Mild, Efficient, and Regioselective Synthesis of Diiodophenylboronic Acid Derivatives via Metal–Iodine Exchange of 5-Substituted 1,2,3-Triiodoarenes
Bibliographic record
Abstract
Unique 2,6-diiodophenylboronic acid and 2,3-diiodophenylboronic acid derivatives have been synthesized via regioselective metal–iodine exchange (MIE) of 5-substituted 1,2,3-triiodoarenes. The regioselectivity of the reaction per se is remarkably controlled by the nature of the C5 substituent providing the desired diiodophenylboronic acids in moderate to good yields and with high site selectivity. The diiodophenylboronic acids were then examined for in vitro antimicrobial activity against four strains of bacteria Micrococcus luteus (ATCC 9341), Bacillus cereus (ATCC 11778), Escherichia coli (ATCC 25922), and Serratia marcescens (ATCC 27117) and one fungal strain Candida albicans using well diffusion assay and dilution method. It indicated that 5-fluoro-2,3-diiodophenylboronic acid possesses the most potent antibacterial and antifungal activity with MIC of 2.6 mg/mL for M. luteus and C. albicans. This report discloses a one-step protocol to access hitherto unknowns 2,6-diiodophenylboronic acid and 2,3-diiodophenylboronic acid derivatives that is scalable, good in scope, no chromatography is needed, and these compounds are difficult to prepare by other means.
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.
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) | 0.001 | 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 | 0.001 |
| 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".