Harmful Gas Molecule Detection Using Lithium Doped Graphene: Theoretical Analysis of Adsorption and Gas-Sensing Properties
Bibliographic record
Abstract
In the present paper, the energetic, structural, geometric, and electronic characteristics of graphene (Gr) were examined with the use of first-principles density functional theory (DFT) methods in relation to adsorption of CO, CO2, NH3, SO2, and N2 molecules and to lithium (Li) contamination.They substituted a single carbon (C) atom with a Li atom, creating an energy gap (Eg) of 0.08 eV.We calculated a range of Eg values by using different doping and adsorption techniques with Li.The highest value we obtained was 0.80 eV.The results indicate that CO, NH3, and SO2 molecules undergo physical adsorption onto the surface of Li-doped graphene (Li-Gr), with adsorption energy (Ead) values of -0.31, 0.56, and 0.93 eV, respectively.On the other hand, CO2 and N2 molecules are chemically bonded to the surface of Li-Gr, with Ead values of -2.44 and -1.08 eV, respectively.In contrast, CO2 and N2 molecules are chemisorbed on the Li-Gr surface, with Ead values of -2.44 and -1.08 eV, respectively.The results of our calculations indicate that Li-Gr may be a suitable sensor for CO, NH3, and SO2 molecules.Using the DFT approach, we determined the optimal and stable electronic configurations of Li-Gr.
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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 | 0.001 | 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".