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Furxhi I, Faccani L, Zanoni I, Brigliadori A, Vespignani M, Costa AL. Design rules applied to silver nanoparticles synthesis: A practical example of machine learning application. Comput Struct Biotechnol J 2024; 25:20-33. [PMID: 38444982 PMCID: PMC10914561 DOI: 10.1016/j.csbj.2024.02.010] [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: 12/12/2023] [Revised: 02/12/2024] [Accepted: 02/14/2024] [Indexed: 03/07/2024] Open
Abstract
The synthesis of silver nanoparticles with controlled physicochemical properties is essential for governing their intended functionalities and safety profiles. However, synthesis process involves multiple parameters that could influence the resulting properties. This challenge could be addressed with the development of predictive models that forecast endpoints based on key synthesis parameters. In this study, we manually extracted synthesis-related data from the literature and leveraged various machine learning algorithms. Data extraction included parameters such as reactant concentrations, experimental conditions, as well as physicochemical properties. The antibacterial efficiencies and toxicological profiles of the synthesized nanoparticles were also extracted. In a second step, based on data completeness, we employed regression algorithms to establish relationships between synthesis parameters and desired endpoints and to build predictive models. The models for core size and antibacterial efficiency were trained and validated using a cross-validation approach. Finally, the features' impact was evaluated via Shapley values to provide insights into the contribution of features to the predictions. Factors such as synthesis duration, scale of synthesis and the choice of capping agents emerged as the most significant predictors. This study demonstrated the potential of machine learning to aid in the rational design of synthesis process and paves the way for the safe-by-design principles development by providing insights into the optimization of the synthesis process to achieve the desired properties. Finally, this study provides a valuable dataset compiled from literature sources with significant time and effort from multiple researchers. Access to such datasets notably aids computational advances in the field of nanotechnology.
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Affiliation(s)
- Irini Furxhi
- CNR-ISSMC (Former ISTEC), National Research Council of Italy-Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy
- Transgero Limited, Limerick, Ireland
| | - Lara Faccani
- CNR-ISSMC (Former ISTEC), National Research Council of Italy-Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy
| | - Ilaria Zanoni
- CNR-ISSMC (Former ISTEC), National Research Council of Italy-Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy
| | - Andrea Brigliadori
- CNR-ISSMC (Former ISTEC), National Research Council of Italy-Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy
| | - Maurizio Vespignani
- CNR-ISSMC (Former ISTEC), National Research Council of Italy-Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy
| | - Anna Luisa Costa
- CNR-ISSMC (Former ISTEC), National Research Council of Italy-Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy
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2
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Gao Y, Zhu Z, Chen Z, Guo M, Zhang Y, Wang L, Zhu Z. Machine learning in nanozymes: from design to application. Biomater Sci 2024; 12:2229-2243. [PMID: 38497247 DOI: 10.1039/d4bm00169a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Nanozymes, a distinctive class of nanomaterials endowed with enzyme-like activity and kinetics akin to enzyme-catalysed reactions, present several advantages over natural enzymes, including cost-effectiveness, heightened stability, and adjustable activity. However, the conventional trial-and-error methodology for developing novel nanozymes encounters growing challenges as research progresses. The advent of artificial intelligence (AI), particularly machine learning (ML), has ushered in innovative design approaches for researchers in this domain. This review delves into the burgeoning role of ML in nanozyme research, elucidating the advancements achieved through ML applications. The review explores successful instances of ML in nanozyme design and implementation, providing a comprehensive overview of the evolving landscape. A roadmap for ML-assisted nanozyme research is outlined, offering a universal guideline for research in this field. In the end, the review concludes with an analysis of challenges encountered and anticipates future directions for ML in nanozyme research. The synthesis of knowledge in this review aims to foster a cross-disciplinary study, propelling the revolutionary field forward.
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Affiliation(s)
- Yubo Gao
- College of Materials Science and Engineering, Qingdao University of Science and Technology, Qingdao, Shandong 266042, China.
