ArticleNanotechnology2015
Physical model for recognition tunneling.
Article in Nanotechnology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Machine learning empowered next generation DNA sequencing: perspective and prospectus.Chemical science · 2024Review
- Electronic analysis of hydrogen-bonded molecular complexes: the case of DNA sensed in a functionalized nanogap.RSC advances · 2023Article
- Functionalized electrodes embedded in nanopores: read-out enhancement?Chemistry, an Asian journal · 2023Review
- Functionalized Nanogap for DNA Read-Out: Nucleotide Rotation and Current-Voltage Curves.Chemphyschem : a European journal of chemical physics and physical chemistry · 2020Article
- Strategies for Development of a Next-Generation Protein Sequencing Platform.Trends in biochemical sciences · 2020Review
- Classification of DNA nucleotides with transverse tunneling currents.Nanotechnology · 2017Article
- Electronic single-molecule identification of carbohydrate isomers by recognition tunnelling.Nature communications · 2016Article
- Unsupervised vector-based classification of single-molecule charge transport data.Nature communications · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Recognition tunneling (RT) identifies target molecules trapped between tunneling electrodes functionalized with recognition molecules that serve as specific chemical linkages between the metal electrodes and the trapped target molecule. Possible applications include single molecule DNA and protein sequencing. This paper addresses several fundamental aspects of RT by multiscale theory, applying both all-atom and coarse-grained DNA models: (1) we show that the magnitude of the observed currents are consistent with the results of non-equilibrium Green's function calculations carried out on a solvated all-atom model. (2) Brownian fluctuations in hydrogen bond-lengths lead to current spikes that are similar to what is observed experimentally. (3) The frequency characteristics of these fluctuations can be used to identify the trapped molecules with a machine-learning algorithm, giving a theoretical underpinning to this new method of identifying single molecule signals.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.