1
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Anand G, Koniusz P, Kumar A, Golding LA, Morgan MJ, Moghadam P. Graph neural networks-enhanced relation prediction for ecotoxicology (GRAPE). JOURNAL OF HAZARDOUS MATERIALS 2024; 472:134456. [PMID: 38703678 DOI: 10.1016/j.jhazmat.2024.134456] [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: 02/05/2024] [Revised: 04/25/2024] [Accepted: 04/25/2024] [Indexed: 05/06/2024]
Abstract
Exposure to toxic chemicals threatens species and ecosystems. This study introduces a novel approach using Graph Neural Networks (GNNs) to integrate aquatic toxicity data, providing an alternative to complement traditional in vivo ecotoxicity testing. This study pioneers the application of GNN in ecotoxicology by formulating the problem as a relation prediction task. GRAPE's key innovation lies in simultaneously modelling 444 aquatic species and 2826 chemicals within a graph, leveraging relations from existing datasets where informative species and chemical features are augmented to make informed predictions. Extensive evaluations demonstrate the superiority of GRAPE over Logistic Regression (LR) and Multi-Layer Perceptron (MLP) models, achieving remarkable improvements of up to a 30% increase in recall values. GRAPE consistently outperforms LR and MLP in predicting novel chemicals and new species. In particular, GRAPE showcases substantial enhancements in recall values, with improvements of ≥ 100% for novel chemicals and up to 13% for new species. Specifically, GRAPE correctly predicts the effects of novel chemicals (104 out of 126) and effects on new species (7 out of 8). Moreover, the study highlights the effectiveness of the proposed chemical features and induced network topology through GNN for accurately predicting metallic (74 out of 86) and organic (612 out of 674) chemicals, showcasing the broad applicability and robustness of the GRAPE model in ecotoxicological investigations. The code/data are provided at https://github.com/csiro-robotics/GRAPE.
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Affiliation(s)
- Gaurangi Anand
- Environment, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Dutton Park 4102, QLD, Australia
| | - Piotr Koniusz
- Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Black Mountain 2601, ACT, Australia.
| | - Anupama Kumar
- Environment, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Waite Campus 5064, SA, Australia
| | - Lisa A Golding
- Environment, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Dutton Park 4102, QLD, Australia
| | - Matthew J Morgan
- Environment, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Black Mountain 2601, ACT, Australia
| | - Peyman Moghadam
- Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Pullenvale 4069, QLD, Australia
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2
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Yang M, Zhu JJ, McGaughey AL, Priestley RD, Hoek EMV, Jassby D, Ren ZJ. Machine Learning for Polymer Design to Enhance Pervaporation-Based Organic Recovery. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:10128-10139. [PMID: 38743597 DOI: 10.1021/acs.est.4c00060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Pervaporation (PV) is an effective membrane separation process for organic dehydration, recovery, and upgrading. However, it is crucial to improve membrane materials beyond the current permeability-selectivity trade-off. In this research, we introduce machine learning (ML) models to identify high-potential polymers, greatly improving the efficiency and reducing cost compared to conventional trial-and-error approach. We utilized the largest PV data set to date and incorporated polymer fingerprints and features, including membrane structure, operating conditions, and solute properties. Dimensionality reduction, missing data treatment, seed randomness, and data leakage management were employed to ensure model robustness. The optimized LightGBM models achieved RMSE of 0.447 and 0.360 for separation factor and total flux, respectively (logarithmic scale). Screening approximately 1 million hypothetical polymers with ML models resulted in identifying polymers with a predicted permeation separation index >30 and synthetic accessibility score <3.7 for acetic acid extraction. This study demonstrates the promise of ML to accelerate tailored membrane designs.
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Affiliation(s)
- Meiqi Yang
- Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544, United States
- Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544, United States
| | - Jun-Jie Zhu
- Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544, United States
- Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544, United States
| | - Allyson L McGaughey
- Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544, United States
- Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544, United States
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, United States
| | - Rodney D Priestley
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, United States
| | - Eric M V Hoek
- Department of Civil & Environmental Engineering, University of California Los Angeles, Los Angeles, California 90095, United States
| | - David Jassby
- Department of Civil & Environmental Engineering, University of California Los Angeles, Los Angeles, California 90095, United States
| | - Zhiyong Jason Ren
- Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544, United States
- Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544, United States
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3
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Fukuda M, Sakai K. 3D porous structure imaging of membranes for medical devices using scanning probe microscopy and electron microscopy: from membrane science points of view. J Artif Organs 2024; 27:83-90. [PMID: 38311666 DOI: 10.1007/s10047-023-01431-x] [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: 10/12/2023] [Accepted: 12/06/2023] [Indexed: 02/06/2024]
Abstract
The evolution of hemodialysis membranes (dialyzer, artificial kidney) was remarkable, since Dow Chemical began manufacturing hollow fiber hemodialyzers in 1968, especially because it involved industrial chemistry, including polymer synthesis and membrane manufacturing process. The development of hemodialysis membranes has brought about the field of medical devices as a major industry. In addition to conventional electron microscopy, scanning probe microscopy (SPM), represented by atomic force microscopy (AFM), has been used in membrane science research on porous membranes for hemodialysis, and membrane science contributes greatly to the hemodialyzer industry. Practical studies of membrane porous structure-function relationship have evolved, and methods for analyzing membrane cross-sectional morphology were developed, such as the ion milling method, which was capable of cutting membrane cross sections on the order of molecular size to obtain smooth surface structures. Recently, following the global pandemic of SARS-CoV-2 infection, many studies on new membranes for extracorporeal membrane oxygenator have been promptly reported, which also utilize membrane science researches. Membrane science is playing a prominent role in membrane-based technologies such as separation and fabrication, for hemodialysis, membrane oxygenator, lithium ion battery separators, lithium recycling, and seawater desalination. These practical studies contribute to the global medical devices industry.
