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Record W3024225389 · doi:10.1149/ma2020-0110859mtgabs

Architectures of Graphene-Based Field-Effect Transistors for Single-Molecule Experiments

2020· article· en· W3024225389 on OpenAlexaff
Amira Bencherif, Monique Tie, Richard Martel, Delphine Bouilly

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsRegroupement Québécois sur les Matériaux de PointeUniversité de Montréal
Fundersnot available
KeywordsGrapheneNanotechnologyMaterials scienceField-effect transistorFabricationNanowireCarbon nanotubeChemical vapor depositionPhotolithographyTransistorWaferVoltageElectrical engineering

Abstract

fetched live from OpenAlex

With progress in the extreme miniaturisation of electronic components and the discovery of low-dimensional conductive materials, it is now possible to assemble field-effect transistors (FETs) that can incorporate single-molecule components as a channel or gate1,2. Such types of FETs have been recently used to detect and study various fundamental mechanisms at the single-molecule scale, among which the folding and unfolding of molecules, hybridization mechanisms, charge transport or chemical reactions1–5. In these experiments, devices were typically fabricated using architectures based on individual 1D materials, such as carbon nanotubes (CNTs) and silicon nanowires. The 1D topology facilitates the isolation of individual molecules in the circuit, but present drawbacks in scalability due to challenges in the growth, purification and/or assembling of such 1D materials into FET circuits. Here, we present new top-down approaches for the fabrication of single-molecule FETs, based on 2D graphene architectures. As CNTs, graphene is made of an hexagonal carbon lattice enabling excellent conductivity as well as carbon-based chemistry to anchor individual molecules, yet its 2D topology is more compatible with wafer-scale fabrication processes. First, we report the fabrication of large arrays of FETs based on graphene ribbons with controlled electrical properties. These arrays were built from high-quality large area graphene synthesized by chemical vapor deposition (CVD), followed by patterning steps using photolithography and plasma etching processes. Then, we report the design of two different architectures for single-molecule experiments: nanoconstrictions and nanogaps. Nanoconstrictions were achieved using electron-beam lithography (EBL), allowing to pattern high-resolution features (50nm) in the graphene channel. Nanogaps were obtained using the electroburning technique to open a gap of a few nanometers in the graphene channel6. We will present the design and fabrication process of these architectures, as well as their characterization using high-resolution microscopy (SEM/AFM) and transport measurements. Finally, we will discuss approached for the single-molecule functionalization for these architectures and their application in conductance-based single-molecule measurements. References 1. Vernick, S. et al. Electrostatic melting in a single-molecule field-effect transistor with applications in genomic identification. Nat. Commun. 8, 1–9 (2017). 2. Guo, X., Gorodetsky, A. A., Hone, J., Barton, J. K. & Nuckolls, C. Conductivity of a single DNA duplex bridging a carbon nanotube gap. Nat. Nanotechnol. 3, 163–167 (2008). 3. He, G., Li, J., Ci, H., Qi, C. & Guo, X. Direct Measurement of Single-Molecule DNA Hybridization Dynamics with Single-Base Resolution. Angew. Chemie - Int. Ed. 55, 9036–9040 (2016). 4. Bouilly, D. et al. Single-molecule reaction chemistry in patterned nanowells. Nano Lett. 16, 4679–4685 (2016). 5. Guo, X. Revealing the direct effect of individual intercalations on DNA conductance toward single-molecule electrical biodetection. J. Mater. Chem. B 3, 5150–5154 (2015). 6. Xu, Q. et al. Single Electron Transistor with Single Aromatic Ring Molecule Covalently Connected to Graphene Nanogaps. Nano Lett. 17, 5335–5341 (2017).

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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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.032
GPT teacher head0.294
Teacher spread0.261 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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