Highly Selective Photo-Oxidation of Methane to Methanol by Fe–Au Site-Supported SrTiO<sub>3</sub> Hollow Nanotubes with Oxygen Vacancies
Bibliographic record
Abstract
Solar-driven methane (CH 4 ) conversion to value-added chemicals with high selectivity remains a long-standing challenge. Here, we present closely attached atomically dispersed Fe species and ultrafine Au-supported SrTiO 3 hollow nanotubes with oxygen vacancies (STO v ) for highly selective CH 4 conversion to CH 3 OH. An impressive CH 3 OH production rate of 7.53 mmol g –1 h –1 with a selectivity up to 95.4% has been achieved, corresponding to an apparent quantum efficiency of 15.8% at 365 nm, representing a record among all of the representative photocatalysts under comparable conditions. Experimental results and theoretical simulations elucidate that the created oxygen vacancies on SrTiO 3 without Ti 3+ facilitate CH 4 adsorption to effectively capture photogenerated holes for producing methyl radicals. In parallel, the photogenerated electrons could be rapidly extracted by the anchored Au and then transferred to the adjacent single-atom Fe sites for activating O 2 to generate the key intermediate Fe–*OOH toward highly selective CH 3 OH production. Significantly, a commendable electron transfer efficiency of 67.5% for O 2 activation is achieved on Fe–Au/STO v based on the quantitative in situ microsecond transient absorption spectra. This work provides a deep understanding of the regulation of both activity and selectivity by the engineering of adjacent sites and the investigation of electron kinetics for O 2 activation during CH 4 photo-oxidation.
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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".