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Affiliation(s)
- Makoto Fukuda
- Department of Biomedical Engineering, Kindai University, 930 Nishimitani, Kinokawa-City, Wakayama, 649-6493, Japan.
| | - Kiyotaka Sakai
- Professor Emeritus of Chemical Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo, 169-8555, Japan
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4
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Xiao H, Feng Y, Goundry WRF, Karlsson S. Organic Solvent Nanofiltration in Pharmaceutical Applications. Org Process Res Dev 2024; 28:891-923. [PMID: 38660379 PMCID: PMC11036530 DOI: 10.1021/acs.oprd.3c00470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 02/22/2024] [Accepted: 02/28/2024] [Indexed: 04/26/2024]
Abstract
Separation and purification in organic solvents are indispensable procedures in pharmaceutical manufacturing. However, they still heavily rely on the conventional separation technologies of distillation and chromatography, resulting in high energy and massive solvent consumption. As an alternative, organic solvent nanofiltration (OSN) offers the benefits of low energy consumption, low solid waste generation, and easy scale-up and incorporation into continuous processes. Thus, there is a growing interest in employing membrane technology in the pharmaceutical area to improve process sustainability and energy efficiency. This Review comprehensively summarizes the recent progress (especially the last 10 years) of organic solvent nanofiltration and its applications in the pharmaceutical industry, including the concentration and purification of active pharmaceutical ingredients, homogeneous catalyst recovery, solvent exchange and recovery, and OSN-assisted peptide/oligonucleotide synthesis. Furthermore, the challenges and future perspectives of membrane technology in pharmaceutical applications are discussed in detail.
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Affiliation(s)
- Hui Xiao
- Early
Chemical Development, Pharmaceutical Sciences, Biopharmaceuticals R&D, AstraZeneca, Macclesfield SK10 2NA, United Kingdom
| | - Yanyue Feng
- Early
Chemical Development, Pharmaceutical Sciences, Biopharmaceuticals R&D, AstraZeneca Gothenburg, SE-431 83 Mölndal, Sweden
| | - William R. F. Goundry
- Early
Chemical Development, Pharmaceutical Sciences, Biopharmaceuticals R&D, AstraZeneca, Macclesfield SK10 2NA, United Kingdom
| | - Staffan Karlsson
- Early
Chemical Development, Pharmaceutical Sciences, Biopharmaceuticals R&D, AstraZeneca Gothenburg, SE-431 83 Mölndal, Sweden
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5
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Glass S, Schmidt M, Merten P, Abdul Latif A, Fischer K, Schulze A, Friederich P, Filiz V. Design of Modified Polymer Membranes Using Machine Learning. ACS APPLIED MATERIALS & INTERFACES 2024; 16. [PMID: 38600824 PMCID: PMC11056926 DOI: 10.1021/acsami.3c18805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 03/19/2024] [Accepted: 03/21/2024] [Indexed: 04/12/2024]
Abstract
Surface modification is an attractive strategy to adjust the properties of polymer membranes. Unfortunately, predictive structure-processing-property relationships between the modification strategies and membrane performance are often unknown. One possibility to tackle this challenge is the application of data-driven methods such as machine learning. In this study, we applied machine learning methods to data sets containing the performance parameters of modified membranes. The resulting machine learning models were used to predict performance parameters, such as the pure water permeability and the zeta potential of membranes modified with new substances. The predictions had low prediction errors, which allowed us to generalize them to similar membrane modifications and processing conditions. Additionally, machine learning methods were able to identify the impact of substance properties and process parameters on the resulting membrane properties. Our results demonstrate that small data sets, as they are common in materials science, can be used as training data for predictive machine learning models. Therefore, machine learning shows great potential as a tool to expedite the development of high-performance membranes while reducing the time and costs associated with the development process at the same time.
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Affiliation(s)
- Sarah Glass
- Institute
of Membrane Research, Helmholtz-Zentrum
Hereon, Max-Planck-Str.
1, Geesthacht 21502, Germany
- Institute
of Theoretical Informatics, Karlsruhe Institute
of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany
| | - Martin Schmidt
- Leibniz
Institute of Surface Engineering (IOM), Permoserstr. 15, Leipzig 04318, Germany
| | - Petra Merten
- Institute
of Membrane Research, Helmholtz-Zentrum
Hereon, Max-Planck-Str.
1, Geesthacht 21502, Germany
| | - Amira Abdul Latif
- Leibniz
Institute of Surface Engineering (IOM), Permoserstr. 15, Leipzig 04318, Germany
| | - Kristina Fischer
- Leibniz
Institute of Surface Engineering (IOM), Permoserstr. 15, Leipzig 04318, Germany
| | - Agnes Schulze
- Leibniz
Institute of Surface Engineering (IOM), Permoserstr. 15, Leipzig 04318, Germany
| | - Pascal Friederich
- Institute
of Theoretical Informatics, Karlsruhe Institute
of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany
- Institute
of Nanotechnology, Karlsruhe Institute of
Technology (KIT), Kaiserstr.
12, 76131 Karlsruhe, Germany
| | - Volkan Filiz
- Institute
of Membrane Research, Helmholtz-Zentrum
Hereon, Max-Planck-Str.
1, Geesthacht 21502, Germany
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6
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Usman J, Abba SI, Baig N, Abu-Zahra N, Hasan SW, Aljundi IH. Design and Machine Learning Prediction of In Situ Grown PDA-Stabilized MOF (UiO-66-NH 2) Membrane for Low-Pressure Separation of Emulsified Oily Wastewater. ACS APPLIED MATERIALS & INTERFACES 2024; 16:16271-16289. [PMID: 38514254 DOI: 10.1021/acsami.4c00752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/23/2024]
Abstract
Significant progress has been made in designing advanced membranes; however, persistent challenges remain due to their reduced permeation rates and a propensity for substantial fouling. These factors continue to pose significant barriers to the effective utilization of membranes in the separation of oil-in-water emulsions. Metal-organic frameworks (MOFs) are considered promising materials for such applications; however, they encounter three key challenges when applied to the separation of oil from water: (a) lack of water stability; (b) difficulty in producing defect-free membranes; and (c) unresolved issue of stabilizing the MOF separating layer on the ceramic membrane (CM) support. In this study, a defect-free hydrolytically stable zirconium-based MOF separating layer was formed through a two-step method: first, by in situ growth of UiO-66-NH2 MOF into the voids of polydopamine (PDA)-functionalized CM during the solvothermal process, and then by facilitating the self-assembly of UiO-66-NH2 with PDA using a pressurized dead-end assembly. A stable MOF separating layer was attained by enriching the ceramic support with amines and hydroxyl groups using PDA, which assisted in the assembly and stabilization of UiO-66-NH2. The PDA-s-UiO-66-NH2-CM membrane displayed air superhydrophilicity and underwater superoleophobicity, demonstrating its oil resistance and high antifouling behavior. The PDA-s-UiO-66-NH2-CM membrane has shown exceptionally high permeability and separation capacity for challenging oil-in-water emulsions. This is attributed to numerous nanochannels from the membrane and its high resistance to oil adhesion. The membranes showed excellent stability over 15 continuous test cycles, which indicates that the developed MOFs separating layers have a low tendency to be clogged by oil droplets during separation. Machine learning-based Gaussian process regression (GPR) models as nonparametric kernel-based probabilistic models were employed to predict the performance efficiency of the PDA-s-UiO-66-NH2-CM membrane in oil-in-water separation. The outcomes were compared with the support vector machine (SVM) and decision tree (DT) algorithm. This efficiency includes various metrics related to its separation accuracy, and the models were developed through feature engineering to identify and utilize the most significant factors affecting the membrane's performance. The results proved the reliability of GPR optimization with the highest prediction accuracy in the validation phase. The average percentage increase of the GPR model compared to the SVM and DT model was 6.11 and 42.94%, respectively.
