Characterizing the Non-Covalent Binding of a Pyrene-Derived Linker for DNA Immobilization on Graphene Field-Effect Transistors
Bibliographic record
Abstract
Graphene field-effect transistors (G-FETs) constitute an emerging platform for biosensing applications. Most genomic applications with G-FETs use single-stranded DNA (ssDNA) as probes in order to capture a specific target DNA sequence and to detect the corresponding change in the electrical response of the sensor1. Controlling the distribution of DNA probes on the graphene surface is crucial to the sensitivity, selectivity and reproducibility of the sensors1. The most popular immobilization approach uses a non-covalent linker molecule named 1-pyrenebutanoic acid succinimidyl ester (PBASE)1. This bifunctional molecule binds to the graphene surface through non-covalent π-π interactions via its aromatic pyrene group, and on the other end, its succinimidyl ester group can form a covalent bond with amine groups added to the terminal end of ssDNA probes. However, the adsorption process of PBASE molecules is not well controlled, which represents a limitation in the optimization of G-FET biosensors. Here, we present an investigation of the kinetics of the non-covalent adsorption of PBASE on graphene, in order to control the density of ssDNA probes for biosensing applications with G-FETs. We fabricated G-FET sensor arrays using CVD-grown graphene and photolithography techniques, as described previously2. First, we investigated the effect of incubation time on PBASE coverage on graphene, using electrical curves as well as Raman hyperspectral imaging (RIMA). We report a significative and reproducible electrical signature for the PBASE compared to the control without PBASE. We find that this electrical signature appears and saturates quickly compared to the timescales usually reported in the literature. Corroborating results were obtained with RIMA spectroscopy, showing a rapid response of the graphene modes following PBASE incubation. Next, we studied the effect of PBASE accumulation on the assembly of ssDNA probes. We will discuss the combined effect of PBASE accumulation and screening effects in saline buffer on the electrical signature of ssDNA probes. Our results will enable a better control on the non-covalent functionalization of graphene with PBASE for the assembly of various biomolecular probes for biosensing applications. 1. Béraud A, Sauvage M, Bazan CM, Tie M, Bencherif A, Bouilly D. Graphene field-effect transistors as bioanalytical sensors: design, operation and performance. Analyst. 2021;(146):403-428. 2.Bazan CM, Béraud A, Nguyen M, Bencherif A, Martel R and Bouilly D. Dynamic Gate controlled of Aryldiazonium chemistry on Graphene field-effect transistors. Nano Lett. 2022;22(7):2635-2642.
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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 | 0.001 |
| 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".