- College of Environment and Safety Engineering, Qingdao University of Science and Technology, Qingdao, Shandong 266042, China.
| | - Zhicheng Zhu
- College of Materials Science and Engineering, Qingdao University of Science and Technology, Qingdao, Shandong 266042, China.
| | - Zhen Chen
- College of Materials Science and Engineering, Qingdao University of Science and Technology, Qingdao, Shandong 266042, China.
| | - Meng Guo
- College of Environment and Safety Engineering, Qingdao University of Science and Technology, Qingdao, Shandong 266042, China.
| | - Yiqing Zhang
- College of Materials Science and Engineering, Qingdao University of Science and Technology, Qingdao, Shandong 266042, China.
| | - Lina Wang
- College of Environment and Safety Engineering, Qingdao University of Science and Technology, Qingdao, Shandong 266042, China.
| | - Zhiling Zhu
- College of Materials Science and Engineering, Qingdao University of Science and Technology, Qingdao, Shandong 266042, China.
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3
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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: 0] [Impact Index Per Article: 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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4
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Huang G, Guo Y, Chen Y, Nie Z. Application of Machine Learning in Material Synthesis and Property Prediction. MATERIALS (BASEL, SWITZERLAND) 2023; 16:5977. [PMID: 37687675 PMCID: PMC10488794 DOI: 10.3390/ma16175977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/22/2023] [Accepted: 08/28/2023] [Indexed: 09/10/2023]
Abstract
Material innovation plays a very important role in technological progress and industrial development. Traditional experimental exploration and numerical simulation often require considerable time and resources. A new approach is urgently needed to accelerate the discovery and exploration of new materials. Machine learning can greatly reduce computational costs, shorten the development cycle, and improve computational accuracy. It has become one of the most promising research approaches in the process of novel material screening and material property prediction. In recent years, machine learning has been widely used in many fields of research, such as superconductivity, thermoelectrics, photovoltaics, catalysis, and high-entropy alloys. In this review, the basic principles of machine learning are briefly outlined. Several commonly used algorithms in machine learning models and their primary applications are then introduced. The research progress of machine learning in predicting material properties and guiding material synthesis is discussed. Finally, a future outlook on machine learning in the materials science field is presented.
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Affiliation(s)
| | | | | | - Zhengwei Nie
- School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China; (G.H.); (Y.G.); (Y.C.)
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5
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Mahawar L, Ramasamy KP, Suhel M, Prasad SM, Živčák M, Brestic M, Rastogi A, Skalicky M. Silicon nanoparticles: Comprehensive review on biogenic synthesis and applications in agriculture. ENVIRONMENTAL RESEARCH 2023:116292. [PMID: 37276972 DOI: 10.1016/j.envres.2023.116292] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 05/22/2023] [Accepted: 05/30/2023] [Indexed: 06/07/2023]
Abstract
Recent advancements in nanotechnology have opened new advances in agriculture. Among other nanoparticles, silicon nanoparticles (SiNPs), due to their unique physiological characteristics and structural properties, offer a significant advantage as nanofertilizers, nanopesticides, nanozeolite and targeted delivery systems in agriculture. Silicon nanoparticles are well known to improve plant growth under normal and stressful environments. Nanosilicon has been reported to enhance plant stress tolerance against various environmental stress and is considered a non-toxic and proficient alternative to control plant diseases. However, a few studies depicted the phytotoxic effects of SiNPs on specific plants. Therefore, there is a need for comprehensive research, mainly on the interaction mechanism between NPs and host plants to unravel the hidden facts about silicon nanoparticles in agriculture. The present review illustrates the potential role of silicon nanoparticles in improving plant resistance to combat different environmental (abiotic and biotic) stresses and the underlying mechanisms involved. Furthermore, our review focuses on providing the overview of various methods exploited in the biogenic synthesis of silicon nanoparticles. However, certain limitations exist in synthesizing the well-characterized SiNPs on a laboratory scale. To bridge this gap, in the last section of the review, we discussed the possible use of the machine learning approach in future as an effective, less labour-intensive and time-consuming method for silicon nanoparticle synthesis. The existing research gaps from our perspective and future research directions for utilizing SiNPs in sustainable agriculture development have also been highlighted.
