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Record W2939769774 · doi:10.3389/fchem.2019.00198

Phosphorothioate DNA Mediated Sequence-Insensitive Etching and Ripening of Silver Nanoparticles

2019· article· en· W2939769774 on OpenAlexafffund
Shengqiang Hu, Po‐Jung Jimmy Huang, Jianxiu Wang, Juewen Liu

Bibliographic record

VenueFrontiers in Chemistry · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Waterloo
FundersChina Scholarship CouncilUniversity of WaterlooNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsDNASilver nanoparticleRipeningSequence (biology)NanoparticleEtching (microfabrication)ChemistryNanotechnologyMaterials scienceBiochemistryFood scienceLayer (electronics)

Abstract

fetched live from OpenAlex

Many DNA-functionalized nanomaterials and biosensors have been reported, but most have ignored the influence of DNA on the stability of nanoparticles. We observed that cytosine-rich DNA oligonucleotides can etch silver nanoparticles (AgNPs). In this work, we showed that phosphorothioate (PS)-modified DNA (PS-DNA) can etch AgNPs independently of DNA sequence, suggesting that the thio-modifications are playing the major role in etching. Compared to unmodified DNA (e.g., poly-cytosine DNA), the concentration of required PS DNA decreases sharply, and the reaction rate increases. Furthermore, etching by PS-DNA occurs quite independent of pH, which is also different from unmodified DNA. The PS-DNA mediated etching could also be controlled well by varying DNA length and conformation, and the number and location of PS modifications. With a higher activity of PS-DNA, the process of etching, ripening, and further etching was taken place sequentially. The etching ability is inhibited by forming duplex DNA and thus etching can be used to measure the concentration of complementary DNA.

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 score0.026
Threshold uncertainty score0.554

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 head0.006
GPT teacher head0.234
Teacher spread0.228 · 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

Citations6
Published2019
Admission routes2
Has abstractyes

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