Chemistry of photogenerated α-phenyl-substituted <i>o</i>-, <i>m</i>-, and <i>p</i>-quinone methides from phenol derivatives in aqueous solution
Bibliographic record
Abstract
The enhanced photochemical reactivity of o-substituted phenols in its propensity to give o-quinone methide (o-QM) intermediates via excited state intramolecular proton transfer (ESIPT) was uncovered by Keith Yates as part of his now classic studies of photohydration of aromatic alkenes, alkynes, and related compounds. Photogeneration of QMs and the study of their chemistry along with potential biological applications are the focus of many groups. In this work, photochemical precursors to o-, m-, and p-QMs based on substituted phenols (hydroxybenzyl alcohols) and related compounds have been studied in aqueous solution as a function of pH and water content. The focus will be on QMs that are stabilized by an α-phenyl substituent, which enhances quantum yields for their formation, with the resulting QMs having longer lifetimes and easier to detect. Noteworthy is that all QM isomers can be photogenerated with the o and m isomers being the most efficient, consistent with the Zimmerman “ortho-meta” effect. m-QMs have formal non-Kekulé structures, and although they can be routinely photogenerated, are found to be most reactive. One m-QM was found to undergo a photocondensation reaction at high pH giving rise to m-substituted oligomers. The mechanism of QM formation in aqueous solution is believed to involve singlet excited phenols that undergo adiabatic deprotonation to give the corresponding photoexcited phenolate ion, which subsequently expels the hydroxide ion (photodehydroxylation). A pathway involving direct loss of water for the o-isomers is also possible in organic solvents.Key words: quinone methides, phenols, excited state acidity, solvolysis, carbocations, meta effect, photopolymerization, non-Kekulé intermediates.
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) | 0.000 | 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) | 0.002 | 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".