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Record W4399133749 · doi:10.1002/cjoc.202400113

Interfacing <scp>DNA</scp> and Aptamers with Gold Nanoparticles: From Fundamental Colloid and Interface Sciences to Biosensors

2024· article· en· W4399133749 on OpenAlex
Yuzhe Ding, Juewen Liu

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

VenueChinese Journal of Chemistry · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsInterfacingChemistryAptamerColloidal goldNanotechnologyBiosensorInterface (matter)NanoparticleColloidMolecular biologyBiochemistryOrganic chemistryPulmonary surfactantComputer science

Abstract

fetched live from OpenAlex

Comprehensive Summary Interfacing DNA oligonucleotides and DNA aptamers with gold nanoparticles has generated numerous functional hybrid materials for sensing, self‐assembly and drug delivery applications. Our lab has been working in this area for 15 years. In this article, the current understanding of the adsorption of DNA to gold nanoparticles is summarized, and related applications in bioconjugation of DNA to gold surface is described. In addition, problems of using gold nanoparticles to signaling aptamer binding are discussed. Finally, re‐selection of aptamers for previously reported targets using the library‐immobilization method is reviewed. What is the most favorite and original chemistry developed in your research group? My most favorite and original work is the study of biointerface chemistry between DNA oligonucleotides and gold nanoparticles enabling rapid DNA bioconjugation by lowering the pH and freezing. How do you get into this specific field? Could you please share some experiences with our readers? My PhD training was focused on catalytic DNA for the detection of metal ions, when I used gold nanoparticles to signal the reactions catalyzed by DNA. When I started my independent career in the University of Waterloo in 2009, I realized that there were many fundamental issues regarding gold nanoparticles and DNA to be studied. My first teaching assignment was a course named ‘Surfaces and Interfaces’. By teaching this course, I learned a lot of surface science concepts that were later used in my research. That was the starting point for me to set up my own research program in this area. What is the most important personality for scientific research? Curiosity, careful observation, critical thinking, and keep trying. How do you keep balance between research and family? Research and personal life don’t have to be always in conflict. New ideas may spark when I am in a relaxed family environment. What are your favorite journals? Journal of the American Chemical Society , Angewandte Chemie International Edition , Nucleic Acids Research , Analytical Chemistry, Langmuir . What are your hobbies? Running; traveling; watching movies.

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 score0.005
Threshold uncertainty score0.470

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 head0.267
Teacher spread0.260 · 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