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Updated: Jun 11, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

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Improving Kinetic Prediction and Structural-Electronic Mechanistic Coherence in the Fenton Process via a Cross-Scale

Sheng Li¹, Ling Yuan¹, Yingying Chu¹

¹State Key Laboratory of Water Pollution Control and Green Resource Recycling, School of the Environment, Nanjing University, Nanjing 210023, China.

Environmental Science & Technology

|June 2, 2026

Summary

This summary is machine-generated.

A new machine learning framework integrates molecular and quantum features to predict contaminant degradation in the Fenton process. This approach enhances mechanistic understanding and optimizes advanced oxidation processes for environmental remediation.

Keywords:

Fenton oxidation cross-scale feature electronic reactivity machine learning molecular structure

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Area of Science:

Environmental Chemistry
Computational Chemistry
Chemical Engineering

Background:

Predicting contaminant degradation kinetics in advanced oxidation processes is complex.
Existing methods struggle to integrate multiscale mechanistic drivers like structure and electronics.

Purpose of the Study:

To develop a unified multiscale machine learning framework for predicting contaminant degradation kinetics.
To link structural and electronic drivers of degradation in the Fenton process.

Main Methods:

Fused molecular fingerprints (MFs) and quantum chemical features (QCFs) of contaminants.
Developed a multiscale machine learning model to predict observed rate constants (k_obs).
Utilized interaction analyses (UMAP, t-SNE) and partial dependence analyses.

Main Results:

The fusion model demonstrated superior predictive performance and robustness over single-scale models.
Feature fusion provided a coherent structural-electronic mechanistic interpretation.
Identified synergistic interactions between environmental factors (e.g., pH) and contaminant properties.

Conclusions:

The proposed framework reconciles mechanistic understanding of contaminant degradation.
Provides a basis for the rational optimization of Fenton-based advanced oxidation processes.
External validation confirmed the model's generalizability to new contaminants and conditions.