Working towards a sample preparation device with carbon nanotubes
Bibliographic record
Abstract
Advances in microfabrication have introduced new possibilities for automated, high-throughput biomedical investigations and analyses. Physical effects such as dielectrophoresis and electroporation can be used to manipulate particles in solution to coordinate a sequence of bioanalytical processing steps. Dielectrophoresis is accomplished with non-uniform electric fields that can polarize particles in suspension and exert static or translational forces. Electroporation is accomplished with high-strength electric fields that can create pores on the plasma membranes of cells. Membrane breakdown under high voltage is associated with cell death and a dispersal of cell contents including nucleic acids and protein. This paper presents summaries of multiple experiments in both dielectrophoresis and electroporation. In the electroporation experiments, carbon nanotubes were used to enhance electric field strengths with the goal of reducing the voltage requirements for portable lab-on-a-chip devices with strict power limitations. The concept is to create a sample preparation device which is capable of separating cells into multiple chambers for cell lysis by carbon nanotubes and releasing their DNA for further analysis.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".