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Record W4306938356 · doi:10.3390/chemosensors10100435

Thiol-Based Probe Linker with Antifouling Properties for Aptasensor Development

2022· article· en· W4306938356 on OpenAlexafffund
Sandro Spagnolo, Katharina Davoudian, Soha Ahmadi, Edmund Chan, Tibor Hianik, Michael Thompson

Bibliographic record

VenueChemosensors · 2022
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoVedecká Grantová Agentúra MŠVVaŠ SR a SAVEuropean Commission
KeywordsQuartz crystal microbalanceBiofoulingAptamerContact angleBiosensorAnalyteChemistryLinkerThiolFoulingCyclic voltammetryCombinatorial chemistryChromatographyMaterials scienceOrganic chemistryElectrochemistryAdsorptionBiochemistry

Abstract

fetched live from OpenAlex

Surfaces with antifouling properties are critical for optimizing biosensors to improve the selectivity and specificity of analyte detection in complex biological samples. This work describes the four-step synthesis of 3-dithiothreitol propanoic acid (DTTCOOH), a new antifouling thiol linker that (a) significantly reduces fouling of raw human serum samples and (b) binds amino receptors via its terminal carboxylic acid group. DTTCOOH was successfully functionalized on quartz crystal microbalance (QCM) discs and used to anchor penicillin-binding aptamers. Relative to bare and coated (11-mercaptoundecanoic acid (MUA) and 1-undecanethiol (UDT)) QCM crystals, DTTCOOH’s antifouling improved by approximately 75–86%. Following aptamer/ethanolamine extension, the modified DTTCOOH layer reduced serum fouling by approximately 95–97% compared to bare and coated (MUA, UDT) crystals. QCM with dissipation (QCM-D) monitoring, contact goniometry, and cyclic voltammetry techniques were used to compare the DTTCOOH surfaces with quartz crystals functionalized with hydrophobic and hydrophilic molecules.

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 score0.063
Threshold uncertainty score0.624

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 studies0.0010.000
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 head0.046
GPT teacher head0.251
Teacher spread0.205 · 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

Citations12
Published2022
Admission routes2
Has abstractyes

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