Heterostructure Engineering of a Reverse Water Gas Shift Photocatalyst
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract To achieve substantial reductions in CO 2 emissions, catalysts for the photoreduction of CO 2 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 CO 2 reduction photocatalysts remains a great challenge. Herein, a metal oxide heterostructure engineering strategy that enables the gas‐phase, photocatalytic, heterogeneous hydrogenation of CO 2 to CO with high performance metrics (i.e., the conversion rate of CO 2 to CO reached as high as 1400 µmol g cat −1 h −1 ) is reported. The catalyst is comprised of indium oxide nanocrystals, In 2 O 3− x (OH) y , nucleated and grown on the surface of niobium pentoxide (Nb 2 O 5 ) nanorods. The heterostructure between In 2 O 3− x (OH) y nanocrystals and the Nb 2 O 5 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 In 2 O 3− x (OH) y material. The defect optimized heterostructure exhibits a 44‐fold higher conversion rate than pristine In 2 O 3− x (OH) y . It also exhibits selective conversion of CO 2 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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it