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Author Spotlight: Designing Sustainable Nanomaterials for Advancing Synthesis and Element Mixing

Author Spotlight: Designing Sustainable Nanomaterials for Advancing Synthesis and Element Mixing

Published on: March 15, 2024

Using Machine Learning to make nanomaterials sustainable.

Janeck J Scott-Fordsmand¹, Mónica J B Amorim²

¹Department of Ecoscience, Aarhus University, 8000 Aarhus, Denmark.

The Science of the Total Environment

|November 21, 2022

Summary

Machine Learning (ML) can advance sustainable development by aiding Environmental Risk Assessments (ERAs). Clearer guidance and data standards are crucial for integrating ML in ERAs, especially for novel materials.

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Area of Science:

Environmental Science
Computer Science
Risk Assessment

Background:

Sustainable development is critical for human survival.
Environmental Risk Assessment (ERA) is key to evaluating environmental safety.
Machine Learning (ML) offers potential for enhancing ERA processes.

Purpose of the Study:

To review the application of ML in various stages of Environmental Risk Assessment (ERA).
To identify the need for standardized guidance on using ML in ERAs for nano- and smart-materials.
To highlight the importance of data quality and bias mitigation in ML for sustainability.

Main Methods:

Literature review of ML applications in ERA.
Analysis of current practices and challenges in ML implementation for ERA.

Keywords:

Environment Machine Learning Nanomaterials Risk assessment Species

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Identification of key areas for future development and standardization.

Main Results:

ML can be applied across most steps of a typical ERA, from data gathering to assessment.
There is a lack of clear guidance and standards for using ML in ERAs.
Addressing data foundations, methodologies, and uncertainties is vital for ML in ERA.

Conclusions:

ML holds significant promise for supporting sustainable development through improved ERAs.
Developing a strategic approach for ML implementation in ERA is essential.
Mitigating historical data biases is crucial for responsible ML deployment in environmental safety assessments.