Co Implanted <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si14.svg"> <mml:mstyle mathvariant="normal"> <mml:mi>Ψ</mml:mi> </mml:mstyle> </mml:math> -graphene: A Non-Noble Metal Single-Atom Catalyst for Proficient CO Oxidation Reaction
Bibliographic record
Abstract
Single-Atom Catalysts (SACs) have procured heightened attention of the research community across the globe due to their astounding catalytic behaviour towards some prime chemical reactions. Herein, the catalytic performance of non-noble metal, cobalt single-atom supported over Ψ -graphene (Co@PG) addressed towards CO oxidation reaction by means of first-principles-based, dispersion corrected density functional theory (DFT) calculations. The higher diffusion barrier of Co over Ψ -graphene evidently authenticate the stronger interaction of Co with Ψ -graphene and robust stability of overall system, hence prelude the cluster formation possibility. Further, the temperate interaction of reactants (CO, O 2 ) affirms that the Co@PG is brilliantly effective in hosting and activating reactants, an indispensable condition to commence any catalytic reaction cycle. The analysis of electronic reactivity descriptors (ERDs) like d-band centre ( ϵ d ) and fractional filling of d-band ( f l ), by employing improved d-band model, emphasize that the minority-spin states of Co atom would engage predominantly in the interaction with the reactants. Latterly, CO oxidation reaction over Co@PG was carried out with two different mechanisms, viz., Langmuir-Hinshelwood (LH) and Eley-Rideal (ER) pathways for detailed analysis. Computation of minimum energy path (MEP) and activation barriers reveal that the Co@PG favours the ER mechanism, with reaction barrier of 0.19 eV and 0.27 eV for first and second half reactions respectively, both of them being highly exothermic validating the viability of the overall process. Accordingly, present investigation insinuates that Co@PG can be a potent, non-noble metal, practically operational catalyst for CO oxidation reaction.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".