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Record W4205479583 · doi:10.1002/chem.202104302

Electronic Circular Dichroism‐Circularly Polarized Raman (eCP‐Raman): A New Form of Chiral Raman Spectroscopy

2022· article· en· W4205479583 on OpenAlexafffund
Guojie Li, Mutasem Alshalalfeh, Josef Kapitán, Petr Bouř, Yunjie Xu

Bibliographic record

VenueChemistry - A European Journal · 2022
Typearticle
Languageen
FieldChemistry
TopicMolecular spectroscopy and chirality
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of EducationCanada Research ChairsUniversity of AlbertaCompute CanadaCanada Foundation for Innovation
KeywordsRaman spectroscopyRaman optical activityVibrational circular dichroismCircular dichroismResonance (particle physics)Resonance Raman spectroscopyMoleculeCoherent anti-Stokes Raman spectroscopyRaman scatteringChemistryAnalytical Chemistry (journal)Materials scienceNuclear magnetic resonanceOpticsCrystallographyPhysicsAtomic physicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract This Concept article summarizes recent work on the development of a new form of chiral Raman spectroscopy, e CP‐Raman, which combines two spectroscopies: electronic circular dichroism (ECD) and circularly polarized Raman (CP‐Raman). First, some puzzling observations while carrying out Raman optical activity (ROA) measurements of several transition metal complexes under resonance are described, as well as the search for the mechanisms responsible. Then an equation for quantifying the e CP‐Raman contribution is presented, followed by several examples of how e CP‐Raman influences the I R − I L spectra of achiral and chiral solvent molecules and of a number of chiral solutes under resonance. The conditions to extract resonance ROA, when the e CP‐Raman contribution is minimized, are also discussed. Finally, we comment on the potential applications of e CP‐Raman.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
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 score0.068
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0120.000

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 head0.008
GPT teacher head0.217
Teacher spread0.209 · 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.

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

Citations16
Published2022
Admission routes2
Has abstractyes

Explore more

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