Architectures of Graphene-Based Field-Effect Transistors for Single-Molecule Experiments
Bibliographic record
Abstract
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).
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 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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".