(Invited) Surface Functionalization of Graphene Field-Effect Transistors for Biosensing Applications
Bibliographic record
Abstract
Graphene field-effect transistors (GFETs) present beneficial features for their application as biomolecular or chemical sensors. Due to their atomically-thin 2D dimensionality, their high electrical conductance is particularly sensitive to small changes in the distribution of charged species near the graphene surface. Taking advantage of this property, GFET sensors have been designed to report the detection or quantitation of various types of biologically-relevant molecular analytes such as nucleic acids, proteins, ions or small molecules. By analyzing data from published literature on GFET bioanalytical sensors, we have recently shown that the detection metrics of such sensors vary enormously between studies, and argued that this variance is mainly driven by disparities in the bio-recognition interface [1]. Indeed, the selectivity of GFET sensors must be engineered, typically by covering the graphene surface with biological molecules having a specific affinity for the chosen analyte (e.g. antibodies to capture the corresponding antigen, ssDNA to capture its complementary sequence). Yet, the coverage, orientation, stability and interactions between immobilized probes, blocking species and captured analytes are often not well known or controlled. In this presentation, I will discuss our efforts to understand and regulate the surface functionalization of graphene field-effect transistors. Using electrical conductance measurements and Raman spectroscopy, I will characterize the response of devices to both covalent chemistry, using aryldiazonium reagents, and non-covalent chemistry, using pyrene derivatives. I will describe our recent developments in controlling these functionalization routes, and compare their use for further bioconjugation with molecular probes for bioanalytical purposes. [1] A. Béraud, M. Sauvage, C. M. Bazán, M. Tie, A. Bencherif and D. Bouilly. Graphene Field-Effect Transistors as Bioanalytical Sensors: Design, Operation and Performance. Analyst 146, 403-428 (2021) https://doi.org/10.1039/D0AN01661F
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".