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Giant Third-Order Nonlinear Response of Liquids at Terahertz Frequencies

2021· article· en· W3159352794 on OpenAlexaff
Anton Tcypkin, Maria O. Zhukova, Maksim Melnik, Irina Vorontsova, Maksim S. Kulya, S. É. Putilin, S. A. Kozlov, Saumya Choudhary, Robert W. Boyd

Bibliographic record

VenuePhysical Review Applied · 2021
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsUniversity of Ottawa
FundersRussian Foundation for Basic Research
KeywordsTerahertz radiationRefractive indexNonlinear systemMoleculePhysicsMaterials scienceOrder (exchange)InfraredTerahertz spectroscopy and technologyNonlinear opticsMolecular physicsAtomic physicsOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

The nonlinear response of liquids in the terahertz regime has recently attracted significant interest, even though very few measurements have been reported. Here, we report on our measurements based on a $z$-scan technique of the nonlinear refractive-index coefficient ${n}_{2}$ at terahertz frequencies for several liquids with noncentrosymmetric molecules, specifically, water, ethanol, and $\ensuremath{\alpha}$-pinene. We describe how the value of ${n}_{2}$ depends on the physical parameters of these molecules. The measured values of ${n}_{2}$ of the liquids in the terahertz region are as much as 6 orders of magnitude larger than their corresponding values in the visible or near-IR. Through a simple theoretical model, we confirm that the predominant source of this large third-order nonlinearity is the second-order perturbative component of the vibrational response of these molecules, which have resonances in the mid-IR.

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 score0.002
Threshold uncertainty score0.005

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.260
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

Citations44
Published2021
Admission routes1
Has abstractyes

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