Hexachlorobenzene (HCB) adsorption onto the surfaces of C60, C59Si, and C59Ge: Insight from DFT, QTAIM, and NCI
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it