Monolithic Chalcogenide Optical Nanocomposites Enable Infrared System Innovation: Gradient Refractive Index Optics
Bibliographic record
Abstract
Abstract The size and weight of conventional imaging systems is defined by costly non‐planar lenses and the complex lens assemblies required to minimize optical aberrations. The ability to engineer gradient refractive index (GRIN) optics has the potential to overcome constraints of traditional homogeneous lenses by reducing the number of components in optical systems. Here, an innovative strategy to realize this goal based on monolithic GRIN media created in Ge‐As‐Se‐Pb chalcogenide infrared nanocomposites is presented. A gradient heat treatment to spatially modulate the volume fraction of high refractive index Pb‐rich nanocrystals within a glass matrix is utilized, providing a GRIN profile while maintaining an optical transparency. A first‐ever correlation of material chemistry and microstructure, processing protocol, and optical property modification resulting in a prototype GRIN structure is presented. The integrated approach and mechanistic understanding illustrated by this versatile modification paradigm provides a platform for new optical functionalities in next‐generation imaging applications.
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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".