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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".