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Yu Y, Hossain MM, Sikder R, Qi Z, Huo L, Chen R, Dou W, Shi B, Ye T. Exploring the potential of machine learning to understand the occurrence and health risks of haloacetic acids in a drinking water distribution system. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 951:175573. [PMID: 39153609 DOI: 10.1016/j.scitotenv.2024.175573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Revised: 08/07/2024] [Accepted: 08/14/2024] [Indexed: 08/19/2024]
Abstract
Determining the occurrence of disinfection byproducts (DBPs) in drinking water distribution system (DWDS) remains challenging. Predicting DBPs using readily available water quality parameters can help to understand DBPs associated risks and capture the complex interrelationships between water quality and DBP occurrence. In this study, we collected drinking water samples from a distribution network throughout a year and measured the related water quality parameters (WQPs) and haloacetic acids (HAAs). 12 machine learning (ML) algorithms were evaluated. Random Forest (RF) achieved the best performance (i.e., R2 of 0.78 and RMSE of 7.74) for predicting HAAs concentration. Instead of using cytotoxicity or genotoxicity separately as the surrogate for evaluating toxicity associated with HAAs, we created a health risk index (HRI) that was calculated as the sum of cytotoxicity and genotoxicity of HAAs following the widely used Tic-Tox approach. Similarly, ML models were developed to predict the HRI, and RF model was found to perform the best, obtaining R2 of 0.69 and RMSE of 0.38. To further explore advanced ML approaches, we developed 3 models using uncertainty-based active learning. Our findings revealed that Categorical Boosting Regression (CAT) model developed through active learning substantially outperformed other models, achieving R2 of 0.87 and 0.82 for predicting concentration and the HRI, respectively. Feature importance analysis with the CAT model revealed that temperature, ions (e.g., chloride and nitrate), and DOC concentration in the distribution network had a significant impact on the occurrence of HAAs. Meanwhile, chloride ion, pH, ORP, and free chlorine were found as the most important features for HRI prediction. This study demonstrates that ML has the potential in the prediction of HAA occurrence and toxicity. By identifying key WQPs impacting HAA occurrence and toxicity, this research offers valuable insights for targeted DBP mitigation strategies.
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Affiliation(s)
- Ying Yu
- School of Environmental Science and Engineering, Xiamen University of Technology, Xiamen 361024, China; Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; Key Laboratory of Water Resources Utilization and Protection, Xiamen city, Xiamen 361005, China
| | - Md Mahjib Hossain
- Department of Civil and Environmental Engineering, South Dakota School of Mines and Technology, Rapid City, SD 57701, USA
| | - Rabbi Sikder
- Department of Civil and Environmental Engineering, South Dakota School of Mines and Technology, Rapid City, SD 57701, USA
| | - Zhenguo Qi
- Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Lixin Huo
- Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Ruya Chen
- School of Environmental Science and Engineering, Zhejiang Gongshang University, Hangzhou 310018, Zhejiang, China.
| | - Wenyue Dou
- Key Laboratory of Industrial Pollution Control and Reuse of Jiangsu Province, College of Environmental Engineering, Xuzhou University of Technology, Xuzhou 221018, China
| | - Baoyou Shi
- Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Tao Ye
- Department of Civil and Environmental Engineering, South Dakota School of Mines and Technology, Rapid City, SD 57701, USA.
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2
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Havelikar U, Ghorpade KB, Kumar A, Patel A, Singh M, Banjare N, Gupta PN. Comprehensive insights into mechanism of nanotoxicity, assessment methods and regulatory challenges of nanomedicines. DISCOVER NANO 2024; 19:165. [PMID: 39365367 PMCID: PMC11452581 DOI: 10.1186/s11671-024-04118-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2024] [Accepted: 09/26/2024] [Indexed: 10/05/2024]
Abstract
Nanomedicine has the potential to transform healthcare by offering targeted therapies, precise diagnostics, and enhanced drug delivery systems. The National Institutes of Health has coined the term "nanomedicine" to describe the use of nanotechnology in biological system monitoring, control, diagnosis, and treatment. Nanomedicine continues to receive increasing interest for the rationalized delivery of therapeutics and pharmaceutical agents to achieve the required response while reducing its side effects. However, as nanotechnology continues to advance, concerns about its potential toxicological effects have also grown. This review explores the current state of nanomedicine, focusing on the types of nanoparticles used and their associated properties that contribute to nanotoxicity. It examines the mechanisms through which nanoparticles exert toxicity, encompassing various cellular and molecular interactions. Furthermore, it discusses the assessment methods employed to evaluate nanotoxicity, encompassing in-vitro and in-vivo models, as well as emerging techniques. The review also addresses the regulatory issues surrounding nanotoxicology, highlighting the challenges in developing standardized guidelines and ensuring the secure translation of nanomedicine into clinical settings. It also explores into the challenges and ethical issues associated with nanotoxicology, as understanding the safety profile of nanoparticles is essential for their effective translation into therapeutic applications.
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Affiliation(s)
- Ujwal Havelikar
- Department of Pharmaceutics, NIMS Institute of Pharmacy, NIMS University Rajasthan, Jaipur, 303121, India
- Pharmacology Division, CSIR-Indian Institute of Integrative Medicine, Canal Road, Jammu, 180001, India
| | - Kabirdas B Ghorpade
- Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER) - Raebareli, Lucknow, Uttar Pradesh, 226002, India
| | - Amit Kumar
- Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER) - Raebareli, Lucknow, Uttar Pradesh, 226002, India
| | - Akhilesh Patel
- Department of Pharmaceutics, NIMS Institute of Pharmacy, NIMS University Rajasthan, Jaipur, 303121, India
| | - Manisha Singh
- Pharmacology Division, CSIR-Indian Institute of Integrative Medicine, Canal Road, Jammu, 180001, India
| | - Nagma Banjare
- Pharmacology Division, CSIR-Indian Institute of Integrative Medicine, Canal Road, Jammu, 180001, India
| | - Prem N Gupta
- Pharmacology Division, CSIR-Indian Institute of Integrative Medicine, Canal Road, Jammu, 180001, India.
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3
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Bandaru S, Arora D, Ganesh KM, Umrao S, Thomas S, Bhaskar S, Chakrabortty S. Recent Advances in Research from Nanoparticle to Nano-Assembly: A Review. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:1387. [PMID: 39269049 PMCID: PMC11397018 DOI: 10.3390/nano14171387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Revised: 08/17/2024] [Accepted: 08/20/2024] [Indexed: 09/15/2024]
Abstract
The careful arrangement of nanomaterials (NMs) holds promise for revolutionizing various fields, from electronics and biosensing to medicine and optics. This review delves into the intricacies of nano-assembly (NA) techniques, focusing on oriented-assembly methodologies and stimuli-dependent approaches. The introduction provides a comprehensive overview of the significance and potential applications of NA, setting the stage for review. The oriented-assembly section elucidates methodologies for the precise alignment and organization of NMs, crucial for achieving desired functionalities. The subsequent section delves into stimuli-dependent techniques, categorizing them into chemical and physical stimuli-based approaches. Chemical stimuli-based self-assembly methods, including solvent, acid-base, biomolecule, metal ion, and gas-induced assembly, are discussed in detail by presenting examples. Additionally, physical stimuli such as light, magnetic fields, electric fields, and temperature are examined for their role in driving self-assembly processes. Looking ahead, the review outlines futuristic scopes and perspectives in NA, highlighting emerging trends and potential breakthroughs. Finally, concluding remarks summarize key findings and underscore the significance of NA in shaping future technologies. This comprehensive review serves as a valuable resource for researchers and practitioners, offering insights into the diverse methodologies and potential applications of NA in interdisciplinary research fields.
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Affiliation(s)
- Shamili Bandaru
- Department of Chemistry, SRM University AP─Andhra Pradesh, Mangalagiri 522240, Andhra Pradesh, India
| | - Deepshika Arora
- Engineering Product Development, Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore 487372, Singapore
| | - Kalathur Mohan Ganesh
- Star Laboratory, Department of Chemistry, Sri Sathya Sai Institute of Higher Learning, Prasanthi Nilayam, Sri Sathya Sai, Puttaparthi 515134, Andhra Pradesh, India
| | - Saurabh Umrao
- Nick Holonyak Jr. Micro and Nanotechnology Laboratory (HMNTL), University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Sabu Thomas
- International and Inter University Centre for Nanoscience and Nanotechnology, Mahatma Gandhi University, Kottayam 686 560, Kerala, India
| | - Seemesh Bhaskar
- Nick Holonyak Jr. Micro and Nanotechnology Laboratory (HMNTL), University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Sabyasachi Chakrabortty
- Department of Chemistry, SRM University AP─Andhra Pradesh, Mangalagiri 522240, Andhra Pradesh, India
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Wang T, Russo DP, Demokritou P, Jia X, Huang H, Yang X, Zhu H. An Online Nanoinformatics Platform Empowering Computational Modeling of Nanomaterials by Nanostructure Annotations and Machine Learning Toolkits. NANO LETTERS 2024; 24:10228-10236. [PMID: 39120132 PMCID: PMC11342361 DOI: 10.1021/acs.nanolett.4c02568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 08/03/2024] [Accepted: 08/06/2024] [Indexed: 08/10/2024]
Abstract
Modern nanotechnology has generated numerous datasets from in vitro and in vivo studies on nanomaterials, with some available on nanoinformatics portals. However, these existing databases lack the digital data and tools suitable for machine learning studies. Here, we report a nanoinformatics platform that accurately annotates nanostructures into machine-readable data files and provides modeling toolkits. This platform, accessible to the public at https://vinas-toolbox.com/, has annotated nanostructures of 14 material types. The associated nanodescriptor data and assay test results are appropriate for modeling purposes. The modeling toolkits enable data standardization, data visualization, and machine learning model development to predict properties and bioactivities of new nanomaterials. Moreover, a library of virtual nanostructures with their predicted properties and bioactivities is available, directing the synthesis of new nanomaterials. This platform provides a data-driven computational modeling platform for the nanoscience community, significantly aiding in the development of safe and effective nanomaterials.
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Affiliation(s)
- Tong Wang
- Tulane
Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, Louisiana 70112, United States
- Division
of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, Louisiana 70112, United States
- Department
of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Daniel P. Russo
- Department
of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Philip Demokritou
- Center
for Nanotechnology and Nanotoxicology, Department of Environmental
Health, T.H. Chan School of Public Health, Harvard University, 655 Huntington Ave, Boston, Massachusetts 02115, United States
- Nanoscience
and Advanced Materials Center, Environmental Occupational Health Sciences
Institute, School of Public Health, Rutgers
University, Piscataway, New Jersey 08854, United States
| | - Xuelian Jia
- Tulane
Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, Louisiana 70112, United States
- Division
of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, Louisiana 70112, United States
- Department
of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Heng Huang
- Department
of Computer Science, University of Maryland
College Park, College
Park, Maryland 20742, United States
| | - Xinyu Yang
- Department
of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Hao Zhu
- Tulane
Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, Louisiana 70112, United States
- Division
of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, Louisiana 70112, United States
- Department
of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
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5
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Carvalho WOF, Aiex Taier Filho MT, Oliveira ON, Mejía-Salazar JR, Pereira de Figueiredo FA. Toward Machine-Learning-Accelerated Design of All-Dielectric Magnetophotonic Nanostructures. ACS APPLIED MATERIALS & INTERFACES 2024; 16:42828-42834. [PMID: 39078874 PMCID: PMC11331439 DOI: 10.1021/acsami.4c06740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 07/08/2024] [Accepted: 07/15/2024] [Indexed: 08/16/2024]
Abstract
All-dielectric magnetophotonic nanostructures are promising for integrated nanophotonic devices with high resolution and sensitivity, but their design requires computationally demanding electromagnetic simulations evaluated through trial and error. In this paper, we propose a machine-learning approach to accelerate the design of these nanostructures. Using a data set of 12 170 samples containing four geometric parameters of the nanostructure and the incidence wavelength, trained neural network and polynomial regression algorithms were capable of predicting the amplitude of the transverse magneto-optical Kerr effect (TMOKE) within a time frame of 10-3 s and mean square error below 4.2%. With this approach, one can readily identify nanostructures suitable for sensing at ultralow analyte concentrations in aqueous solutions. As a proof of principle, we used the machine-learning models to determine the sensitivity (S = |Δθres/Δna|) of a nanophotonic grating, which is competitive with state-of-the-art systems and exhibits a figure of merit of 672 RIU-1. Furthermore, researchers can use the predictions of TMOKE peaks generated by the algorithms to assess the suitability for experimental setups, adding a layer of utility to the machine-learning methodology.
