Parallel Synthesis and Rapid Photochemical Screening of Organosoluble Ru<sup>II</sup> Photosensitizers
Bibliographic record
Abstract
Ru(II) complexes of heteroaromatic ligands are photosensitizers of interest in such applications as photovoltaic cells. Their bulk preparation is tedious, time-consuming, and expensive. Their assessments, by measurement of the individual excited-state lifetimes, is incomplete and requires specialized equipment and expertise, as well as time. The identification of new, promising photosensitizers would, therefore, greatly benefit from any time- and cost-saving protocol, if absolute purity is not required for assessment. This paper details a protocol for the fairly rapid preparation, in parallel and on a small scale, of organosoluble Ru(II) complexes in a state ready for screening for photosensitization ability. The protocol was tested with a small set of bidentate ligands, generating 20 possible complexes, many of which are known. The protocol was found to produce predominantly the desired species in all cases except those with three different ligands. The batch screening results for the remaining 16 complexes were entirely consistent with those obtained with pure samples of the most promising materials prepared in bulk and were consistent with known photophysical properties.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".