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Affiliation(s)
- Lovely Mahawar
- Department of Plant Physiology, Faculty of Agrobiology and Food Resources, Slovak University of Agriculture, Nitra, Slovakia.
| | | | - Mohammad Suhel
- Ranjan Plant Physiology and Biochemistry Laboratory, Department of Botany, University of Allahabad, Prayagraj, India
| | - Sheo Mohan Prasad
- Ranjan Plant Physiology and Biochemistry Laboratory, Department of Botany, University of Allahabad, Prayagraj, India
| | - Marek Živčák
- Department of Plant Physiology, Faculty of Agrobiology and Food Resources, Slovak University of Agriculture, Nitra, Slovakia
| | - Marian Brestic
- Department of Plant Physiology, Faculty of Agrobiology and Food Resources, Slovak University of Agriculture, Nitra, Slovakia.
| | - Anshu Rastogi
- Laboratory of Bioclimatology, Department of Ecology and Environmental Protection, Faculty of Environmental Engineering and Mechanical Engineering, Poznan University of Life Sciences, Piątkowska 94, 60-649, Poznań, Poland
| | - Milan Skalicky
- Department of Botany and Plant Physiology, Czech University of Life Sciences Prague, Czech Republic
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6
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Feltin N, Crouzier L, Delvallée A, Pellegrino F, Maurino V, Bartczak D, Goenaga-Infante H, Taché O, Marguet S, Testard F, Artous S, Saint-Antonin F, Salzmann C, Deumer J, Gollwitzer C, Koops R, Sebaïhi N, Fontanges R, Neuwirth M, Bergmann D, Hüser D, Klein T, Hodoroaba VD. Metrological Protocols for Reaching Reliable and SI-Traceable Size Results for Multi-Modal and Complexly Shaped Reference Nanoparticles. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:993. [PMID: 36985886 PMCID: PMC10057439 DOI: 10.3390/nano13060993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 02/26/2023] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
The study described in this paper was conducted in the framework of the European nPSize project (EMPIR program) with the main objective of proposing new reference certified nanomaterials for the market in order to improve the reliability and traceability of nanoparticle size measurements. For this purpose, bimodal populations as well as complexly shaped nanoparticles (bipyramids, cubes, and rods) were synthesized. An inter-laboratory comparison was organized for comparing the size measurements of the selected nanoparticle samples performed with electron microscopy (TEM, SEM, and TSEM), scanning probe microscopy (AFM), or small-angle X-ray scattering (SAXS). The results demonstrate good consistency of the measured size by the different techniques in cases where special care was taken for sample preparation, instrument calibration, and the clear definition of the measurand. For each characterization method, the calibration process is described and a semi-quantitative table grouping the main error sources is proposed for estimating the uncertainties associated with the measurements. Regarding microscopy-based techniques applied to complexly shaped nanoparticles, data dispersion can be observed when the size measurements are affected by the orientation of the nanoparticles on the substrate. For the most complex materials, hybrid approaches combining several complementary techniques were tested, with the outcome being that the reliability of the size results was improved.
