Temperature-dependent structural changes and hydration of CsLiB<sub>6</sub>O<sub>10</sub>
Bibliographic record
Abstract
This presentation features a combined photo-crystallography and Density Functional Theory study on two Ru-based complexes that have potential application in optical data storage [1].Solid-state linkage photo-isomerism is the transformative process, that yields a binary structural signature from 0 (ground-state) to 1 (the photo-isomer) [2].The compounds are based on the general series of materials, [Ru(SO 2 )(NH 3 ) 4 X]Y, where X is trans to the SO 2 ligand.In this particular study, X = isonicotinamide, Y = tosylate 2 ; X = H 2 O and Y = camphorsulfonate.The SO 2 is the photoactive ligand, converting from S-end bound (η 1 ) to side-bound (η 2 ) coordination with the Ru metal centre [3].Photo-crystallography experiments reveal the 3-D geometry of these light-activated molecular species [4], with up to 27% photoconversion efficiency.Complementary DFT calculations quantify a relationship between the photoconversion fraction and the size of the reaction cavity, i.e. the void surrounding the SO 2 ligand within the crystal lattice.The associated energetics of the ligand photoisomerism are presented in tandem with these findings.The relevance of these findings to the optical data storage industry are discussed; in particular, their role in helping to solve the current challenges in securing suitable materials for industrial application.This leads to the ultimate goal of being able to tailor an optoelectronic material for a given device application.
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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".