Failure Analysis of Nickel-Coated Anodes in Zinc-Air Hybrid Flow Batteries
Bibliographic record
Abstract
A zinc-air flow battery system pumps "fuel” (a zinc particle/KOH slurry) from a fuel tank to a fuel cell stack, where the zinc particles are combined with oxygen from the air to form zincate ions and produce electricity. The zincate-rich electrolyte is then returned to the fuel tank. During the charging cycle, the electrolyte is passed to the zinc regenerator, where electricity (from renewable sources such as solar or wind) is utilized to convert the zincate ions to zinc particles. The regenerated fuel is pumped back into the fuel tank for the discharge process. Nickel is considered for use as the anode during zinc regeneration as it has been shown to be an active catalyst for the oxygen evolution reaction (OER). However, nickel electrodes pose manufacturing challenges due to machinability issues. Alternatively, nickel can be coated on a machinable metal substrate to improve scalability. These electrodes are subjected to open circuit voltage (OCV), OER, and the hydrogen evolution reaction (HER) during operation of zinc-air flow batteries. The electrodes have been observed to fail during prolonged voltage cycling due to nickel coating delamination, which manifests itself as blistering, flaking, and discoloration. It is hypothesized that this may be due to electrolyte penetration into the pores of the nickel coating during operation. The present work is aimed at analyzing and mitigating the coating delamination process through characterization of various Ni coating recipes. As-fabricated and cycled electrodes are characterized using various microstructural techniques, including optical microscopy, x-ray diffraction (XRD), scanning and transmission electron microscopy (SEM and TEM), and x-ray tomography. Coated electrodes are also evaluated electrochemically and the results are correlated with the microstructural analysis. The overall goal of the work is to understand the failure mechanisms and apply the knowledge to fabricate improved coatings for OER electrodes.
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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 | 0.001 | 0.002 |
| 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) | 0.000 | 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".