Hydrovoltaic Nanogenerators for Self‐Powered Sweat Electrolyte Analysis
Bibliographic record
Abstract
Abstract Human sweat comprises various electrolytes that are health status indicators. Conventional potentiometric electrolyte sensors require an electrical power source, which is expensive, bulky, and requires a complex architecture. Herein, this work demonstrates an electric nanogenerator fabricated using silicon nanowire (SiNW) arrays comprising modified carbon nanoparticles. The SiNW arrays platform is demonstrated as an effective self‐powered sensor for sweat electrolyte analysis. It has been shown that an evaporation‐induced water flow in nanochannels can yield an open‐circuit voltage ( V oc ) of 0.45 V and a short‐circuit current of 10.2 µA at room temperature as a result of overlapped electric double layers. The electrolyte in the water flow results in a V oc decrease due to the charge shielding effect. The V oc is inversely proportional to the electrolyte concentration. The fabricated hydrovoltaic device shows the capability for sensing electrolytes in human sweat, which is useful in evaluating the hydration status of volunteers following intense physical exercise. The device depicts a novel response mechanism compared to conventional electrochemical sensors. Furthermore, the hydrovoltaic device shows a maximum output power of 1.42 µW, and as such has been successfully shown to drive various electronic devices including light‐emitting diodes, a calculator, and an electronic timer.
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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 | 0.000 | 0.001 |
| 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) | 0.000 | 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".