Computational demystification of iron carbonyls formation under syngas environment
Bibliographic record
Abstract
Abstract Iron pentacarbonyl (IPC) gas forms upon the reaction of carbon monoxide with Fe containing metallic surfaces under gas reforming conditions. IPC formation can sometimes reach alarming levels that cause metal loss, pipeline thinning corrosion, catalyst poisoning, and contamination of sensitive industrial equipment. In this work, we demystify using multiscale computational modeling the mechanism of Iron pentacarbonyl formation: Density functional theory (DFT) is used to explore various catalytic reactions that involve a Fe adatom reacting with adsorbed carbon monoxide. Our calculated carbonyls desorption barriers on a perfect and clean Fe surface are too high to allow the carbonyls to form then desorb at temperatures <500 K at the rates reported experimentally. Most importantly, our calculations indicate that a high CO surface coverage, in addition to the presence of Fe adatoms, favors carbonyl formation and its desorption towards the flowing gas medium. Using insights extracted from ab initio molecular dynamics simulations, we propose that the most plausible IPC formation mechanism consists of: (1) on surface reactions of adsorbed CO molecules with an Fe adatom to form iron tricarbonyl (Fe(CO)3*) molecules; (2) an adsorbate assisted movement of iron tricarbonyl on top of the CO adlayer; and (3) the interaction of iron tricarbonyl with CO molecules from the gaseous medium eventually leading to iron adatom removal as Fe(CO)5 gas.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".