Mechanistic Insight into the Limiting Factors of Graphene-Based Environmental Sensors
Bibliographic record
Abstract
Graphene has demonstrated great promise for technological use, yet control over material growth and understanding of how material imperfections affect the performance of devices are challenges that hamper the development of applications. In this work, we reveal new insight into the connections between the performance of the graphene devices as environmental sensors and the microscopic details of the interactions at the sensing surface. We monitor changes in the resistance of the chemical-vapor deposition grown graphene devices as exposed to different concentrations of ethanol. We perform thermal surface treatments after the devices are fabricated, use scanning probe microscopy to visualize their effects down to nanometer scale and correlate them with the measured performance of the device as an ethanol sensor. Our observations are compared to theoretical calculations of charge transfers between molecules and the graphene surface. We find that, although often overlooked, the surface cleanliness after device fabrication is responsible for the device performance and reliability. These results further our understanding of the mechanisms of sensing in graphene-based environmental sensors and pave the way to optimizing such devices, especially for their miniaturization, as with decreasing size of the active zone the potential role of contaminants will rise.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".