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Record W2021347663 · doi:10.1021/ac101164n

Nanostructuring of Sensors Determines the Efficiency of Biomolecular Capture

2010· letter· en· W2021347663 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnalytical Chemistry · 2010
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchOntario Centres of ExcellenceGenome Canada
KeywordsChemistryNucleic acidNanotechnologyBiosensorMonolayerNanostructureNucleic acid detectionMoleculeSmall moleculeBiochemistryMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

The detection of biologically important molecules such as proteins and nucleic acids is of high and growing interest in the diagnosis of diseases from cancer to infectious and cardiovascular disease. The use of nanostructures to enhance sensitivity in biomolecular detection has now been reported in a broad range of assays. Here we provide direct evidence that the display of nucleic acid probe molecules on a nanostructured surface dramatically enhances hybridization efficiency compared to the case of the same probe molecules tethered on a smoother surface. Another factor expected to influence hybridization is the density of the probe monolayer. Remarkably, we find herein that the effect of nanostructuring dominates over probe density: the benefits of a high degree of nanostructuring can more than overcome the influence of dense probe packing. The results obtained herein give guidance to the development of high-performance biosensors for medical and environmental 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.

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 categoriesResearch integrity
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 score0.030
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.001
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 head0.006
GPT teacher head0.248
Teacher spread0.242 · 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