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Affiliation(s)
- William O. F. Carvalho
- Sao
Carlos Institute of Physics, University
of Sao Paulo, CP 369, São
Carlos, São Paulo 13560-970, Brazil
| | | | - Osvaldo N. Oliveira
- Sao
Carlos Institute of Physics, University
of Sao Paulo, CP 369, São
Carlos, São Paulo 13560-970, Brazil
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6
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Xu R, Liu S, Yang M, Yang G, Luo Z, Ran R, Zhou W, Shao Z. Advancements and prospects of perovskite-based fuel electrodes in solid oxide cells for CO 2 electrolysis to CO. Chem Sci 2024; 15:11166-11187. [PMID: 39055001 PMCID: PMC11268505 DOI: 10.1039/d4sc03306j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Accepted: 06/26/2024] [Indexed: 07/27/2024] Open
Abstract
Carbon dioxide (CO2) electrolysis to carbon monoxide (CO) is a very promising strategy for economically converting CO2, with high-temperature solid oxide electrolysis cells (SOECs) being regarded as the most suitable technology due to their high electrode reaction kinetics and nearly 100% faradaic efficiency, while their practical application is highly dependent on the performance of their fuel electrode (cathode), which significantly determines the cell activity, selectivity, and durability. In this review, we provide a timely overview of the recent progress in the understanding and development of fuel electrodes, predominantly based on perovskite oxides, for CO2 electrochemical reduction to CO (CO2RR) in SOECs. Initially, the current understanding of the reaction mechanisms over the perovskite electrocatalyst for CO synthesis from CO2 electrolysis in SOECs is provided. Subsequently, the recent experimental advances in fuel electrodes are summarized, with importance placed on perovskite oxides and their modification, including bulk doping with multiple elements to introduce high entropy effects, various methods for realizing surface nanoparticles or even single atom catalyst modification, and nanocompositing. Additionally, the recent progress in numerical modeling-assisted fast screening of perovskite electrocatalysts for high-temperature CO2RR is summarized, and the advanced characterization techniques for an in-depth understanding of the related fundamentals for the CO2RR over perovskite oxides are also reviewed. The recent pro-industrial application trials of the CO2RR in SOECs are also briefly discussed. Finally, the future prospects and challenges of SOEC cathodes for the CO2RR are suggested.
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Affiliation(s)
- Ruijia Xu
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University Nanjing 211816 China
| | - Shuai Liu
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University Nanjing 211816 China
| | - Meiting Yang
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University Nanjing 211816 China
| | - Guangming Yang
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University Nanjing 211816 China
| | - Zhixin Luo
- WA School of Mines: Minerals, Energy & Chemical Engineering (WASM-MECE), Curtin University Perth WA 6102 Australia
| | - Ran Ran
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University Nanjing 211816 China
| | - Wei Zhou
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University Nanjing 211816 China
| | - Zongping Shao
- WA School of Mines: Minerals, Energy & Chemical Engineering (WASM-MECE), Curtin University Perth WA 6102 Australia
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7
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Hwang W, Kwon S, Lee WB, Kim Y. Self-assembly prediction of architecture-controlled bottlebrush copolymers in solution using graph convolutional networks. SOFT MATTER 2024; 20:4905-4915. [PMID: 38867573 DOI: 10.1039/d4sm00453a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2024]
Abstract
The investigation of bottlebrush copolymer self-assembly in solution involves a comprehensive approach integrating simulation and experimental research, due to their unique physical characteristics. However, the intricate architecture of bottlebrush copolymers and the diverse solvent conditions introduce a wide range of parameter spaces. In this study, we investigated the solution self-assembly behavior of bottlebrush copolymers by combining dissipative particle dynamics (DPD) simulation results and machine learning (ML) including graph convolutional networks (GCNs). The architecture of bottlebrush copolymers is encoded by graphs including connectivity, side chain length, bead types, and interaction parameters of DPD simulation. Using GCN, we accurately predicted the single chain properties of bottlebrush copolymers with over 95% accuracy. Furthermore, phase behavior was precisely predicted using these single chain properties. Shapley additive explanations (SHAP) values of single chain properties to the various self-assembly morphologies were calculated to investigate the correlation between single chain properties and morphologies. In addition, we analyzed single chain properties and phase behavior as a function of DPD interaction parameters, extracting relevant physical properties for vesicle morphology formation. This work paves the way for tailored design in solution of self-assembled nanostructures of bottlebrush copolymers, offering a GCN framework for precise prediction of self-assembly morphologies under various chain architectures and solvent conditions.
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Affiliation(s)
- Wooseop Hwang
- Department of Materials Science and Engineering, Korea University, Seoul 02841, Republic of Korea.
| | - Sangwoo Kwon
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea.
| | - Won Bo Lee
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea.
| | - YongJoo Kim
- Department of Materials Science and Engineering, Korea University, Seoul 02841, Republic of Korea.
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8
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Bassani CL, van Anders G, Banin U, Baranov D, Chen Q, Dijkstra M, Dimitriyev MS, Efrati E, Faraudo J, Gang O, Gaston N, Golestanian R, Guerrero-Garcia GI, Gruenwald M, Haji-Akbari A, Ibáñez M, Karg M, Kraus T, Lee B, Van Lehn RC, Macfarlane RJ, Mognetti BM, Nikoubashman A, Osat S, Prezhdo OV, Rotskoff GM, Saiz L, Shi AC, Skrabalak S, Smalyukh II, Tagliazucchi M, Talapin DV, Tkachenko AV, Tretiak S, Vaknin D, Widmer-Cooper A, Wong GCL, Ye X, Zhou S, Rabani E, Engel M, Travesset A. Nanocrystal Assemblies: Current Advances and Open Problems. ACS NANO 2024; 18:14791-14840. [PMID: 38814908 DOI: 10.1021/acsnano.3c10201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
We explore the potential of nanocrystals (a term used equivalently to nanoparticles) as building blocks for nanomaterials, and the current advances and open challenges for fundamental science developments and applications. Nanocrystal assemblies are inherently multiscale, and the generation of revolutionary material properties requires a precise understanding of the relationship between structure and function, the former being determined by classical effects and the latter often by quantum effects. With an emphasis on theory and computation, we discuss challenges that hamper current assembly strategies and to what extent nanocrystal assemblies represent thermodynamic equilibrium or kinetically trapped metastable states. We also examine dynamic effects and optimization of assembly protocols. Finally, we discuss promising material functions and examples of their realization with nanocrystal assemblies.
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Affiliation(s)
- Carlos L Bassani
- Institute for Multiscale Simulation, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
| | - Greg van Anders
- Department of Physics, Engineering Physics, and Astronomy, Queen's University, Kingston, Ontario K7L 3N6, Canada
| | - Uri Banin
- Institute of Chemistry and the Center for Nanoscience and Nanotechnology, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Dmitry Baranov
- Division of Chemical Physics, Department of Chemistry, Lund University, SE-221 00 Lund, Sweden
| | - Qian Chen
- University of Illinois, Urbana, Illinois 61801, USA
| | - Marjolein Dijkstra
- Soft Condensed Matter & Biophysics, Debye Institute for Nanomaterials Science, Utrecht University, 3584 CC Utrecht, The Netherlands
| | - Michael S Dimitriyev
- Department of Polymer Science and Engineering, University of Massachusetts, Amherst, Massachusetts 01003, USA
- Department of Materials Science and Engineering, Texas A&M University, College Station, Texas 77843, USA
| | - Efi Efrati
- Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel
- James Franck Institute, The University of Chicago, Chicago, Illinois 60637, USA
| | - Jordi Faraudo
- Institut de Ciencia de Materials de Barcelona (ICMAB-CSIC), Campus de la UAB, E-08193 Bellaterra, Barcelona, Spain
| | - Oleg Gang
- Department of Chemical Engineering, Columbia University, New York, New York 10027, USA
- Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York 10027, USA
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York 11973, USA
| | - Nicola Gaston
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, Department of Physics, The University of Auckland, Auckland 1142, New Zealand
| | - Ramin Golestanian
- Max Planck Institute for Dynamics and Self-Organization (MPI-DS), 37077 Göttingen, Germany
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford OX1 3PU, UK
| | - G Ivan Guerrero-Garcia
- Facultad de Ciencias de la Universidad Autónoma de San Luis Potosí, 78295 San Luis Potosí, México
| | - Michael Gruenwald
- Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, USA
| | - Amir Haji-Akbari
- Department of Chemical and Environmental Engineering, Yale University, New Haven, Connecticut 06511, USA
| | - Maria Ibáñez
- Institute of Science and Technology Austria (ISTA), 3400 Klosterneuburg, Austria
| | - Matthias Karg
- Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
| | - Tobias Kraus
- INM - Leibniz-Institute for New Materials, 66123 Saarbrücken, Germany
- Saarland University, Colloid and Interface Chemistry, 66123 Saarbrücken, Germany
| | - Byeongdu Lee
- X-ray Science Division, Argonne National Laboratory, Lemont, Illinois 60439, USA
| | - Reid C Van Lehn
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53717, USA
| | - Robert J Macfarlane
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA
| | - Bortolo M Mognetti
- Center for Nonlinear Phenomena and Complex Systems, Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Arash Nikoubashman
- Leibniz-Institut für Polymerforschung Dresden e.V., 01069 Dresden, Germany
- Institut für Theoretische Physik, Technische Universität Dresden, 01069 Dresden, Germany
| | - Saeed Osat
- Max Planck Institute for Dynamics and Self-Organization (MPI-DS), 37077 Göttingen, Germany
| | - Oleg V Prezhdo
- Department of Chemistry, University of Southern California, Los Angeles, CA 90089, USA
- Department of Physics and Astronomy, University of Southern California, Los Angeles, California 90089, USA
| | - Grant M Rotskoff
- Department of Chemistry, Stanford University, Stanford, California 94305, USA
| | - Leonor Saiz
- Department of Biomedical Engineering, University of California, Davis, California 95616, USA
| | - An-Chang Shi
- Department of Physics & Astronomy, McMaster University, Hamilton, Ontario L8S 4M1, Canada
| | - Sara Skrabalak
- Department of Chemistry, Indiana University, Bloomington, Indiana 47405, USA
| | - Ivan I Smalyukh
- Department of Physics and Chemical Physics Program, University of Colorado, Boulder, Colorado 80309, USA
- International Institute for Sustainability with Knotted Chiral Meta Matter, Hiroshima University, Higashi-Hiroshima City 739-0046, Japan
| | - Mario Tagliazucchi
- Universidad de Buenos Aires, Ciudad Universitaria, C1428EHA Ciudad Autónoma de Buenos Aires, Buenos Aires 1428 Argentina
| | - Dmitri V Talapin
- Department of Chemistry, James Franck Institute and Pritzker School of Molecular Engineering, The University of Chicago, Chicago, Illinois 60637, USA
- Center for Nanoscale Materials, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Alexei V Tkachenko
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York 11973, USA
| | - Sergei Tretiak
- Theoretical Division and Center for Integrated Nanotechnologies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - David Vaknin
- Iowa State University and Ames Lab, Ames, Iowa 50011, USA
| | - Asaph Widmer-Cooper
- ARC Centre of Excellence in Exciton Science, School of Chemistry, University of Sydney, Sydney, New South Wales 2006, Australia
- The University of Sydney Nano Institute, University of Sydney, Sydney, New South Wales 2006, Australia
| | - Gerard C L Wong
- Department of Bioengineering, University of California, Los Angeles, California 90095, USA
- Department of Chemistry and Biochemistry, University of California, Los Angeles, California 90095, USA
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, CA 90095, USA
- California NanoSystems Institute, University of California, Los Angeles, CA 90095, USA
| | - Xingchen Ye
- Department of Chemistry, Indiana University, Bloomington, Indiana 47405, USA
| | - Shan Zhou
- Department of Nanoscience and Biomedical Engineering, South Dakota School of Mines and Technology, Rapid City, South Dakota 57701, USA
| | - Eran Rabani
- Department of Chemistry, University of California and Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
- The Raymond and Beverly Sackler Center of Computational Molecular and Materials Science, Tel Aviv University, Tel Aviv 69978, Israel
| | - Michael Engel
- Institute for Multiscale Simulation, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
| | - Alex Travesset
- Iowa State University and Ames Lab, Ames, Iowa 50011, USA
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Dhoble S, Wu TH, Kenry. Decoding Nanomaterial-Biosystem Interactions through Machine Learning. Angew Chem Int Ed Engl 2024; 63:e202318380. [PMID: 38687554 DOI: 10.1002/anie.202318380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Indexed: 05/02/2024]
Abstract
The interactions between biosystems and nanomaterials regulate most of their theranostic and nanomedicine applications. These nanomaterial-biosystem interactions are highly complex and influenced by a number of entangled factors, including but not limited to the physicochemical features of nanomaterials, the types and characteristics of the interacting biosystems, and the properties of the surrounding microenvironments. Over the years, different experimental approaches coupled with computational modeling have revealed important insights into these interactions, although many outstanding questions remain unanswered. The emergence of machine learning has provided a timely and unique opportunity to revisit nanomaterial-biosystem interactions and to further push the boundary of this field. This minireview highlights the development and use of machine learning to decode nanomaterial-biosystem interactions and provides our perspectives on the current challenges and potential opportunities in this field.
