Nonmonotonic dynamics in Lifshitz-Slyozov-Wagner theory: Ostwald ripening in nanoparticle catalysts
Bibliographic record
Abstract
Nanoparticle catalysts dispersed on high-surface-area electronic support materials are used in a wide range of applications. Nano-sized particles afford a high active surface area per unit volume of an electrocatalytic medium. However, the gain in active surface area for desired surface reactions is offset in part by enhanced rates of degradation processes that cause losses in catalyst mass, catalyst surface area, and electrocatalytic activity. A dynamic model of surface-area-loss phenomena based on the theories of Lifshitz and Slyozov [J. Phys. Chem. Solids 19, 35 (1961)], Wagner [Z. Elektrochem. 65, 581 (1961)], and Smoluchowski [Z. Phys. Chem. 92, 129 (1917)] is presented. A population balance equation in particle space accounts for nanoparticle dissolution, redeposition, and coagulation. It relates kinetic rates of these processes to the evolution of the particle-size distribution and its moments. Our analysis of the temporal dynamics of the number density, mean radii, surface area, and mass moments focuses on the important case of reaction-limited Ostwald ripening. Transient solutions reveal unique scaling relationships between the moments of the evolving distribution. Diagnostic criteria established from the scaling relationships are applied to previously published experimental degradation data for supported nanoparticle catalysts in polymer electrolyte fuel cells.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".