Softwood biochar-supported Bi2WO6 for photocatalytic degradation of organic contaminant mixture in river water: Role of pyrolysis temperature and surface functionality
Bibliographic record
Abstract
The design of efficient biochar-supported photocatalysts for the removal of emerging contaminants requires a fundamental understanding of the influence of biochar properties on photocatalyst structure and performance. This study presents a comprehensive investigation of the effects of biochar feedstock and pyrolysis temperature on the structural, optical, and photocatalytic properties of biochar-supported bismuth tungstate (Bi 2 WO 6 ) composites for the degradation of 1,3-diphenylguanidine (DPG) and other emerging contaminants. Biochar derived from two softwood feedstocks (cedar and a mix of pine and spruce) was pyrolyzed at different temperatures (300 °C, 400 °C, 500 °C, and 600 °C) to serve as a support for Bi 2 WO 6 . This work reveals the critical impact of biochar surface area, porosity, and redox-active functional groups on the interaction with Bi 2 WO 6 , which influence the crystal orientation, surface area, redox properties, and the resulting composite photocatalytic behavior. Composites synthesized using biochar pyrolyzed at 400 °C demonstrated optimal performance and a 74-fold enhancement in DPG photocatalytic rate constant compared to bare Bi 2 WO 6 . The optimal composite was further applied to treat river water spiked with five organic contaminants, achieving 94.36 % removal of the total mixture and a55.4 % reduction in total organic carbon, confirming its effectiveness in complex matrices. This study highlights the critical role of biochar support properties in enhancing the photocatalytic activity of Bi 2 WO 6 and establishes guidelines for the development of sustainable photocatalysts for water treatment applications. • Softwood biochar pyrolyzed at 300–600 °C were investigated as supports for Bi 2 WO 6 (BW). • Composites supported on biochar pyrolyzed at 400 °C exhibited optimal surface area, crystal orientation, and redox functionality. • BW/P400 demonstrated a 74-fold improvement in activity and over 94 % removal of a mixture of contaminants in river water.
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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".