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Affiliation(s)
- Sagar Dhoble
- Department of Pharmacology and Toxicology, R. Ken Coit College of Pharmacy, University of Arizona, Tucson, AZ 85721, USA
| | - Tzu-Hsien Wu
- Department of Pharmacology and Toxicology, R. Ken Coit College of Pharmacy, University of Arizona, Tucson, AZ 85721, USA
| | - Kenry
- Department of Pharmacology and Toxicology, R. Ken Coit College of Pharmacy, University of Arizona, Tucson, AZ 85721, USA
- University of Arizona Cancer Center, University of Arizona, Tucson, AZ 85721, USA
- BIO5 Institute, University of Arizona, Tucson, AZ 85721, USA
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10
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Cao Z, Barati Farimani O, Ock J, Barati Farimani A. Machine Learning in Membrane Design: From Property Prediction to AI-Guided Optimization. NANO LETTERS 2024; 24:2953-2960. [PMID: 38436240 PMCID: PMC10941251 DOI: 10.1021/acs.nanolett.3c05137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 03/05/2024]
Abstract
Porous membranes, either polymeric or two-dimensional materials, have been extensively studied because of their outstanding performance in many applications such as water filtration. Recently, inspired by the significant success of machine learning (ML) in many areas of scientific discovery, researchers have started to tackle the problem in the field of membrane design using data-driven ML tools. In this Mini Review, we summarize research efforts on three types of applications of machine learning in membrane design, including (1) membrane property prediction using ML, (2) gaining physical insight and drawing quantitative relationships between membrane properties and performance using explainable artificial intelligence, and (3) ML-guided design, optimization, or virtual screening of membranes. On top of the review of previous research, we discuss the challenges associated with applying ML for membrane design and potential future directions.
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Affiliation(s)
- Zhonglin Cao
- Department
of Mechanical Engineering, Carnegie Mellon
University, Pittsburgh Pennsylvania 15213, United States
| | - Omid Barati Farimani
- Department
of Mechanical Engineering, Carnegie Mellon
University, Pittsburgh Pennsylvania 15213, United States
| | - Janghoon Ock
- Department
of Chemical Engineering, Carnegie Mellon
University, Pittsburgh Pennsylvania 15213, United States
| | - Amir Barati Farimani
- Department
of Mechanical Engineering, Carnegie Mellon
University, Pittsburgh Pennsylvania 15213, United States
- Department
of Chemical Engineering, Carnegie Mellon
University, Pittsburgh Pennsylvania 15213, United States
- Machine
Learning Department, Carnegie Mellon University, Pittsburgh Pennsylvania 15213, United States
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11
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Dong H, Lin J, Tao Y, Jia Y, Sun L, Li WJ, Sun H. AI-enhanced biomedical micro/nanorobots in microfluidics. LAB ON A CHIP 2024; 24:1419-1440. [PMID: 38174821 DOI: 10.1039/d3lc00909b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Human beings encompass sophisticated microcirculation and microenvironments, incorporating a broad spectrum of microfluidic systems that adopt fundamental roles in orchestrating physiological mechanisms. In vitro recapitulation of human microenvironments based on lab-on-a-chip technology represents a critical paradigm to better understand the intricate mechanisms. Moreover, the advent of micro/nanorobotics provides brand new perspectives and dynamic tools for elucidating the complex process in microfluidics. Currently, artificial intelligence (AI) has endowed micro/nanorobots (MNRs) with unprecedented benefits, such as material synthesis, optimal design, fabrication, and swarm behavior. Using advanced AI algorithms, the motion control, environment perception, and swarm intelligence of MNRs in microfluidics are significantly enhanced. This emerging interdisciplinary research trend holds great potential to propel biomedical research to the forefront and make valuable contributions to human health. Herein, we initially introduce the AI algorithms integral to the development of MNRs. We briefly revisit the components, designs, and fabrication techniques adopted by robots in microfluidics with an emphasis on the application of AI. Then, we review the latest research pertinent to AI-enhanced MNRs, focusing on their motion control, sensing abilities, and intricate collective behavior in microfluidics. Furthermore, we spotlight biomedical domains that are already witnessing or will undergo game-changing evolution based on AI-enhanced MNRs. Finally, we identify the current challenges that hinder the practical use of the pioneering interdisciplinary technology.
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Affiliation(s)
- Hui Dong
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
| | - Jiawen Lin
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.
| | - Yihui Tao
- Department of Automation Control and System Engineering, University of Sheffield, Sheffield, UK
| | - Yuan Jia
- Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, China
| | - Lining Sun
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
| | - Wen Jung Li
- Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China
| | - Hao Sun
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
- Research Center of Aerospace Mechanism and Control, Harbin Institute of Technology, Harbin, China
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12
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Komoto Y, Ryu J, Taniguchi M. Total variation denoising-based method of identifying the states of single molecules in break junction data. DISCOVER NANO 2024; 19:20. [PMID: 38285285 PMCID: PMC10825082 DOI: 10.1186/s11671-024-03963-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 01/22/2024] [Indexed: 01/30/2024]
Abstract
Break junction (BJ) measurements provide insights into the electrical properties of diverse molecules, enabling the direct assessment of single-molecule conductances. The BJ method displays potential for use in determining the dynamics of individual molecules, single-molecule chemical reactions, and biomolecules, such as deoxyribonucleic acid and ribonucleic acid. However, conductance data obtained via single-molecule measurements may be susceptible to fluctuations due to minute structural changes within the junctions. Consequently, clearly identifying the conduction states of these molecules is challenging. This study aims to develop a method of precisely identifying conduction state traces. We propose a novel single-molecule analysis approach that employs total variation denoising (TVD) in signal processing, focusing on the integration of information technology with measured single-molecule data. We successfully applied this method to simulated conductance traces, effectively denoise the data, and elucidate multiple conduction states. The proposed method facilitates the identification of well-defined plateau lengths and supervised machine learning with enhanced accuracies. The introduced TVD-based analytical method is effective in elucidating the states within the measured single-molecule data. This approach exhibits the potential to offer novel perspectives regarding the formation of molecular junctions, conformational changes, and cleavage.
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Affiliation(s)
- Yuki Komoto
- SANKEN, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan.
- Artificial Intelligence Research Center, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan.
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan.
| | - Jiho Ryu
- SANKEN, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan
| | - Masateru Taniguchi
- SANKEN, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan
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13
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Tang Y, Liu Y, Zhang D, Zheng J. Perspectives on Theoretical Models and Molecular Simulations of Polymer Brushes. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2024; 40:1487-1502. [PMID: 38153400 DOI: 10.1021/acs.langmuir.3c03253] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
Polymer brushes have witnessed extensive utilization and progress, driven by their distinct attributes in surface modification, tethered group functionality, and tailored interactions at the nanoscale, enabling them for various scientific and industrial applications of coatings, sensors, switchable/responsive materials, nanolithography, and lab-on-a-chips. Despite the wealth of experimental investigations into polymer brushes, this review primarily focuses on computational studies of antifouling polymer brushes with a strong emphasis on achieving a molecular-level understanding and structurally designing antifouling polymer brushes. Computational exploration covers three realms of thermotical models, molecular simulations, and machine-learning approaches to elucidate the intricate relationship between composition, structure, and properties concerning polymer brushes in the context of nanotribology, surface hydration, and packing conformation. Upon acknowledging the challenges currently faced, we extend our perspectives toward future research directions by delineating potential avenues and unexplored territories. Our overarching objective is to advance our foundational comprehension and practical utilization of polymer brushes for antifouling applications, leveraging the synergy between computational methods and materials design to drive innovation in this crucial field.
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Affiliation(s)
- Yijing Tang
- Department of Chemical, Biomolecular, and Corrosion Engineering, The University of Akron, Akron, Ohio 44325, United States
| | - Yonglan Liu
- Department of Chemical, Biomolecular, and Corrosion Engineering, The University of Akron, Akron, Ohio 44325, United States
| | - Dong Zhang
- The Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Jie Zheng
- Department of Chemical, Biomolecular, and Corrosion Engineering, The University of Akron, Akron, Ohio 44325, United States
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14
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Zhou Y, Wang Y, Peijnenburg W, Vijver MG, Balraadjsing S, Fan W. Using Machine Learning to Predict Adverse Effects of Metallic Nanomaterials to Various Aquatic Organisms. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:17786-17795. [PMID: 36730792 DOI: 10.1021/acs.est.2c07039] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The wide production and use of metallic nanomaterials (MNMs) leads to increased emissions into the aquatic environments and induces high potential risks. Experimentally evaluating the (eco)toxicity of MNMs is time-consuming and expensive due to the multiple environmental factors, the complexity of material properties, and the species diversity. Machine learning (ML) models provide an option to deal with heterogeneous data sets and complex relationships. The present study established an in silico model based on a machine learning properties-environmental conditions-multi species-toxicity prediction model (ML-PEMST) that can be applied to predict the toxicity of different MNMs toward multiple aquatic species. Feature importance and interaction analysis based on the random forest method indicated that exposure duration, illumination, primary size, and hydrodynamic diameter were the main factors affecting the ecotoxicity of MNMs to a variety of aquatic organisms. Illumination was demonstrated to have the most interaction with the other features. Moreover, incorporating additional detailed information on the ecological traits of the test species will allow us to further optimize and improve the predictive performance of the model. This study provides a new approach for ecotoxicity predictions for organisms in the aquatic environment and will help us to further explore exposure pathways and the risk assessment of MNMs.
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Affiliation(s)
- Yunchi Zhou
- School of Space and Environment, Beihang University, Beijing100191, China
| | - Ying Wang
- School of Space and Environment, Beihang University, Beijing100191, China
| | - Willie Peijnenburg
- Institute of Environmental Science (CML), Leiden University, Leiden2300, RA, The Netherlands
- Center for Safety of Substances and Products, National Institute of Public Health and the Environment (RIVM), Bilthoven3720, BA, The Netherlands
| | - Martina G Vijver
- Institute of Environmental Science (CML), Leiden University, Leiden2300, RA, The Netherlands
| | - Surendra Balraadjsing
- Institute of Environmental Science (CML), Leiden University, Leiden2300, RA, The Netherlands
| | - Wenhong Fan
- School of Space and Environment, Beihang University, Beijing100191, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing100191, China
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15
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Zhao J, Chen R, Liu S, Zhou S, Xu M, Dai F. Corrosion Degree Evaluation of Polymer Anti-Corrosive Oil Well Cement under an Acidic Geological Environment Using an Artificial Neural Network. Polymers (Basel) 2023; 15:4441. [PMID: 38006165 PMCID: PMC10674542 DOI: 10.3390/polym15224441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 10/31/2023] [Accepted: 11/05/2023] [Indexed: 11/26/2023] Open
Abstract
Oil well cement is prone to corrosion and damage in carbon dioxide (CO2) acidic gas wells. In order to improve the anti-corrosion ability of oil well cement, polymer resin was used as the anti-corrosion material. The effect of polymer resin on the mechanical and corrosion properties of oil well cement was studied. The corrosion law of polymer anti-corrosion cement in an acidic gas environment was studied. The long-term corrosion degree of polymer anti-corrosion cement was evaluated using an improved neural network model. The cluster particle algorithm (PSO) was used to improve the accuracy of the neural network model. The results indicate that in acidic gas environments, the compressive strength of polymer anti-corrosion cement was reduced under the effect of CO2, and the corrosion depth was increased. The R2 of the prediction model PSO-BPNN3 is 0.9970, and the test error is 0.0136. When corroded for 365 days at 50 °C and 25 MPa pressure of CO2, the corrosion degree of the polymer anti-corrosion cement was 43.6%. The corrosion depth of uncorroded cement stone is 76.69%, which is relatively reduced by 33.09%. The corrosion resistance of cement can be effectively improved by using polymer resin. Using the PSO-BP neural network to evaluate the long-term corrosion changes of polymer anti-corrosion cement under complex acidic gas conditions guides the evaluation of its corrosion resistance.
