Predicting CO<sub>2</sub> adsorption and reactivity on transition metal surfaces using popular density functional theory methods
Bibliographic record
Abstract
In this work, with Ni (110) as a model catalyst surface and CO2 as an adsorbate, a performance study of Density Functional Theory methods (functionals) is performed. CO being a possible intermediate in CO2 conversion reactions, binding energies of both, CO2 and CO, are calculated on the Ni surface and are compared with experimental data. OptPBE-vdW functional correctly predicts CO2 binding energy on Ni (−62 kJ/mol), whereas CO binding energy is correctly predicted by the rPBE-vdW functional (−138 kJ/mol). The difference in computed adsorption energies by different functionals is attributed to the calculation of gas phase CO2. Three alternate reaction systems based on a different number of C=O double bonds present in the gas phase molecule are considered to replace CO2. The error in computed adsorption energy is directly proportional to the number of C=O double bonds present in the gas phase molecule. Additionally, both functionals predict similar carbon–oxygen activation barrier (40 kJ/mol) and equivalent C1s shifts for probe species (−2.6 eV for CCH3 and +1.5 eV CO3−), with respect to adsorbed CO2. Thus, by including a correction factor of 28 kJ/mol for the computed CO2 gas phase energy, we suggest using rPBE-vdW functional to investigate CO2 conversion reactions on different metals.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".