Sensitivity and Adsorption Energy Analysis of B and Ga Doped Graphene/Silicene for HCl Gas Sensing
Bibliographic record
Abstract
Detecting highly toxic HCl fumes conveniently, quickly, and reliably is essential due to its potential human health hazards.Therefore, it is important to track trace amounts of HCl using sensors.Sensors based on the two-dimensional materials graphene and graphene-like materials have attracted widespread attention due to their sensitivity, especially when grafting them with other molecules or atoms to alter the electronic and structural properties.This work used DFT to investigate the adsorption mechanism of pure and (B, Ga) doped graphene/silicene on the hazardous gas HCl.Adsorption energy, charge transfer, sensitivity, and density of state were Calculated.The adsorption energy of pure Graphene/silicene are (-0.1306,-0.2857) with sensitivity (1.132, 5.311) respectively.Doping graphene and graphene-like structures with (B, Ga) atoms significantly enhances the adsorption energy of graphene.This suggests that doped graphene performs better than pure graphene in applications involving HCl gas adsorption and sensors.GNR_DopB has a (19.41eV) adsorption energy and (51.58%) sensitivity to HCl gas, while the adsorption energy of GNR_DopGa is (-0.958eV), and it has the highest sensitivity to gas among the calculated models (195.03%).The results showed that graphene doped with Ga atoms has high sensitivity to HCl gas.In contrast, graphene doped with B atoms has high adsorption energy and High sensitivity, indicating that it can be used as suitable equipment for high-efficiency sensing HCl gas.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".