MétaCan
Menu
Back to cohort
Record W2099360393 · doi:10.1002/chem.201500949

Patterned Paper Sensors Printed with Long‐Chain DNA Aptamers

2015· article· en· W2099360393 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

VenueChemistry - A European Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsAptamer3d printedDNANanotechnologyComputational biologyComputer scienceEngineeringBiologyMaterials scienceGeneticsBiomedical engineering

Abstract

fetched live from OpenAlex

There is growing interest in developing printable paper sensors to enable rapid testing of analytes for environmental, food safety, and clinical applications. A major challenge is to find suitable bioinks that are amenable to high-speed printing and remain functional after printing. We report on a simple and effective approach wherein an aqueous ink composed of megadalton-sized tandem repeating structure-switching DNA aptamers (concatemeric aptamers) is used to rapidly create patterned paper sensors on filter paper by inkjet printing. These concatemeric aptamer reporters remain immobilized at the point of printing through strong adsorption but retain sufficient segmental mobility to undergo structure switching and fluorescence signaling to provide both qualitative and quantitative detection of small molecules and protein targets. The convenience of inkjet printing allows for the patterning of internally referenced sensors with multiplexed detection, and provides a generic platform for on-demand printing of sensors even in remote locations.

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 score0.444
Threshold uncertainty score0.564

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 head0.011
GPT teacher head0.181
Teacher spread0.169 · 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