An imidazoacridine-based TADF material as an effective organic photosensitizer for visible-light-promoted [2 + 2] cycloaddition
Bibliographic record
Abstract
Energy transfer (EnT) is a fundamental activation process in visible-light-promoted photocycloaddition reactions. This work describes the performance of imidazoacridine-based TADF materials for visible-light mediated triplet-triplet EnT photocatalysis. The TADF material ACR-IMAC has been discovered as an inexpensive, high-performance organic alternative to the commonly used metal-based photosensitizers for visible-light EnT photocatalysis. The efficiency of ACR-IMAC as a photosensitizer is comparable with Ir-based photosensitizers in both intra- and intermolecular [2 + 2] cycloadditions. ACR-IMAC mediated both dearomative and non-dearomative [2 + 2] cycloadditions in good yields, with high regio- and diastereocontrol. Cyclobutane-containing bi- tri- and tetracylic scaffolds were successfully prepared, with broad tolerance toward functional groups relevant to drug discovery campaigns. Fluorescence quenching experiments, time-correlated single-photon counting, and transient absorption spectroscopy were also conducted to provide insight into the reaction and evidence for an EnT mechanism.
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".