Sonochemical synthesis of electrocatalysts for low-temperature water electrolysers
Bibliographic record
Abstract
An important step in the process of producing hydrogen a viable method is to improve the efficiency and reduce the cost of low-temperature water electrolysers. One of the most crucial components is the catalyst used to drive the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER). Traditionally, the nanosized electrocatalysts are synthesized through a chemical reduction method involving a strong reducing agent like sodium borohydride, polyol, etc. Being able to control the nucleation and growth and therefore the size of the nanocatalysts, however, is not straightforward with the chemical reduction method where the use of surfactants is heavily relied upon, thus complicating the method for the industry. An alternative synthesis route involves the in-situ generation of radicals to serve as reducing agents through high power ultrasound (20 kHz–1 MHz) in a process where water is split into OH- and H-radicals referred to as water sonolysis. This presentation highlights the effects of ultrasonication frequencies, ultrasonication, times, pH solutions, reducing agents, and different saturation gasses on the generation of metallic nanoparticles and their subsequent electrocatalytic activities towards the HER and OER in mild acidic and alkaline environments. A series of physical characterizations on these sonochemically prepared nanoelectrocatalysts will be shown and discussed.
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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 | 0.001 |
| 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".