Separation of nanoparticles by a nano-orifice based DC-dielectrophoresis method in a pressure-driven flow
Bibliographic record
Abstract
A novel DC-dielectrophoresis (DEP) method employing a pressure-driven flow for the continuous separation of micro/nano-particles is presented in this paper. To generate the DEP force, a small voltage difference is applied to produce a non-uniformity of the electric field across a microchannel via a larger orifice of several hundred microns on one side of the channel wall and a smaller orifice of several hundred nanometers on the opposite channel wall. The particles experience a DEP force when they move with the flow through the vicinity of the small orifice, where the strongest electrical field gradient exists. Experiments were conducted to demonstrate the separation of 1 μm and 3 μm polystyrene particles by size by adjusting the applied electrical potentials. In order to separate smaller nanoparticles, the electrical conductivity of the suspending solution is adjusted so that the polystyrene nanoparticles of a given size experience positive DEP while the polystyrene nanoparticles of another size experience negative DEP. Using this method, the separation of 51 nm and 140 nm nanoparticles and the separation of 140 nm and 500 nm nanoparticles were demonstrated. In comparison with the microfluidic DC-DEP methods reported in the literature which utilize hurdles or obstacles to induce the non-uniformity of an electric field, a pair of asymmetrical orifices on the channel side walls is used in this method to generate a strong electrical field gradient and has advantages such as capability of separating nanoparticles, and locally applied lower electrical voltages to minimize the Joule heating effect.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".