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Record W4391607925 · doi:10.1002/adsr.202300170

Anti‐Fouling Polymer or Peptide‐Modified Electrochemical Biosensors for Improved Biosensing in Complex Media

2024· article· en· W4391607925 on OpenAlexafffund
Survanshu Saxena, Payel Sen, Leyla Soleymani, Todd Hoare

Bibliographic record

VenueAdvanced Sensor Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsBiosensorFoulingBiofoulingAnalyteContext (archaeology)Materials scienceNanotechnologyBiochemical engineeringChemistryChromatographyMembraneEngineeringBiologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Electrochemical biosensing represents a highly effective technology for detecting disease biomarkers given its high sensitivity, low and clinically relevant limit of detection, and cost effectiveness. However, in complex media such as urine, blood, sweat or saliva, biosensing performance can be significantly impacted by electrode biofouling by proteins, cells, lipids, and other matrix components. Such biofouling leads to reduced signal from the target analyte coupled with an elevated background signal, resulting in poor signal‐to‐noise ratios (SNRs), reduced sensitivity, and lower specificity. This comprehensive review describes the design of anti‐fouling polymers and peptides as a potential solution to prevent or suppress electrochemical biosensor fouling. Various anti‐fouling polymers and peptides developed for improved biosensing in complex media are summarized in the context of their mechanism(s) of anti‐fouling, methods of deposition, and practical applications. Recent advances and persistent challenges in the field are also reviewed to provide perspectives on new directions toward enhancing anti‐fouling in electrochemical biosensors.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.066
GPT teacher head0.402
Teacher spread0.336 · 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.

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

Citations55
Published2024
Admission routes2
Has abstractyes

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