(Invited) Biomolecular Sensors Based on Nanocarbon Field-Effect Transistors: Advances in Design, Fabrication and Characterization
Bibliographic record
Abstract
Nanoscale field-effect transistors (FETs) embedded in microfluidics form a promising technology as compact and portable lab-on-a-chip biosensors for applications in biology and medicine. Such sensors can be designed to monitor a variety of biochemical mechanisms, at the ensemble or single-molecule scale, through fluctuations in the electrical conductance of the circuit. In particular, nanocarbon materials, i.e. carbon nanotubes and graphene, are materials of choice for FET biosensors, due to their high electrical conductance, the sensitivity of their electronic properties to the surrounding environment, and the versatility of their carbon-based surface chemistry. In this presentation, I will report on recent developments and key considerations in the design, fabrication and characterization of such nanocarbon-based biomolecular FET sensors. In particular, I will discuss approaches for chemical functionalization and structural patterning of nanocarbon materials to optimize their coupling with molecules. I will also describe hardware and software developments for the analysis of single-molecule measurements in hybrid biomolecule-nanocarbon FET devices.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.014 |
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".