Field enhancement in microfluidic semiconductor nanowire array
Bibliographic record
Abstract
Nano-material integrated microfluidic platforms are increasingly being considered to accelerate biological sample preparation and molecular diagnostics. A major challenge in this context is the generation of high electric fields for electroporation of cell membranes. In this paper, we have studied a novel mechanism of generating a high electric field in the microfluidic channels by using an array of semiconductor nanowires. When an electrostatic field is applied across a semiconductor nanowire array, the electric field is localized near the nanowires and the field strength is higher than what was reported previously with various other micro-geometries. Nanowires made of ZnO, Si, and Si–SiO2 and their orientation and array spacing are considered design parameters. It is observed that for a given ratio of the spacing between nanowires to the diameter, the electric field enhancement near the edges of ZnO nanowires is nearly 30 times higher compared to Si or Si–SiO2 nanowire arrays. This enhancement is a combined effect of the unique geometry with a pointed tip with a hexagonal cross section, the piezoelectric and the spontaneous polarization in the ZnO nanowires, and the electro-kinetics of the interface fluid. Considering the field localization phenomena, the trajectories of E. coli cells in the channel are analyzed. For a given inter-nanowire spacing and an applied electric field, the channels with ZnO nanowire arrays have a greater probability of cell lysis in comparison to Si-based nanowire arrays. Detailed correlations between the cell lysis probability with the inter-nanowire spacing and the applied electric field are reported.
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How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".