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Affiliation(s)
- Jamilu Usman
- Interdisciplinary Research Centre for Membranes and Water Security (IRC-MWS), King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Sani I Abba
- Interdisciplinary Research Centre for Membranes and Water Security (IRC-MWS), King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Nadeem Baig
- Interdisciplinary Research Centre for Membranes and Water Security (IRC-MWS), King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Nidal Abu-Zahra
- Materials Science and Engineering Department, University of Wisconsin-Milwaukee, 3200 North Cramer Street, Milwaukee, Wisconsin 53201, United States
| | - Shadi W Hasan
- Center for Membranes and Advanced Water Technology (CMAT), Department of Chemical and Petroleum Engineering, Khalifa University of Science and Technology, P.O. Box 127788 Abu Dhabi, United Arab Emirates
| | - Isam H Aljundi
- Interdisciplinary Research Centre for Membranes and Water Security (IRC-MWS), King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
- Chemical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
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7
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Wang H, Zeng J, Dai R, Wang Z. Understanding Rejection Mechanisms of Trace Organic Contaminants by Polyamide Membranes via Data-Knowledge Codriven Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:5878-5888. [PMID: 38498471 DOI: 10.1021/acs.est.3c08523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
Data-driven machine learning (ML) provides a promising approach to understanding and predicting the rejection of trace organic contaminants (TrOCs) by polyamide (PA). However, various confounding variables, coupled with data scarcity, restrict the direct application of data-driven ML. In this study, we developed a data-knowledge codriven ML model via domain-knowledge embedding and explored its application in comprehending TrOC rejection by PA membranes. Domain-knowledge embedding enhanced both the predictive performance and the interpretability of the ML model. The contribution of key mechanisms, including size exclusion, charge effect, hydrophobic interaction, etc., that dominate the rejections of the three TrOC categories (neutral hydrophilic, neutral hydrophobic, and charged TrOCs) was quantified. Log D and molecular charge emerge as key factors contributing to the discernible variations in the rejection among the three TrOC categories. Furthermore, we quantitatively compared the TrOC rejection mechanisms between nanofiltration (NF) and reverse osmosis (RO) PA membranes. The charge effect and hydrophobic interactions possessed higher weights for NF to reject TrOCs, while the size exclusion in RO played a more important role. This study demonstrated the effectiveness of the data-knowledge codriven ML method in understanding TrOC rejection by PA membranes, providing a methodology to formulate a strategy for targeted TrOC removal.
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Affiliation(s)
- Hejia Wang
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Jin Zeng
- School of Software Engineering, Tongji University, Shanghai 201804, China
| | - Ruobin Dai
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Zhiwei Wang
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
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8
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Du W, Ma F, Zhang B, Zhang J, Wu D, Sharman E, Jiang J, Wang Y. Spectroscopy-Guided Deep Learning Predicts Solid-Liquid Surface Adsorbate Properties in Unseen Solvents. J Am Chem Soc 2024; 146:811-823. [PMID: 38157302 PMCID: PMC10785802 DOI: 10.1021/jacs.3c10921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 01/03/2024]
Abstract
Accurately and rapidly acquiring the microscopic properties of a material is crucial for catalysis and electrochemistry. Characterization tools, such as spectroscopy, can be a valuable tool to infer these properties, and when combined with machine learning tools, they can theoretically achieve fast and accurate prediction results. However, on the path to practical applications, training a reliable machine learning model is faced with the challenge of uneven data distribution in a vast array of non-negligible solvent types. Herein, we employ a combination of the first-principles-based approach and data-driven model. Specifically, we utilize density functional theory (DFT) to calculate theoretical spectral data of CO-Ag adsorption in 23 different solvent systems as a data source. Subsequently, we propose a hierarchical knowledge extraction multiexpert neural network (HMNN) to bridge the knowledge gaps among different solvent systems. HMNN undergoes two training tiers: in tier I, it learns fundamental quantitative spectra-property relationships (QSPRs), and in tier II, it inherits the fundamental QSPR knowledge from previous steps through a dynamic integration of expert modules and subsequently captures the solvent differences. The results demonstrate HMNN's superiority in estimating a range of molecular adsorbate properties, with an error range of less than 0.008 eV for zero-shot predictions on unseen solvents. The findings underscore the usability, reliability, and convenience of HMNN and could pave the way for real-time access to microscopic properties by exploiting QSPR.
