Non-Noble Electrocatalysts for O<sub>2</sub> Reduction: How Does Heat Treatment Affect Their Activity and Structure? Part I. Model for Carbon Black Gasification by NH<sub>3</sub>: Parametric Calibration and Electrochemical Validation
Bibliographic record
Abstract
In part I of this paper, a model for the gasification of pristine carbon blacks by NH 3 is developed. Here, it is applied to investigate non-noble catalysts for the O 2 reduction reaction (ORR) in fuel cells. These catalysts are produced by pyrolyzing a furnace black, loaded with 0.2 wt % Fe, in NH 3 . The model predicts (i) the gasification rate of an initially pore-free particle of furnace black and (ii) the internal porous network thereby created. The model assumes two rate constants for the gasification of carbon black: one for the graphitic crystallites and one for the disordered matrix phase. The model is fitted to experimental time-evolutions of (i) weight loss of carbon black and (ii) specific surface area measured during the synthesis of non-noble catalysts for the ORR. The fittings yield a rate constant that is ten times larger for the gasification of the disordered phase than that for the graphitic phase. For a 42 nm diameter particle, the model predicts that (i) the gas−carbon reaction, and therefore the internal porous network, occurs in an outer shell of thickness ≤8 nm and (ii) the particle shrinks beyond 45% weight loss. Experimentally, the micropore area created during the heat treatment controls the activity for the ORR of such catalysts. The model is able to reproduce the experimental micropore area if the fraction of disordered carbon of the pristine carbon black particle is assumed larger in the core (35%) than in the periphery (20%). Thus, in agreement with experimentals the model tells that Fe/N/C sites for the ORR are created when the micropore area increases (0 to 30−35 wt % loss) and destroyed when it decreases (weight loss >40%).
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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) | 0.000 | 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 | 0.001 |
| 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".