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Affiliation(s)
- Jun Zhao
- China Oilfield Services Limited, Langfang 065200, China
| | - Rongyao Chen
- School of Petroleum Engineering, Yangtze University, Wuhan 430100, China
- National Engineering Research Center for Oil and Gas Drilling and Completion Technology, Yangtze University, Wuhan 430100, China
| | - Shikang Liu
- China Oilfield Services Limited, Langfang 065200, China
| | - Shanshan Zhou
- School of Petroleum Engineering, Yangtze University, Wuhan 430100, China
- National Engineering Research Center for Oil and Gas Drilling and Completion Technology, Yangtze University, Wuhan 430100, China
| | - Mingbiao Xu
- School of Petroleum Engineering, Yangtze University, Wuhan 430100, China
- National Engineering Research Center for Oil and Gas Drilling and Completion Technology, Yangtze University, Wuhan 430100, China
| | - Feixu Dai
- Tarim Oilfield Production Capacity Construction Division, Korla 841000, China
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16
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Cui S, Gao Y, Huang Y, Shen L, Zhao Q, Pan Y, Zhuang S. Advances and applications of machine learning and deep learning in environmental ecology and health. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 335:122358. [PMID: 37567408 DOI: 10.1016/j.envpol.2023.122358] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 08/02/2023] [Accepted: 08/08/2023] [Indexed: 08/13/2023]
Abstract
Machine learning (ML) and deep learning (DL) possess excellent advantages in data analysis (e.g., feature extraction, clustering, classification, regression, image recognition and prediction) and risk assessment and management in environmental ecology and health (EEH). Considering the rapid growth and increasing complexity of data in EEH, it is of significance to summarize recent advances and applications of ML and DL in EEH. This review summarized the basic processes and fundamental algorithms of the ML and DL modeling, and indicated the urgent needs of ML and DL in EEH. Recent research hotspots such as environmental ecology and restoration, environmental fate of new pollutants, chemical exposures and risks, chemical hazard identification and control were highlighted. Various applications of ML and DL in EEH demonstrate their versatility and technological revolution, and present some challenges. The perspective of ML and DL in EEH were further outlined to promote the innovative analysis and cultivation of the ML-driven research paradigm.
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Affiliation(s)
- Shixuan Cui
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China
| | - Yuchen Gao
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Yizhou Huang
- Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China
| | - Lilai Shen
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Qiming Zhao
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Yaru Pan
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Shulin Zhuang
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China.
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17
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Williamson E, Brutchey RL. Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis. Inorg Chem 2023; 62:16251-16262. [PMID: 37767941 PMCID: PMC10565808 DOI: 10.1021/acs.inorgchem.3c02697] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Indexed: 09/29/2023]
Abstract
The design of inorganic materials for various applications critically depends on our ability to manipulate their synthesis in a rational, robust, and controllable fashion. Different from the conventional trial-and-error approach, data-driven techniques such as the design of experiments (DoE) and machine learning are an effective and more efficient way to predictably control materials synthesis. Here, we present a Viewpoint on recent progress in leveraging such techniques for predicting and controlling the outcomes of inorganic materials synthesis. We first compare how the design choice (statistical DoE vs machine learning) affects the type of control it can offer over the resulting product attributes, information elucidated, and experimental cost. These attributes are supported by discussing select case studies from the recent literature that highlight the power of these techniques for materials synthesis. The influence of experimental bias is next discussed, followed finally by our perspectives on the major challenges in the widespread implementation of predictable and controllable materials synthesis using data-driven techniques.
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Affiliation(s)
- Emily
M. Williamson
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States
| | - Richard L. Brutchey
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States
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18
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Williamson E, Sun Z, Tappan BA, Brutchey RL. Predictive Synthesis of Copper Selenides Using a Multidimensional Phase Map Constructed with a Data-Driven Classifier. J Am Chem Soc 2023; 145:17954-17964. [PMID: 37540836 PMCID: PMC10436277 DOI: 10.1021/jacs.3c05490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Indexed: 08/06/2023]
Abstract
Copper selenides are an important family of materials with applications in catalysis, plasmonics, photovoltaics, and thermoelectrics. Despite being a binary material system, the Cu-Se phase diagram is complex and contains multiple crystal structures in addition to several metastable structures that are not found on the thermodynamic phase diagram. Consequently, the ability to synthetically navigate this complex phase space poses a significant challenge. We demonstrate that data-driven learning can successfully map this phase space in a minimal number of experiments. We combine soft chemistry (chimie douce) synthetic methods with multivariate analyses via classification techniques to enable predictive phase determination. A surrogate model was constructed with experimental data derived from a design matrix of four experimental variables: C-Se bond strength of the selenium precursor, time, temperature, and solvent composition. The reactions in the surrogate model resulted in 11 distinct phase combinations of copper selenide. These data were used to train a classification model that predicts the phase with 95.7% accuracy. The resulting decision tree enabled conclusions to be drawn about how the experimental variables affect the phase and provided prescriptive synthetic conditions for specific phase isolation. This guided the accelerated phase targeting in a minimum number of experiments of klockmannite CuSe, which could not be isolated in any of the reactions used to construct the surrogate model. The reaction conditions that the model predicted to synthesize klockmannite CuSe were experimentally validated, highlighting the utility of this approach.
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Affiliation(s)
- Emily
M. Williamson
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States
| | - Zhaohong Sun
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States
| | - Bryce A. Tappan
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States
| | - Richard L. Brutchey
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States
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19
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Ostadhossein F, Moitra P, Alafeef M, Sar D, D’Souza S, Benig LF, Nelappana M, Huang X, Soares J, Zhang K, Pan D. Ensemble and single-particle level fluorescent fine-tuning of carbon dots via positional changes of amines toward "supervised" oral microbiome sensing. JOURNAL OF BIOMEDICAL OPTICS 2023; 28:082807. [PMID: 37427335 PMCID: PMC10324603 DOI: 10.1117/1.jbo.28.8.082807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/30/2023] [Accepted: 06/01/2023] [Indexed: 07/11/2023]
Abstract
Significance Carbon dots (CDs) have attracted a host of research interest in recent years mainly due to their unique photoluminescence (PL) properties that make them applicable in various biomedical areas, such as imaging and image-guided therapy. However, the real mechanism underneath the PL is a subject of wide controversy and can be investigated from various angles. Aim Our work investigates the effect of the isomeric nitrogen position as the precursor in the synthesis of CDs by shedding light on their photophysical properties on the single particles and ensemble level. Approach To this end, we adopted five isomers of diaminopyridine (DAP) and urea as the precursors and obtained CDs during a hydrothermal process. The various photophysical properties were further investigated in depth by mass spectroscopy. CD molecular frontier orbital analyses aided us in justifying the fluorescence emission profile on the bulk level as well as the charge transfer processes. As a result of the varying fluorescent responses, we indicate that these particles can be utilized for machine learning (ML)-driven sensitive detection of oral microbiota. The sensing results were further supported by density functional theoretical calculations and docking studies. Results The generating isomers have a significant effect on the overall photophysical properties at the bulk/ensembled level. On the single-particle level, although some of the photophysical properties such as average intensity remained the same, the overall differences in brightness, photo-blinking frequency, and bleaching time between the five samples were conceived. The various photophysical properties could be explained based on the different chromophores formed during the synthesis. Overall, an array of CDs was demonstrated herein to achieve ∼ 100 % separation efficacy in segregating a mixed oral microbiome culture in a rapid (< 0.5 h ), high-throughput manner with superior accuracy. Conclusions We have indicated that the PL properties of CDs can be regulated by the precursors' isomeric position of nitrogen. We emancipated this difference in a rapid method relying on ML algorithms to segregate the dental bacterial species as biosensors.
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Affiliation(s)
- Fatemeh Ostadhossein
- University of Illinois at Urbana-Champaign, Department of Bioengineering, Urbana, Illinois, United States
- Carle Foundation Hospital, Mills Breast Cancer Institute, Urbana, Illinois, United States
- University of Illinois at Urbana-Champaign, Beckman Institute of Advanced Science and Technology, Urbana, Illinois, United States
| | - Parikshit Moitra
- The Pennsylvania State University, Department of Nuclear Engineering, State College, Pennsylvania, United States
| | - Maha Alafeef
- University of Illinois at Urbana-Champaign, Department of Bioengineering, Urbana, Illinois, United States
- The Pennsylvania State University, Department of Nuclear Engineering, State College, Pennsylvania, United States
| | - Dinabandhu Sar
- University of Illinois at Urbana-Champaign, Department of Bioengineering, Urbana, Illinois, United States
- Carle Foundation Hospital, Mills Breast Cancer Institute, Urbana, Illinois, United States
| | - Shannon D’Souza
- University of Illinois at Urbana-Champaign, Department of Bioengineering, Urbana, Illinois, United States
- Carle Foundation Hospital, Mills Breast Cancer Institute, Urbana, Illinois, United States
| | - Lily F. Benig
- University of Illinois at Urbana-Champaign, Department of Bioengineering, Urbana, Illinois, United States
- Carle Foundation Hospital, Mills Breast Cancer Institute, Urbana, Illinois, United States
| | - Michael Nelappana
- University of Illinois at Urbana-Champaign, Department of Bioengineering, Urbana, Illinois, United States
- Carle Foundation Hospital, Mills Breast Cancer Institute, Urbana, Illinois, United States
| | - Xuedong Huang
- Fudan University, Department of Chemistry, Shanghai, China
| | - Julio Soares
- University of Illinois at Urbana‐Champaign, Frederick Seitz Materials Research Laboratory, Urbana, Illinois, United States
| | - Kai Zhang
- University of Illinois at Urbana-Champaign, School of Molecular and Cellular Biology, Department of Biochemistry, Urbana, Illinois, United States
| | - Dipanjan Pan
- University of Illinois at Urbana-Champaign, Department of Bioengineering, Urbana, Illinois, United States
- Carle Foundation Hospital, Mills Breast Cancer Institute, Urbana, Illinois, United States
- University of Illinois at Urbana-Champaign, Beckman Institute of Advanced Science and Technology, Urbana, Illinois, United States
- The Pennsylvania State University, Department of Nuclear Engineering, State College, Pennsylvania, United States
- The Pennsylvania State University, Department of Materials Science and Engineering, University Park, Pennsylvania, United States
- The Materials Research Institute, Millennium Science Complex, University Park, Pennsylvania, United States
- Huck Institutes of the Life Sciences, University Park, Pennsylvania, United States
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20
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Bhattacharya S, Sahoo A, Baitalik S. Human brain-inspired chemical artificial intelligence tools for the analysis and prediction of the anion-sensing characteristics of an imidazole-based luminescent Os(II)-bipyridine complex. Dalton Trans 2023; 52:6749-6762. [PMID: 37129261 DOI: 10.1039/d3dt00327b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Neural network and decision tree-based soft computing techniques are implemented in this work for the thorough analysis of the multichannel anion-sensing characteristics of an Os(II)-bipyridine complex derived from imidazole-4,5-bis(benzimidazole) ligand. With the aid of three imidazole NH protons in its outer coordination sphere, a substantial change in the spectral response as well as OsII/OsIII potential is made possible upon treating with anions of varying basicity. Initial hydrogen bonding between NH protons and anions and thereafter complete proton transfer from the complex backbone probably take place in the process. The deprotonation of the complex by specific anions and restoration to its original form by acid is also reversible. The responsiveness of the new compound is complex enough to imitate multiple sophisticated binary and ternary Boolean logic (BL) functions (NOT logic, combinational logic, traffic signal, set-reset flip-flop logic, and ternary NOR logic) by employing its spectral and redox outputs upon the action of suitable anions and acid in a proper sequence. Executing sensing investigations on altering the amount of the anions within a widespread range is often time-consuming and tedious. To overcome the lacuna, we implemented multiple soft computing techniques, viz., fuzzy logic (FL), artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and decision tree (DT) regression, for the thorough analysis and prediction of the experimentally observed results. The outcomes obtained from different techniques were compared among themselves as well as with the experimental data and utilized for the proper modeling of the anion-sensing behaviors of the complex.
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Affiliation(s)
- Sohini Bhattacharya
- Department of Chemistry, Inorganic Chemistry Section, Jadavpur University, Kolkata-700032, India.
| | - Anik Sahoo
- Department of Chemistry, Inorganic Chemistry Section, Jadavpur University, Kolkata-700032, India.
| | - Sujoy Baitalik
- Department of Chemistry, Inorganic Chemistry Section, Jadavpur University, Kolkata-700032, India.
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21
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Chan WCW. Principles of Nanoparticle Delivery to Solid Tumors. BME FRONTIERS 2023; 4:0016. [PMID: 37849661 PMCID: PMC10085247 DOI: 10.34133/bmef.0016] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 03/08/2023] [Indexed: 10/19/2023] Open
Abstract
The effective treatment of patients with cancer hinges on the delivery of therapeutics to a tumor site. Nanoparticles provide an essential transport system. We present 5 principles to consider when designing nanoparticles for cancer targeting: (a) Nanoparticles acquire biological identity in vivo, (b) organs compete for nanoparticles in circulation, (c) nanoparticles must enter solid tumors to target tumor components, (d) nanoparticles must navigate the tumor microenvironment for cellular or organelle targeting, and (e) size, shape, surface chemistry, and other physicochemical properties of nanoparticles influence their transport process to the target. This review article describes these principles and their application for engineering nanoparticle delivery systems to carry therapeutics to tumors or other disease targets.
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Affiliation(s)
- Warren C W Chan
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
- Terrence Donnelly Center for Cellular & Biomolecular Research, University of Toronto, Toronto, ON M5 3E1, Canada
- Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada
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22
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Scott-Fordsmand JJ, Amorim MJB. Using Machine Learning to make nanomaterials sustainable. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 859:160303. [PMID: 36410486 DOI: 10.1016/j.scitotenv.2022.160303] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 11/06/2022] [Accepted: 11/15/2022] [Indexed: 06/16/2023]
Abstract
Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.
