Self-Powered Humidity Sensors Based on SnS<sub>2</sub> Nanosheets
Bibliographic record
Abstract
With the advent of the Internet of Things (IoT), the development of self-powered sensors has received much attention. Introducing triboelectric nanogenerators (TENGs) as a power source that converts mechanical movement into electrical signals has been admired recently. Moreover, the monitoring of humidity has become enormously essential in several technological contexts from environment monitoring to biomedical applications, thus joining these two subjects provides a huge benefit in achieving self-powered humidity sensors. Here, in this research, facile, low-priced and self-powered humidity sensors are fabricated utilizing transition-metal dichalcogenides (TMD) nanosheets. Semi-vertical SnS 2 nanosheets are synthesized through a modified chemical vapor deposition method at moderate heat treatment (500 °C) utilizing pristine sensor’s substrate and sulfur element on a laser grooved fluorine tin-doped oxide (FTO)/glass. To achieve a self-powered device, the integrated contact-separated (CS) TENG of FTO and Kapton/Al has been coupled with the projected humidity sensor, through the impedance matching circuit. This self-powered SnS 2 humidity sensor demonstrated an outstanding response of about 400%, fast response and recovery times (∼4, and 7 s), and long-term stability (at least 5 months). Furthermore, the humidity effect on the electrical performance of the SnS 2 layer was studied through first-principle simulations. Based on calculated adsorption energies, charge transfer, electronic band structures, and density of states, H 2 O molecules physisorbed on the SnS 2 nanosheet with a strong adsorption energy of −1.82 eV and 0.0208 e/molecule charges transferred from H 2 O molecules to the surface. Our density functional theory calculations shed light on the humidity sensing mechanism by illustrating that both Sn and Sulfur atoms could act as adsorption sites. The introduced battery-free and self-powered humidity sensors have great conceivable application in wearable/portable electronics and also smart homes.
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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".