Exhaling‐Driven Hydroelectric Nanogenerators for Stand‐Alone Nonmechanical Breath Analyzing
Bibliographic record
Abstract
Abstract The analysis of exhaled breath is an increasingly important role in the provision of security and in the management of personal healthcare. The development of self‐powered, reliable, miniature low cost and noninvasive devices is fundamental to practical applications. However, most state‐of‐the‐art self‐powered systems are incorporating mechanical nanogenerators, which would promote contact failures of electronics and limit minimization of the monitoring system. This work outlines a new solution to this problem based on a self‐powered breath analyzer integrated with a hydroelectric nanogenerator (HENG), in which the nanogenerator extracts electrical power from biochemical energy. The output signal from the sensor in this device is highly sensitive to the concentration of ethanol exhaled in breath down to low detection limitation of 50 ppm. A high dynamic range is observed whereby a signal response of ≈80% relative to peak value is obtained under the exposure to gas containing 100 ppm of ethanol. Unlike conventional self‐powered breathing analyzers based on piezoelectric or triboelectric nanogenerators, mechanical vibrations are eliminated. The availability of this compact breath analyzer provides a new detection regime for gas sensing, and should facilitate the design of a wide range of self‐powered systems incorporated in the next generation of innovative 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 | 0.000 |
| 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".