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Affiliation(s)
| | - Mónica J B Amorim
- Department of Biology & CESAM, University of Aveiro, 3810-193 Aveiro, Portugal.
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23
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Masson JF, Biggins JS, Ringe E. Machine learning for nanoplasmonics. NATURE NANOTECHNOLOGY 2023; 18:111-123. [PMID: 36702956 DOI: 10.1038/s41565-022-01284-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 10/27/2022] [Indexed: 06/18/2023]
Abstract
Plasmonic nanomaterials have outstanding optoelectronic properties potentially enabling the next generation of catalysts, sensors, lasers and photothermal devices. Owing to optical and electron techniques, modern nanoplasmonics research generates large datasets characterizing features across length scales. Furthermore, optimizing syntheses leading to specific nanostructures requires time-consuming multiparametric approaches. These complex datasets and trial-and-error practices make nanoplasmonics research ripe for the application of machine learning (ML) and advanced data processing methods. ML algorithms capture relationships between synthesis, structure and performance in a way that far exceeds conventional simulation and theory approaches, enabling effective performance optimization. For example, neural networks can tailor the nanostructure morphology to target desired properties, identify synthetic conditions and extract quantitative information from complex data. Here we discuss the nascent field of ML for nanoplasmonics, describe the opportunities and limitations of ML in nanoplasmonic research, and conclude that ML is potentially transformative, especially if the community curates and shares its big data.
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Affiliation(s)
- Jean-Francois Masson
- Département de chimie, Quebec Center for Advanced Materials, Regroupement québécois sur les matériaux de pointe, and Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage, Université de Montréal, Montréal, Quebec, Canada.
| | - John S Biggins
- Engineering Department, University of Cambridge, Cambridge, UK.
| | - Emilie Ringe
- Department of Material Science and Metallurgy, University of Cambridge, Cambridge, UK.
- Department of Earth Science, University of Cambridge, Cambridge, UK.
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24
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Wang T, Russo DP, Bitounis D, Demokritou P, Jia X, Huang H, Zhu H. Integrating structure annotation and machine learning approaches to develop graphene toxicity models. CARBON 2023; 204:484-494. [PMID: 36845527 PMCID: PMC9957041 DOI: 10.1016/j.carbon.2022.12.065] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Modern nanotechnology provides efficient and cost-effective nanomaterials (NMs). The increasing usage of NMs arises great concerns regarding nanotoxicity in humans. Traditional animal testing of nanotoxicity is expensive and time-consuming. Modeling studies using machine learning (ML) approaches are promising alternatives to direct evaluation of nanotoxicity based on nanostructure features. However, NMs, including two-dimensional nanomaterials (2DNMs) such as graphenes, have complex structures making them difficult to annotate and quantify the nanostructures for modeling purposes. To address this issue, we constructed a virtual graphenes library using nanostructure annotation techniques. The irregular graphene structures were generated by modifying virtual nanosheets. The nanostructures were digitalized from the annotated graphenes. Based on the annotated nanostructures, geometrical nanodescriptors were computed using Delaunay tessellation approach for ML modeling. The partial least square regression (PLSR) models for the graphenes were built and validated using a leave-one-out cross-validation (LOOCV) procedure. The resulted models showed good predictivity in four toxicity-related endpoints with the coefficient of determination (R2) ranging from 0.558 to 0.822. This study provides a novel nanostructure annotation strategy that can be applied to generate high-quality nanodescriptors for ML model developments, which can be widely applied to nanoinformatics studies of graphenes and other NMs.
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Affiliation(s)
- Tong Wang
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA
| | - Daniel P. Russo
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA
| | - Dimitrios Bitounis
- Center for Nanotechnology and Nanotoxicology, Department of Environmental Health, T.H. Chan School of Public Health, Harvard University, 655 Huntington Ave, Boston, MA 02115, USA
- Nanoscience and Advanced Materials Center, Environmental Occupational Health Sciences Institute, School of Public Health, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Philip Demokritou
- Center for Nanotechnology and Nanotoxicology, Department of Environmental Health, T.H. Chan School of Public Health, Harvard University, 655 Huntington Ave, Boston, MA 02115, USA
- Nanoscience and Advanced Materials Center, Environmental Occupational Health Sciences Institute, School of Public Health, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Xuelian Jia
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA
| | - Heng Huang
- Department of Electrical and Computer Engineering, Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Boulevard, Pittsburgh, Pennsylvania, USA
| | - Hao Zhu
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA
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25
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A comprehensive deep learning method for empirical spectral prediction and its quantitative validation of nano-structured dimers. Sci Rep 2023; 13:1129. [PMID: 36670171 PMCID: PMC9860028 DOI: 10.1038/s41598-023-28076-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 01/12/2023] [Indexed: 01/22/2023] Open
Abstract
Nanophotonics exploits the best of photonics and nanotechnology which has transformed optics in recent years by allowing subwavelength structures to enhance light-matter interactions. Despite these breakthroughs, design, fabrication, and characterization of such exotic devices have remained through iterative processes which are often computationally costly, memory-intensive, and time-consuming. In contrast, deep learning approaches have recently shown excellent performance as practical computational tools, providing an alternate avenue for speeding up such nanophotonics simulations. This study presents a DNN framework for transmission, reflection, and absorption spectra predictions by grasping the hidden correlation between the independent nanostructure properties and their corresponding optical responses. The proposed DNN framework is shown to require a sufficient amount of training data to achieve an accurate approximation of the optical performance derived from computational models. The fully trained framework can outperform a traditional EM solution using on the COMSOL Multiphysics approach in terms of computational cost by three orders of magnitude. Furthermore, employing deep learning methodologies, the proposed DNN framework makes an effort to optimise design elements that influence the geometrical dimensions of the nanostructure, offering insight into the universal transmission, reflection, and absorption spectra predictions at the nanoscale. This paradigm improves the viability of complicated nanostructure design and analysis, and it has a lot of potential applications involving exotic light-matter interactions between nanostructures and electromagnetic fields. In terms of computational times, the designed algorithm is more than 700 times faster as compared to conventional FEM method (when manual meshing is used). Hence, this approach paves the way for fast yet universal methods for the characterization and analysis of the optical response of nanophotonic systems.
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26
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Wang Y, Zhou Y. Recent Progress on Anti-Humidity Strategies of Chemiresistive Gas Sensors. MATERIALS (BASEL, SWITZERLAND) 2022; 15:ma15248728. [PMID: 36556531 PMCID: PMC9784667 DOI: 10.3390/ma15248728] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 12/03/2022] [Accepted: 12/05/2022] [Indexed: 05/14/2023]
Abstract
In recent decades, chemiresistive gas sensors (CGS) have been widely studied due to their unique advantages of expedient miniaturization, simple fabrication, easy operation, and low cost. As one ubiquitous interference factor, humidity dramatically affects the performance of CGS, which has been neglected for a long time. With the rapid development of technologies based on gas sensors, including the internet of things (IoT), healthcare, environment monitoring, and food quality assessing, the humidity interference on gas sensors has been attracting increasing attention. Inspiringly, various anti-humidity strategies have been proposed to alleviate the humidity interference in this field; however, comprehensive summaries of these strategies are rarely reported. Therefore, this review aims to summarize the latest research advances on humidity-independent CGS. First, we discussed the humidity interference mechanism on gas sensors. Then, the anti-humidity strategies mainly including surface engineering, physical isolation, working parameters modulation, humidity compensation, and developing novel gas-sensing materials were successively introduced in detail. Finally, challenges and perspectives of improving the humidity tolerance of gas sensors were proposed for future research.
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27
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Singh M, Sharma D, Garg M, Kumar A, Baliyan A, Rani R, Kumar V. Current understanding of biological interactions and processing of DNA origami nanostructures: Role of machine learning and implications in drug delivery. Biotechnol Adv 2022; 61:108052. [DOI: 10.1016/j.biotechadv.2022.108052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 10/13/2022] [Accepted: 10/20/2022] [Indexed: 11/02/2022]
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28
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Williamson EM, Ghrist AM, Karadaghi LR, Smock SR, Barim G, Brutchey RL. Creating ground truth for nanocrystal morphology: a fully automated pipeline for unbiased transmission electron microscopy analysis. NANOSCALE 2022; 14:15327-15339. [PMID: 36214256 DOI: 10.1039/d2nr04292d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Control over colloidal nanocrystal morphology (size, size distribution, and shape) is important for tailoring the functionality of individual nanocrystals and their ensemble behavior. Despite this, traditional methods to quantify nanocrystal morphology are laborious. New developments in automated morphology classification will accelerate these analyses but the assessment of machine learning models is limited by human accuracy for ground truth, causing even unsupervised machine learning models to have inherent bias. Herein, we introduce synthetic image rendering to solve the ground truth problem of nanocrystal morphology classification. By simulating 2D images of nanocrystal shapes via a function of high-dimensional parameter space, we trained a convolutional neural network to link unique morphologies to their simulated parameters, defining nanocrystal morphology quantitatively rather than qualitatively. An automated pipeline then processes, quantitatively defines, and classifies nanocrystal morphology from experimental transmission electron microscopy (TEM) images. Using improved computer vision techniques, 42 650 nanocrystals were identified, assessed, and labeled with quantitative parameters, offering a 600-fold improvement in efficiency over best-practice manual measurements. A classification algorithm was trained with a prediction accuracy of 99.5%, which can successfully analyze a range of concave, convex, and irregular nanocrystal shapes. The resulting pipeline was applied to differentiating two syntheses of nominally cuboidal CsPbBr3 nanocrystals and uniquely classifying binary nickel sulfide nanocrystal phase based on morphology. This pipeline provides a simple, efficient, and unbiased method to quantify nanocrystal morphology and represents a practical route to construct large datasets with an absolute ground truth for training unbiased morphology-based machine learning algorithms.
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Affiliation(s)
- Emily M Williamson
- Department of Chemistry, University of Southern California, Los Angeles, CA 90089, USA.
| | - Aaron M Ghrist
- Department of Chemistry, University of Southern California, Los Angeles, CA 90089, USA.
| | - Lanja R Karadaghi
- Department of Chemistry, University of Southern California, Los Angeles, CA 90089, USA.
| | - Sara R Smock
- Department of Chemistry, University of Southern California, Los Angeles, CA 90089, USA.
| | - Gözde Barim
- Department of Chemistry, University of Southern California, Los Angeles, CA 90089, USA.
| | - Richard L Brutchey
- Department of Chemistry, University of Southern California, Los Angeles, CA 90089, USA.
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29
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Rebollo R, Oyoun F, Corvis Y, El-Hammadi MM, Saubamea B, Andrieux K, Mignet N, Alhareth K. Microfluidic Manufacturing of Liposomes: Development and Optimization by Design of Experiment and Machine Learning. ACS APPLIED MATERIALS & INTERFACES 2022; 14:39736-39745. [PMID: 36001743 DOI: 10.1021/acsami.2c06627] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Liposomes constitute the most exploited drug-nanocarrier with several liposomal drugs on the market. Microfluidic-based preparation methods stand up as a promising approach with high reproducibility and the ability to scale up. In this study, liposomes composed of DOPC, cholesterol, and DSPE-PEG 2000 with different molar ratios were fabricated using a microfluidic system. Process and conditions were optimized by applying design of experiments (DoE) principles. Furthermore, data were used to build an artificial neural network (ANN) model, to predict size and polydispersity index (PDI). Sets of runs were designed by DoE and performed on a micromixer microfluidic chip. Lipids' molar ratio and the process parameters, i.e. total flow rate (TFR) and flow rate ratio (FRR), were found to be the most influential factors on the formation of vesicles with target size and PDI under 100 nm and lower than 0.2, respectively. Size and PDI were predicted by the ANN model for 3 preparations with defined experimental conditions. The results showed no significant difference in size and PDI between the preparations and their values calculated with the ANN. In conclusion, production of optimized liposomes with high reproducibility was achieved by the application of microfluidic manufacturing processes, DoE, and Artificial Intelligence (AI). Microfluidic-based preparation methods assisted by computational tools would enable a faster development and clinical transfer of nanobased medications.
