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Related Experiment Video

Updated: Jul 16, 2026

Physical, Chemical and Biological Characterization of Six Biochars Produced for the Remediation of Contaminated Sites
09:39

Physical, Chemical and Biological Characterization of Six Biochars Produced for the Remediation of Contaminated Sites

Published on: November 28, 2014

Physics-Informed Machine Learning for Optimized and Sustainable Biochar Water Treatment.

Qingyang Liu1, Bing Bai2

  • 1Co-Innovation Center for the Sustainable Forestry in Southern China, College of Ecology and Environment, Nanjing Forestry University, Nanjing 210037, China.

Molecules (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

This study proposes integrating physics-informed machine learning (PIML) with life cycle assessment (LCA) to advance biochar water treatment. This novel approach aims to overcome empirical limitations for sustainable, large-scale application.

Keywords:
biocharlife cycle assessmentphysics-informed machine learningsustainability assessmentwastewater treatment

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Last Updated: Jul 16, 2026

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Published on: November 28, 2014

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Assessment of Waste-Derived Biochars on the Health and Biological Activity of Soil
10:31

Assessment of Waste-Derived Biochars on the Health and Biological Activity of Soil

Published on: October 10, 2025

Area of Science:

  • Environmental Engineering
  • Materials Science
  • Computational Science

Background:

  • Biochar water treatment faces challenges due to reliance on empirical data and black-box models.
  • Current methods hinder large-scale application and sustainable implementation.
  • A need exists for a more integrated and physically consistent approach.

Purpose of the Study:

  • To propose a novel research paradigm integrating physics-informed machine learning (PIML) and life cycle assessment (LCA) for biochar water treatment.
  • To address the limitations of empirical experimentation and black-box models in the field.
  • To outline pathways and identify research gaps for advancing biochar technology.

Main Methods:

  • Conceptual framework integrating PIML and LCA with bidirectional information flow.
  • Embedding fundamental physical laws (adsorption, kinetics, thermodynamics) into PIML architectures.
  • Extending the framework to a closed-loop water-energy-soil-food system for holistic management.

Main Results:

  • The proposed framework enables simultaneous material design and sustainability assessment.
  • Ensures physical consistency in PIML models by incorporating physical laws.
  • Identifies explainability and cross-scale generalization as critical gaps for industrial deployment.

Conclusions:

  • The integrated PIML-LCA framework offers a promising direction for sustainable biochar water treatment.
  • Addressing identified research gaps is crucial for industrial adoption and closed-loop resource management.
  • This perspective provides a roadmap for future research towards sustainable biochar technology implementation.