Engineered biochar supported bismuth tungstate: Unveiling the influence of precursor concentrations and biochar dosage for the solar photocatalysis of 1,3-diphenylguanidine in secondary municipal effluent
Bibliographic record
Abstract
In this work, engineered hierarchal structures of biochar-supported bismuth tungstate are synthesized for wastewater treatment applications. The integration of biochar supports resulted in an increase in the surface area, reduction in crystallite size, and exposure of the active facets of Bi 2 WO 6 . Controlled Bi 2 WO 6 morphologies were obtained by varying the precursor concentrations, causing a remarkable transition from mixed microspheres to flower-like structures and, ultimately, to nanosheets. The optimal composite demonstrated its efficiency in a spiked wastewater secondary effluent, achieving 97.74 % degradation of 1,3-diphenylguanidine (DPG) under 8 h of simulated solar light irradiation. The treatment also reduced the toxicity and COD of the spiked secondary effluent matrix. The primary reactive oxygen species detected were O 2 •– and h + . Additionally, the photocatalytic degradation mechanism and possible degradation by-products of DPG were identified. These results highlight the significant influence of the biochar supports and synthesis parameters on the activity of biochar-supported photocatalysts, paving the way for the design of superior photocatalyst materials for wastewater treatment applications.
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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".