Characterizing the thermal ablation of cells for silicon biosensors
Bibliographic record
Abstract
Being able to restore the surface of biosensors to their initial material-free state after immobilization of the biological target can facilitate biosensor development and reduce measurement costs. Thermal ablation has been recently proposed as a new method to conveniently accomplish this using high temperature heating with a hot plate. Key variables related to chip heating are characterized to optimize the effectiveness of thermal ablation. Testing was performed using Poly-L- Lysine-functionalized silicon-on-insulator chips and E. coli. An analysis based on image processing demonstrates that the maximum temperature reached by the chip and the length of time the chip is held at the maximum temperature significantly affect cell removal, while the heat rate at which the chip goes from room temperature to the maximum temperature does not affect cell removal. It was found that nearly all cells can be removed by heating the chip at 425°C for 10 s or by heating the chip at 375°C for 100 s.
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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 | 0.000 |
| 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".