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Affiliation(s)
- René Rebollo
- Université Paris Cité, CNRS, INSERM, UTCBS (Chemical and Biological Technologies for Health Group), 4 avenue de l'observatoire, 75006Paris, France
| | - Feras Oyoun
- Université Paris Cité, CNRS, INSERM, UTCBS (Chemical and Biological Technologies for Health Group), 4 avenue de l'observatoire, 75006Paris, France
| | - Yohann Corvis
- Université Paris Cité, CNRS, INSERM, UTCBS (Chemical and Biological Technologies for Health Group), 4 avenue de l'observatoire, 75006Paris, France
| | - Mazen M El-Hammadi
- Department of Pharmacy and Pharmaceutical Technology, Faculty of Pharmacy, University of Seville, c/Prof. García González n◦2, 41012Seville, Spain
| | - Bruno Saubamea
- Université Paris Cité, US25 INSERM, UMS3612 CNRS, Plateforme Imagerie Cellulaire et Moléculaire, 75006Paris, France
| | - Karine Andrieux
- Université Paris Cité, CNRS, INSERM, UTCBS (Chemical and Biological Technologies for Health Group), 4 avenue de l'observatoire, 75006Paris, France
| | - Nathalie Mignet
- Université Paris Cité, CNRS, INSERM, UTCBS (Chemical and Biological Technologies for Health Group), 4 avenue de l'observatoire, 75006Paris, France
| | - Khair Alhareth
- Université Paris Cité, CNRS, INSERM, UTCBS (Chemical and Biological Technologies for Health Group), 4 avenue de l'observatoire, 75006Paris, France
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30
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Fan Z, Jiang C, Wang Y, Wang K, Marsh J, Zhang D, Chen X, Nie L. Engineered extracellular vesicles as intelligent nanosystems for next-generation nanomedicine. NANOSCALE HORIZONS 2022; 7:682-714. [PMID: 35662310 DOI: 10.1039/d2nh00070a] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Extracellular vesicles (EVs), as natural carriers of bioactive cargo, have a unique micro/nanostructure, bioactive composition, and characteristic morphology, as well as fascinating physical, chemical and biochemical features, which have shown promising application in the treatment of a wide range of diseases. However, native EVs have limitations such as lack of or inefficient cell targeting, on-demand delivery, and therapeutic feedback. Recently, EVs have been engineered to contain an intelligent core, enabling them to (i) actively target sites of disease, (ii) respond to endogenous and/or exogenous signals, and (iii) provide treatment feedback for optimal function in the host. These advances pave the way for next-generation nanomedicine and offer promise for a revolution in drug delivery. Here, we summarise recent research on intelligent EVs and discuss the use of "intelligent core" based EV systems for the treatment of disease. We provide a critique about the construction and properties of intelligent EVs, and challenges in their commercialization. We compare the therapeutic potential of intelligent EVs to traditional nanomedicine and highlight key advantages for their clinical application. Collectively, this review aims to provide a new insight into the design of next-generation EV-based theranostic platforms for disease treatment.
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Affiliation(s)
- Zhijin Fan
- School of Medicine, South China University of Technology, Guangzhou 510006, P. R. China.
- Research Center of Medical Sciences, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, P. R. China
| | - Cheng Jiang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Yichao Wang
- Department of Clinical Laboratory Medicine, Tai Zhou Central Hospital (Taizhou University Hospital), Taizhou 318000, P. R. China
| | - Kaiyuan Wang
- Department of Pharmaceutics, Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, 110016, P. R. China
| | - Jade Marsh
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Da Zhang
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, P. R. China.
| | - Xin Chen
- School of Chemical Engineering and Technology, Shaanxi Key Laboratory of Energy Chemical Process Intensification, Institute of Polymer Science in Chemical Engineering, Xi'an Jiao Tong University, Xi'an 710049, P. R. China.
| | - Liming Nie
- Research Center of Medical Sciences, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, P. R. China
- School of Medicine, South China University of Technology, Guangzhou 510006, P. R. China.
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31
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Guo K, Buehler MJ. Rapid prediction of protein natural frequencies using graph neural networks. DIGITAL DISCOVERY 2022; 1:277-285. [PMID: 35769204 PMCID: PMC9189858 DOI: 10.1039/d1dd00007a] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 03/28/2022] [Indexed: 11/21/2022]
Abstract
Natural vibrational frequencies of proteins help to correlate functional shifts with sequence or geometric variations that lead to negligible changes in protein structures, such as point mutations related to disease lethality or medication effectiveness. Normal mode analysis is a well-known approach to accurately obtain protein natural frequencies. However, it is not feasible when high-resolution protein structures are not available or time consuming to obtain. Here we provide a machine learning model to directly predict protein frequencies from primary amino acid sequences and low-resolution structural features such as contact or distance maps. We utilize a graph neural network called principal neighborhood aggregation, trained with the structural graphs and normal mode frequencies of more than 34 000 proteins from the protein data bank. combining with existing contact/distance map prediction tools, this approach enables an end-to-end prediction of the frequency spectrum of a protein given its primary sequence.
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Affiliation(s)
- Kai Guo
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology 77 Massachusetts Ave. 1-165 Cambridge Massachusetts 02139 USA +1 617 452 2750
- Institute of High Performance Computing, ASTAR Singapore 138632 Singapore
| | - Markus J Buehler
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology 77 Massachusetts Ave. 1-165 Cambridge Massachusetts 02139 USA +1 617 452 2750
- Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology 77 Massachusetts Ave. Cambridge Massachusetts 02139 USA
- Center for Materials Science and Engineering 77 Massachusetts Ave Cambridge Massachusetts 02139 USA
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32
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Olszta M, Hopkins D, Fiedler KR, Oostrom M, Akers S, Spurgeon SR. An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2022; 28:1-11. [PMID: 35686442 DOI: 10.1017/s1431927622012065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Artificial intelligence (AI) promises to reshape scientific inquiry and enable breakthrough discoveries in areas such as energy storage, quantum computing, and biomedicine. Scanning transmission electron microscopy (STEM), a cornerstone of the study of chemical and materials systems, stands to benefit greatly from AI-driven automation. However, present barriers to low-level instrument control, as well as generalizable and interpretable feature detection, make truly automated microscopy impractical. Here, we discuss the design of a closed-loop instrument control platform guided by emerging sparse data analytics. We hypothesize that a centralized controller, informed by machine learning combining limited a priori knowledge and task-based discrimination, could drive on-the-fly experimental decision-making. This platform may unlock practical, automated analysis of a variety of material features, enabling new high-throughput and statistical studies.
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Affiliation(s)
- Matthew Olszta
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Derek Hopkins
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Kevin R Fiedler
- College of Arts and Sciences, Washington State University - Tri-Cities, Richland, WA 99354, USA
| | - Marjolein Oostrom
- National Security Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Sarah Akers
- National Security Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Steven R Spurgeon
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
- Department of Physics, University of Washington, Seattle, WA 98195, USA
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33
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Kumar R. Materiomically Designed Polymeric Vehicles for Nucleic Acids: Quo Vadis? ACS APPLIED BIO MATERIALS 2022; 5:2507-2535. [PMID: 35642794 DOI: 10.1021/acsabm.2c00346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Despite rapid advances in molecular biology, particularly in site-specific genome editing technologies, such as CRISPR/Cas9 and base editing, financial and logistical challenges hinder a broad population from accessing and benefiting from gene therapy. To improve the affordability and scalability of gene therapy, we need to deploy chemically defined, economical, and scalable materials, such as synthetic polymers. For polymers to deliver nucleic acids efficaciously to targeted cells, they must optimally combine design attributes, such as architecture, length, composition, spatial distribution of monomers, basicity, hydrophilic-hydrophobic phase balance, or protonation degree. Designing polymeric vectors for specific nucleic acid payloads is a multivariate optimization problem wherein even minuscule deviations from the optimum are poorly tolerated. To explore the multivariate polymer design space rapidly, efficiently, and fruitfully, we must integrate parallelized polymer synthesis, high-throughput biological screening, and statistical modeling. Although materiomics approaches promise to streamline polymeric vector development, several methodological ambiguities must be resolved. For instance, establishing a flexible polymer ontology that accommodates recent synthetic advances, enforcing uniform polymer characterization and data reporting standards, and implementing multiplexed in vitro and in vivo screening studies require considerable planning, coordination, and effort. This contribution will acquaint readers with the challenges associated with materiomics approaches to polymeric gene delivery and offers guidelines for overcoming these challenges. Here, we summarize recent developments in combinatorial polymer synthesis, high-throughput screening of polymeric vectors, omics-based approaches to polymer design, barcoding schemes for pooled in vitro and in vivo screening, and identify materiomics-inspired research directions that will realize the long-unfulfilled clinical potential of polymeric carriers in gene therapy.
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Affiliation(s)
- Ramya Kumar
- Department of Chemical & Biological Engineering, Colorado School of Mines, 1613 Illinois St, Golden, Colorado 80401, United States
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34
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Tan EX, Chen Y, Lee YH, Leong YX, Leong SX, Stanley CV, Pun CS, Ling XY. Incorporating plasmonic featurization with machine learning to achieve accurate and bidirectional prediction of nanoparticle size and size distribution. NANOSCALE HORIZONS 2022; 7:626-633. [PMID: 35507320 DOI: 10.1039/d2nh00146b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Determination of nanoparticle size and size distribution is important because these key parameters dictate nanomaterials' properties and applications. Yet, it is only accomplishable using low-throughput electron microscopy. Herein, we incorporate plasmonic-domain-driven feature engineering with machine learning (ML) for accurate and bidirectional prediction of both parameters for complete characterization of nanoparticle ensembles. Using gold nanospheres as our model system, our ML approach achieves the lowest prediction errors of 2.3% and ±1.0 nm for ensemble size and size distribution respectively, which is 3-6 times lower than previously reported ML or Mie approaches. Knowledge elicitation from the plasmonic domain and concomitant translation into featurization allow us to mitigate noise and boost data interpretability. This enables us to overcome challenges arising from size anisotropy and small sample size limitations to achieve highly generalizable ML models. We further showcase inverse prediction capabilities, using size and size distribution as inputs to generate spectra with LSPRs that closely match experimental data. This work illustrates a ML-empowered total nanocharacterization strategy that is rapid (<30 s), versatile, and applicable over a wide size range of 200 nm.
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Affiliation(s)
- Emily Xi Tan
- Division of Chemistry and Biological Chemistry, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, 637371, Singapore.
| | - Yichao Chen
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, 637371, Singapore
| | - Yih Hong Lee
- Division of Chemistry and Biological Chemistry, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, 637371, Singapore.
| | - Yong Xiang Leong
- Division of Chemistry and Biological Chemistry, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, 637371, Singapore.
| | - Shi Xuan Leong
- Division of Chemistry and Biological Chemistry, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, 637371, Singapore.
| | - Chelsea Violita Stanley
- Division of Chemistry and Biological Chemistry, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, 637371, Singapore.
| | - Chi Seng Pun
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, 637371, Singapore
| | - Xing Yi Ling
- Division of Chemistry and Biological Chemistry, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, 637371, Singapore.
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35
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Modulating the Filamentary-Based Resistive Switching Properties of HfO2 Memristive Devices by Adding Al2O3 Layers. ELECTRONICS 2022. [DOI: 10.3390/electronics11101540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The resistive switching properties of HfO2 based 1T-1R memristive devices are electrically modified by adding ultra-thin layers of Al2O3 into the memristive device. Three different types of memristive stacks are fabricated in the 130 nm CMOS technology of IHP. The switching properties of the memristive devices are discussed with respect to forming voltages, low resistance state and high resistance state characteristics and their variabilities. The experimental I–V characteristics of set and reset operations are evaluated by using the quantum point contact model. The properties of the conduction filament in the on and off states of the memristive devices are discussed with respect to the model parameters obtained from the QPC fit.
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36
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Moreira M, Hillenkamp M, Divitini G, Tizei LHG, Ducati C, Cotta MA, Rodrigues V, Ugarte D. Improving Quantitative EDS Chemical Analysis of Alloy Nanoparticles by PCA Denoising: Part II. Uncertainty Intervals. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2022; 28:1-9. [PMID: 35431023 DOI: 10.1017/s1431927622000551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Analytical studies of nanoparticles (NPs) are frequently based on huge datasets derived from hyperspectral images acquired using scanning transmission electron microscopy. These large datasets require machine learning computational tools to reduce dimensionality and extract relevant information. Principal component analysis (PCA) is a commonly used procedure to reconstruct information and generate a denoised dataset; however, several open questions remain regarding the accuracy and precision of reconstructions. Here, we use experiments and simulations to test the effect of PCA processing on data obtained from AuAg alloy NPs a few nanometers wide with different compositions. This study aims to address the reliability of chemical quantification after PCA processing. Our results show that the PCA treatment mitigates the contribution of Poisson noise and leads to better quantification, indicating that denoised results may be reliable from the point of view of both uncertainty and accuracy for properly planned experiments. However, the initial data need to be of sufficient quality: these results can only be obtained if the signal-to-noise ratio of input data exceeds a minimal value to avoid the occurrence of random noise bias in the PCA reconstructions.
