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Record W4401068195 · doi:10.1021/acssensors.4c00861

Self-Assembled Monolayer Transporters Enable Reagentless Analysis of Small Molecule Analytes

2024· article· en· W4401068195 on OpenAlex
Connor D. Flynn, Kimberly T. Riordan, Tiana L. Young, Dingran Chang, Zhenwei Wu, Scott E. Isaacson, Hanie Yousefi, Jagotamoy Das, Shana O. Kelley

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

VenueACS Sensors · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Toronto
FundersChan Zuckerberg InitiativeNorthwestern University
KeywordsMonolayerSmall moleculeAnalyteNanotechnologyChemistryBiosensorElectrochemistryMoleculeElectrodeMaterials scienceChromatographyOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

The detection of small molecules beyond glucose remains an ongoing challenge in the field of biomolecular sensing owing to their small size, diverse structures, and lack of alternative non-enzymatic sensing methods. Here, we present a new reagentless electrochemical approach for small molecule detection that involves directed movement of electroactive analytes through a self-assembled monolayer to an electrode surface. Using this method, we demonstrate detection of several physiologically relevant small molecules as well as the capacity for the system to operate in several biological fluids. We anticipate that this mechanism will further improve our capacity for small molecule measurement and provide a new generalizable monolayer-based technique for electrochemical assessment of various electroactive analytes.

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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.257
Teacher spread0.247 · 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