Oxynitride-surface engineering of rhodium-decorated gallium nitride for efficient thermocatalytic hydrogenation of carbon dioxide to carbon monoxide
Bibliographic record
Abstract
Abstract Upcycling of carbon dioxide towards fuels and value-added chemicals poses an opportunity to overcome challenges faced by depleting fossil fuels and climate change. Herein, combining highly controllable molecular beam epitaxy growth of gallium nitride (GaN) under a nitrogen-rich atmosphere with subsequent air annealing, a tunable platform of gallium oxynitride (GaN1-xOx) nanowires is built to anchor rhodium (Rh) nanoparticles for carbon dioxide hydrogenation. By correlatively employing various spectroscopic and microscopic characterizations, as well as density functional theory calculations, it is revealed that the engineered oxynitride surface of GaN works in synergy with Rh to achieve a dramatically reduced energy barrier. Meanwhile, the potential-determining step is switched from *COOH formation into *CO desorption. As a result, significantly improved CO activity of 127 mmol‧gcat−1‧h−1 is achieved with high selectivity of >94% at 290 °C under atmospheric pressure, which is three orders of magnitude higher than that of commercial Rh/Al2O3. Furthermore, capitalizing on the high dispersion of the Rh species, the architecture illustrates a decent turnover frequency of 270 mol CO per mol Rh per hour over 9 cycles of operation. This work presents a viable strategy for promoting CO2 refining via surface engineering of an advanced support, in collaboration with a suitable metal cocatalyst.
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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".