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Record W2885342463 · doi:10.1002/adma.201803628

Ultralow Dispersion Multicomponent Thin‐Film Chalcogenide Glass for Broadband Gradient‐Index Optics

2018· article· en· W2885342463 on OpenAlexaff
Myungkoo Kang, Andrew Swisher, Alexej Pogrebnyakov, Liu Leo Liu, Andrew G. Kirk, Stephen R. Aiken, Laura Sisken, Charmayne Lonergan, Justin Cook, Teodor Malendevych, Fedor Kompan, Ivan Divliansky, Leonid Glebov, M. C. Richardson, Clara Rivero‐Baleine, Carlo G. Pantano, Theresa S. Mayer, Kathleen Richardson

Bibliographic record

VenueAdvanced Materials · 2018
Typearticle
Languageen
FieldMaterials Science
TopicPhase-change materials and chalcogenides
Canadian institutionsLockheed Martin (Canada)
FundersDefense Advanced Research Projects Agency
KeywordsMaterials scienceRefractive indexChalcogenideAmorphous solidLaserDispersion (optics)OptoelectronicsOpticsThin filmInfraredChalcogenide glassFemtosecondGratingPhotothermal therapyNanocrystalBand gapNanotechnology

Abstract

fetched live from OpenAlex

A novel photothermal process to spatially modulate the concentration of sub-wavelength, high-index nanocrystals in a multicomponent Ge-As-Pb-Se chalcogenide glass thin film resulting in an optically functional infrared grating is demonstrated. The process results in the formation of an optical nanocomposite possessing ultralow dispersion over unprecedented bandwidth. The spatially tailored index and dispersion modification enables creation of arbitrary refractive index gradients. Sub-bandgap laser exposure generates a Pb-rich amorphous phase transforming on heat treatment to high-index crystal phases. Spatially varying nanocrystal density is controlled by laser dose and is correlated to index change, yielding local index modification to ≈+0.1 in the mid-infrared.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.024
GPT teacher head0.278
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations51
Published2018
Admission routes1
Has abstractyes

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