A computational investigation of the thermodynamics and kinetics of multiple single-step electron transfers of various Ni- and chalcogen-containing complexes
Bibliographic record
Abstract
Continued increase in green house gas emissions will lead to irreversible and unpredictable damage to the environment and biosphere. CO2 alone contributes to about two-thirds of the energy imbalance causing climate change. The bulk of these CO2 emissions can be traced to fossil fuels, which presently account for 82% of this green house gas. Developing effective renewable–alternative fuel sources is crucial to mitigate the harmful effects of fossil fuels. One such alternative fuel source is molecular hydrogen, which can be produced by the photocatalytic splitting of water using metal-based catalysts. This study investigates the thermodynamics and kinetics associated with single-step protonations and reductions of Ni[(S2C2H2)(N2C2H4)], Ni[(Se2C2H2)(N2C2H4)], and Ni[(Te2C2H2)(N2C2H4)] complexes using density functional theory. Results found diselenolene to be the least endergonic for the formation of the triply reduced, doubly protonated species with a Gibbs protonation energy of 79.6 kJ mol−1. Ditellurolene and dithiolene were slightly more endergonic with Gibbs protonation energies of 87.7 and 91.6 kJ mol−1, respectively. The most thermodynamically favorable pathways were found to be the ECCEE pathway for dithiolene and diselenolene, whereas the CECEE mechanism was found to be most favorable for ditellurolene. Kinetically, it was found that there were two feasible pathways for all three complexes. The first was a CCEEE pathway and the second was a CECEE pathway. All complexes were found to have similar Gibbs activation energies.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".