Optimizing the Toehold-Mediated Strand Displacement\nReaction on the Nanoparticle Surface by Altering the\nSurface DNA Density for the Design of a microRNA NanoOptical Sensor
Bibliographic record
Abstract
We present a method for modulating the kinetics and thermodynamic properties of aggregation and disassembly processes of DNA-functionalized nanoparticles. Specifically, we examine factors influencing the toehold strand-displacement reaction on nanoparticle surfaces. Gold nanoparticles were functionalized with oligonucleotide sequences with varying surface density by incorporating diluent DNA strands. The hybridization of DNA yields aggregates which then disassemble via a strand-displacement reaction by the target sequence. Localized surface plasmon resonance of gold nanoparticles and fluorescently tagged DNA strands were employed to gain an understanding of the aggregation and disassembly steps. The surface density of DNA impacts the aggregation kinetics, the melting temperature and the target-induced disassembly of these nanoaggregates. It does so by modulating the cooperativity and attinebility of the oligonucleotides, the electrostatic repulsion between the nanoparticles and the accessibility of the linkers to the target nucleic acid. A dramatic decrease in the initiation time and increase in the rate of disassembly are achieved by optimizing the surface density. Our work provides insight into the strand-displacement reaction on nanoparticle surfaces that underpins various sensing and DNA-driven nanomachine applications. This fundamental understanding allowed the design of a label-free, low cost and miniaturized biosensing platform based on the disassembly of core-satellite nanoassemblies. We successfully manipulate the system for the rapid and selective detection of a nucleic acid biomarker microRNA-210, enabling diverse biological applicability
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".