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Record W4285398646 · doi:10.1149/ma2022-019721mtgabs

The Electrostatic Gating of Carbon Nanotube Field-Effect Biosensors Characterized at the Molecular Scale Using Simulations

2022· article· en· W4285398646 on OpenAlexaff
Sébastien Côté, Delphine Bouilly, Normand Mousseau

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicNanopore and Nanochannel Transport Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsBiomoleculeCarbon nanotubeNanotubeBiosensorNanotechnologyMolecular dynamicsElectrostaticsChemistryChemical physicsMaterials scienceComputational chemistry

Abstract

fetched live from OpenAlex

Carbon nanotube field-effect biosensors (CNT-bioFETs) are ultraminiaturized devices that can be used to measure single-molecule kinetics of biomolecules on time scales going from a few microseconds to several minutes, as demonstrated for nucleic acid hybridization [1] and folding [2] as well as for enzyme function [3]. Experiments indicate that the sensitivity of CNT-bioFETs originates from the interplay between the nanotube’s conductance, which is monitored by the device, and the electrostatic potential generated by the biomolecule under investigation, which is localized on the nanotube [4,5]. The measured conductance exhibits characteristic transitions between two levels (or more) as a function of time, as the biomolecule folds or performs its function. Yet, the origin of this electrostatic gating of the carbon nanotube by a single biomolecule is not well understood at the molecular scale. To bridge this gap, we employ molecular dynamics (MD) and Hamiltonian replica exchange (HREX) simulations to unveil: (1) the interactions between the biomolecule and the nanotube to which it is attached in the device and (2) the electrostatic potential on the nanotube as the state of the biomolecule changes. We address these questions by considering three prototypical cases: the function of the Lysozyme protein, the hybridization of 10-nt DNA sequence and the folding of a DNA G-quadruplex, which were previously characterized using CNT-bioFETs [1-5]. Our simulations show that the lysozyme, the 10-nt DNA sequence and the DNA G-quadruplex interact differently with the nanotube to which they are attached. Consequently, the electrostatic potential (ESP) that they generate on the nanotube is very sensitive to the type and state of the biomolecule. When compared to experiment, the ESP distribution for the with-ligand and without-ligand states of the Lysozyme protein are in line with the measured two-level conductance by CNT-bioFETs. For the DNAs, however, the ESP distribution for their different states does not agree with the measured two-level conductance. Experiments imply that the DNA strand is not interacting with the nanotube, which is not what our simulations suggest. The reason for this apparent conflict could arise from the impact of the external electric field imposed by the gate electrode in CNT-bioFETs on highly charged systems such as DNAs, as supported by our recent simulations. The significance of this work is twofold. First, it contributes to a better understanding of the inner working of carbon nanotube field-effect biosensors, which is crucially needed to support the development of these promising devices in the lab. Second, it provides the structural ensemble of the biomolecules and their interactions with the nanotube in these devices, which can serve as a starting point for a finer characterization of their effect on the carbon nanotube’s conductance at the ab initio level. [1] S. Sorgenfrei et al. Nat Nanotechnol, 2011, 6, 126-132. [2] D. Bouilly et al. Nano Lett, 2016, 16, 4679-4685. [3] Y. Choi et al. Science, 2012, 335, 319-324. [4] S. Sorgenfrei et al. Nano Lett, 2011, 11, 3739-3743. [5] Y. Choi et al. Nano Lett, 2013, 13, 625-631.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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

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 head0.008
GPT teacher head0.219
Teacher spread0.212 · 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 designSimulation or modeling
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
Published2022
Admission routes1
Has abstractyes

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