Nanoelectronic Biomolecular Sensors Based on Graphene Field-Effect Transistors for Protein Biomarker Detection
Bibliographic record
Abstract
Field-effect transistor devices based on functionalized graphene (G-FETs) are a promising technology for biomolecular sensing applications due to the several advantages they present, including the label-free detection of biomolecules with direct electrical read-out, real-time detection and multiplexing capability. The development of highly selective and sensitive sensors for protein biomarkers is especially desirable to open novel technological avenues for the early detection and monitoring of biomarkers associated with cancer diseases. In this presentation, I’ll describe our recent progress on the design and development of a label-free immunosensor based on antibody-modified graphene field-effect transistors for protein biomarker detection. Specifically, monoclonal antibodies were selected to target a protein biomarker specific to MLL translocated acute myeloid leukemia and we tested approaches for their immobilization on graphene. We found that the antibodies can spontaneously adhere to the graphene surface but this adhesion is partially reversible under solution flow. In order to stabilize the immobilization of antibodies, we developed a protocol based on electrochemically-driven chemistry to form stable covalent anchor groups at the graphene surface which can then capture antibodies. We optimized the rate of formation of anchor groups at the surface in order to maximize the density of anchor groups while maintaining high electrical currents in the graphene. We were then able to record a specific electrical signature showing irreversible immobilization of antibodies on the graphene surface, thus allowing further optimization of target detection. Employing a combination of microfluidics and real-time electrical measurements, we then investigated the response of the sensors to non-specific interactions with graphene as well as their response to specific antibody-antigen interactions in phosphate buffer solution. Finally, the sensors response to different concentrations of target protein was characterized to assess their performance parameters.
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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 | 0.000 | 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".