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Record W3159909318 · doi:10.1039/d1cp01399h

Utility of far-field effects from tip-assisted Raman spectroscopy for the detection of a monolayer of diblock copolymer reverse micelles for nanolithography

2021· article· en· W3159909318 on OpenAlexafffund
Ramis Arbi, Lok Shu Hui, Maria Dittrich, Ayse Turak

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2021
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsThe Scarborough HospitalToronto Public HealthUniversity of TorontoMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and Science
KeywordsCopolymerRaman spectroscopyMonolayerMicelleNanolithographyMaterials scienceRaman scatteringNanotechnologyField (mathematics)ChemistryOpticsOrganic chemistryPolymerPhysicsFabricationComposite material

Abstract

fetched live from OpenAlex

A modified set-up for Raman spectroscopy is proposed to utilize an AFM probe in a regime beyond the dependence on near field optics. Possible mechanisms for the observed enhancement have been explored through comparisons to spectra from other enhanced Raman techniques, including surface enhanced Raman, interference enhanced Raman and polarized Raman spectroscopies. The effects of polarization, focusing and interference are heightened when near field effects are diminished, giving rise to spectral enhancement. This technique allows for the characterization of a sub-20 nm monolayer of polystyrene-block-poly(2 vinyl pyridine) reverse micelles and paves the way for a promising method of non-destructive analysis of large self-assembled arrays of colloids.

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 score0.002

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)0.0010.000

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 head0.014
GPT teacher head0.252
Teacher spread0.238 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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