Effect of Reduced Graphene Oxide Film Thickness on a Chemiresistor’s Response to Volatile Organic Compounds and Warfare Agents
Bibliographic record
Abstract
We explore the performance of a chemiresistor sensor array based on thin layers of reduced graphene oxide (rGO). The rGO is deposited with a spray coating technique to fabricate three samples of different layer thicknesses, which are characterized by atomic force microscopy (AFM) and Raman spectroscopy. We expose the chemiresistors to water vapor, three volatile organic compounds (VOC), ethanol, acetone, and formaldehyde, and two simulants of chemical warfare agents (CWA), dimethyl-methyl phosphonate (DMMP) and dipropylene glycol monomethyl ether (DPGME). The rGO-based sensors show noticeable changes in resistance upon parts per million variations of the analyte concentrations. The largest detection sensitivity 0.02%/ppm is observed with DPGME. Furthermore, we investigate a thickness-dependent signal that depends on the nature of the analyte. We show that comparing the signal measured with only a few rGO layers of different thicknesses can be used to distinguish formaldehyde from other VOC and DMMP from DPGME. Our findings represent a step toward the development of practical sensor arrays based on low cost, scalable graphene-based materials, enabling both sensitive and selective detection of analytes.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".