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Affiliation(s)
- Murilo Moreira
- Instituto de Fisica "Gleb Wataghin", Universidade Estadual de Campinas-UNICAMP, 13083-859 Campinas, SP, Brazil
| | - Matthias Hillenkamp
- Instituto de Fisica "Gleb Wataghin", Universidade Estadual de Campinas-UNICAMP, 13083-859 Campinas, SP, Brazil
- Institute of Light and Matter, Université Claude Bernard Lyon 1, CNRS, UMR5306, F-69622 Villeurbanne, France
| | - Giorgio Divitini
- Department of Materials Science and Metallurgy, University of Cambridge, CambridgeCB3 0FS, UK
- Electron Spectroscopy and Nanoscopy Group, Istituto Italiano di Tecnologia, via Morego 30, Genoa, Italy
| | - Luiz H G Tizei
- Laboratoire de Physique des Solides, Université Paris-Saclay, CNRS, 91405 Orsay, France
| | - Caterina Ducati
- Department of Materials Science and Metallurgy, University of Cambridge, CambridgeCB3 0FS, UK
| | - Monica A Cotta
- Instituto de Fisica "Gleb Wataghin", Universidade Estadual de Campinas-UNICAMP, 13083-859 Campinas, SP, Brazil
| | - Varlei Rodrigues
- Instituto de Fisica "Gleb Wataghin", Universidade Estadual de Campinas-UNICAMP, 13083-859 Campinas, SP, Brazil
| | - Daniel Ugarte
- Instituto de Fisica "Gleb Wataghin", Universidade Estadual de Campinas-UNICAMP, 13083-859 Campinas, SP, Brazil
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37
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Beckham JL, Wyss KM, Xie Y, McHugh EA, Li JT, Advincula PA, Chen W, Lin J, Tour JM. Machine Learning Guided Synthesis of Flash Graphene. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2106506. [PMID: 35064973 DOI: 10.1002/adma.202106506] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 12/15/2021] [Indexed: 06/14/2023]
Abstract
Advances in nanoscience have enabled the synthesis of nanomaterials, such as graphene, from low-value or waste materials through flash Joule heating. Though this capability is promising, the complex and entangled variables that govern nanocrystal formation in the Joule heating process remain poorly understood. In this work, machine learning (ML) models are constructed to explore the factors that drive the transformation of amorphous carbon into graphene nanocrystals during flash Joule heating. An XGBoost regression model of crystallinity achieves an r2 score of 0.8051 ± 0.054. Feature importance assays and decision trees extracted from these models reveal key considerations in the selection of starting materials and the role of stochastic current fluctuations in flash Joule heating synthesis. Furthermore, partial dependence analyses demonstrate the importance of charge and current density as predictors of crystallinity, implying a progression from reaction-limited to diffusion-limited kinetics as flash Joule heating parameters change. Finally, a practical application of the ML models is shown by using Bayesian meta-learning algorithms to automatically improve bulk crystallinity over many Joule heating reactions. These results illustrate the power of ML as a tool to analyze complex nanomanufacturing processes and enable the synthesis of 2D crystals with desirable properties by flash Joule heating.
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Affiliation(s)
- Jacob L Beckham
- Department of Chemistry, Rice University, 6100 Main Street MS 222, Houston, TX, 77005, USA
| | - Kevin M Wyss
- Department of Chemistry, Rice University, 6100 Main Street MS 222, Houston, TX, 77005, USA
| | - Yunchao Xie
- Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, MO, 65211, USA
| | - Emily A McHugh
- Department of Chemistry, Rice University, 6100 Main Street MS 222, Houston, TX, 77005, USA
| | - John Tianci Li
- Department of Chemistry, Rice University, 6100 Main Street MS 222, Houston, TX, 77005, USA
| | - Paul A Advincula
- Department of Chemistry, Rice University, 6100 Main Street MS 222, Houston, TX, 77005, USA
| | - Weiyin Chen
- Department of Chemistry, Rice University, 6100 Main Street MS 222, Houston, TX, 77005, USA
| | - Jian Lin
- Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, MO, 65211, USA
| | - James M Tour
- Department of Chemistry, Smalley-Curl Institute, NanoCarbon Center, Welch Institute for Advanced Materials, Department of Materials Science and Nanoengineering, Department of Computer Science, Rice University, 6100 Main Street MS 222, Houston, TX, 77005, USA
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38
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Effective of Smart Mathematical Model by Machine Learning Classifier on Big Data in Healthcare Fast Response. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:6927170. [PMID: 35251298 PMCID: PMC8890881 DOI: 10.1155/2022/6927170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 02/02/2022] [Accepted: 02/07/2022] [Indexed: 11/17/2022]
Abstract
In the past few years, big data related to healthcare has become more important, due to the abundance of data, the increasing cost of healthcare, and the privacy of healthcare. Create, analyze, and process large and complex data that cannot be processed by traditional methods. The proposed method is based on classifying data into several classes using the data weight derived from the features extracted from the big data. Three important criteria were used to evaluate the study as well as to benchmark the current study with previous studies using a standard dataset.
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39
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Glavin NR, Ajayan PM, Kar S. Quantum Materials Manufacturing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022:e2109892. [PMID: 35195312 DOI: 10.1002/adma.202109892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 01/13/2022] [Indexed: 06/14/2023]
Abstract
The quantum age is just around the corner. As quantum systems become more stable, robust, and mainstream, tackling the challenge of high-throughput manufacturing will require further developments in materials synthesis, characterization, assembly, and diagnostics. As the building blocks of future technologies scale down to atomic and molecular scales, a paradigm shift in manufacturing will begin to take shape. Inspired by a quantum manufacturing world that elevates the Materials Genome Initiative to the next level, a "human-in-the-loop" framework for high-throughput manufacturing, which addresses key opportunities and challenges to be overcome, is outlined.
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Affiliation(s)
- Nicholas R Glavin
- Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, OH, 45433, USA
| | - Pulickel M Ajayan
- Materials Science and Nano Engineering, Rice University, Houston, TX, 77005, USA
| | - Swastik Kar
- Department of Physics, Northeastern University, Boston, MA, 02115, USA
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40
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Liu K, Salvati A, Sabirsh A. Physiology, pathology and the biomolecular corona: the confounding factors in nanomedicine design. NANOSCALE 2022; 14:2136-2154. [PMID: 35103268 DOI: 10.1039/d1nr08101b] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The biomolecular corona that forms on nanomedicines in different physiological and pathological environments confers a new biological identity. How the recipient biological system's state can potentially affect nanomedicine corona formation, and how this can be modulated, remains obscure. With this perspective, this review summarizes the current knowledge about the content of biological fluids in various compartments and how they can be affected by pathological states, thus impacting biomolecular corona formation. The content of representative biological fluids is explored, and the urgency of integrating corona formation, as an essential component of nanomedicine designs for effective cargo delivery, is highlighted.
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Affiliation(s)
- Kai Liu
- Advanced Drug Delivery, Pharmaceutical Sciences, R&D, AstraZeneca, Gothenburg, Sweden.
| | - Anna Salvati
- Department of Nanomedicine & Drug Targeting, Groningen Research Institute of Pharmacy, University of Groningen, Groningen 9713AV, The Netherlands
| | - Alan Sabirsh
- Advanced Drug Delivery, Pharmaceutical Sciences, R&D, AstraZeneca, Gothenburg, Sweden.
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41
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Verma S, Chugh S, Ghosh S, Rahman BMA. Artificial Neural Network Modelling for Optimizing the Optical Parameters of Plasmonic Paired Nanostructures. NANOMATERIALS 2022; 12:nano12010170. [PMID: 35010120 PMCID: PMC8746605 DOI: 10.3390/nano12010170] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 12/26/2021] [Accepted: 12/29/2021] [Indexed: 02/01/2023]
Abstract
The Artificial Neural Network (ANN) has become an attractive approach in Machine Learning (ML) to analyze a complex data-driven problem. Due to its time efficient findings, it has became popular in many scientific fields such as physics, optics, and material science. This paper presents a new approach to design and optimize the electromagnetic plasmonic nanostructures using a computationally efficient method based on the ANN. In this work, the nanostructures have been simulated by using a Finite Element Method (FEM), then Artificial Intelligence (AI) is used for making predictions of associated sensitivity (S), Full Width Half Maximum (FWHM), Figure of Merit (FOM), and Plasmonic Wavelength (PW) for different paired nanostructures. At first, the computational model is developed by using a Finite Element Method (FEM) to prepare the dataset. The input parameters were considered as the Major axis, a, the Minor axis, b, and the separation gap, g, which have been used to calculate the corresponding sensitivity (nm/RIU), FWHM (nm), FOM, and plasmonic wavelength (nm) to prepare the dataset. Secondly, the neural network has been designed where the number of hidden layers and neurons were optimized as part of a comprehensive analysis to improve the efficiency of ML model. After successfully optimizing the neural network, this model is used to make predictions for specific inputs and its corresponding outputs. This article also compares the error between the predicted and simulated results. This approach outperforms the direct numerical simulation methods for predicting output for various input device parameters.
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Affiliation(s)
- Sneha Verma
- School of Mathematics, Computer Science and Engineering, City University of London, London EC1V 0HB, UK; (S.C.); (B.M.A.R.)
- Correspondence:
| | - Sunny Chugh
- School of Mathematics, Computer Science and Engineering, City University of London, London EC1V 0HB, UK; (S.C.); (B.M.A.R.)
| | - Souvik Ghosh
- Department of Electronics and Electrical Engineering, University College London, London EC1V 0HB, UK;
| | - B. M. Azizur Rahman
- School of Mathematics, Computer Science and Engineering, City University of London, London EC1V 0HB, UK; (S.C.); (B.M.A.R.)
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42
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Niu B, Chen Y, Zhang L, Tan J. Organic–inorganic hybrid nanomaterials prepared via polymerization-induced self-assembly: recent developments and future opportunities. Polym Chem 2022. [DOI: 10.1039/d2py00180b] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
This review highlights recent developments in the preparation of organic–inorganic hybrid nanomaterials via polymerization-induced self-assembly.
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Affiliation(s)
- Bing Niu
- Department of Polymeric Materials and Engineering, School of Materials and Energy, Guangdong University of Technology, Guangzhou 510006, China
| | - Ying Chen
- Guangdong Provincial Key Laboratory of Functional Soft Condensed Matter, Guangzhou 510006, China
| | - Li Zhang
- Department of Polymeric Materials and Engineering, School of Materials and Energy, Guangdong University of Technology, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory of Functional Soft Condensed Matter, Guangzhou 510006, China
| | - Jianbo Tan
- Department of Polymeric Materials and Engineering, School of Materials and Energy, Guangdong University of Technology, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory of Functional Soft Condensed Matter, Guangzhou 510006, China
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43
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Smith CW, Hizir MS, Nandu N, Yigit MV. Algorithmically Guided Optical Nanosensor Selector (AGONS): Guiding Data Acquisition, Processing, and Discrimination for Biological Sampling. Anal Chem 2021; 94:1195-1202. [PMID: 34964601 DOI: 10.1021/acs.analchem.1c04379] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Here, we report a biomarker-free detection of various biological targets through a programmed machine learning algorithm and an automated computational selection process termed algorithmically guided optical nanosensor selector (AGONS). The optical data processed/used by algorithms are obtained through a nanosensor array selected from a library of nanosensors through AGONS. The nanosensors are assembled using two-dimensional nanoparticles (2D-nps) and fluorescently labeled single-stranded DNAs (F-ssDNAs) with random sequences. Both 2D-np and F-ssDNA components are cost-efficient and easy to synthesize, allowing for scaled-up data collection essential for machine learning modeling. The nanosensor library was subjected to various target groups, including proteins, breast cancer cells, and lethal-7 (let-7) miRNA mimics. We have demonstrated that AGONS could select the most essential nanosensors while achieving 100% predictive accuracy in all cases. With this approach, we demonstrate that machine learning can guide the design of nanosensor arrays with greater predictive accuracy while minimizing manpower, material cost, computational resources, instrumentation usage, and time. The biomarker-free detection attribute makes this approach readily available for biological targets without any detectable biomarker. We believe that AGONS can guide optical nanosensor array setups, opening broader opportunities through a biomarker-free detection approach for most challenging biological targets.
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Affiliation(s)
- Christopher W Smith
- Department of Chemistry, University at Albany, State University of New York, 1400 Washington Avenue, Albany, New York 12222, United States.,The RNA Institute, University at Albany, State University of New York, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Mustafa Salih Hizir
- Department of Chemistry, University at Albany, State University of New York, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Nidhi Nandu
- Department of Chemistry, University at Albany, State University of New York, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Mehmet V Yigit
- Department of Chemistry, University at Albany, State University of New York, 1400 Washington Avenue, Albany, New York 12222, United States.,The RNA Institute, University at Albany, State University of New York, 1400 Washington Avenue, Albany, New York 12222, United States
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44
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Bouchet D, Rachbauer LM, Rotter S, Mosk AP, Bossy E. Optimal Control of Coherent Light Scattering for Binary Decision Problems. PHYSICAL REVIEW LETTERS 2021; 127:253902. [PMID: 35029434 DOI: 10.1103/physrevlett.127.253902] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 11/18/2021] [Indexed: 05/25/2023]
Abstract
Because of quantum noise fluctuations, the rate of error achievable in decision problems involving several possible configurations of a scattering system is subject to a fundamental limit known as the Helstrom bound. Here, we present a general framework to calculate and minimize this bound using coherent probe fields with tailored spatial distributions. As an example, we experimentally study a target located in between two disordered scattering media. We first show that the optimal field distribution can be directly identified using a general approach based on scattering matrix measurements. We then demonstrate that this optimal light field successfully probes the presence of the target with a number of photons that is reduced by more than 2 orders of magnitude as compared to unoptimized fields.
