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Record W4384407266 · doi:10.46932/sfjdv4n4-028

Theoretical-experimental method of nonlinear optics: the Z-scan technique

2023· article· en· W4384407266 on OpenAlexfundno aff
Antonio-Alfonso Rodriguez-Rosales, Omar Guillermo Morales Saavedra, Fabiola Cristina Rodríguez Estrada, Juan Antonio Murillo Vargas

Bibliographic record

VenueSouth Florida Journal of Development · 2023
Typearticle
Languageen
FieldEngineering
TopicNonlinear Optical Materials Studies
Canadian institutionsnot available
FundersCanadian Institute for Theoretical Astrophysics
KeywordsNonlinear opticsNonlinear systemOpticsRefractive indexInterpretation (philosophy)Nonlinear opticalLiquid crystalAbsorption (acoustics)Z-scan techniqueSimple (philosophy)LaserPhysicsComputer scienceQuantum mechanics

Abstract

fetched live from OpenAlex

We did this work to help professors and students of physics and engineering in Optics and Lasers courses who require a theoretical-experimental introductory framework to nonlinear optics. We showed in this article the basic principles of the theory, focusing on third-order nonlinear optical phenomena, using the well-known Z-scan technique for its demonstration. For this, we propose the design of a simple experiment that the interested party can do in an optics laboratory. Once the data has been obtained and processed, we implemented a practical method of interpretation of the typical graphs obtained by this technique to carry out the calculations that allow the measurement of the refractive index and the nonlinear absorption coefficient in materials that present these optical properties, as the case exemplified here of nematic liquid crystals doped with an organic dye.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.004

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 head0.263
Teacher spread0.250 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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