Graphene Oxide as a Sensing Material for Gas Detection Based on Nanomechanical Sensors in the Static Mode
Bibliographic record
Abstract
Graphene is a key material for gas sensing applications owing to its high specific surface area and vast chemical modification potential. To fully utilize the potential of graphene, a sensing platform independent of conductive properties is required. In this study, we employed membrane-type surface stress sensors (MSS)—A kind of nanomechanical sensor operated in the static mode—As a sensing platform and utilized graphene oxide (GO) as a gas sensing material. MSS detect surface stress caused by gas sorption; therefore, chemically modified graphene with low conductivity can be utilized as a gas sensing material. We evaluated the sensing performance of a GO-coated MSS by measuring its responses to five gases. We demonstrated with the GO-coated MSS the feasibility of GO as a gas sensing material for static mode nanomechanical sensors and revealed its high selectivity to water vapor. Moreover, we investigated the sensing mechanism of the GO-coated MSS by comparing it with the sensing performance of MSS coated with reduced graphene oxide and graphite powder and deduced key factors for sensitivity and selectivity. Considering the high sensitivity of the GO-coated MSS and the compact measurement system that MSS can realize, the present study provides a new perspective on the sensing applications of graphene.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".