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Record W4386867039 · doi:10.1149/ma2023-0191158mtgabs

(Invited) Predicting the Response of Carbon Nanotube Field-Effect Biosensors Using Simulations

2023· article· en· W4386867039 on OpenAlexaff
Sébastien Côté, Rebecca E. A. Gwyther, Chang‐Seuk Lee, Delphine Bouilly, Matteo Palma

Bibliographic record

VenueECS Meeting Abstracts · 2023
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversité de MontréalCegep de Saint Jerome
Fundersnot available
KeywordsCarbon nanotubeBiomoleculeBiosensorNanotubeAnalyteNanotechnologyMaterials scienceMoleculeChemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Objectives Carbon nanotube field-effect biosensors (bioCNTFETs) are a promising platform to detect biomolecular analytes because they are highly sensitive, specific, low-cost, portable, and integrable into arrays for multiplexed sensing [1]. Their sensitivity originates from carbon nanotubes having electrical properties that are strongly dependent on nearby biomolecules. As a result, the biosensor detects an electrical current through the network of nanotubes that depends on the concentration of analyte. To achieve specificity, carbon nanotubes need to be functionalized with a probe molecule that binds to a specific target analyte. However, it has been demonstrated that the response of bioCNTFETs strongly depends on the attachment site on the probe molecule to the carbon nanotube [2], and this variability is impairing the development of bioCNTFETs. To address this challenge, we are developing an integrated approach combining simulation and experiment: simulations are used to predict which attachment sites on the binding protein are expected to generate the greatest response, then devices are fabricated using phenyl azide photochemistry to precisely attach the binding protein via these sites on the nanotubes. To validate our approach, we characterized a bioCNTFET covalently functionalized with the beta-lactamase inhibitor protein II (BLIP-II) to detect beta-lactamases such as TEM-1 and KPC-2 that are associated with antimicrobial resistance. Results Our simulation protocol is based on molecular dynamics to sample the biomolecule-nanotube interactions and then on Poisson-Boltzmann calculations to estimate the electrostatic potential generated on the nanotube’s surface by the conformations of the biomolecule [3]. Since electrostatic potential changes on the surface of the nanotubes in the BLIP-II biosensor are expected to cause the electrical signal changes detected, we specifically quantify that as TEM-1 or KPC-2 binds to the covalently attached BLIP-II to the nanotube. We looked at two different attachment sites on BLIP-II – A41 and T213 – that are expected to produce a different response. For the A41 site, we show that the electrostatic potential doesn’t change significantly as TEM-1 or KPC-2 binds to BLIP-II because they are too far from the nanotube. However, the T213 site allows significant electrostatic potential changes: TEM-1 shifts the potential to more negative values, while KPC-2 shifts the potential to less negative values. The predictions from our simulations agree with measurements done on devices. For the A41 site, the measured conductance doesn’t change significantly upon binding of TEM-1 or KPC-2, indicating that the electrostatic potential on the nanotube doesn’t change much, as predicted. For the T213 site, the measured conductance increases significantly upon binding of TEM-1, while it decreases significantly for KPC-2. This suggests more p-type carriers in the nanotube (due to a more negative potential on the nanotube) as TEM-1 binds, but less p-type carriers (due to a less negative potential) as KPC-2 binds, as predicted. Conclusions We have developed a simulation protocol that predicts, in agreement with experimental measurements, the impact of the probe’s attachment site on the conductance modulation detected by carbon-nanotube field-effect biosensors. This supports the development of a truly integrated approach combining simulation and experiment to consistently design and fabricate these biosensors to achieve optimized sensitivity. References [1] Lee, C. S. et al. Chembiochem 2022 , e202200282. [2] Xu, X. et al. Angew. Chem. Int. Ed. 2021 , 60 , 20184-20189. [3] Côté, S. et al. Phys. Chem. Chem. Phys. 2022 , 24 , 4174-4186. Figure 1

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.015
GPT teacher head0.247
Teacher spread0.232 · 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 teacher head, 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
Published2023
Admission routes1
Has abstractyes

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