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Record W3024430631 · doi:10.1149/ma2020-016649mtgabs

Characterizing the Interactions of DNA Oligomers with Graphene Field-Effect Transistors

2020· article· en· W3024430631 on OpenAlexaff
Madline Sauvage, Claudia M. Bazán, Amira Bencherif, Delphine Bouilly

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsGrapheneBiosensorMaterials scienceField-effect transistorNanotechnologyMonolayerBiomoleculeOptoelectronicsTransistorSubstrate (aquarium)Voltage

Abstract

fetched live from OpenAlex

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

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.247
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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