The Electrostatic Gating of Carbon Nanotube Field-Effect Biosensors Characterized at the Molecular Scale Using Simulations
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».