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Record W2088921177 · doi:10.1364/josab.21.002213

Nondestructive interferometric determination of χ^(2)(z) spatial distribution induced in thermally poled silica glasses

2004· article· en· W2088921177 on OpenAlexaff
Vincent Tréanton, Nicolas Godbout, Suzanne Lacroix

Bibliographic record

VenueJournal of the Optical Society of America B · 2004
Typearticle
Languageen
FieldMaterials Science
TopicGlass properties and applications
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsPolingMaterials scienceOpticsPhase (matter)InterferometryAmplitudeSIGNAL (programming language)Nonlinear systemQuartzPlanarSecond-harmonic generationNondestructive testingAnalytical Chemistry (journal)Composite materialOptoelectronicsFerroelectricityPhysicsChemistryLaserDielectric

Abstract

fetched live from OpenAlex

A new nondestructive characterization technique, based on a modified Maker fringe measurement, is reported that allows the complete determination of complex spatial distributions of the nonlinear χ(2)(z) coefficient in planar samples. Each sample under test was stacked together with a phase reference quartz plate to retrieve both amplitude and phase of the optical second-harmonic signal. The added phase information permits the precise determination of the location of the optically nonlinear region within the sample. Hemicylindrical lenses are used to obtain greater internal propagation angles, with an important increase in the information about the nonlinearity distribution. The technique is demonstrated for two Infrasil glass plates thermally poled in vacuum under the same temperature, voltage, and duration conditions. Very different distributions were obtained for these samples, one nonlinear layer being buried 4 μm under the anodic surface while the other was not, the latter exhibiting maximum χ(2) three times larger than the former. This difference in the χ(2)(z) distribution is explained in terms of charge injection during the poling process.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.066
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.013
GPT teacher head0.250
Teacher spread0.237 · 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 teacher head, 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

Citations11
Published2004
Admission routes1
Has abstractyes

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