Tailoring facet sensitivity in anatase titania for selective photocatalytic oxidation of methane to formaldehyde
Bibliographic record
Abstract
Photocatalytic methane oxidation is a promising route to produce formaldehyde, yet achieving high efficiency, selectivity, and stability with cost-effective systems remains challenging. Here, we uncover facet sensitivity in the methane photocatalytic oxidation over anatase TiO 2 {001}/{101} junctions. The truncated octahedral bipyramid, exposing 62 % {001} and 38 % {101} facets, exhibits superior photocatalytic performance for methane-to-formaldehyde conversion under ambient conditions. Band structure, carrier dynamics, and mechanistic studies reveal that surface-bound methoxy species (OCH 3 ) act as key intermediates, facilitated by enhanced charge separation and transfer across the {001}/{101} facet junctions. The higher OCH 3 /•OH (hydroxyl radicals) ratio promotes selective methane oxidation to HCHO while suppressing deep oxidation to CO 2 . Furthermore, integration into a microtube reactor with optimized light harvesting and gas–solid–liquid mass transfer boosts performance, achieving a high formaldehyde production rate of 280 mmol g cat −1 h −1 L −1 (2.24 µmol h −1 ) with 100 % selectivity in liquid-phase. This work offers an efficient and scalable approach to catalyst and process engineering for sustainable formaldehyde production via photocatalytic methane conversion. • Facet sensitivity in the methane oxidation over anatase TiO 2 {001}/{101} junctions. • Surface-bound methoxy species (*OCH 3 ) act as key intermediates. • Integration into an optimized microtube reactor boosts the performance. • 100 % formaldehyde selectivity in the liquid phase has been achieved.
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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".