(Invited) Designing Microscale Structures for Enhanced Function of Electrocatalysts for Alkaline Water Electrolysis
Bibliographic record
Abstract
Water electrolysis has already been implemented for many decades on an industrial scale. It does, however, remain an active area of research and development for the pursuit of materials that have enhanced electrocatalytic performance and prolonged durability. For example, the accumulation of gas bubbles on the surfaces of electrodes during water electrolysis remains a challenge. Persistent bubbles on the surfaces of electrocatalysts can block electrolyte access to the electrode and reduce the electrochemically active surface area. At current densities that are relevant to industrial applications an extensive portion of the electrode can become covered with bubbles. There are a range of solutions that are being utilized in industrial settings, but also additional solutions being sought to optimize the efficiency of these systems. These solutions include applied shear flows across the surfaces of the electrodes, the implementation of ultrasonic induced cavitation, and an increase in electrolyte temperature. These solutions require further expenditures of energy and decrease the overall energy efficiency of the system. Alternative approaches may yield a lower energy demand for maintaining accessibility of the electrolyte to the electrochemically active surface area. One approach is through the design of electrode surface architectures that can assist with the removal of gas bubbles during water electrolysis. This work includes a review of the progress made in preparing structured electrodes for the alkaline based water electrolysis and specifically for the oxygen evolution reaction. These structures can influence the dynamics that occur at the electrode-electrolyte interface, such as the growth, coalescence, and release of oxygen gas bubbles. Understanding the correlations between the structures on the electrodes and their influence on the function of these materials towards the gas evolution are sought to guide the future development of optimal surface structures that are self-cleaning. Designs of electrode surface architectures are sought that can improve the efficiency of electrodes toward this and other gas evolution reactions.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.020 |
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".