Effect of point defects in armchair graphene nanoribbons for biosensing of Methanethiol biomarkers: A DFT Study
Bibliographic record
Abstract
In this study, we probe the surface engineered (vacancy and Stone Wales defective) hydrogen passivated armchair graphene nanoribbon with a width of 7 atoms as a biosensing material to detect methanethiol biomarkers. Induction of defects results in the decrement of the bandgap by the formation of the trapping states. After geometry optimisation, it is found that only vacancy induced armchair graphene nanoribbon can form strong binding with the biomarker, whereas Stone Wales defect induced armchair graphene nanoribbon show disassociation with the biomarker by having positive adsorption energy. Using density functional theory, the electronic properties of a vacancy-induced armchair graphene nanoribbon with and without adsorption of methanethiol are calculated, showing that methanethiol adsorption leads to increment in the bandgap of the host material by eliminating the trapping states. The work function and electron affinity change after adsorption. As there are significant changes in the electronic properties, vacancy induced armchair graphene nanoribbon show high sensing capabilities toward methanethiol biomarkers.
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 | 0.000 | 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".