Nanochannel electrodes facilitating interfacial transport for PEM water electrolysis
Bibliographic record
Abstract
Proton-exchange membrane water electrolyzers (PEMWEs) are a promising technology for green hydrogen production; however, interfacial transport behaviors are poorly understood, hindering device performance and longevity. Here, we first utilized finite-gap electrolyzer to demonstrate the possibility of proton transfer through water in PEMWEs. The measured high-frequency resistances (HFRs) exhibit a linear trend with increasing gap distance, where extrapolation shows a lower value compared with HFRs in regular zero-gap electrolyzers, indicating that ohmic resistance could be further reduced. We introduce nanochannels to facilitate mass transport, as evidenced by both liquid-fed and vapor-fed electrolysis. Nanochannel electrodes achieve a voltage reduction of 190 mV at 9 A·cm −2 compared with the Ir-PTEs without nanochannels. Furthermore, nanochannel electrodes show negligible degradation through 100,000 accelerated-stress tests and over 2,000 h of operation at 1.8 A·cm −2 with a decay rate of 11.66 μV·h −1 . These results provide new insights into localized transport dynamics for PEMWEs and highlight the significance of interfacial engineering for electrochemical devices.
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 | 0.000 | 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 | 0.000 |
| 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".