Interaction of carbon monoxide with small metal clusters: a DFT, electrochemical, and FTIR study
Bibliographic record
Abstract
The adsorption of CO molecules onto small metal clusters was studied using density functional theory (DFT) calculations, and experimental electrochemical and attenuated total reflection-Fourier transform infrared spectroscopic (ATR-FTIR) techniques were used to examine CO adsorbed onto nanostructures of similar composition. The adsorption strengths and CO vibrational stretching frequencies were calculated and analyzed for clusters of the form M–CO for all of the period 4, 5, and 6 d-block transition metals. A direct link between the νCO and the population of d orbitals of the metal was observed. All possible binding sites for CO on clusters of the form Pd4–CO, Pd2Pt2–CO, and Pd2Au2–CO were determined and the corresponding adsorption energies and CO stretching frequencies were examined. Pure Pd and bimetallic PdPt and PdAu nanostructures were fabricated and used as catalysts for the adsorption and electrochemical oxidation of CO. The relative quantities of CO molecules adsorbed to surface of the catalysts decrease in the order of PdPt > Pd > PdAu, consistent with our DFT results. The location of νCO bands of CO adsorbed onto the nanostructured catalysts were determined by means of ATR-FTIR spectroscopy and were found to have values close to that predicted by DFT. This paper shows that DFT calculations on very small metal clusters Mn–CO (n ≤ 4) can be a simple but effective way of screening catalysts for their adsorbing properties.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".