Surface and Catalytic Properties of Molten In–Sn and In–Ni for Methane Dry Reforming and Pyrolysis
Bibliographic record
Abstract
Molten metals have been recently shown as promising catalysts for the pyrolysis and dry reforming of methane. Herein, we examine the surface properties of molten In–Sn and In–Ni alloys and their influence on the catalytic performance. In–Sn shows higher CO 2 and CH 4 conversions than either pure In or pure Sn, whereas In–Ni has higher conversions for CO 2 than pure In. To understand the reason for this, we quantified the surface composition using surface tension measurements and ab initio molecular dynamics (AIMD) simulations. We find that In–Sn has similar surface compositions to its bulk, whereas In–Ni has heavily enriched In surfaces. Density functional theory calculations indicate that both Sn and Ni modify the electronic state of In, which is supported by XPS measurements. A qualitative correlation is identified between the surface charge on In and measured activity. Accumulation experiments showed minimal oxygen uptake at the steady state for both In–Sn and In–Ni systems. AIMD simulations are performed to understand the nature of intermediate oxygen and carbon species formed during the reaction and their effect on the surface composition of the alloy. The findings in this work highlight the crucial role of alloying elements in tuning catalyst surfaces, demonstrating the role of electronic effects, oxidation behavior, and intermediate accumulation.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".