An efficient lignin-biochar/ZnAl2O4/Bi2MoO6 composite photocatalyst for degradation of organic chlorine pollutants
Bibliographic record
Abstract
Lignin, with its intrinsic polyphenolic structure, high carbon content, and diverse functional groups, has emerged as promising precursor for carbon-based materials in pollutant elimination. In this work, lignin-biochar (LC) was fabricated and integrated into LC/ZnAl 2 O 4 /Bi 2 MoO 6 composites with adsorption-photocatalysis synergy, employed within an intimate coupling of photocatalysis and biodegradation (ICPB) system for AOX degradation. The formation of Bi-O-C bonds between lignin-biochar and Bi 2 MoO 6 provides atomic-level channel for accelerating electron transfer. Notably, the 1 wt%LC/0.5 wt%ZnAl 2 O 4 /Bi 2 MoO 6 (LM3) sample showed superior degradation efficiency, with the apparent rate constants of 13.82-fold (methylene blue, MB) and 5.56-fold (p-chlorophenol, 4-CP) higher than those of the pristine Bi 2 MoO 6 , respectively. DFT analyses demonstrated strong chemical adsorption interactions between 4-CP and lignin-biochar, with the highest adsorption energy of −46.3152 eV observed for LC/ZnAl 2 O 4 /Bi 2 MoO 6 . The synergistic adsorption-photocatalysis mechanism of the LC/ZnAl 2 O 4 /Bi 2 MoO 6 composite was systematically explored, highlighting the critical role of lignin-biochar in enhancing adsorption, charge separation, and reactive oxygen species generation. The ICPB system demonstrated effective degradation of refractory 4-CP and bamboo ECF bleaching effluent. The visible-light driven ICPB system (VCPB) achieved 78.30 % TOC removal for 4-CP effluent within 24 h. Furthermore, the VCPB process achieved 80.03 % COD and 71.03 % AOX removals for an industrial bamboo ECF bleaching effluent sample, with long-term operational stability, while fostering robust microbial activity and enhanced biofilm diversity. The synergistic mechanism of the system between adsorption, photocatalysis, and biodegradation was discussed. This work provides a novel approach for designing bio-based photocatalysts for the degradation of refractory industrial effluents.
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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".