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Record W4281400277 · doi:10.21203/rs.3.rs-1642827/v1

Scalable nonlinear helical dichroism in chiral and achiral molecules

2022· preprint· en· W4281400277 on OpenAlexaff
Jean-Luc Bégin, Ashish Jain, A. M. Parks, Felix Hufnagel, P. B. Corkum, Ebrahim Karimi, Thomas Brabec, V. R. Bhardwaj

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldChemistry
TopicMolecular spectroscopy and chirality
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCircular dichroismNonlinear systemMoleculeMaterials scienceCrystallographyChemistryPhysicsQuantum mechanicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Chiral interactions are prevalent in nature, driving a variety of bio-chemical processes. Discerning the two non-superimposable mirror images of a chiral molecule, known as enantiomers, requires interaction with a chiral reagent with known handedness. Circularly polarized light beams are often used as a chiral reagent. Here, we demonstrate efficient chiral sensitivity with linearly polarized helical light beams carrying an orbital angular momentum of $\pm\it{l\hbar}$, in which the handedness is defined by the twisted wavefront structure tracing a left- or right-handed corkscrew pattern as it propagates in space. By probing nonlinear optical response, we show that helicity dependent nonlinear absorption occurs even in achiral molecules and can be precisely controlled. We model this effect by considering induced multipole moments in light-matter interactions. Design and control of light-matter interactions with helical light opens new opportunities in chiroptical spectroscopy, light-driven molecular machines, optical switching, and in-situ ultrafast probing of chiral systems and magnetic materials.

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 score0.001
Threshold uncertainty score0.004

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.390
Teacher spread0.352 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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