Surface oxide growth on platinum electrode in aqueous trifluoromethanesulfonic acid
Bibliographic record
Abstract
Platinum in the form of nanoparticles is the key and most expensive component of polymer electrolyte membrane fuel cells, while trifluoromethanesulfonic acid (CF3SO3H) is the smallest fluorinated sulfonic acid. Nafion, which acts as both electrolyte and separator in fuel cells, contains -CF2SO3H groups. Consequently, research on the electrochemical behaviour of Pt in aqueous CF3SO3H solutions creates important background knowledge that can benefit fuel cell development. In this contribution, Pt electro-oxidation is studied in 0.1 M aqueous CF3SO3H as a function of the polarization potential (E(p), 1.10 ≤ E(p) ≤ 1.50 V), polarization time (t(p), 10(0) ≤ t(p) ≤ 10(4) s), and temperature (T, 278 ≤ T ≤ 333 K). The critical thicknesses (X1), which determines the applicability of oxide growth theories, is determined and related to the oxide thickness (d(ox)). Because X1 > d(ox) for the entire range of E(p), t(p), and T values, the formation of Pt surface oxide follows the interfacial place-exchange or the metal cation escape mechanism. The mechanism of Pt electro-oxidation is revised and expanded by taking into account possible interactions of cations, anions, and water molecules with Pt. A modified kinetic equation for the interfacial place exchange is proposed. The application of the interfacial place-exchange and metal cation escape mechanisms leads to an estimation of the Pt(δ+)-O(δ-) surface dipole (μ(PtO)), and the potential drop (V(ox)) and electric field (E(ox)) within the oxide. The Pt-anion interactions affect the oxidation kinetics by indirectly influencing the electric field within the double layer and the surface oxide.
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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".