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Affiliation(s)
- Dorian Bouchet
- Université Grenoble Alpes, CNRS, LIPhy, 38000 Grenoble, France
| | - Lukas M Rachbauer
- Institute for Theoretical Physics, Vienna University of Technology (TU Wien), 1040 Vienna, Austria
| | - Stefan Rotter
- Institute for Theoretical Physics, Vienna University of Technology (TU Wien), 1040 Vienna, Austria
| | - Allard P Mosk
- Nanophotonics, Debye Institute for Nanomaterials Science and Center for Extreme Matter and Emergent Phenomena, Utrecht University, P.O. Box 80000, 3508 TA Utrecht, Netherlands
| | - Emmanuel Bossy
- Université Grenoble Alpes, CNRS, LIPhy, 38000 Grenoble, France
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45
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Pang J, Bachmatiuk A, Yang F, Liu H, Zhou W, Rümmeli MH, Cuniberti G. Applications of Carbon Nanotubes in the Internet of Things Era. NANO-MICRO LETTERS 2021; 13:191. [PMID: 34510300 PMCID: PMC8435483 DOI: 10.1007/s40820-021-00721-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 08/11/2021] [Indexed: 05/07/2023]
Abstract
The post-Moore's era has boosted the progress in carbon nanotube-based transistors. Indeed, the 5G communication and cloud computing stimulate the research in applications of carbon nanotubes in electronic devices. In this perspective, we deliver the readers with the latest trends in carbon nanotube research, including high-frequency transistors, biomedical sensors and actuators, brain-machine interfaces, and flexible logic devices and energy storages. Future opportunities are given for calling on scientists and engineers into the emerging topics.
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Affiliation(s)
- Jinbo Pang
- Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy, Institute for Advanced Interdisciplinary Research (iAIR), Universities of Shandong, University of Jinan, Shandong, Jinan, 250022, People's Republic of China.
| | - Alicja Bachmatiuk
- PORT Polish Center for Technology Development, Łukasiewicz Research Network, Ul. Stabłowicka 147, 54-066, Wrocław, Poland
- Centre of Polymer and Carbon Materials, Polish Academy of Sciences, M. Curie-Sklodowskiej 34, 41-819, Zabrze, Poland
| | - Feng Yang
- Department of Chemistry, Southern University of Science and Technology, Shenzhen, 518055, People's Republic of China
| | - Hong Liu
- Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy, Institute for Advanced Interdisciplinary Research (iAIR), Universities of Shandong, University of Jinan, Shandong, Jinan, 250022, People's Republic of China
- State Key Laboratory of Crystal Materials, Center of Bio & Micro/Nano Functional Materials, Shandong University, 27 Shandanan Road, Jinan, 250100, People's Republic of China
| | - Weijia Zhou
- Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy, Institute for Advanced Interdisciplinary Research (iAIR), Universities of Shandong, University of Jinan, Shandong, Jinan, 250022, People's Republic of China
| | - Mark H Rümmeli
- College of Energy, Institute for Energy and Materials Innovations, Soochow University, Suzhou, Soochow, 215006, People's Republic of China
- Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province, Soochow University, Suzhou, 215006, People's Republic of China
- Centre of Polymer and Carbon Materials, Polish Academy of Sciences, M. Curie Sklodowskiej 34, 41-819, Zabrze, Poland
- Institute for Complex Materials, Leibniz Institute for Solid State and Materials Research Dresden (IFW Dresden), 20 Helmholtz Strasse, 01069, Dresden, Germany
- Institute of Environmental Technology, VŠB-Technical University of Ostrava, 17. Listopadu 15, Ostrava, 708 33, Czech Republic
| | - Gianaurelio Cuniberti
- Institute for Materials Science and Max Bergmann Center of Biomaterials, Center for Advancing Electronics Dresden, Technische Universität Dresden, 01069, Dresden, Germany.
- Dresden Center for Computational Materials Science, Dresden Center for Intelligent Materials (GCL DCIM), Technische Universität Dresden, 01062, Dresden, Germany.
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46
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Abstract
The field of nanotechnology has been a significant research focus in the last thirty years. This emphasis is due to the unique optical, electrical, magnetic, chemical and biological properties of materials approximately ten thousand times smaller than the diameter of a hair strand. Researchers have developed methods to synthesize and characterize large libraries of nanomaterials and have demonstrated their preclinical utility. We have entered a new phase of nanomedicine development, where the focus is to translate these technologies to benefit patients. This review article provides an overview of nanomedicine's unique properties, the current state of the field, and discusses the challenge of clinical translation. Finally, we discuss the need to build and strengthen partnerships between engineers and clinicians to create a feedback loop between the bench and bedside. This partnership will guide fundamental studies on the nanoparticle-biological interactions, address clinical challenges and change the development and evaluation of new drug delivery systems, sensors, imaging agents and therapeutic systems.
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Affiliation(s)
- Shrey Sindhwani
- From the, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Warren C W Chan
- From the, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.,Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada.,Department of Chemistry, University of Toronto, Toronto, ON, Canada.,Faculty of Applied Science and Engineering, University of Toronto, Toronto, ON, Canada.,Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.,Materials Science and Engineering, University of Toronto, Toronto, ON, Canada
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47
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Rincón-López J, Almanza-Arjona YC, Riascos AP, Rojas-Aguirre Y. When Cyclodextrins Met Data Science: Unveiling Their Pharmaceutical Applications through Network Science and Text-Mining. Pharmaceutics 2021; 13:1297. [PMID: 34452258 PMCID: PMC8399453 DOI: 10.3390/pharmaceutics13081297] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 08/14/2021] [Accepted: 08/16/2021] [Indexed: 12/21/2022] Open
Abstract
We present a data-driven approach to unveil the pharmaceutical technologies of cyclodextrins (CDs) by analyzing a dataset of CD pharmaceutical patents. First, we implemented network science techniques to represent CD patents as a single structure and provide a framework for unsupervised detection of keywords in the patent dataset. Guided by those keywords, we further mined the dataset to examine the patenting trends according to CD-based dosage forms. CD patents formed complex networks, evidencing the supremacy of CDs for solubility enhancement and how this has triggered cutting-edge applications based on or beyond the solubility improvement. The networks exposed the significance of CDs to formulate aqueous solutions, tablets, and powders. Additionally, they highlighted the role of CDs in formulations of anti-inflammatory drugs, cancer therapies, and antiviral strategies. Text-mining showed that the trends in CDs for aqueous solutions, tablets, and powders are going upward. Gels seem to be promising, while patches and fibers are emerging. Cyclodextrins' potential in suspensions and emulsions is yet to be recognized and can become an opportunity area. This is the first unsupervised/supervised data-mining approach aimed at depicting a landscape of CDs to identify trending and emerging technologies and uncover opportunity areas in CD pharmaceutical research.
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Affiliation(s)
- Juliana Rincón-López
- Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Ciudad Universitaria, Mexico City 04510, Mexico;
| | - Yara C. Almanza-Arjona
- Instituto de Ciencias Aplicadas y Tecnología, Universidad Nacional Autónoma de México, Ciudad Universitaria, Mexico City 04510, Mexico;
| | - Alejandro P. Riascos
- Instituto de Física, Universidad Nacional Autónoma de México, Ciudad Universitaria, Mexico City 04510, Mexico
| | - Yareli Rojas-Aguirre
- Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Ciudad Universitaria, Mexico City 04510, Mexico;
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48
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Azuri I, Rosenhek-Goldian I, Regev-Rudzki N, Fantner G, Cohen SR. The role of convolutional neural networks in scanning probe microscopy: a review. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2021; 12:878-901. [PMID: 34476169 PMCID: PMC8372315 DOI: 10.3762/bjnano.12.66] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 07/23/2021] [Indexed: 05/13/2023]
Abstract
Progress in computing capabilities has enhanced science in many ways. In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials design through image analysis. Deep learning has the ability to identify abstract characteristics embedded within a data set, subsequently using that association to categorize, identify, and isolate subsets of the data. Scanning probe microscopy measures multimodal surface properties, combining morphology with electronic, mechanical, and other characteristics. In this review, we focus on a subset of deep learning algorithms, that is, convolutional neural networks, and how it is transforming the acquisition and analysis of scanning probe data.
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Affiliation(s)
- Ido Azuri
- Weizmann Institute of Science, Department of Life Sciences Core Facilities, Rehovot 76100, Israel
| | - Irit Rosenhek-Goldian
- Weizmann Institute of Science, Department of Chemical Research Support, Rehovot 76100, Israel
| | - Neta Regev-Rudzki
- Weizmann Institute of Science, Department of Biomolecular Sciences, Rehovot 76100, Israel
| | - Georg Fantner
- École Polytechnique Fédérale de Lausanne, Laboratory for Bio- and Nano-Instrumentation, CH1015 Lausanne, Switzerland
| | - Sidney R Cohen
- Weizmann Institute of Science, Department of Chemical Research Support, Rehovot 76100, Israel
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Peng F, Jeong S, Ho A, Evans CL. Recent progress in plasmonic nanoparticle-based biomarker detection and cytometry for the study of central nervous system disorders. Cytometry A 2021; 99:1067-1078. [PMID: 34328262 DOI: 10.1002/cyto.a.24489] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/28/2021] [Accepted: 07/19/2021] [Indexed: 11/07/2022]
Abstract
Neurological disorders affect hundreds of millions of people around the world, are often life-threatening, untreatable, and can result in debilitating symptoms. The high prevalence of these disorders, which feature biochemical or structural abnormalities in neuronal systems, has spurned innovations in both rapid and early detection to assist in the selection of appropriate treatment strategies to improve the patients' quality of life. Plasmonic nanoparticles (PNPs), a versatile and promising class of nanomaterials, are widely utilized in numerous imaging techniques, drug delivery systems, and biomarker detection methods. Recently, PNP-based nanoprobes have attracted considerable attention for the early diagnosis of neurological disorders. Gold nanoparticles (AuNPs), with high local surface plasmon resonance (LSPR) signals, have been particularly well exploited as probes for dynamic biomarker detection, with quantification sensitivity demonstrated down to the single-molecule level. In this review, we will discuss the possibilities of PNPs in the methodological development for rapid neurological disease identification. In addition, we will also describe a new digital cytometry method that combines dark-field imaging and machine learning for precise biomarker enumeration on single cells. The aim of this review is to attract researchers working on the future development of new plasmonic nanoprobe-based strategies for the diagnosis of neurological disorders.
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Affiliation(s)
- Fei Peng
- Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Sinyoung Jeong
- Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Alexander Ho
- Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Conor L Evans
- Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
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50
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Tang Y, Zhang J, He D, Miao W, Liu W, Li Y, Lu G, Wu F, Wang S. GANDA: A deep generative adversarial network conditionally generates intratumoral nanoparticles distribution pixels-to-pixels. J Control Release 2021; 336:336-343. [PMID: 34197860 DOI: 10.1016/j.jconrel.2021.06.039] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/19/2021] [Accepted: 06/25/2021] [Indexed: 12/22/2022]
Abstract
Intratumoral nanoparticles (NPs) distribution is critical for the success of nanomedicine in imaging and treatment, but computational models to describe the NPs distribution remain unavailable due to the complex tumor-nano interactions. Here, we develop a Generative Adversarial Network for Distribution Analysis (GANDA) to describe and conditionally generates the intratumoral quantum dots (QDs) distribution after i.v. injection. This deep generative model is trained automatically by 27,775 patches of tumor vessels and cell nuclei decomposed from whole-slide images of 4 T1 breast cancer sections. The GANDA model can conditionally generate images of intratumoral QDs distribution under the constraint of given tumor vessels and cell nuclei channels with the same spatial resolution (pixels-to-pixels), minimal loss (mean squared error, MSE = 1.871) and excellent reliability (intraclass correlation, ICC = 0.94). Quantitative analysis of QDs extravasation distance (ICC = 0.95) and subarea distribution (ICC = 0.99) is allowed on the generated images without knowing the real QDs distribution. We believe this deep generative model may provide opportunities to investigate how influencing factors affect NPs distribution in individual tumors and guide nanomedicine optimization for molecular imaging and personalized treatment.
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Affiliation(s)
- Yuxia Tang
- Department of Radiology, Jinling Hospital, Nanjing, Jiangsu 210000, Nanjing Medical University, China
| | - Jiulou Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Doudou He
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wenfang Miao
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wei Liu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yang Li
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Guangming Lu
- Department of Radiology, Jinling Hospital, Nanjing, Jiangsu 210000, Nanjing Medical University, China.
| | - Feiyun Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
| | - Shouju Wang
- Department of Radiology, Jinling Hospital, Nanjing, Jiangsu 210000, Nanjing Medical University, China; Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
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