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Affiliation(s)
- Wenjie Du
- Key
Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, China
- School
of Software Engineering, University of Science
and Technology of China, Hefei, Anhui 230026, China
- Suzhou
Institute for Advanced Research, University
of Science and Technology of China, Suzhou, Jiangsu 215123, China
| | - Fenfen Ma
- Key
Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, China
- School
of Chemistry and Materials Science, University
of Science and Technology of China, Hefei, Anhui 230026, China
- Gusu
Laboratory of Materials, Suzhou, Jiangsu 215123, China
| | - Baicheng Zhang
- Key
Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, China
- School
of Chemistry and Materials Science, University
of Science and Technology of China, Hefei, Anhui 230026, China
| | - Jiahui Zhang
- School
of Software Engineering, University of Science
and Technology of China, Hefei, Anhui 230026, China
- Suzhou
Institute for Advanced Research, University
of Science and Technology of China, Suzhou, Jiangsu 215123, China
| | - Di Wu
- School
of Software Engineering, University of Science
and Technology of China, Hefei, Anhui 230026, China
- Suzhou
Institute for Advanced Research, University
of Science and Technology of China, Suzhou, Jiangsu 215123, China
| | - Edward Sharman
- Department
of Neurology, University of California, Irvine, California 92697, United States
| | - Jun Jiang
- Key
Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, China
- School
of Chemistry and Materials Science, University
of Science and Technology of China, Hefei, Anhui 230026, China
| | - Yang Wang
- Key
Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, China
- School
of Software Engineering, University of Science
and Technology of China, Hefei, Anhui 230026, China
- Suzhou
Institute for Advanced Research, University
of Science and Technology of China, Suzhou, Jiangsu 215123, China
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9
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Wu L, Wang M, Rong L, Wang W, Chen L, Wu Q, Sun H, Huang X, Zou X. Structural effects of sulfonamides on the proliferation dynamics of sulfonamide resistance genes in the sequencing batch reactors and the mechanism. J Environ Sci (China) 2024; 135:161-173. [PMID: 37778792 DOI: 10.1016/j.jes.2022.11.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/22/2022] [Accepted: 11/22/2022] [Indexed: 10/03/2023]
Abstract
Antibiotic resistance genes (ARGs) can be easily promoted by antibiotics, however, the structural effects of antibiotics on the proliferation of ARGs dynamic and the associated mechanisms remain obscure in, especially, activated sludge sequencing batch reactors. In the present study, the effects of 9 sulfonamides (SAs) with different structures on the proliferation dynamic of sulfonamide resistance genes (Suls) in the activated sludge sequencing batch reactors and the corresponding mechanisms were determined (30 days), and the results showed that the largest proliferation value (∆AR) of Suls dynamic for SAs (sulfachloropyridazine) was approximately 2.9 times than that of the smallest one (sulfadiazine). The proliferation of Suls was significantly related to the structural features (minHBint6, SssNH, SHBd and SpMax2_Bhm) that represent the biological activity of SAs. To interpret the phenomenon, a mechanistic model was developed and the results indicated that the biodegradation of SAs (T1/2) rather than conjugative transfer frequency or mutation frequency tends to be the key process for affecting Suls proliferation. T1/2 was proved to be dependent on the interactions between SAs and receptors (Ebinding), the cleavage mode (bond dissociation energy), and the site of nucleophilic assault. Besides, the metagenomic analysis showed that SAs posed significant effect on antibiotic resistome and Tnp31 played a vital role in the proliferation of Suls. Overall, our findings provide important insight into a theoretical basis for understanding the structural effects of SAs on the proliferation of ARGs in SBR systems.
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Affiliation(s)
- Ligui Wu
- College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; School of Life Science, Jinggangshan University, Ji'an 343009, China
| | - Mingyu Wang
- School of Life Science, Jinggangshan University, Ji'an 343009, China
| | - Lingling Rong
- School of Life Science, Jinggangshan University, Ji'an 343009, China
| | - Wenbiao Wang
- Key Laboratory of Organic Compound Pollution Control Engineering (MOE), School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
| | - Linwei Chen
- School of Life Science, Jinggangshan University, Ji'an 343009, China
| | - Qiaofeng Wu
- Fuzhou Urban and Rural Construction Group Co. Ltd., Fuzhou 350007, China
| | - Haoyu Sun
- Key Laboratory of Organic Compound Pollution Control Engineering (MOE), School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
| | - Xiangfeng Huang
- College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.
| | - Xiaoming Zou
- School of Life Science, Jinggangshan University, Ji'an 343009, China.
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10
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Sun Y, Zhao Z, Tong H, Sun B, Liu Y, Ren N, You S. Machine Learning Models for Inverse Design of the Electrochemical Oxidation Process for Water Purification. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:17990-18000. [PMID: 37189261 DOI: 10.1021/acs.est.2c08771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
In this study, a machine learning (ML) framework is developed toward target-oriented inverse design of the electrochemical oxidation (EO) process for water purification. The XGBoost model exhibited the best performances for prediction of reaction rate (k) based on training the data set relevant to pollutant characteristics and reaction conditions, indicated by Rext2 of 0.84 and RMSEext of 0.79. Based on 315 data points collected from the literature, the current density, pollutant concentration, and gap energy (Egap) were identified to be the most impactful parameters available for the inverse design of the EO process. In particular, adding reaction conditions as model input features allowed provision of more available information and an increase in the sample size of the data set to improve the model accuracy. The feature importance analysis was performed for revealing the data pattern and feature interpretation by using Shapley additive explanations (SHAP). The ML-based inverse design for the EO process was generalized to a random case for tailoring the optimum conditions with phenol and 2,4-dichlorophenol (2,4-DCP) serving as model pollutants. The resulting predicted k values were close to the experimental k values by experimental verification, accounting for the relative error lower than 5%. This study provides a paradigm shift from conventional trial-and-error mode to data-driven mode for advancing research and development of the EO process by a time-saving, labor-effective, and environmentally friendly target-oriented strategy, which makes electrochemical water purification more efficient, more economic, and more sustainable in the context of global carbon peaking and carbon neutrality.
