Self‐Powered Wearable Electronics Based on Moisture Enabled Electricity Generation
Bibliographic record
Abstract
Abstract Most state‐of‐the‐art electronic wearable sensors are powered by batteries that require regular charging and eventual replacement, which would cause environmental issues and complex management problems. Here, a device concept is reported that can break this paradigm in ambient moisture monitoring—a new class of simple sensors themselves can generate moisture‐dependent voltage that can be used to determine the ambient humidity level directly. It is demonstrated that a moisture‐driven electrical generator, based on the diffusive flow of water in titanium dioxide (TiO2) nanowire networks, can yield an output power density of up to 4 µW cm−2 when exposed to a highly moist environment. This performance is two orders of magnitude better than that reported for carbon‐black generators. The output voltage is strongly dependent on humidity of ambient environment. As a big breakthrough, this new type of device is successfully used as self‐powered wearable human‐breathing monitors and touch pads, which is not achievable by any existing moisture‐induced‐electricity technology. The availability of high‐output self‐powered electrical generators will facilitate the design and application of a wide range of new innovative flexible electronic devices.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".