Cover Picture: Role of Temperature in Controlling Performance of Photorefractive Organic Glasses (ChemPhysChem 7/2003)
Bibliographic record
Abstract
Abstract The cover picture shows the underlying mechanism and one of the applications of the photorefractive effect, which produces a spatial modulation of the refractive index of a material under nonuniform illumination. As illustrated on the right side, the photorefractive effect begins with light and dark fringes produced by intersecting laser beams, which, in the presence of an applied electric field E0, produce charge separation. The mobile charges (primarily holes) move and eventually trap in dark regions, leading to a space–charge electric field (green). This field produces a refractive index change, that is, a hologram that can diffract light. A key feature of the effect is that it leads to asymmetric energy transfer between the two beams incident on the material. An application of this effect is image amplification, which is demonstrated on the top left side of the picture where the image of the number 5, carried by a weak beam, is amplified in the presence of a strong beam. Among the best photorefractive materials developed thus far are organic, amorphous glasses, the properties of which depend critically on the glass transition temperature (Tg) and photoconductivity (σph), as well as polarizability anisotropy (Δα) and hyperpolarizability (β) of the molecules. The picture shows the structure of the photoconductive, nonlinear optical chromophore DCDHF‐6 that forms a high‐performance low‐molecular weight photorefractive glass. The detailed properties of several photorefractive glasses containing DCDHF derivatives are described in the article by Ostroverkhova et al. on page 732.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.250 | 0.078 |
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".