Mechanism of Cell Lysis in Microfluidic Channel With Integrated Nanocomposite Electrodes
Bibliographic record
Abstract
This paper reports on the characterization of an integrated micro-fluidic platform for controlled electrical lysis of biological cells and subsequent extraction of intracellular biomolecules. The proposed methodology is capable of high throughput electrical cell lysis facilitated by nano-composite coated electrodes. The nano-composites are synthesized using Carbon Nanotube and ZnO nanorod dispersion in polymer. Bacterial cells are used to demonstrate the lysis performance of these nanocomposite electrodes. Investigation of electrical lysis in the microchannel is carried out under different parameters, one with continuous DC application and the other under DC biased AC electric field. Lysis in DC field is dependent on optimal field strength and governed by the cell type. By introducing the AC electrical field, the electrokinetics is controlled to prevent cell clogging in the micro-channel and ensure uniform cell dispersion and lysis. Lysis mechanism is analyzed with time-resolved fluorescence imaging which reveal the time scale of electrical lysis and explain the dynamic behavior of GFP-expressing E. coli cells under the electric field induced by nanocomposite electrodes. The DNA and protein samples extracted after lysis are compared with those obtained from a conventional chemical lysis method by using a UV–Visible spectroscopy and fluorimetry. The paper also focuses on the mechanistic understanding of the nano-composite coating material and the film thickness on the leakage charge densities which lead to differential lysis efficiency.
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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 | 0.001 |
| Open science | 0.001 | 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".