Systematic refinement of experimental practices to improve repeatability in flow battery cycling
Bibliographic record
Abstract
Flow batteries represent one of the leading options for large-scale, long-duration energy storage. In recent years, research into this technology has accelerated, with numerous innovative studies focusing on electrolytes, membranes, and electrode materials. Despite this, there is presently no clear set of testing protocols followed during full-cell testing of flow batteries and the experimental techniques detailed in published literature are often insufficient to reproduce results. Furthermore, testing to quantify the repeatability of experiments is not often reported. In this work, various aspects of an experimental procedure developed from the peer-reviewed literature are refined, with voltage efficiency, coulombic efficiency, energy efficiency, and electrolyte utilization used as indicators of repeatability. A set of improved testing protocols are presented for researchers to consider when conducting charge–discharge testing, and additional factors to be reported and studied in the context of repeatability are suggested.
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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 | 0.058 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".