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Affiliation(s)
- Ye Sun
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, P. R. China
| | - Zhiyuan Zhao
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, P. R. China
| | - Hailong Tong
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, P. R. China
- State Key Laboratory of Veterinary Biotechnology, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin 150069, P. R. China
| | - Baiming Sun
- State Key Laboratory of Veterinary Biotechnology, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin 150069, P. R. China
| | - Yanbiao Liu
- College of Environmental Science and Engineering, Textile Pollution Controlling Engineering Center of the Ministry of Ecology and Environment, Donghua University, Shanghai 201620, China
| | - Nanqi Ren
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, P. R. China
| | - Shijie You
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, P. R. China
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11
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Jeong N, Epsztein R, Wang R, Park S, Lin S, Tong T. Exploring the Knowledge Attained by Machine Learning on Ion Transport across Polyamide Membranes Using Explainable Artificial Intelligence. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:17851-17862. [PMID: 36917705 DOI: 10.1021/acs.est.2c08384] [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
Recent studies have increasingly applied machine learning (ML) to aid in performance and material design associated with membrane separation. However, whether the knowledge attained by ML with a limited number of available data is enough to capture and validate the fundamental principles of membrane science remains elusive. Herein, we applied explainable artificial intelligence (XAI) to thoroughly investigate the knowledge learned by ML on the mechanisms of ion transport across polyamide reverse osmosis (RO) and nanofiltration (NF) membranes by leveraging 1,585 data from 26 membrane types. The Shapley additive explanation method based on cooperative game theory was used to unveil the influences of various ion and membrane properties on the model predictions. XAI shows that the ML can capture the important roles of size exclusion and electrostatic interaction in regulating membrane separation properly. XAI also identifies that the mechanisms governing ion transport possess different relative importance to cation and anion rejections during RO and NF filtration. Overall, we provide a framework to evaluate the knowledge underlying the ML model prediction and demonstrate that ML is able to learn fundamental mechanisms of ion transport across polyamide membranes, highlighting the importance of elucidating model interpretability for more reliable and explainable ML applications to membrane selection and design.
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Affiliation(s)
- Nohyeong Jeong
- Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Razi Epsztein
- Department of Civil and Environmental Engineering, Technion - Israel Institute of Technology, Haifa 32000, Israel
| | - Ruoyu Wang
- Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, Tennessee 37235-1831, United States
| | - Shinyun Park
- Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Shihong Lin
- Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, Tennessee 37235-1831, United States
- Department of Chemical and Bimolecular Engineering, Vanderbilt University, Nashville, Tennessee 37235-1831, United States
| | - Tiezheng Tong
- Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, Colorado 80523, United States
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12
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Wang M, Shi GM, Zhao D, Liu X, Jiang J. Machine Learning-Assisted Design of Thin-Film Composite Membranes for Solvent Recovery. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:15914-15924. [PMID: 37814603 DOI: 10.1021/acs.est.3c04773] [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: 10/11/2023]
Abstract
Organic solvents are extensively utilized in industries as raw materials, reaction media, and cleaning agents. It is crucial to efficiently recover solvents for environmental protection and sustainable manufacturing. Recently, organic solvent nanofiltration (OSN) has emerged as an energy-efficient membrane technology for solvent recovery; however, current OSN membranes are largely fabricated by trial-and-error methods. In this study, for the first time, we develop a machine learning (ML) approach to design new thin-film composite membranes for solvent recovery. The monomers used in interfacial polymerization, along with membrane, solvent and solute properties, are featurized to train ML models via gradient boosting regression. The ML models demonstrate high accuracy in predicting OSN performance including solvent permeance and solute rejection. Subsequently, 167 new membranes are designed from 40 monomers and their OSN performance is predicted by the ML models for common solvents (methanol, acetone, dimethylformamide, and n-hexane). New top-performing membranes are identified with methanol permeance superior to that of existing membranes. Particularly, nitrogen-containing heterocyclic monomers are found to enhance microporosity and contribute to higher permeance. Finally, one new membrane is experimentally synthesized and tested to validate the ML predictions. Based on the chemical structures of monomers, the ML approach developed here provides a bottom-up strategy toward the rational design of new membranes for high-performance solvent recovery and many other technologically important applications.
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Affiliation(s)
- Mao Wang
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117576, Singapore
| | - Gui Min Shi
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117576, Singapore
| | - Daohui Zhao
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117576, Singapore
| | - Xinyi Liu
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117576, Singapore
| | - Jianwen Jiang
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117576, Singapore
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13
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Raza A, Chohan TA, Buabeid M, Arafa ESA, Chohan TA, Fatima B, Sultana K, Ullah MS, Murtaza G. Deep learning in drug discovery: a futuristic modality to materialize the large datasets for cheminformatics. J Biomol Struct Dyn 2023; 41:9177-9192. [PMID: 36305195 DOI: 10.1080/07391102.2022.2136244] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 10/08/2022] [Indexed: 10/31/2022]
Abstract
Artificial intelligence (AI) development imitates the workings of the human brain to comprehend modern problems. The traditional approaches such as high throughput screening (HTS) and combinatorial chemistry are lengthy and expensive to the pharmaceutical industry as they can only handle a smaller dataset. Deep learning (DL) is a sophisticated AI method that uses a thorough comprehension of particular systems. The pharmaceutical industry is now adopting DL techniques to enhance the research and development process. Multi-oriented algorithms play a crucial role in the processing of QSAR analysis, de novo drug design, ADME evaluation, physicochemical analysis, preclinical development, followed by clinical trial data precision. In this study, we investigated the performance of several algorithms, including deep neural networks (DNN), convolutional neural networks (CNN) and multi-task learning (MTL), with the aim of generating high-quality, interpretable big and diverse databases for drug design and development. Studies have demonstrated that CNN, recurrent neural network and deep belief network are compatible, accurate and effective for the molecular description of pharmacodynamic properties. In Covid-19, existing pharmacological compounds has also been repurposed using DL models. In the absence of the Covid-19 vaccine, remdesivir and oseltamivir have been widely employed to treat severe SARS-CoV-2 infections. In conclusion, the results indicate the potential benefits of employing the DL strategies in the drug discovery process.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ali Raza
- Department of pharmaceutical chemistry, Faculty of Pharmacy, The University of Lahore, Pakistan
- Institute of Molecular Biology and Biochemistry, The University of Lahore, Pakistan
| | - Talha Ali Chohan
- Institute of Molecular Biology and Biochemistry, The University of Lahore, Pakistan
- Institute of Pharmaceutical Science, UVAS, Lahore, Pakistan
| | - Manal Buabeid
- Department of Clinical Sciences, College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates
| | - El-Shaima A Arafa
- Department of Clinical Sciences, College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates
- Centre of Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates
| | | | - Batool Fatima
- Department of biochemistry, Bahauddin Zakariya University, Multan, Pakistan
| | - Kishwar Sultana
- Department of pharmaceutical chemistry, Faculty of Pharmacy, The University of Lahore, Pakistan
| | - Malik Saad Ullah
- Department of Pharmacy, Government College University, Faisalabad, Pakistan
| | - Ghulam Murtaza
- Department of Pharmacy, COMSATS University Islamabad, Lahore Campus, Pakistan
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14
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Pereira TO, Abbasi M, Arrais JP. Enhancing reinforcement learning for de novo molecular design applying self-attention mechanisms. Brief Bioinform 2023; 24:bbad368. [PMID: 37903414 DOI: 10.1093/bib/bbad368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 09/04/2023] [Accepted: 09/26/2023] [Indexed: 11/01/2023] Open
Abstract
The drug discovery process can be significantly improved by applying deep reinforcement learning (RL) methods that learn to generate compounds with desired pharmacological properties. Nevertheless, RL-based methods typically condense the evaluation of sampled compounds into a single scalar value, making it difficult for the generative agent to learn the optimal policy. This work combines self-attention mechanisms and RL to generate promising molecules. The idea is to evaluate the relative significance of each atom and functional group in their interaction with the target, and to utilize this information for optimizing the Generator. Therefore, the framework for de novo drug design is composed of a Generator that samples new compounds combined with a Transformer-encoder and a biological affinity Predictor that evaluate the generated structures. Moreover, it takes the advantage of the knowledge encapsulated in the Transformer's attention weights to evaluate each token individually. We compared the performance of two output prediction strategies for the Transformer: standard and masked language model (MLM). The results show that the MLM Transformer is more effective in optimizing the Generator compared with the state-of-the-art works. Additionally, the evaluation models identified the most important regions of each molecule for the biological interaction with the target. As a case study, we generated synthesizable hit compounds that can be putative inhibitors of the enzyme ubiquitin-specific protein 7 (USP7).
