Graphene Based Sensors for Air Quality Monitoring - Preliminary Development Evaluation
Bibliographic record
Abstract
Indoor air pollution can induce adverse health effects on building occupants and pose a significant role in health worldwide. To avoid such effects, it is extremely important to monitor and control common indoor pollutants such as CO2, VOCs, and relative humidity. Therefore, this work focuses on recent advances in the field of graphene-based gas sensors, emphasizing the use of modified graphene that broadly expands the range of nanomaterials sensors. Graphene films were grown on copper by chemical vapor deposition (CVD) and transferred to arbitrary substrates. After synthesis, the samples were functionalized with Al2O3 by ALD and characterized by a large set of experimental techniques such as XPS, Raman, and SEM. The results demonstrated that graphene was successfully synthesized and transferred to SiO2, glass, and polymer. As a proof-of-concept, ALD of Al2O3 was performed on the graphene surface to produce a graphene/metal oxide nanostructure towards the development of nanocomposites for gas sensing. From this perspective, a laboratory prototype device based on measuring the electrical properties of the graphene sample as a function of the gas absorption is under development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".