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Affiliation(s)
- Nicolas Feltin
- Laboratoire National de Métrologie et d’Essais (LNE), 29 Avenue Roger Hennequin, 78197 Trappes, France
| | - Loïc Crouzier
- Laboratoire National de Métrologie et d’Essais (LNE), 29 Avenue Roger Hennequin, 78197 Trappes, France
| | - Alexandra Delvallée
- Laboratoire National de Métrologie et d’Essais (LNE), 29 Avenue Roger Hennequin, 78197 Trappes, France
| | - Francesco Pellegrino
- Dipartimento di Chimica and NIS Inter-Department Centre, University of Torino, Via P. Giuria, 10125 Torino, Italy
| | - Valter Maurino
- Dipartimento di Chimica and NIS Inter-Department Centre, University of Torino, Via P. Giuria, 10125 Torino, Italy
| | - Dorota Bartczak
- National Measurement Laboratory, LGC Limited, Queens Road, Teddington TW11 0LY, UK
| | | | - Olivier Taché
- CEA, CNRS, NIMBE, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - Sylvie Marguet
- CEA, CNRS, NIMBE, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - Fabienne Testard
- CEA, CNRS, NIMBE, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - Sébastien Artous
- CEA, Liten, DTNM, Université Grenoble Alpes, 38000 Grenoble, France
| | | | - Christoph Salzmann
- Federal Institute for Materials Research and Testing (BAM), Unter den Eichen 44-46, 12203 Berlin, Germany
| | - Jérôme Deumer
- Physikalisch-Technische Bundesanstalt (PTB), Abbestraße 2–12, 10587 Berlin, Germany
| | - Christian Gollwitzer
- Physikalisch-Technische Bundesanstalt (PTB), Abbestraße 2–12, 10587 Berlin, Germany
| | - Richard Koops
- VSL National Metrology Institute, Thjsseweg 11, 2629 JA Delft, The Netherlands
| | - Noham Sebaïhi
- National Standards (SMD), FPS Economy, 16 Bd du Roi Albert II, B-1000 Brussels, Belgium
| | - Richard Fontanges
- Pollen Metrology, 122 Rue du Rocher de Lorzier, Novespace A, 38430 Moirans, France
| | - Matthias Neuwirth
- Physikalisch-Technische Bundesanstalt (PTB), Bundesallee 100, 38116 Braunschweig, Germany
| | - Detlef Bergmann
- Physikalisch-Technische Bundesanstalt (PTB), Bundesallee 100, 38116 Braunschweig, Germany
| | - Dorothee Hüser
- Physikalisch-Technische Bundesanstalt (PTB), Bundesallee 100, 38116 Braunschweig, Germany
| | - Tobias Klein
- Physikalisch-Technische Bundesanstalt (PTB), Bundesallee 100, 38116 Braunschweig, Germany
| | - Vasile-Dan Hodoroaba
- Federal Institute for Materials Research and Testing (BAM), Unter den Eichen 44-46, 12203 Berlin, Germany
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7
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The Application of Artificial Intelligence in Magnetic Hyperthermia Based Research. FUTURE INTERNET 2022. [DOI: 10.3390/fi14120356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
The development of nanomedicine involves complex nanomaterial research involving magnetic nanomaterials and their use in magnetic hyperthermia. The selection of the optimal treatment strategies is time-consuming, expensive, unpredictable, and not consistently effective. Delivering personalized therapy that obtains maximal efficiency and minimal side effects is highly important. Thus, Artificial Intelligence (AI) based algorithms provide the opportunity to overcome these crucial issues. In this paper, we briefly overview the significance of the combination of AI-based methods, particularly the Machine Learning (ML) technique, with magnetic hyperthermia. We considered recent publications, reports, protocols, and review papers from Scopus and Web of Science Core Collection databases, considering the PRISMA-S review methodology on applying magnetic nanocarriers in magnetic hyperthermia. An algorithmic performance comparison in terms of their types and accuracy, data availability taking into account their amount, types, and quality was also carried out. Literature shows AI support of these studies from the physicochemical evaluation of nanocarriers, drug development and release, resistance prediction, dosing optimization, the combination of drug selection, pharmacokinetic profile characterization, and outcome prediction to the heat generation estimation. The papers reviewed here clearly illustrate that AI-based solutions can be considered as an effective supporting tool in drug delivery, including optimization and behavior of nanocarriers, both in vitro and in vivo, as well as the delivery process. Moreover, the direction of future research, including the prediction of optimal experiments and data curation initiatives has been indicated.
