A Self-Powered Nanogenerator for the Electrical Protection of Integrated Circuits from Trace Amounts of Liquid
Bibliographic record
Abstract
Abstract With the increase in the use of electronic devices in many different environments, a need has arisen for an easily implemented method for the rapid, sensitive detection of liquids in the vicinity of electronic components. In this work, a high-performance power generator that combines carbon nanoparticles and TiO2 nanowires has been fabricated by sequential electrophoretic deposition (EPD). The open-circuit voltage and short-circuit current of a single generator are found to exceed 0.7 V and 100 μA when 6 μL of water was applied. The generator is also found to have a stable and reproducible response to other liquids. An output voltage of 0.3 V was obtained after 244, 876, 931, and 184 μs, on exposure of the generator to 6 μL of water, ethanol, acetone, and methanol, respectively. The fast response time and high sensitivity to liquids show that the device has great potential for the detection of small quantities of liquid. In addition, the simple easily implemented sequential EPD method ensures the high mechanical strength of the device. This compact, reliable device provides a new method for the sensitive, rapid detection of extraneous liquids before they can impact the performance of electronic circuits, particularly those on printed circuit board.
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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 | 0.001 | 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".