UV Illumination as a Method to Improve the Performance of Gas Sensors Based on Graphene Field-Effect Transistors
Bibliographic record
Abstract
The ability to detect and recognize airborne chemical species is essential to enable applications in security, health, and environmental monitoring. Here, we report a sensing platform based on graphene field-effect transistor (GFET) devices combined with optical illumination for the detection of volatile compounds. We compare the change in resistance of GFET sensors upon exposure to analytes such as ethanol, dimethyl methylphosphonate (DMMP), and water vapors with and without the presence of a local illuminating ultraviolet (UV) light-emitting diode (LED). Our results show that UV illumination acts as a control knob for the electronic transport properties of graphene, increasing the device's response to ethanol, water, and DMMP, up to a factor of 54, and enabling ppb-level detection of DMMP at 800 ppb without chemical functionalization of the graphene layer. The sensing response can be optimized to reveal an analyte-specific interplay between the induced changes in carrier concentration and mobility of the GFET. These findings provide a pathway to enhancing the sensitivity of GFET sensors and a differentiation channel to improve their selectivity.
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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) | 0.001 | 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 | 0.001 |
| 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".