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Record W4401833666 · doi:10.1002/ange.202410353

Rapid Thermal Drying Synthesis of Nonthiolated Spherical Nucleic Acids with Stability Rivaling Thiolated DNA

2024· article· en· W4401833666 on OpenAlexafffund
Xin Wang, Zhansen Yang, Zihe Li, Kunlun Huang, Nan Cheng, Juewen Liu

Bibliographic record

VenueAngewandte Chemie · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsUniversity of Waterloo
FundersKey Technologies Research and Development ProgramNatural Sciences and Engineering Research Council of CanadaNational Key Research and Development Program of ChinaChina Scholarship CouncilUniversity of Waterloo
KeywordsNucleic acidThermal stabilityDNAChemistryChemical engineeringCombinatorial chemistryNanotechnologyMaterials scienceOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

Abstract Attaching DNA oligonucleotides to gold nanoparticles (AuNPs) to prepare spherical nucleic acids (SNAs) has offered tremendous insights into surface chemistry with resulting bioconjugates serving as critical reagents in biosensors and nanotechnology. While thiolated DNA is generally required to achieve stable conjugates, we herein communicate that using a thermal drying method, a high DNA density and excellent SNA stability was achieved using nonthiolated DNA, rivaling the performance of thiolated DNA such as surviving 1 M NaCl, 2 month stability in 0.3 M NaCl and working in 50 % serum. A poly‐adenine block with as few as two consecutive terminal adenine bases is sufficient for anchoring on AuNPs. By side‐by‐side comparison with the salt‐aging method, the conjugation mechanism was attributed to competitive adenine adsorption at high temperature along with an extremely high DNA concentration upon drying. Bioanalytical applications of nonthiolated SNAs were validated in both solution and paper‐based sensor platforms, facilitating cost‐effective applications for SNAs.

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 score0.004
Threshold uncertainty score0.749

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 head0.012
GPT teacher head0.223
Teacher spread0.211 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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