Nanocarbon-Based Field-Effect Transistor Biosensors (bioFETs) for Real-Time Detection of DNA Sequences
Bibliographic record
Abstract
Field-effect transistor devices based on functionalized nanocarbon materials (2-D graphene thin layers and 1-D carbon nanotubes) are a promising technology for biomolecular sensing applications. In particular, this type of devices allows for label-free detection of DNA hybridization with the advantage of an easy and direct electrical readout in comparison with most routine techniques. In this work, we report on the design and fabrication of nanocarbon-based bioFETs for DNA hybridization detection. Employing a combination of microfluidics and real-time electrical measurements, we investigated the response of bioFETs to single-stranded DNA (ssDNA) probe tethering and hybridization with complementary target ssDNA in saline buffer solution. The bioFETs were assembled from individual carbon nanotubes or graphene nanoribbons connected by pairs of metallic electrodes using photolithography techniques. Transfer curves of bioFETs in saline buffer were measured using an immersed pseudo-reference electrode. We obtained transfer curves showing current modulation with high OFF-current at the charge-neutrality point. Probe and target ssDNA were chosen as 22-nucleotide-long complementary sequences. Probe tethering on nanocarbon was obtained using diazonium chemistry followed by EDC/NHS coupling of an amine moiety placed at the extremity of the DNA probe [1]. Transfer characteristics were measured before/after probe and target injection. Moreover, to further characterize the bioFETs response, probe immobilization and probe-target hybridization were recorded in real-time using electrical readout. The development of highly selective and ultra-sensitive sensors for DNA will open novel technological avenues to improve the detection of genetic biomarkers for various diseases. [1] Bouilly, D. et al. Nano Letters 16, 4679–4685 (2016)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".