Influence of peripheral halogenation on axial phenoxylation kinetics of boron subphthalocyanines
Bibliographic record
Abstract
We investigate the effect of peripheral fluorination and chlorination on the rate of axial phenoxylation of boron subphthalocyanines (BsubPcs), with Br and Cl as axial ligands, specifically Br-BsubPc, Br-F[Formula: see text]BsubPc, Br-Cl[Formula: see text]BsubPc, Cl-BsubPc, and Cl-F[Formula: see text]BsubPc. For this study, we use various solvents at their reflux temperature to acquire conversion of axial phenoxylation and obtain kinetic data. We found the experimentally observed reactivity of the BsubPcs used in this study followed Br-BsubPc > Cl-BsubPc > Br-F[Formula: see text]BsubPc [Formula: see text] Br-Cl[Formula: see text]SubPc >> Cl-F[Formula: see text]BsubPc. This shows that peripheral fluorination or chlorination inhibits the rate of axial phenoxylation and confirms the axial B–Br bonds to be more reactive than B–Cl bonds. Density functional theory (DFT) calculations confirm the observed kinetic data and suggest that phenoxylation proceeds primarily via the Torres mechanism.
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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) | 0.000 | 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".