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Affiliation(s)
- Tiago O Pereira
- Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Univ Coimbra, Coimbra, Portugal
| | - Maryam Abbasi
- Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Univ Coimbra, Coimbra, Portugal
| | - Joel P Arrais
- Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Univ Coimbra, Coimbra, Portugal
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15
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Lee YJ, Chen L, Nistane J, Jang HY, Weber DJ, Scott JK, Rangnekar ND, Marshall BD, Li W, Johnson JR, Bruno NC, Finn MG, Ramprasad R, Lively RP. Data-driven predictions of complex organic mixture permeation in polymer membranes. Nat Commun 2023; 14:4931. [PMID: 37582784 PMCID: PMC10427679 DOI: 10.1038/s41467-023-40257-2] [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: 01/05/2023] [Accepted: 07/17/2023] [Indexed: 08/17/2023] Open
Abstract
Membrane-based organic solvent separations are rapidly emerging as a promising class of technologies for enhancing the energy efficiency of existing separation and purification systems. Polymeric membranes have shown promise in the fractionation or splitting of complex mixtures of organic molecules such as crude oil. Determining the separation performance of a polymer membrane when challenged with a complex mixture has thus far occurred in an ad hoc manner, and methods to predict the performance based on mixture composition and polymer chemistry are unavailable. Here, we combine physics-informed machine learning algorithms (ML) and mass transport simulations to create an integrated predictive model for the separation of complex mixtures containing up to 400 components via any arbitrary linear polymer membrane. We experimentally demonstrate the effectiveness of the model by predicting the separation of two crude oils within 6-7% of the measurements. Integration of ML predictors of diffusion and sorption properties of molecules with transport simulators enables for the rapid screening of polymer membranes prior to physical experimentation for the separation of complex liquid mixtures.
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Affiliation(s)
- Young Joo Lee
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Lihua Chen
- School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Janhavi Nistane
- School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Hye Youn Jang
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Dylan J Weber
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Joseph K Scott
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Neel D Rangnekar
- ExxonMobil Technology and Engineering Company, Annandale, NJ, 08801, USA
| | - Bennett D Marshall
- ExxonMobil Technology and Engineering Company, Annandale, NJ, 08801, USA
| | - Wenjun Li
- ExxonMobil Technology and Engineering Company, Annandale, NJ, 08801, USA
| | - J R Johnson
- ExxonMobil Technology and Engineering Company, Annandale, NJ, 08801, USA
| | - Nicholas C Bruno
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - M G Finn
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Rampi Ramprasad
- School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
| | - Ryan P Lively
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
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16
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Dai M, Xiao G, Shao M, Zhang YS. The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review. BIOSENSORS 2023; 13:389. [PMID: 36979601 PMCID: PMC10046732 DOI: 10.3390/bios13030389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 02/22/2023] [Accepted: 03/07/2023] [Indexed: 06/18/2023]
Abstract
Organs-on-chips (OoCs) are miniature microfluidic systems that have arguably become a class of advanced in vitro models. Deep learning, as an emerging topic in machine learning, has the ability to extract a hidden statistical relationship from the input data. Recently, these two areas have become integrated to achieve synergy for accelerating drug screening. This review provides a brief description of the basic concepts of deep learning used in OoCs and exemplifies the successful use cases for different types of OoCs. These microfluidic chips are of potential to be assembled as highly potent human-on-chips with complex physiological or pathological functions. Finally, we discuss the future supply with perspectives and potential challenges in terms of combining OoCs and deep learning for image processing and automation designs.
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Affiliation(s)
- Manna Dai
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
- Computing and Intelligence Department, Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Singapore
| | - Gao Xiao
- College of Environment and Safety Engineering, Fuzhou University, Fuzhou 350108, China
- Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Ming Shao
- Department of Computer and Information Science, College of Engineering, University of Massachusetts Dartmouth, North Dartmouth, MA 02747, USA
| | - Yu Shrike Zhang
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Cambridge, MA 02139, USA
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17
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Luo J, Li M, Hoek EMV, Heng Y. Supercomputing and machine learning-aided optimal design of high permeability seawater reverse osmosis membrane systems. Sci Bull (Beijing) 2023:S2095-9273(23)00075-0. [PMID: 36774298 DOI: 10.1016/j.scib.2023.01.039] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 01/06/2023] [Accepted: 01/28/2023] [Indexed: 02/05/2023]
Abstract
Concentration polarization (CP) should limit the energy and cost reducing benefits of high permeability seawater reverse osmosis (SWRO) membranes operating at a water flux higher than normal one. Herein, we propose a multiscale optimization framework coupling membrane permeability, feed spacer design (sub-millimeter scale) and system design (meter scale) via computational fluid dynamics and system level modeling using advanced supercomputing in conjunction with machine learning. Simulation results suggest energy consumption could be reduced by 27.5% (to 1.66 kWh m-3) predominantly through the use of high permeability SWRO membranes (12.2%) and a two-stage design (14.5%). Without optimization, CP approaches 1.52 at the system inlet, whereas the optimized CP is limited to 1.20. This work elucidates the optimized permeability, module design, operating scheme and benefits of high permeability SWRO membranes in seawater desalination.
