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Record W2132978634 · doi:10.1021/ma400955u

Accurate New Method for Molecular Orientation Quantification Using Polarized Raman Spectroscopy

2013· article· en· W2132978634 on OpenAlexaff
Marie Richard‐Lacroix, Christian Pellerin

Bibliographic record

VenueMacromolecules · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsRaman spectroscopyOrientation (vector space)PolymerHigh-density polyethyleneSpectroscopyBiological systemMaterials scienceRange (aeronautics)Statistical physicsComputer scienceOpticsChemical physicsChemistryPolyethylenePhysicsMathematicsGeometry

Abstract

fetched live from OpenAlex

The physical properties of polymers are strongly affected by their molecular orientation. In this paper, we demonstrate for the first time a new and improved Raman spectroscopy method to characterize this key parameter. In recent years, Raman spectroscopy has emerged as an indispensable tool for this purpose, but its widespread use is still largely restricted by the experimental complexity and the limitations imposed by the standard quantification procedure, referred in this article as the depol constant (DC) method. We have very recently proposed and established theoretically a simplified quantification approach that is based on the most probable orientation distribution (MPD method). Herein, we demonstrate its experimental validity and its wide applicability by studying a series of samples from three highly dissimilar polymers (HDPE, PET, and PS), and covering the full possible orientation range. We show that the new MPD method overcomes the experimental and theoretical difficulties faced with the current DC method and that it leads to more accurate orientation values. We expect that this method will greatly extend the accessibility of Raman spectroscopy for molecular orientation studies of polymer systems.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.158
Threshold uncertainty score0.749

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.025
GPT teacher head0.390
Teacher spread0.365 · 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.

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

Citations76
Published2013
Admission routes1
Has abstractyes

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