Optimizing electroporation assisted silver nanoparticle delivery into living C666 cells for surface-enhanced Raman spectroscopy
Bibliographic record
Abstract
Electroporation assisted metallic nanoparticle delivery has been shown by our previous work to significantly reduce the time of sample preparation for surface-enhanced Raman spectroscopy (SERS) measurements of biological cells. In this paper, we report our experimental work to optimize the electroporation parameters, including adjustment of the pulse pattern, operation temperature, and electroporation buffer, for fastest delivery of silver nanoparticles into living C666 cells (a human nasopharyngeal carcinoma cell line). The delivery efficiency was evaluated by the integrated intensity of whole cell SERS spectrum. Our work concluded that the silver nanoparticle delivery rate is best under the electroporation condition of using 4 consecutive 350 V (875 V/cm) rectangular electric pulses of 1, 10, 10 and 1 ms durations, respectively. Low temperature (0–4°C) is necessary for keeping cell viability during the electroporation process and it also improves the delivery efficiency of silver nanoparticles. The serum in the buffer has no obvious effect on the delivery 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".