Aerosol-jet printing of flexible green graphene humidity sensors for IoT applications
Bibliographic record
Abstract
This research focuses on designing and developing highly sensitive flexible graphene sensors for humidity detection. First, green graphene inks were prepared with triton X-100 (Ge-GTr) as a dispersant and loaded gelatin as a binder. Then, flexible graphene sensors were fabricated by printing the graphene inks with aerosol-jet on top of screen-printed carbon electrodes. High gelatin-modified sensors (0.5Ge-GTr and 1Ge-GTr) exhibited a good linear response in relative humidity (RH) range of 30%RH–90%RH with a good sensitivity of 0.55/%RH at 25°C and with fast response in a second range. Moreover, they showed good stability to temperature fluctuation ranged from 22°C to 70°C. The humidity sensing mechanism depends on the surface coverage of graphene by the hydrophilic coating. The electrons transfer can explain the sensing mechanism for sensors of GTr, and 0.1Ge-GTr. In contrast, sensors' response could be better explained by gelatin swelling for 0.25Ge-GTr, 0.5Ge-GTr, and 1Ge-GTr sensors upon humidity detection. The proposed humidity sensors (0.5Ge-GTr and 1Ge-GTr) are green, highly sensitive, and with a fast response to human breathing, making them good candidates for healthcare applications such as respiration sensors for facial masks.
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.
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) | 0.001 | 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".