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8
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In Situ Assembly of Well-Defined MoS2 Slabs on Shape-Tailored Anatase TiO2 Nanostructures: Heterojunctions Role in Phenol Photodegradation. Catalysts 2022. [DOI: 10.3390/catal12111414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
MoS2/TiO2-based nanostructures have attracted extensive attention due to their high performance in many fields, including photocatalysis. In this contribution, MoS2 nanostructures were prepared via an in situ bottom-up approach at the surface of shape-controlled TiO2 nanoparticles (TiO2 nanosheets and bipyramids). Furthermore, a multi-technique approach by combining electron microscopy and spectroscopic methods was employed. More in detail, the morphology/structure and vibrational/optical properties of MoS2 slabs on TiO2 anatase bipyramidal nanoparticles, mainly exposing {101} facets, and on TiO2 anatase nanosheets exposing both {001} and {101} facets, still covered by MoS2, were compared. It was shown that unlike other widely used methods, the bottom-up approach enabled the atomic-level growth of well-defined MoS2 slabs on TiO2 nanostructures, thus aiming to achieve the most effective chemical interactions. In this regard, two kinds of synergistic heterojunctions, namely, crystal face heterojunctions between anatase TiO2 coexposed {101} and {001} facets and semiconductor heterojunctions between MoS2 and anatase TiO2 nanostructures, were considered to play a role in enhancing the photocatalytic activity, together with a proper ratio of (101), (001) coexposed surfaces.
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9
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High-throughput synthesis of silver nanoplates and optimization of optical properties by machine learning. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.118009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Vincent J, Lau KS, Evyan YCY, Chin SX, Sillanpää M, Chia CH. Biogenic Synthesis of Copper-Based Nanomaterials Using Plant Extracts and Their Applications: Current and Future Directions. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:3312. [PMID: 36234439 PMCID: PMC9565561 DOI: 10.3390/nano12193312] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 09/19/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
Plants have been used for multiple purposes over thousands of years in various applications such as traditional Chinese medicine and Ayurveda. More recently, the special properties of phytochemicals within plant extracts have spurred researchers to pursue interdisciplinary studies uniting nanotechnology and biotechnology. Plant-mediated green synthesis of nanomaterials utilises the phytochemicals in plant extracts to produce nanomaterials. Previous publications have demonstrated that diverse types of nanomaterials can be produced from extracts of numerous plant components. This review aims to cover in detail the use of plant extracts to produce copper (Cu)-based nanomaterials, along with their robust applications. The working principles of plant-mediated Cu-based nanomaterials in biomedical and environmental applications are also addressed. In addition, it discusses potential biotechnological solutions and new applications and research directions concerning plant-mediated Cu-based nanomaterials that are yet to be discovered so as to realise the full potential of the plant-mediated green synthesis of nanomaterials in industrial-scale production and wider applications. This review provides readers with comprehensive information, guidance, and future research directions concerning: (1) plant extraction, (2) plant-mediated synthesis of Cu-based nanomaterials, (3) the applications of plant-mediated Cu-based nanomaterials in biomedical and environmental remediation, and (4) future research directions in this area.
