Benchmarking performance: A round-robin testing for liquid alkaline electrolysis
Bibliographic record
Abstract
Liquid alkaline water electrolysis has gained considerable interest in recent years due to its promising role in an energy sector based on renewable energy sources. Its main advantage is the low investment cost of industrial alkaline water electrolyzers compared to other electrolysis technologies. A challenge remains in developing cost-efficient materials, stable in corrosive electrolytes, and offering competitive cell performance. Although there are many publications in liquid alkaline electrolysis, there is insufficient standardization of experimental conditions and procedures, reference materials, and hardware. As a result, comparability and reproducibility suffer, significantly slowing down research progress. This manuscript presents the initial efforts towards the development of such reference hardware and procedures within the framework of Task 30 Electrolysis in the Technology Collaboration Programme on Advanced Fuel Cells (AFC TCP) of the International Energy Agency (IEA). For this purpose, a homogenized setup including the electrolysis cell, functional materials, experimental conditions, and a test protocol was developed. The protocol and hardware were tested simultaneously at eleven different institutions in Europe and North America. To evaluate the success of this approach, polarization and run-in data were collected and analyzed for comparison, and performance differences were calculated. Significant disparities between the laboratories were observed and some key influence factors were identified: iron content in the electrolyte resulted to be a main source of deviation between experiments, along with temperature control and the conditioning of the cells. The results suggest that additional attention to detailed experimental conditions should be paid to obtain meaningful performance data in future research.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".