Magnetically Recoverable Fe3O4@BNPs@ZnO-ZnS Nanocomposite with Machine Learning Optimization for Enhanced Photocatalytic Water Purification
Bibliographic record
Abstract
In this study, we developed a magnetically recoverable Fe₃O₄@BNPs@ZnO-ZnS nanocomposite for enhanced photocatalytic degradation of organic pollutants in wastewater, with machine learning (ML) optimization for process prediction.The nanocomposite exhibited superior photocatalytic activity under UV irradiation (10 W), achieving removal efficiencies of 99.7% for trifluralin, 97.2% for dimethoate, and 96.5% for Congo Red within 120 minutes.Compared to traditional ZnO-only catalysts, which typically exhibit <80% removal under similar conditions, the proposed system improves degradation efficiency by up to 25% and shortens equilibrium time by 20-40 minutes.The composite's enhanced performance is attributed to synergistic bandgap tuning and extended charge carrier lifetimes (8.7 ns vs. 2.1 ns in bare ZnO).Characterization techniques, including XRD, FTIR, and FESEM, confirmed successful synthesis and structural integrity.Additionally, machine learning algorithms, including Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Regression (SVR), were trained on experimental data to predict pollutant removal and concentration ratios with high accuracy (R² ≥ 0.96).The nanocomposite also demonstrates excellent magnetic recoverability (<1% catalyst loss per cycle).Notably, these ML models outperformed conventional kinetic models such as Langmuir-Hinshelwood, which generally exhibit lower accuracy (R² ≈ 0.85-0.90)and limited generalizability across varying operational conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".