Polycyclic Aromatic Hydrocarbons Adsorption onto Graphene: A DFT and AIMD Study
Bibliographic record
Abstract
Density functional theory (DFT) calculations and ab-initio molecular dynamics (AIMD) simulations were performed to understand graphene and its interaction with polycyclic aromatic hydrocarbons (PAHs) molecules. The adsorption energy was predicted to increase with the number of aromatic rings in the adsorbates, and linearly correlate with the hydrophobicity of PAHs. Additionally, the analysis of the electronic properties showed that PAHs behave as mild n-dopants and introduce electrons into graphene; but do not remarkably modify the band gap of graphene, indicating that the interaction between PAHs and graphene is physisorption. We have also discovered highly sensitive strain dependence on the adsorption strength of PAHs onto graphene surface. The AIMD simulation indicated that a sensitive and fast adsorption process of PAHs can be achieved by choosing graphene as the adsorbent. These findings are anticipated to shed light on the future development of graphene-based materials with potential applications in the capture and removal of persistent aromatic pollutants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".