Interfacing <scp>DNA</scp> and Aptamers with Gold Nanoparticles: From Fundamental Colloid and Interface Sciences to Biosensors
Why this work is in the frame
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Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it