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Affiliation(s)
- Jiu Luo
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China; National Supercomputing Center in Guangzhou (NSCC-GZ), Guangzhou 510006, China; Guangdong Province Key Laboratory of Computational Science, Guangzhou 510006, China
| | - Mingheng Li
- Department of Chemical and Materials Engineering, California State Polytechnic University, Pomona CA 91768, USA
| | - Eric M V Hoek
- Department of Civil & Environmental Engineering, California NanoSystems Institute and Institute of the Environment & Sustainability, University of California, Los Angeles (UCLA), Los Angeles CA 90095, USA; Energy Storage & Distributed Resources Division, Lawrence Berkeley National Laboratory, Berkeley CA 94720, USA
| | - Yi Heng
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China; National Supercomputing Center in Guangzhou (NSCC-GZ), Guangzhou 510006, China; Guangdong Province Key Laboratory of Computational Science, Guangzhou 510006, China.
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18
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Wang J, Tian K, Li D, Chen M, Feng X, Zhang Y, Wang Y, Van der Bruggen B. Machine learning in gas separation membrane developing: ready for prime time. Sep Purif Technol 2023. [DOI: 10.1016/j.seppur.2023.123493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
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19
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Ignacz G, Beke AK, Szekely G. Data-driven investigation of process solvent and membrane material on organic solvent nanofiltration. J Memb Sci 2023. [DOI: 10.1016/j.memsci.2023.121519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
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20
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Zhu T, Zhang Y, Tao C, Chen W, Cheng H. Prediction of organic contaminant rejection by nanofiltration and reverse osmosis membranes using interpretable machine learning models. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159348. [PMID: 36228787 DOI: 10.1016/j.scitotenv.2022.159348] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 09/21/2022] [Accepted: 10/06/2022] [Indexed: 06/16/2023]
Abstract
Efficiency improvement in contaminant removal by nanofiltration (NF) and reverse osmosis (RO) membranes is a multidimensional process involving membrane material selection and experimental condition optimization. It is unrealistic to explore the contributions of diverse influencing factors to the removal rate by trial-and-error experimentation. However, the advanced machine learning (ML) method is a powerful tool to simulate this complex decision-making process. Here, 4 traditional learning algorithms (MLR, SVM, ANN, kNN) and 4 ensemble learning algorithms (RF, GBDT, XGBoost, LightGBM) were applied to predict the removal efficiency of contaminants. Results reported here demonstrate that ensemble models showed significantly better predictive performance than traditional models. More importantly, this study achieved a compelling tradeoff between accuracy and interpretability for ensemble models with an effective model interpretation approach, which revealed the mutual interaction mechanism between the membrane material, contaminants and experimental conditions in membrane separation. Additionally, feature selection was for the first time achieved based on the aforementioned model interpretation method to determine the most important variable influencing the contaminant removal rate. Ultimately, the four ensemble models retrained by the selected variables achieved distinguished prediction performance (R2adj = 92.4 %-99.5 %). MWCO (membrane molecular weight cut-off), McGowan volume of solute (V) and molecular weight (MW) of the compound were demonstrated to be the most important influencing factors in contaminant removal by the NF and RO processes. Overall, the proposed methods in this study can facilitate versatile complex decision-making processes in the environmental field, particularly in contaminant removal by advanced physicochemical separation processes.
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Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China.
| | - Yu Zhang
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
| | - Cuicui Tao
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
| | - Wenxuan Chen
- School of Civil Engineering, Southeast University, Nanjing 210096, China
| | - Haomiao Cheng
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
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21
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Feasibility of several commercial membranes to recover valuable phenolic compounds from extracts of wet olive pomace through organic-solvent nanofiltration. Sep Purif Technol 2023. [DOI: 10.1016/j.seppur.2022.122396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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22
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Krupková A, Müllerová M, Petrickovic R, Strašák T. On the Edge between Organic Solvent Nanofiltration and Ultrafiltration: Characterization of Regenerated Cellulose Membrane with Aspect on Dendrimer Purification and Recycling. Sep Purif Technol 2023. [DOI: 10.1016/j.seppur.2023.123141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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23
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Deep Learning-Based Predictive Control of Injection Velocity in Injection Molding Machines. ADVANCES IN POLYMER TECHNOLOGY 2022. [DOI: 10.1155/2022/7662264] [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
Rapid and reliable optimal control of injection molding machines (IMMs) is critical for the effective production of injection-molded goods, especially in the situation of restricted computer resources of embedded equipment in IMMs. In this paper, an optimal tracking injection velocity control problem arising in a typical IMM is studied. An effective hybrid intelligent control approach with less computing resources for real-time implementation based on the deep learning (DL) method to mimic the classical model predictive control rule is developed to deal with the tracking control of the injection speed. The proposed method utilizes the gated recurrent unit neural network to learn and predict the optimal time series control process data produced by the traditional model predictive controller. The benefits of this approach over the conventional optimization method are illustrated through simulation results, which show that the convergent DL-based controller can effectively avoid the complex calculation in the control process of IMMs and meet the requirements of more robustness and resist environmental uncertainty to a certain level and can be potentially implemented in embedded hardware much more efficiently and conveniently with a smaller memory footprint and faster computation time.