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Affiliation(s)
- Jei Vincent
- Materials Science Program, Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Kam Sheng Lau
- Materials Science Program, Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Yang Chia-Yan Evyan
- Faculty of Engineering, Science and Technology, Nilai University, Nilai 71800, Negeri Sembilan, Malaysia
| | - Siew Xian Chin
- ASASIpintar Program, Pusat GENIUS@Pintar Negara, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Mika Sillanpää
- Materials Science Program, Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
- Department of Chemical Engineering, School of Mining, Metallurgy and Chemical Engineering, University of Johannesburg, P.O. Box 17011, Doornfontein 2028, South Africa
- Sustainable Membrane Technology Research Group (SMTRG), Chemical Engineering Department, Persian Gulf University, Bushehr P.O. Box 75169-13817, Iran
- Zhejiang Rongsheng Environmental Protection Paper Co. LTD, NO.588 East Zhennan Road, Pinghu Economic Development Zone, Zhejiang 314213, China
| | - Chin Hua Chia
- Materials Science Program, Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
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Karthikeyan M, Mahapatra DM, Razak ASA, Abahussain AA, Ethiraj B, Singh L. Machine learning aided synthesis and screening of HER catalyst: Present developments and prospects. CATALYSIS REVIEWS 2022:1-31. [DOI: 10.1080/01614940.2022.2103980] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 07/13/2022] [Indexed: 09/02/2023]
Affiliation(s)
- M Karthikeyan
- Department of Environmental Science, SRM University-AP, Amaravati, India
| | - Durga Madhab Mahapatra
- Department of Chemical Engineering, Energy Cluster, School of Engineering, University of Petroleum and Energy Studies, Energy Acres, Dehradun, India
| | - Abdul Syukor Abd Razak
- Faculty of Civil Engineering Technology, Universiti Malaysia Pahang (UMP), Kuantan, Malaysia
| | | | - Baranitharan Ethiraj
- Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India
| | - Lakhveer Singh
- Department of Chemistry, Sardar Patel University, Mandi,India
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12
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Mai H, Le TC, Chen D, Winkler DA, Caruso RA. Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery. Chem Rev 2022; 122:13478-13515. [PMID: 35862246 DOI: 10.1021/acs.chemrev.2c00061] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.
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Affiliation(s)
- Haoxin Mai
- Applied Chemistry and Environmental Science, School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
| | - Tu C Le
- School of Engineering, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
| | - Dehong Chen
- Applied Chemistry and Environmental Science, School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
| | - David A Winkler
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia.,Biochemistry and Chemistry, La Trobe University, Kingsbury Drive, Bundoora, Victoria 3042, Australia.,School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Rachel A Caruso
- Applied Chemistry and Environmental Science, School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
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13
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Maurino V, Pellegrino F, Picotto GB, Ribotta L. Quantitative three-dimensional characterization of critical sizes of non-spherical TiO 2 nanoparticles by using atomic force microscopy. Ultramicroscopy 2022; 234:113480. [PMID: 35151042 DOI: 10.1016/j.ultramic.2022.113480] [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: 09/21/2021] [Revised: 12/22/2021] [Accepted: 01/30/2022] [Indexed: 10/19/2022]
Abstract
Since both size and shape of nanoparticles are challenging to be quantitatively measured, traceable 3D measurements are nowadays an issue. 3D nanometrology plays a crucial role to reduce the uncertainty of measurements, improve traceable calibration of samples and implement new approaches, models, and methodologies in the study of the nanomaterials. AFM measurement of nanoparticles with unusual shape represent a non-trivial challenge due to the convolution with the finite size of the tip. In this work, geometric approaches for the determination of critical sizes of TiO2 anatase bipyramids and nanosheets are described. An uncertainty budget is estimated for each nanoparticle size with the aim of assessing the different sources of error to obtain a more reliable and consistent result. The combined standard uncertainties are respectively less than 5% and 10% of the dimensions of bipyramids and nanosheets. Due to the stability and monomodal distribution of their critical sizes, bipyramids and nanosheets are suitable to apply as candidate reference materials at the nanoscale. Moreover, quantitative measurements of shape and texture descriptors are discussed in order to understand the quality of the synthetized batch.
