Computational Investigation into the Ni(SeNHC<sub>2</sub>(CN)<sub>2</sub>)<sub>2</sub> and Ni(SNHC<sub>2</sub>(CN)<sub>2</sub>)<sub>2</sub> Complexes as Potential Catalysts for Hydrogen Production
Bibliographic record
Abstract
To reduce our carbon footprint, we must look at alternative non-carbon-containing fuels to prevent continued global climate change. One environmentally friendly alternative fuel is molecular hydrogen. Herein the Ni(SeNHC 2 (CN) 2 ) 2 complex was studied using DFT to determine the thermodynamics associated with the electrocatalytic formation of H 2 (g). From the calculated thermodynamics, it appears that the Ni(SeNHC 2 (CN) 2 ) 2 complex is predicted to catalyze the production of H 2 gas under mildly reducing conditions relative to the SHE. Notably, the thermodynamics are better than the values calculated for the analogous Ni(SNHC 2 (CN) 2 ) 2 complex which has been shown experimentally to catalyze the formation of H 2 gas in aqueous solution. Regarding possible kinetic reactivity, the HOMO–LUMO gap energies were calculated. From the gap energies, it is expected that the Se-containing compounds would be more reactive to electron transfer in the third reduction step, meaning therefore that a smaller overpotential would be needed to drive the reduction of Red2-H 2 relative to S Red2-H 2 in agreement with past experimental work. Thus, the use of Se in such compounds may offer a means to improve the catalysts for H 2 production.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".