Heterostructure Engineering of a Reverse Water Gas Shift Photocatalyst
Bibliographic record
Abstract
Abstract To achieve substantial reductions in CO2 emissions, catalysts for the photoreduction of CO2 into value‐added chemicals and fuels will most likely be at the heart of key renewable‐energy technologies. Despite tremendous efforts, developing highly active and selective CO2 reduction photocatalysts remains a great challenge. Herein, a metal oxide heterostructure engineering strategy that enables the gas‐phase, photocatalytic, heterogeneous hydrogenation of CO2 to CO with high performance metrics (i.e., the conversion rate of CO2 to CO reached as high as 1400 µmol g cat−1 h−1) is reported. The catalyst is comprised of indium oxide nanocrystals, In2O3−x(OH)y, nucleated and grown on the surface of niobium pentoxide (Nb2O5) nanorods. The heterostructure between In2O3−x(OH)y nanocrystals and the Nb2O5 nanorod support increases the concentration of oxygen vacancies and prolongs excited state (electron and hole) lifetimes. Together, these effects result in a dramatically improved photocatalytic performance compared to the isolated In2O3−x(OH)y material. The defect optimized heterostructure exhibits a 44‐fold higher conversion rate than pristine In2O3−x(OH)y. It also exhibits selective conversion of CO2 to CO as well as long‐term operational stability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".