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24
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Materials discovery of ion-selective membranes using artificial intelligence. Commun Chem 2022; 5:132. [PMID: 36697945 PMCID: PMC9814132 DOI: 10.1038/s42004-022-00744-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 09/29/2022] [Indexed: 01/28/2023] Open
Abstract
Significant attempts have been made to improve the production of ion-selective membranes (ISMs) with higher efficiency and lower prices, while the traditional methods have drawbacks of limitations, high cost of experiments, and time-consuming computations. One of the best approaches to remove the experimental limitations is artificial intelligence (AI). This review discusses the role of AI in materials discovery and ISMs engineering. The AI can minimize the need for experimental tests by data analysis to accelerate computational methods based on models using the results of ISMs simulations. The coupling with computational chemistry makes it possible for the AI to consider atomic features in the output models since AI acts as a bridge between the experimental data and computational chemistry to develop models that can use experimental data and atomic properties. This hybrid method can be used in materials discovery of the membranes for ion extraction to investigate capabilities, challenges, and future perspectives of the AI-based materials discovery, which can pave the path for ISMs engineering.
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25
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Enantioselective nanofiltration using predictive process modeling: Bridging the gap between materials development and process requirements. J Memb Sci 2022. [DOI: 10.1016/j.memsci.2022.121020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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26
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Hatakeyama-Sato K, Adachi H, Umeki M, Kashikawa T, Kimura K, Oyaizu K. Automated Design of Li + -Conducting Polymer by Quantum-Inspired Annealing. Macromol Rapid Commun 2022; 43:e2200385. [PMID: 35759445 DOI: 10.1002/marc.202200385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 06/05/2022] [Indexed: 11/07/2022]
Abstract
Automated molecule design by computers has been an essential topic in materials informatics. Still, generating practical structures is not easy because of the difficulty in treating material stability, synthetic difficulty, mechanical properties, and other miscellaneous parameters, often leading to the generation of junk molecules. We tackle the problem by introducing supervised/unsupervised machine learning and quantum-inspired annealing. Our autonomous molecular design system can help experimental researchers discover practical materials more efficiently. Like the human design process, new molecules are explored based on knowledge of existing compounds. A new solid-state polymer electrolyte for lithium-ion batteries is designed and synthesized, giving a promising room temperature conductivity of 10-5 S/cm with reasonable thermal, chemical, and mechanical properties. This article is protected by copyright. All rights reserved.
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Affiliation(s)
| | - Hiroki Adachi
- Department of Applied Chemistry, Waseda University, Tokyo, 169-8555, Japan
| | - Momoka Umeki
- Department of Applied Chemistry, Waseda University, Tokyo, 169-8555, Japan
| | | | | | - Kenichi Oyaizu
- Department of Applied Chemistry, Waseda University, Tokyo, 169-8555, Japan
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27
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Use of Artificial Intelligence Methods for Predicting the Strength of Recycled Aggregate Concrete and the Influence of Raw Ingredients. MATERIALS 2022; 15:ma15124194. [PMID: 35744254 PMCID: PMC9229192 DOI: 10.3390/ma15124194] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 06/07/2022] [Accepted: 06/09/2022] [Indexed: 01/27/2023]
Abstract
Cracking is one of the main problems in concrete structures and is affected by various parameters. The step-by-step laboratory method, which includes casting specimens, curing for a certain period, and testing, remains a source of worry in terms of cost and time. Novel machine learning methods for anticipating the behavior of raw materials on the ultimate output of concrete are being introduced to address the difficulties outlined above such as the excessive consumption of time and money. This work estimates the splitting-tensile strength of concrete containing recycled coarse aggregate (RCA) using artificial intelligence methods considering nine input parameters and 154 mixes. One individual machine learning algorithm (support vector machine) and three ensembled machine learning algorithms (AdaBoost, Bagging, and random forest) are considered. Additionally, a post hoc model-agnostic method named SHapley Additive exPlanations (SHAP) was performed to study the influence of raw ingredients on the splitting-tensile strength. The model's performance was assessed using the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Then, the model's performance was validated using k-fold cross-validation. The random forest model, with an R2 of 0.96, outperformed the AdaBoost models. The random forest models with greater R2 and lower error (RMSE = 0.49) had superior performance. It was revealed from the SHAP analysis that the cement content had the highest positive influence on the splitting-tensile strength of the recycled aggregate concrete and the primary contact of cement is with water. The feature interaction plot shows that high water content has a negative impact on the recycled aggregate concrete (RAC) splitting-tensile strength, but the increased cement content had a beneficial effect.
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28
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Yokoyama D, Suzuki S, Asakura T, Kikuchi J. Chemometric Analysis of NMR Spectra and Machine Learning to Investigate Membrane Fouling. ACS OMEGA 2022; 7:12654-12660. [PMID: 35474825 PMCID: PMC9025983 DOI: 10.1021/acsomega.1c06891] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 03/02/2022] [Indexed: 05/26/2023]
Abstract
Efficient membrane filtration requires the understanding of the membrane foulants and the functional properties of different membrane types in water purification. In this study, dead-end filtration of aquaculture system effluents was performed and the membrane foulants were investigated via nuclear magnetic resonance (NMR) spectroscopy. Several machine learning models (Random Forest; RF, Extreme Gradient Boosting; XGBoost, Support Vector Machine; SVM, and Neural Network; NN) were constructed, one to predict the maximum transmembrane pressure, for revealing the chemical compounds causing fouling, and the other to classify the membrane materials based on chemometric analysis of NMR spectra, for determining their effect on the properties of the different membrane types tested. Especially, RF models exhibited high accuracy; the important chemical shifts observed in both the regression and classification models suggested that the proportional patterns of sugars and proteins are key factors in the fouling progress and the classification of membrane types. Therefore, the proposed strategy of chemometric analysis of NMR spectra is suitable for membrane research, which aims at investigating comprehensively the fouling phenomenon and how the foulants and environmental conditions vary according to the filtration systems.
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Affiliation(s)
- Daiki Yokoyama
- RIKEN
Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Graduate
School of Medical Life Science, Yokohama
City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Sosei Suzuki
- Graduate
School of Medical Life Science, Yokohama
City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Taiga Asakura
- RIKEN
Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Graduate
School of Medical Life Science, Yokohama
City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Jun Kikuchi
- RIKEN
Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Graduate
School of Medical Life Science, Yokohama
City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Graduate
School of Bioagricultural Sciences, Nagoya
University, 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810, Japan
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