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Physical model for recognition tunneling.

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.

SUBMITTER: Krstic P

PROVIDER: S-EPMC4353715 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Physical model for recognition tunneling.

Krstić Predrag P Ashcroft Brian B Lindsay Stuart S

Nanotechnology 20150203 8

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 ar ...[more]

PMID: 25650375

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Project description:BackgroundIn the city of Maastricht in the Netherlands, a highway crossing several deprived neighborhoods was tunneled in 2016. The vacant space on top of this tunnel was redesigned and prioritized for pedestrians and cyclists. The aim of this study was to evaluate the effect of this major infrastructural change, named the Green Carpet, on total and transport-based physical activity (PA) levels.MethodsParticipants (≥18 years) were part of one of three area-based exposure groups. The maximal exposure group lived in neighborhoods directly bordering the Green Carpet. The minimal exposure group consisted of individuals living at the other side of the city, and the no exposure group consisted of individuals living in a nearby city. Actual use of the new infrastructure was incorporated as a second measure of exposure. Data were collected before and 3-15 months after the opening of the Green Carpet. Device-based measurements were conducted to obtain PA levels and collect location data. Changes in PA over time and intervention effects were determined using linear mixed models.ResultsPA levels in the Green Carpet area increased for the maximal and minimal exposure groups, but did not lead to an increase in total or transport-based PA. For the no exposure group, transport-based MVPA decreased and transport-based SB increased. The significant interaction (time x exposure) for transport-based SB, indicated differences in trends between the no exposure and maximal exposure group (B=-3.59, 95% CI - 7.15; -0.02) and minimal exposure group (B= -4.02, 95% CI -7.85, -0.19). Trends in the results based on analyses focusing on actual use and non-use of the new infrastructure were similar to those of the area-based analyses.ConclusionsResults suggest that the Green Carpet led to more PA in this specific area, but did not increase the total volume of PA. The area-based differences might reflect the differences between users and non-users, but we should be careful when interpreting these results, due to possible interference of selective mobility bias. This paper reflects that the relationship between infrastructure and PA is not unambiguous.Trial registrationThis research was retrospectively registered at the Netherlands Trial Register ( NL8108 ).

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Physical model for recognition tunneling.

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Physical model for recognition tunneling.

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