Infrared Glass–Ceramics with Multidispersion and Gradient Refractive Index Attributes
Bibliographic record
Abstract
Abstract Infrared (IR) glass–ceramics (GCs) hold the potential to dramatically expand the range of optical material solutions available for use in bulk and planar optical systems in the IR. Current material solutions are limited to single‐ or polycrystalline materials and traditional IR‐transparent optical glasses. GCs that can be processed with spatial control and extent of induced crystallization present the opportunity to realize an effective refractive index variation, enabling arbitrary gradient refractive index elements with tailored optical function. This work discusses the role of the parent glass composition and morphology on nanocrystal phase formation in a multicomponent chalcogenide glass. Through a two‐step heat treatment protocol, a Ge–As–Pb–Se glass is converted to an optical nanocomposite where the type, volume fraction, and refractive index of the precipitated crystalline phase(s) define the resulting nanocomposite's optical properties. This modification results in a giant variation in infrared Abbe number, the magnitude of which can be tuned with control of crystal phase formation. The impact of these attributes on the GCs' refractive index, transmission, dispersion, and thermo‐optic coefficient is discussed. A systematic protocol for engineering homogeneous or gradient changes in optical function is presented and validated through experimental demonstration employing this understanding.
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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) | 0.001 | 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".