Electric tweezers: Experimental study of positive dielectrophoresis-based positioning and orientation of a nanorod
Bibliographic record
Abstract
The manipulation of individual micrometer sized objects has been the focus of significant research efforts over the last few years. A previously proposed method for the arbitrary manipulation of nanoparticles is experimentally demonstrated. This method employs dielectrophoretic forces for the planar control of the motion and orientation of such nanoparticles between a set of microfabricated electrodes. Each electrode is approximated as a set of sources, namely, an unknown point charge and induced dipole. Imposing constraints on the electric field at the location of the particle and requiring self-consistency uniquely determine the sources. They can then be subsequently used to determine the set of electrode voltages that creates an electric field that will produce the prescribed orientation and force on the particle. The drag coefficients of a nanorod are experimentally determined by sequentially applying a constant force both parallel and perpendicular to its axis and observing a resulting motion. With the drag coefficients in hand, the velocity rather than force can be prescribed, and the rod is directed to move accurately at oblique angles to its orientation. The rod is in a constant state of unstable equilibrium and requires negative feedback to maintain a fixed position. The automation of such feedback is demonstrated, allowing a controlled travel of the nanostructures over complex paths.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".