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Affiliation(s)
- Valter Maurino
- Department of Chemistry and Centro Interdipartimentale NIS, Università degli di Torino, Via Pietro Giuria 7, Torino 10125, Italy; UniTO-ITT Joint Lab, Via Gioacchino Quarello 15/A, Torino 10135, Italy
| | - Francesco Pellegrino
- Department of Chemistry and Centro Interdipartimentale NIS, Università degli di Torino, Via Pietro Giuria 7, Torino 10125, Italy; UniTO-ITT Joint Lab, Via Gioacchino Quarello 15/A, Torino 10135, Italy
| | - Gian Bartolo Picotto
- Istituto Nazionale di Ricerca Metrologica (INRiM), Strada delle Cacce 91, Torino 10135, Italy
| | - Luigi Ribotta
- Istituto Nazionale di Ricerca Metrologica (INRiM), Strada delle Cacce 91, Torino 10135, Italy; Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino 10129, Italy.
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Merging data curation and machine learning to improve nanomedicines. Adv Drug Deliv Rev 2022; 183:114172. [PMID: 35189266 PMCID: PMC9233944 DOI: 10.1016/j.addr.2022.114172] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/28/2022] [Accepted: 02/16/2022] [Indexed: 12/12/2022]
Abstract
Nanomedicine design is often a trial-and-error process, and the optimization of formulations and in vivo properties requires tremendous benchwork. To expedite the nanomedicine research progress, data science is steadily gaining importance in the field of nanomedicine. Recently, efforts have explored the potential to predict nanomaterials synthesis and biological behaviors via advanced data analytics. Machine learning algorithms process large datasets to understand and predict various material properties in nanomedicine synthesis, pharmacologic parameters, and efficacy. "Big data" approaches may enable even larger advances, especially if researchers capitalize on data curation methods. However, the concomitant use of data curation processes needed to facilitate the acquisition and standardization of large, heterogeneous data sets, to support advanced data analytics methods such as machine learning has yet to be leveraged. Currently, data curation and data analytics areas of nanotechnology-focused data science, or 'nanoinformatics', have been proceeding largely independently. This review highlights the current efforts in both areas and the potential opportunities for coordination to advance the capabilities of data analytics in nanomedicine.
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15
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Lagostina V, Romeo E, Maria Ferrari A, Maurino V, Chiesa M. Monomeric (VO2+) and dimeric mixed valence (V2O33+) vanadium species at the surface of shape controlled TiO2 anatase nano crystals. J Catal 2022. [DOI: 10.1016/j.jcat.2021.12.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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16
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Machine learning assisted optimization of blending process of polyphenylene sulfide with elastomer using high speed twin screw extruder. Sci Rep 2021; 11:24079. [PMID: 34911974 PMCID: PMC8674312 DOI: 10.1038/s41598-021-03513-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 12/06/2021] [Indexed: 11/08/2022] Open
Abstract
Random forest regression was applied to optimize the melt-blending process of polyphenylene sulfide (PPS) with poly(ethylene-glycidyl methacrylate-methyl acrylate) (E-GMA-MA) elastomer to improve the Charpy impact strength. A training dataset was constructed using four elastomers with different GMA and MA contents by varying the elastomer content up to 20 wt% and the screw rotation speed of the extruder up to 5000 rpm at a fixed barrel temperature of 300 °C. Besides the controlled parameters, the following measured parameters were incorporated into the descriptors for the regression: motor torque, polymer pressure, and polymer temperatures monitored by infrared-ray thermometers installed at four positions (T1 to T4) as well as the melt viscosity and elastomer particle diameter of the product. The regression without prior knowledge revealed that the polymer temperature T1 just after the first kneading block is an important parameter next to the elastomer content. High impact strength required high elastomer content and T1 below 320 °C. The polymer temperature T1 was much higher than the barrel temperature and increased with the screw speed due to the heat of shear. The overheating caused thermal degradation, leading to a decrease in the melt viscosity and an increase in the particle diameter at high screw speed. We thus reduced the barrel temperature to keep T1 around 310 °C. This increased the impact strength from 58.6 kJ m−2 as the maximum in the training dataset to 65.3 and 69.0 kJ m−2 at elastomer contents of 20 and 30 wt%, respectively.
