Dielectrophoresis of gold nanoparticles conjugated to DNA origami structures
Bibliographic record
Abstract
DNA nanostructures are promising construction materials to bridge the gap between self-assembly of functional molecules and conventional top-down fabrication methods in nanotechnology. Their positioning onto specific locations of a microstructured substrate is an important task towards this aim. Here we study manipulation and positioning of pristine and of gold nanoparticle-conjugated tubular DNA origami structures using ac dielectrophoresis. The dielectrophoretic behavior was investigated employing fluorescence microscopy. For the pristine origami, a significant dielectrophoretic response was found to take place in the megahertz range, whereas, due to the higher polarizability of the metallic nanoparticles, the nanoparticle/DNA hybrid structures required a lower electrical field strength and frequency for a comparable trapping at the edges of the electrode structure. The nanoparticle conjugation additionally resulted in a remarkable alteration of the DNA structure arrangement. The growth of linear, chain-like structures in between electrodes at applied frequencies in the megahertz range was observed. The long-range chain formation is caused by a local, gold nanoparticle-induced field concentration along the DNA nanostructures, which in turn, creates dielectrophoretic forces that enable the observed self-alignment of the hybrid structures.
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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) | 0.000 | 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 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".