Characterizing the Interactions of DNA Oligomers with Graphene Field-Effect Transistors
Bibliographic record
Abstract
Introduction: Graphene field-effect transistors (G-FETs) constitute an emerging platform for biosensing applications. Indeed, graphene is an ideal material for the detection of biomolecules: every atom of its monolayer structure is in contact with its environment, resulting in an electrical conductivity that is highly responsive to local electrostatic fluctuations from adjacent molecules. For genomic applications, most detection methods use DNA probe sequences bound to the graphene surface, in order to capture a specific target DNA sequence and detect the corresponding change in the electrical response of the sensor1 - 2. However, the effect of interactions between DNA and graphene on the electrical conductance is still not fully understood. Here, we investigate specifically the adsorption of short DNA oligomers on graphene field-effect transistors, in order to model and control the effect of such interactions in biosensing applications with G-FETs. Methods and Results: First, we fabricated G-FET sensors as follow3: Using photolithography techniques, an array of source and drain electrodes were patterned in gold on a Si wafer with a SiO2 insulator layer, as well as a common on-chip gate electrode in platinum. High-quality monolayer CVD-grown graphene was transferred onto the substrate and then etched to create 6 x 4 µm ribbons between each source-drain pair. Transfer curves (Isd vs. Vg) performed in saline buffer solution revealed a conductance minimum at the charge neutrality point of the graphene. Devices were then exposed to solutions of DNA, consisting in 22-single-stranded nucleotides (ssDNA) or double-stranded nucleotides (dsDNA) DNA oligomers diluted in 0,01X PBS buffer. Selected G-FET devices were exposed to different ssDNA concentrations during 15 min, followed by washing steps with 0,01X PBS. Electrical curves were recorded before, during and after each DNA exposure. In this presentation, we will present results showing that ssDNA exposure causes a left-shift of the charge neutrality point above a concentration threshold, and that this shift is proportional to ssDNA concentration. In addition, non-covalent adsorption of ssDNA on graphene appears to be reversible upon washing. Finally, we will discuss differences between the adsorption of dsDNA and ssDNA. Conclusion and Relevance: Our results suggest that unspecific DNA adsorption on graphene can lead to a G-FET response, which needs to be modeled, compensated and /or passivated in biosensing experiments, especially in order to achieve low detection limits for target sequences in complex biological media. References: 1. Hwang MT, Landon PB, Lee J, et al. Highly specific SNP detection using 2D graphene electronics and DNA strand displacement. Proc Natl Acad Sci. 2016;113(26):7088-7093. doi:10.1073/pnas.1603753113 2. Cai B, Wang S, Huang L, Ning Y, Zhang Z, Zhang G. Ultrasensitive Label-Free Detection of PNA À DNA Hybridization by Reduced Graphene Oxide Field-E ff ect Transistor. 2014;(3):2632-2638. doi:Doi 10.1021/Nn4063424 3. Bazan CM, Bencherif A, Sauvage M, Huliganga E, Borduas G, Bouilly D.Fabrication of Nanocarbon-Based Field-Effect Transistor Biosensors for Electronic Detection of DNA Sequences. ECS Trans. 2018;85(13):499-507. doi:10.1149/08513.0499ecst
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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 | 0.000 |
| 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".