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17
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Wen H, Luna-Romera JM, Riquelme JC, Dwyer C, Chang SLY. Statistically Representative Metrology of Nanoparticles via Unsupervised Machine Learning of TEM Images. NANOMATERIALS 2021; 11:nano11102706. [PMID: 34685147 PMCID: PMC8539342 DOI: 10.3390/nano11102706] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/07/2021] [Accepted: 10/06/2021] [Indexed: 11/16/2022]
Abstract
The morphology of nanoparticles governs their properties for a range of important applications. Thus, the ability to statistically correlate this key particle performance parameter is paramount in achieving accurate control of nanoparticle properties. Among several effective techniques for morphological characterization of nanoparticles, transmission electron microscopy (TEM) can provide a direct, accurate characterization of the details of nanoparticle structures and morphology at atomic resolution. However, manually analyzing a large number of TEM images is laborious. In this work, we demonstrate an efficient, robust and highly automated unsupervised machine learning method for the metrology of nanoparticle systems based on TEM images. Our method not only can achieve statistically significant analysis, but it is also robust against variable image quality, imaging modalities, and particle dispersions. The ability to efficiently gain statistically significant particle metrology is critical in advancing precise particle synthesis and accurate property control.
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Affiliation(s)
- Haotian Wen
- School of Materials Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia
- Correspondence: (H.W.); (S.L.Y.C.)
| | - José María Luna-Romera
- Software and Computing Systems, Universidad de Sevilla, 41004 Seville, Spain; (J.M.L.-R.); (J.C.R.)
| | - José C. Riquelme
- Software and Computing Systems, Universidad de Sevilla, 41004 Seville, Spain; (J.M.L.-R.); (J.C.R.)
| | - Christian Dwyer
- Electron Imaging and Spectroscopy Tools, Sydney, NSW 2219, Australia;
| | - Shery L. Y. Chang
- School of Materials Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia
- Mark Wainwright Analytical Centre, Electron Microscope Unit, University of New South Wales, Sydney, NSW 2052, Australia
- Correspondence: (H.W.); (S.L.Y.C.)
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18
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Sordello F, Pellegrino F, Prozzi M, Minero C, Maurino V. Controlled Periodic Illumination Enhances Hydrogen Production by over 50% on Pt/TiO 2. ACS Catal 2021; 11:6484-6488. [PMID: 34306809 PMCID: PMC8294008 DOI: 10.1021/acscatal.1c01734] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 05/13/2021] [Indexed: 12/28/2022]
Abstract
Efficient solar water photosplitting is plagued by large overpotentials of the HER and OER. Even with a noble metal catalyst, the hydrogen evolution reaction can be limited by the strong M-H bonding over some metals, such as Pt, Pd, and Rh, inhibiting hydrogen desorption. H absorption is regulated by the potential at the metal nanoparticles. Through controlled periodic illumination of a Pt/TiO2 suspension, we hypothesized a fast variation of the photopotential that induced catalytic surface resonance on the metal, resulting in more than a 50% increase of the efficiency at frequencies higher than 80 Hz.
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Affiliation(s)
- F. Sordello
- Dipartimento
di Chimica and NIS Center, University of
Torino, Via P. Giuria
7, 10125 Torino, Italy
| | - F. Pellegrino
- Dipartimento
di Chimica and NIS Center, University of
Torino, Via P. Giuria
7, 10125 Torino, Italy
- JointLAB
UniTo-ITT Automotive, Via Quarello 15/A, 10135 Torino, Italy
| | - M. Prozzi
- Dipartimento
di Chimica and NIS Center, University of
Torino, Via P. Giuria
7, 10125 Torino, Italy
| | - C. Minero
- Dipartimento
di Chimica and NIS Center, University of
Torino, Via P. Giuria
7, 10125 Torino, Italy
| | - V. Maurino
- Dipartimento
di Chimica and NIS Center, University of
Torino, Via P. Giuria
7, 10125 Torino, Italy
- JointLAB
UniTo-ITT Automotive, Via Quarello 15/A, 10135 Torino, Italy
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