Immobilization of Protein Probes on Graphene Field-Effect Transistors for Biomolecular Sensing
Bibliographic record
Abstract
The development of biosensor arrays able to detect specific protein interactions is highly needed to precisely dissect fundamental biological systems and to broaden the scope of biomarker detection, especially to refine diagnostics of subtyped diseases such as cancer. Graphene-based field-effect transistor sensors (GFETs) are a promising emerging technology for such biomolecular sensing applications: their atomically-thin surface provides label-free sensitivity to biomolecules via direct electrostatic interactions, and their small footprint enables compact, rapid and parallelized assays. The selectivity of GFET sensors, however, requires functionalization of the graphene surface with a layer of bio-recognition species (“probe molecules”). In this presentation, we will describe our recent progress in immobilizing proteins as probe molecules on graphene field-effect transistors, specifically (1) monoclonal antibodies against a biomarker specific to MLL-translocated acute myeloid leukemia and (2) small Ras GTPase proteins regulated by various effector proteins. First, we will describe our GFET sensor design, based on on-chip arrays of GFETs made from CVD-grown graphene, mounted with a multi-channel delivery flow-cell enabling parallel assays on sub-ensembles of sensors. We will then present our investigation of protein immobilization on graphene. We found that antibodies and small proteins can spontaneously adhere to the graphene surface, but this adhesion is partially reversible under solution flow. To create stable anchor groups at the graphene surface which can then capture the protein probes, we propose to use covalent chemistry on the graphene surface. In particular, we developed a protocol based on electrochemically-driven aryldiazonium chemistry to increase the rate of formation and the density of anchor groups on the graphene surface. Using time-resolved electrical measurements, we observed a specific electrical signal associated with the irreversible immobilization of protein probes on the graphene surface. Finally, we will discuss the use of such protein-functionalized GFETs for the detection of specific probe-target interactions, for applications in fundamental biophysics and cancer diagnostics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".