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Record W2883430543 · doi:10.1002/anie.201806489

Self‐Assembled Functional DNA Superstructures as High‐Density and Versatile Recognition Elements for Printed Paper Sensors

2018· article· en· W2883430543 on OpenAlexafffund
Meng Liu, Qiang Zhang, Balamurali Kannan, Gianluigi A. Botton, Jie Yang, Leyla Soleymani, John D. Brennan, Yingfu Li

Bibliographic record

VenueAngewandte Chemie International Edition · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaCanada Foundation for Innovation
KeywordsDNADeoxyribozymePhysisorptionAptamerBiosensorNanotechnologyChemistryNucleic acidSelf-assemblyRolling circle replicationCombinatorial chemistryAdsorptionMaterials scienceBiochemistryOrganic chemistryPolymeraseMolecular biologyBiology

Abstract

fetched live from OpenAlex

Micrometer-sized functional nucleic acid (FNA) superstructures (denoted as 3D DNA) were examined as a unique class of biorecognition elements to produce highly functional bioactive paper surfaces. 3D DNA containing repeating sequences of either a DNA aptamer or DNAzyme was created from long-chain products of rolling circle amplification followed by salt aging. The resulting 3D DNA retained its original spherical shape upon inkjet printing and adhered strongly to the paper surface via physisorption. 3D DNA paper sensors showed resistance to degradation by nucleases, suppressed nonspecific protein adsorption, and provided a much higher surface density of functional DNA relative to monomeric FNAs, making such species ideally suited for development of paper-based 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)0.000
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 score0.014
Threshold uncertainty score0.550

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 head0.262
Teacher spread0.251 · 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

Citations70
Published2018
Admission routes2
Has abstractyes

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