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Record W3168379178 · doi:10.1021/jacs.1c04115

Complex Liquid Crystal Emulsions for Biosensing

2021· article· en· W3168379178 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of the American Chemical Society · 2021
Typearticle
Languageen
FieldMaterials Science
TopicLiquid Crystal Research Advancements
Canadian institutionsnot available
FundersOffice of Naval ResearchNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsChemistryLiquid crystalBiosensorDopantReflection (computer programming)NanotechnologyMoleculeChemical engineeringOrganic chemistryOptoelectronicsDopingMaterials science

Abstract

fetched live from OpenAlex

Herein we describe a highly responsive optical biosensor based on dynamic complex liquid crystal (LC) emulsions. These emulsions are simple to prepare and consist of immiscible chiral nematic liquid crystals (N*) and fluorocarbon oils. In this work, we exploit the N* selective reflection to build a new sensing paradigm. Our detection strategy is based on changes in the LC/water interfacial activity of boronic acid polymeric surfactants caused by reversible interactions with IgG antibodies at the LC interface. Such biomolecular recognition events can vary the pitch length of the N* organization due to the presence of binaphthyl units in the polymeric structure, which are known to be powerful chiral dopants. We demonstrate that these interface-triggered reflection changes can be used as an effective optical read-out for the detection of the foodborne pathogen Salmonella.

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.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.037
GPT teacher head0.335
Teacher spread0.298 · 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