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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".