Hexachlorobenzene (HCB) adsorption onto the surfaces of C60, C59Si, and C59Ge: Insight from DFT, QTAIM, and NCI
Bibliographic record
Abstract
Several reports have shown that nanomaterials have been found to be effective for gas sensing application and the adsorption of hazardous organochloride. Herein, dispersion corrected density functionals; D3-B3LYP-D3, ωB97XD, M06-2X and PBE0 all at the 6-311G (d) basis set was used to study the interactions of metal-doped fullerenes (silicon (C59Si) and germanium (C59Ge)) with Hexachlorobenzene (C6Cl6) gas molecule. The adsorption properties of the adsorbents viz; the pure fullerene nanocage (C60) and the doped system; Silicon (C59Si) and Germanium (C59Ge) were all investigated in terms of reactivity, stability, bond order, intermolecular interaction, van der waals, and weak interaction as well as adsorption energy. The reactivity levels of the examined surfaces were observed within the same range at the B3LYP-D3/6-311G (d) level of theory to be 5.996 eV, 5.309 eV, and 5.188 eV for C60, C59Si and C59Ge adsorbents respectively. From our calculation for adsorption energies; the high negative value -1.010 eV of for the C59Ge nanocage suggests that the doped surface adsorbs hexachlorobenzene better in comparison to the other surfaces and adsorption is thermodynamically favored. The results for natural bond orbitals (NBO), quantum theory of atoms in molecules (QTAIM), and non-covalent interaction (NCI) were consistent across all systems and favored physical adsorption.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".