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Related Experiment Video

Updated: Jan 7, 2026

Preparation of Biomass-based Mesoporous Carbon with Higher Nitrogen-/Oxygen-chelating Adsorption for CuII Through Microwave Pre-Pyrolysis
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Unveiling sulfonamide adsorption on biochar using explainable machine learning.

Deepu Kumar Jha1

  • 1Department of Chemical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, India.

Bioresource Technology
|December 24, 2025
PubMed
Summary
This summary is machine-generated.

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Machine learning accurately predicts biochar adsorption of sulfonamide antibiotics. Gradient boosting models identify biochar surface area as key for removing these contaminants from water.

Area of Science:

  • Environmental Chemistry
  • Materials Science
  • Computational Chemistry

Background:

  • Sulfonamide antibiotics are persistent aquatic contaminants, posing risks of antimicrobial resistance.
  • Biochar shows potential for sulfonamide removal, but optimization is challenging and resource-intensive.

Purpose of the Study:

  • To develop and validate machine learning models for predicting biochar adsorption capacity of sulfonamides.
  • To identify key molecular, structural, and operational factors influencing sulfonamide adsorption by biochar.

Main Methods:

  • Applied machine learning algorithms including artificial neural networks, Gaussian process regression, and gradient boosting.
  • Utilized grid-search cross-validation for model optimization.
  • Employed SHAP and partial dependence analysis for model explainability.
Keywords:
Antibiotics removalModel interpretabilityPredictive analysisPyrolyzed biomassWater remediation

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Last Updated: Jan 7, 2026

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Main Results:

  • Gradient boosting demonstrated superior performance with R² = 0.997, RMSE = 6.06 mg g⁻¹, and MAE = 2.18 mg g⁻¹.
  • BET surface area was identified as the primary factor influencing adsorption.
  • Initial concentration and dosage were significant operational factors, with pore diameter and volume having secondary effects.

Conclusions:

  • The developed machine learning framework provides an interpretable approach to understanding structure-performance relationships in biochar adsorption.
  • This predictive model can guide efficient biochar selection and optimization for sulfonamide removal in water treatment.