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Record W4253524511 · doi:10.22215/etd/2016-11319

A Theoretical Study of Maker Fringe Measurements in Poled Multi-Layer Silica Structures Focusing on the Impact of Layer Quantity and Spacing

2016· dissertation· en· W4253524511 on OpenAlexaff
Pouyan Nasr

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMaterials Science
TopicGlass properties and applications
Canadian institutionsCarleton University
Fundersnot available
KeywordsPolingLinearitySecond-harmonic generationNonlinear systemLayer (electronics)Silica glassMaterials scienceSymmetry (geometry)HarmonicOpticsNonlinear opticsLaserOptoelectronicsElectronic engineeringEngineeringComposite materialPhysicsAcousticsMathematicsGeometry

Abstract

fetched live from OpenAlex

A second order nonlinearity in silica glass, which does not exist intrinsically, can be achieved through thermal poling. The poling process breaks the intrinsic symmetry of the silica allowing for even ordered non-linearities. After poling was first successfully demonstrated in 1991, the achievable nonlinearity has remained weak (after several attempts). Recently, some progress has been made through the development of multi-layer silica structures created through alternating dopant concentration. Larger observed nonlinearities in these samples (through the creation of second harmonic radiation) suggest that this may have created a distributed non-linearity that could result in more efficient frequency doubled laser sources. The impact of layer spacing on the observed nonlinearity has remained an open question. In this thesis a numerical model is developed with the goal investigating the impact of the quantity and spacing between layers on second harmonic generation. The thesis begins with a brief history on poling techniques and various attempts to induce greater second order nonlinearities in silica. We then develop the theoretical model that describes second harmonic generation in multi-layer structures. This section is followed by a description of our experiment which basically is simulation model and the thesis finishes with a discussion of the conclusions drawn from the model.

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 score0.302
Threshold uncertainty score0.511

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 head0.093
GPT teacher head0.360
Teacher spread0.266 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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