Flexible screen-printed SiC-based humidity sensors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Humidity sensors are essential components in modern technology, spanning applications from residential appliances to the Internet of Things (IoT). However, conventional commercial sensors are typically rigid, constrained by narrow relative humidity (%RH) operating ranges, and require complex fabrication processes. In this study, we present a highly sensitive cubic silicon carbide (3C–SiC) nanoparticle-based relative humidity sensor, fabricated via serigraphic printing on to 5 mil thick flexible polyimide (Kapton®) substrate. Devices are tested across a broad humidity range of 10–90%RH at ambient temperature and their performance is evaluated in a controlled humidity chamber. The sensor exhibits a robust response of 45.2% R/R0, with a sensitivity of 5.34 Ω/%RH, an adsorption time of 18 seconds, and a desorption time of 46 seconds. Additionally, the device demonstrates low hysteresis of 6.5% at 60%RH, with excellent repeatability and stability over 3.5 hours of continuous cycling. To showcase their potential for real-world applications, the printed sensors are integrated into a commercial KN95 mask for monitoring respiration parameters, such as respiration rate. This integration highlights the potential for future exploration in human health monitoring, utilizing fully printed, low-cost sensing devices. This study reports a highly sensitive silicon carbide nanoparticle-based relative humidity sensor fabricated via serigraphic printing. The active 3C-SiC layer and silver electrodes are printed directly onto thick flexible polyimide (Kapton®) substrates. The printed sensors are integrated into a commercial KN95 mask for monitoring respiration parameters, showing the potential for future exploration in human health monitoring, utilizing fully printed, low-cost sensing devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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 it