Computational study on the interactions of functionalized C<sub>24</sub>NC (NC=C, –OH, –NH<sub>2</sub>, –COOH, and B) with chloroethylphenylbutanoic acid
Bibliographic record
Abstract
Computational chemistry approach based on density functional theory (DFT) was utilized to investigate the interaction, adsorption behaviour, electronic and structural properties of nanostructured complexes formed by 4-(4-(bis(2-chloroethyl)amino)phenyl)butanoic acid (CPB) and all carbon fullerene nanocage, (C24NC), boron functionalized carbon nanocage (C24NC@B@CPB), carboxylate functionalized (C24NC@COOH@CPB), amino functionalized (C24NC@NH2@CPB), and hydroxy functionalized (C24NC@OH@CPB) nanostructured materials. To understand effectively the interaction of the drug and surface, topological analysis was conducted via the atoms in molecule (quantum theory of atoms in molecules) and noncovalent interaction approach. Electronic properties such as quantum chemical descriptors, natural bond orbital and nonlinear optics are equally considered and reported. All computations were achieved at the B3LYP-D3 and ωB97XD levels of theory with the 6-311++G(d, p) basis set. The results indicate that the adsorption energy of CPB on C24NC and its functionalized derivatives are in the range of −0.52 to 2.89 eV indicating that physisorption and chemisorption mechanism are prevalent mechanisms of adsorption. C23B@CPB, C24OH@CPB, and C24NH2@CPB were observed to possess the best characteristics to be considered as transport vehicles for CPB due to their strong adsorption nature (chemisorption) and solubility in solution.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".