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Record W2026482556 · doi:10.1021/bm8014126

Preparation of Functional Aptamer Films Using Layer-by-Layer Self-Assembly

2009· article· en· W2026482556 on OpenAlexaff
Yasir Sultan, Ryan Walsh, Carlos M. Monreal, Maria C. DeRosa

Bibliographic record

VenueBiomacromolecules · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsCarleton UniversityAgriculture and Agri-Food Canada
Fundersnot available
KeywordsAptamerQuartz crystal microbalanceLayer by layerPolyelectrolyteChemistryOligonucleotideDendrimerLayer (electronics)Self-assemblyNanotechnologyChemical engineeringMaterials scienceDNAPolymer chemistryBiochemistryOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

Advances in many aptamer-based applications will require a better understanding of how an aptamer's molecular recognition ability is affected by its incorporation into a suitable matrix. In this study, we investigated whether a model aptamer system, the sulforhodamine B aptamer, would retain its binding ability while embedded in a multilayer polyelectrolyte film. Thin films consisting of poly(diallyldimethylammonium chloride) as the polycation and both poly(sodium 4-styrene-sulfonate) and the aptamer as the polyanions were deposited by the layer-by-layer approach and were compared to films prepared using calf thymus DNA or a random single-stranded oligonucleotide. Data from UV-vis spectroscopy, quartz crystal microbalance studies, confocal microscopy, and time of flight secondary ion mass spectrometry confirm that the aptamer's recognition of its target is retained, with no loss of specificity and only a modest reduction of binding affinity, while it is incorporated within the thin film. These findings open up a raft of new opportunities for the development and application of aptamer-based functional thin films.

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 score0.002
Threshold uncertainty score0.686

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 head0.016
GPT teacher head0.307
Teacher spread0.291 · 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

Citations65
Published2009
Admission routes1
Has abstractyes

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