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Record W2774937919 · doi:10.1021/acs.nanolett.7b04877

Molecular Rotors for Universal Quantitation of Nanoscale Hydrophobic Interfaces in Microplate Format

2017· article· en· W2774937919 on OpenAlexaff
Paul W. Bisso, Michelle Tai, Hari Katepalli, Nicolas Bertrand, Daniel Blankschtein, Róbert Langer

Bibliographic record

VenueNano Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicCharacterization and Applications of Magnetic Nanoparticles
Canadian institutionsUniversité Laval
FundersDivision of Graduate EducationNational Institute of Biomedical Imaging and BioengineeringNational Cancer Institute
KeywordsNanoscopic scaleNanotechnologyMaterials scienceChemistryAnalytical Chemistry (journal)Chromatography

Abstract

fetched live from OpenAlex

Hydrophobic self-assembly pairs diverse chemical precursors and simple formulation processes to access a vast array of functional colloids. Exploration of this design space, however, is stymied by lack of broadly general, high-throughput colloid characterization tools. Here, we show that a narrow structural subset of fluorescent, zwitterionic molecular rotors, dialkylaminostilbazolium sulfonates [DASS] with intermediate-length alkyl tails, fills this major analytical void by quantitatively sensing hydrophobic interfaces in microplate format. DASS dyes supersede existing interfacial probes by avoiding off-target fluorogenic interactions and dye aggregation while preserving hydrophobic partitioning strength. To illustrate the generality of this approach, we demonstrate (i) a microplate-based technique for measuring mass concentration of small (20-200 nm), dilute (submicrogram sensitivity) drug delivery nanoparticles; (ii) elimination of particle size, surfactant chemistry, and throughput constraints on quantifying the complex surfactant/metal oxide adsorption isotherms critical for environmental remediation and enhanced oil recovery; and (iii) more reliable self-assembly onset quantitation for chemically and structurally distinct amphiphiles. These methods could streamline the development of nanotechnologies for a broad range of applications.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.294

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 head0.008
GPT teacher head0.224
Teacher spread0.216 · 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
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
Published2017
Admission routes1
Has abstractyes

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