Tracking the Electrocatalytic Activity of a Single Palladium Nanoparticle for the Hydrogen Evolution Reaction
Bibliographic record
Abstract
The nanoparticle-based electrocatalysts' performance is directly related to their working conditions. In general, a number of nanoparticles are uncontrollably fixed on a millimetre-sized electrode for electrochemical measurements. However, it is hard to reveal the maximum electrocatalytic activity owing to the aggregation and detachment of nanoparticles on the electrode surface. To solve this problem, here, we take the hydrogen evolution reaction (HER) catalyzed by palladium nanoparticles (Pd NPs) as a model system to track the electrocatalytic activity of single Pd NPs by stochastic collision electrochemistry and ensemble electrochemistry, respectively. Compared with the nanoparticle fixed working condition, Pd NPs in the nanoparticle diffused working condition results in a 2-5 orders magnitude enhancement of electrocatalytic activity for HER at various bias potential. Stochastic collision electrochemistry with high temporal resolution gives further insights into the accurate study of NPs' electrocatalytic performance, enabling to dramatically enhance electrocatalytic efficiency.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".