Ab-Initio Modeling of the Electrochemical Potential Effect in Facilitating Heterogeneous Reactions on Catalysts Surface
Bibliographic record
Abstract
Theoretical modeling of the effect of electrochemical potential on reaction rate will be presented. Using ab-initio Density Functional Theory (DFT), adsorption and dissociation energies are calculated at the atomistic level for the model ethylene oxidation reaction on a ruthenium oxide slab RuO2 (110). Using the surface charging method, known as grand-canonical DFT, the number of electrons is changed on the slab surface to mimic an electrochemical potential application and thus a change in the work function of the surface. Next, a fitted curve is found for the calculated adsorption energies as a function of the changed potential. A similar approach is used for the calculation of dissociation energies (using minimum energy pathway MEP method). The end models showed an electrochemical potential effect on the adsorption energy of reactants as well as on their dissociation energy. This type of modeling helps us understand on the atomistic level what occurs on the surface of a catalyst in a heterogeneous type of reaction under the effect of an electrochemical potential.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".