Novel Field-Effect Transistor Sensor for DNA Storage Monitoring
Bibliographic record
Abstract
This article presents a novel open gate junction field-effect transistor (OG-JFET)-based sensor that can be used for various life science applications including deoxyribonucleic acid (DNA) storage monitoring. We put forward the design, modeling, implementation, and characterization of OG-JFET sensor using a foundry process through CMC Microsystems. We also demonstrate and discuss the functionality and applicability of the proposed sensor for monitoring DNA samples suitable for DNA storage applications. Synthetic storage has emerged as an intriguing data storage solution with high density and long-term preservation potential. Most common modalities include the conversion of digital data into synthesized nucleotides, the physical storage of DNA materials, and reading out the data via sequencing and other computational processes. Among these, this article tackles the challenge of physical storage monitoring of DNA materials by developing a sensor for measurement of DNA samples in dry conditions. The proposed sensor reveals a linear response of sensor toward DNA concentration in ultra-pure water. Across a 0.7 mm2sensing area, a DNA mass concentration from approximately 100–400 ng/$\mu \text{L}$has been detected using OG-JFET demonstrating a sensitivity of 30$\mu \text{A}$/(ng/$\mu \text{L}$). These performance quantities imply a promising role for OG-JFETs in emerging biotechnology applications including DNA storage monitoring.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".