Photochemical Acceleration of Ammonia Production by Pt<sub>1</sub>-Pt<i><sub>n</sub></i>-TiN Reduction and N<sub>2</sub> Activation
Bibliographic record
Abstract
Stable metal nitrides (MN) are promising materials to fit the future “green” ammonia–hydrogen nexus. Either through catalysis or chemical looping, the reductive hydrogenation of MN to MN 1– x is a necessary step to generate ammonia. However, encumbered by the formation of kinetically stable M-NH 1─3 surface species, this reduction step remains challenging under mild conditions. Herein, we discovered that deleterious Ti-NH 1─3 accumulation on TiN can be circumvented photochemically with supported single atoms and clusters of platinum (Pt 1 -Pt n ) under N 2 –H 2 conditions. The photochemistry of TiN selectively promoted Ti-NH formation, while Pt 1 -Pt n effectively transformed any formed Ti-NH into free ammonia. The generated ammonia was found to originate mainly from TiN reduction with a minor contribution from N 2 activation. The knowledge accrued from this fundamental study could serve as a springboard for the development of MN materials for more efficient ammonia production to potentially disrupt the century-old fossil-powered Haber–Bosch process.
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) | 0.001 | 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".