Photocatalytic oxidation of methanol to formaldehyde on bismuth-based semiconductors
Bibliographic record
Abstract
Methanol is widely applied in photocatalysis as a scavenger of holes, and is also studied as a model system for heterogeneous photocatalysis for the production of formaldehyde. Compared to commercial processes for formaldehyde production via thermal catalytic methanol oxidation, photocatalytic oxidation of methanol to formaldehyde may be more promising when considering the following aspects: 1) lower reaction temperature and pressure (generally operated at room temperature and ambient pressure); 2) lower cost of the energy source (such as solar light) and 3) easy-to-design reactive system. Photocatalytic methanol oxidation was carried out using four different bismuth-based semiconductors (BBS), Bi 2 WO 6 , Bi 2 MoO 6 , BiOBr and BiVO 4 , under varying system temperature (5–50 °C), bubbling speed (0.1–1.0 LPM), catalyst dosage (0.2–2.0 g/L), and initial methanol concentration (12.5–250 mM). It was found that the formaldehyde formation rate for all photocatalysts increased as a function of each of these system parameters. Of these four BBS, it was found that Bi 2 WO 6 had the highest formaldehyde formation rate (0.081 mM/h). This work provides a new approach to produce formaldehyde using photocatalysis, and future work has also been proposed.
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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) | 0.001 | 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".