Infrared Glass–Ceramics with Multidispersion and Gradient Refractive Index Attributes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it