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RESEARCH PAPER · The Science of The Total Environment · 2022

Using Machine Learning to make nanomaterials sustainable

Janeck James Scott-Fordsmand, Mónica J.B. Amorim

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Using Machine Learning to make nanomaterials sustainableOriginalLeontief y España: una reflexión sobre las tablas imput/output y su relevancia para la economía y los economistas españoles5Nanossegurança para inovação sustentável: a avaliação da toxicidade e ciclo de vida de um nanoproduto é essencial para garantir que a sua produção e aplicações prosperem; sem provocar impactos negativos aos trabalhadores, consumidores e ao meio ambiente0Systems toxicology to advance human and environmental hazard assessment: A roadmap for advanced materials24Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations104TiO2 nanoparticles' library toxicity (UV and non-UV exposure) – High-throughput in vivo transcriptomics reveals mechanisms7Machine Learning Modeling and Insights into the Structural Foundations of Polymyxin-like Antimicrobials1Functional Material Systems Enabled by Automated Data Extraction and Machine Learning33NFSDense201: microstructure image classification based on non-fixed size patch division with pre-trained DenseNet201 layers8Identifying multiple soil pollutions of potentially contaminated sites based on multi-gate mixture-of-experts network28Intelligent Materials and Nanomaterials Improving Physical Properties and Control Oriented on Electronic Implementations24Nanoinformatics Applied to Smart Nanomaterials0Innovations in Wastewater Treatment: Harnessing Mathematical Modeling and Computer Simulations with Cutting-Edge Technologies and Advanced Control Systems1A comprehensive systematic literature review of ML in nanotechnology for sustainable development41Artificial Intelligence and Machine Learning in Pharmacokinetics and Pharmacodynamic Studies3Sustainable Nanomaterials in Machine Learning: Occurrence and Applications1Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures74Quantitative gas-phase transmission electron microscopy: Where are we now and what comes next?16Recent Development of Nanomaterials for Chemical Engineering8The coefficient of conformism of a correlative prediction (CCCP): Building up reliable nano-QSPRs/QSARs for endpoints of nanoparticles in different experimental conditions encoded via quasi-SMILES19Application of ML in Agriculture Field With Its Enhancement Methods0Building species trait-specific nano-QSARs: Model stacking, navigating model uncertainties and limitations, and the effect of dataset size19Governance of advanced materials: Shaping a safe and sustainable future21Definition, detection, and tracking of nanowaste in foods: Challenges and perspectives11Let the two sides of the same coin meet—Environmental health and safety-oriented development of functional nanomaterials for environmental remediations28Machine Learning Allowed Interpreting Toxicity of a Fe-Doped CuO NM Library Large Data Set─An Environmental In Vivo Case Study3Application of Machine Learning in Nanotoxicology: A Critical Review and Perspective32A systematic review of current AI techniques used in the context of the SDGs53Energy efficiency and conservation using machine learning0Machine Learning-Driven Multidomain Nanomaterial Design: From Bibliometric Analysis to Applications82Enabling Fast AI-Driven Inverse Design of a Multifunctional Nanosurface by Parallel Evolution Strategies3Exploring temperature-dependent photoluminescence dynamics of colloidal CdSe nanoplatelets using machine learning approach8Advanced carbon nanomaterials for high-performance energy storage technologies8Advanced computational techniques: Bridging metaheuristic optimization and deep learning for material design through image enhancement9Carbon-Based Nanocomposites for Ecosystem Remediation: The AI Approach0Machine learning and image analysis in diatom taxonomy: Progress and prospects1Microbial Biosynthesis of Biostimulant Nanomaterials and Nanofertilizers3Nanotechnology-enabled soil management for sustainable agriculture: interactions, challenges, and prospects37Computational condensed matter science contributions to addressing water emerging contaminant pollution: a comprehensive review6Harnessing nanoparticles for reshaping tumor immune microenvironment of hepatocellular carcinoma6Leveraging Artificial Intelligence and Machine Learning for Characterizing Protein Corona, Nanobiological Interactions, and Advancing Drug Discovery42The latest breakthroughs in green and hybrid nanoparticle synthesis for multifaceted environmental applications19Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare109A comprehensive overview of AI–nanotech convergence for a resilient future5Cellulose-based nanomaterials in targeted tumor chemotherapy: A comprehensive review of design, delivery, and clinical potential11The emerging role of machine learning in nanomaterials research: applications, challenges, and future directions8Methods and tools for the safety assessment part of the European Commission’s safe and sustainable by design framework when applied to advanced materials4Silver integrated hybrids and nanocomposites for next-generation biomedicine: Beyond antimicrobial coatings toward smart sense–response–heal platforms16Advances in synthesis and characterization of bionanomaterials by using artificial intelligence and machine learning techniques: A critical review3Aromatic Plants and Their Role in Metal Nanoparticle Synthesis, Characterisation, and Application0Artificial Intelligence Applications in Bioenergy and Biomaterials Development via Fungal Biotechnology: Challenges and Perspectives0Artificial intelligence-driven nanomaterial design and applications: advances, challenges, and future directions0Toward sustainable strips: a review of materials and environmental considerations for future health monitoring0Safe and sustainable by design with ML/AI: A transformative approach to advancing nanotechnology6The Strategic Role of 2D Nanomaterials in Grid Modernization3Harmonizing ecotoxicological data reporting: A guide for toxicologists and chemists0Advances in artificial intelligence and machine learning for toxicity prediction in computational toxicology: a comprehensive review0From natural-extract variability to reproducible nanomaterials: AI-QbD strategies for green synthesis from agro-food waste0

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  1. Leontief y España: una reflexión sobre las tablas imput/output y su relevancia para la economía y los economistas españoles

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  3. Systems toxicology to advance human and environmental hazard assessment: A roadmap for advanced materials

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  4. Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

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  5. TiO2 nanoparticles' library toxicity (UV and non-UV exposure) – High-throughput in vivo transcriptomics reveals mechanisms

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  6. Machine Learning Modeling and Insights into the Structural Foundations of Polymyxin-like Antimicrobials

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  7. Functional Material Systems Enabled by Automated Data Extraction and Machine Learning

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  8. NFSDense201: microstructure image classification based on non-fixed size patch division with pre-trained DenseNet201 layers

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  10. Intelligent Materials and Nanomaterials Improving Physical Properties and Control Oriented on Electronic Implementations

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  12. Innovations in Wastewater Treatment: Harnessing Mathematical Modeling and Computer Simulations with Cutting-Edge Technologies and Advanced Control Systems

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  15. Sustainable Nanomaterials in Machine Learning: Occurrence and Applications

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  16. Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

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  17. Quantitative gas-phase transmission electron microscopy: Where are we now and what comes next?

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  18. Recent Development of Nanomaterials for Chemical Engineering

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  19. The coefficient of conformism of a correlative prediction (CCCP): Building up reliable nano-QSPRs/QSARs for endpoints of nanoparticles in different experimental conditions encoded via quasi-SMILES

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  20. Application of ML in Agriculture Field With Its Enhancement Methods

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  23. Definition, detection, and tracking of nanowaste in foods: Challenges and perspectives

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