Proton Pachinko: Probing Excited‐State Intramolecular Proton Transfer of St. John's Wort‐Derived Fluorescent Photosensitizer Hypericin with Ultrafast Spectroscopy**
Bibliographic record
Abstract
Abstract Hypericin from St. John's wort has been used as a potent photosensitizer, but its working mechanism remains elusive which hinders its rational design for improved functionality. We implement ultrafast spectroscopy and quantum calculations to track the excited‐state dynamics in an intricate hydrogen‐bonding network of hypericin in solution. Using femtosecond transient absorption (fs‐TA), we track excited state intramolecular proton transfer (ESIPT) via a previously unreported blueshift of a long‐wavelength stimulated emission (SE) band with excitation‐dependent dynamics in various solvents, owing to the dominant Q7,14 tautomer that undergoes bidirectional ESIPT. This finding is corroborated by ground‐state femtosecond stimulated Raman spectroscopy (GS‐FSRS) and density functional theory (DFT) calculations. Moreover, contrasting the neutral and anionic forms of hypericin enables us to reveal an intramolecular charge transfer step underlying ESIPT. We demonstrate UV and visible excitations as an integral platform to provide direct insights into the photophysics and origin for phototoxicity of hypericin. Such mechanistic insights into the excited state of hypericin will power its future development and use.
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) | 0.001 | 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".