Hydrogen evolution reaction on electrodes with different PT/C loadings by electrochemical impedance spectroscopy
Bibliographic record
Abstract
One of the most widely studied reactions in electrochemistry is the hydrogen evolution reaction (HER). HER is important for the development of water hydrolysis and fuel cell technologies. Because hydrogen-substrate interaction determines oxygen reduction efficiency, an understanding of the chemical and electronic state of hydrogen adsorbed on the electrocatalyst surface is required. Electrochemical impedance spectroscopy (EIS) is a proven highly efficient technique for interface characterization and kinetic parameter determination for different reactions carried out on interfaces. This article presented a study that utilized EIS for characterizing electrodes under HER by implementing a rotating disc electrode with different carbon supported platinum nanoparticles loadings and different potentials in acidic solutions. The results collected by EIS were analyzed in terms of equivalent circuits to calculate different parameters which were compared by statistical analysis. The study also considered the Volmer, Heyrovsky and Tafel steps in the HER reaction as well as a single electro-absorbed intermediate species. The article discussed the experimental set-up with reference to measurements, simulation and fitting. Parameters analysis using ANOVA were reviewed. It was concluded that an increase in impedance occurs when platinum loading decreases in both high and low frequencies. 22 refs., 1 tab., 5 figs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".