Comparative Study of H<sub>2</sub>S Gas Sensing: Pristine vs. Cobalt-Functionalized C<sub>2</sub>N Using First-Principles Modeling
Bibliographic record
Abstract
The exceptional physical and electrical characteristics of two-dimensional Nitrogenated Holey Graphene (C2N) devices highlight their significance. C2N-based sensors demonstrate remarkable sensitivity, stability, and responsiveness compared to other solid-state sensors. The conductivity of C2N experiences shifts upon exposure to a wide array of organic and inorganic substances, enabling the detection of various target molecules through these conductivity alterations. Using first-principles transport simulations, a sensor device incorporating Nitrogenated Holey Graphene (C2N) is specifically designed to detect varying concentrations of hydrogen sulfide (H2S) gas molecules. Through the utilization of the Quantumwise Atomistix Toolkit (ATK), a simulator for nanoscale semiconductor devices, a C2N-based sensor is simulated in this study. This work studies the effectiveness of C2N sensors, both pristine and functionalized with Co, for detecting varying concentrations of hydrogen sulfide (H2S) gas molecules. Our findings reveal that the Co-functionalized C2N sensor performs better than the pristine counterpart. Through simulations, we demonstrate the sensor's ability to detect single and double H2S molecules with 20% higher sensitivity and 15% improved selectivity compared to pristine C2N. This research highlights the potential of C2N-based sensors in gas sensing applications, including environmental monitoring and industrial safety, and notably in biomedical applications such as medical diagnostics through breath analysis for disease markers. The effectiveness of employing density functional theory for sensor analysis and electronic transport calculations is also highlighted.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".