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Record W4214565968 · doi:10.1080/23746149.2022.2043185

Multi-responsive micro/nanogels for optical sensing

2022· article· en· W4214565968 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in Physics X · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsNanogelMaterials scienceNanotechnologySelf-healing hydrogelsNaked eyePolymerRational designColloidBiomoleculeChemical engineeringFluorescenceDrug deliveryPolymer chemistryOpticsComposite materialEngineering

Abstract

fetched live from OpenAlex

Micro/nanogels are unique materials that exhibit the properties of both colloids and hydrogels, i.e. being colloids they exhibit a large specific surface area, while they are hydrophilic and porous allowing them to swell to a great degree with water. Engineering micro/nanogels, through the rational design of various polymer compositions and/or optical structures, can enable them to respond to a myriad of stimuli, e.g. temperature, pH, biomolecules, CO2, light, and electricity. These multi-responsive micro/nanogels and their assemblies, are capable of recognizing and transducing analyte signals into changes in optical properties observable spectroscopically or via the naked eye, allowing their use as optical sensors. In this review, we have highlighted recent state-of-the-art examples of stimuli-responsive micro/nanogel-based systems for optical sensors.

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.482
Threshold uncertainty score0.529

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 head0.015
GPT teacher head0.282
Teacher spread0.267 · 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