Tuning transition metal carbide activity by surface metal alloying: a case study on CO <sub>2</sub> capture and activation
Bibliographic record
Abstract
CO2 is one of the main actors in the greenhouse effect and its removal from the atmosphere is becoming an urgent need. Thus, CO2 capture and storage (CCS) and CO2 capture and usage (CCU) are intensively investigated technologies to decrease the concentration of atmospheric CO2. Both CCS and CCU require appropriate materials to adsorb/release and adsorb/activate CO2, respectively. Recently, it has been theoretically and experimentally shown that transition metal carbides (TMC) are able to capture, store, and activate CO2. To further improve the adsorption capacity of these materials, a deep understanding of the atomic level processes involved is essential. In the present work, we theoretically investigate the possible effects of surface metal doping of these TMCs by taking TiC as a textbook case and Cr, Hf, Mo, Nb, Ta, V, W, and Zr as dopants. Using periodic slab models with large supercells and state-of-the-art density functional theory based calculations we show that CO2 adsorption is enhanced by doping with metals down a group but worsened along the d series. Adsorption sites, dispersion and coverage appear to play a minor, secondary constant effect. The dopant-induced adsorption enhancement is highly biased by the charge rearrangement at the surface. In all cases, CO2 activation is found but doping can shift the desorption temperature by up to 135 K.
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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) | 0.001 | 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) | 0.001 | 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".