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Record W4413623724 · doi:10.1063/5.0286254

Sum frequency dual null angle approach and its use in surface hyperpolarizability ratio measurements

2025· article· en· W4413623724 on OpenAlexafffund
A. Kumarasiri, Peter Yang, Dennis K. Hore

Bibliographic record

VenueThe Journal of Chemical Physics · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHyperpolarizabilityNull (SQL)OpticsSum-frequency generationPolarization (electrochemistry)SpectroscopyBeam (structure)PhysicsNonlinear systemComputational physicsNonlinear opticsChemistryNonlinear opticalComputer scienceLaserQuantum mechanics

Abstract

fetched live from OpenAlex

Null angle measurements are a recognized method for accurately determining the ratio of optical constants in linear and nonlinear optical spectroscopy. Here, we extend the established null angle scheme in vibrational sum-frequency generation where the sum-frequency beam is linearly polarized at ±45° to include a second scheme where the IR beam is polarized at ±45°. We illustrate that measurement of the null angles obtained in both schemes may be used together to calibrate the SFG response between three polarization combinations. We then demonstrate that these two null angles provide the required phase information to determine the surface hyperpolarizability ratio, even at buried interfaces where calibration is typically more difficult, and without requiring a heterodyne scheme. This makes extracting the electronic structure information directly from the SFG spectra more accurate and truly independent of the molecular orientation distribution.

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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.038
GPT teacher head0.270
Teacher spread0.232 · 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
GenreMethods

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
Published2025
Admission routes2
Has abstractyes

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