High rate CO2 photoreduction using flame annealed TiO2 nanotubes
Bibliographic record
Abstract
The photocatalytic reduction of CO2 into light hydrocarbons using sunlight and water is a challenging reaction involving eight electron transfer steps; nevertheless, it has great potential to address the problem of rising anthropogenic carbon emissions and enable the use of fossil fuels in a sustainable way. Several decades after its first use, TiO2 remains one of the best performing and most durable photocatalysts for CO2 reduction albeit with a poor visible light absorption capacity. We have used flame annealing to improve the response of TiO2 to visible photons and engineered a nanotubular morphology with square-shaped cross-sections in flame-annealed nanotubes. An enhanced CH4 yield was achieved in the photoreduction of CO2 using flame annealed TiO2 nanotubes, and isotope labeled experiments confirmed the reaction products to originate from the CO2 reactant.
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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".