Chromium oxide nanoparticles in‐situ immobilized onto nitrogen‐doped carbon plates with boosted catalytic activity toward nitrogen reduction reaction
Bibliographic record
Abstract
Abstract A chromium oxide‐based nanocomposite (Cr 2 O 3 @NC) is designed and prepared via a simple pyrolysis route with Cr‐based metal organic framework (MOF) as a template. The research results indicate that Cr 2 O 3 nanoparticles have an average size of ~70 nm and are in situ formed and imbedded onto the Cr‐based MOF‐derived 2D N‐doped carbon microplates. When employed as an inexpensive electrocatalyst for nitrogen reduction reaction (NRR) to synthesize ammonia, Cr 2 O 3 @NC demonstrates an improved and stable catalytic activity in comparison with bare Cr 2 O 3 . A large ammonia production rate of 29.42 μg mg −1 cat h −1 under a lower potential of −0.4 V versus reversible hydrogen electrode (RHE) can be acquired with a Faradic efficiency of 9.89% in sodium sulphate solution. Additionally, a satisfactory selectivity can also be achieved without hydrazine byproduct. The greatly promoted catalytic activity of Cr 2 O 3 @NC is regarded to be concerned with its desirable structures such as 2D planar topological structure with expanded active surface area, abundant catalytic sites, and effective combination with conductive carbon.
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".