An electrocyte‐inspired moisture‐driven electricity generator for self‐sustained water electrolysis
Bibliographic record
Abstract
Abstract Harvesting electricity from ubiquitous moisture offers the promise of clean power for self‐sustained systems. Despite extensive efforts, achieving high‐power electricity generation remains challenging. Existing studies mimicking electric eels’ electrogenesis to enhance their electrical performance focused on the two‐membrane structure that linearly adds up the voltage, but their current output was either transient or limited to microamperes, because of the large resistance for ion diffusing across material interfaces. Here, we report an electrocyte‐inspired moisture‐driven electricity generator (EMEG) made from an interphase‐mediated Janus film. The continuous interphase significantly alleviates the ion migration resistance, boosting the current output to 150 µA and sustaining the voltage of 0.8 V continuously for more than 1000 h. We also show that integrated EMEGs were easily assembled to self‐powered smart watch for emergency rescue. Furthermore, the integrated EMEGs achieved self‐sustained and moisture‐powered water splitting with a steady hydrogen production. Our results provide a rational for bio‐inspired designs toward green and sustained power generation.
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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 | 0.000 | 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".