Dataset Information

Bridging the Gap: Rewritable Electronics Using Real-Time Light-Induced Dielectrophoresis on Lithium Niobate.

ABSTRACT: In the context of micro-electronics, the real-time manipulation and placement of components using optics alone promises a route towards increasingly dynamic systems, where the geometry and function of the device is not fixed at the point of fabrication. Here, we demonstrate physically reconfigurable circuitry through light-induced dielectrophoresis on lithium niobate. Using virtual electrodes, patterned by light, to trap, move, and chain individual micro-solder-beads in real-time via dielectrophoresis, we demonstrate rewritable electrical contacts which can make electrical connections between surface-bound components. The completed micro-solder-bead bridges were found to have relatively low resistances that were not solely dominated by the number of interfaces, or the number of discrete beads, in the connection. Significantly, these connections are formed without any melting/fusing of the beads, a key feature of this technique that enables reconfigurability. Requiring only a low-power (~3.5?mW) laser source to activate, and without the need for external power supply or signal generation, the all-optical simplicity of virtual-electrodes may prove significant for the future development of reconfigurable electronic systems.

SUBMITTER: Sperling JR

PROVIDER: S-EPMC5575290 | biostudies-other | 2017 Aug

REPOSITORIES: biostudies-other

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Bridging the Gap: Rewritable Electronics Using Real-Time Light-Induced Dielectrophoresis on Lithium Niobate.

Sperling Justin R JR Neale Steven L SL Clark Alasdair W AW

Scientific reports 20170829 1

In the context of micro-electronics, the real-time manipulation and placement of components using optics alone promises a route towards increasingly dynamic systems, where the geometry and function of the device is not fixed at the point of fabrication. Here, we demonstrate physically reconfigurable circuitry through light-induced dielectrophoresis on lithium niobate. Using virtual electrodes, patterned by light, to trap, move, and chain individual micro-solder-beads in real-time via dielectroph ...[more]

PMID: 28851963

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Project description:Introduction:Biomedical and translational research often relies on the evaluation of patients or specimens that meet specific clinical or laboratory criteria. The typical approach used to identify biospecimens is a manual, retrospective process that exists outside the clinical workflow. This often makes biospecimen collection cost prohibitive and prevents the collection of analytes with short stability times. Emerging data architectures offer novel approaches to enhance specimen-identification practices. To this end, we present a new tool that can be deployed in a real-time environment to automate the identification and notification of available biospecimens for biomedical research. Methods:Real-time clinical and laboratory data from Cloverleaf (Infor, NY, NY) were acquired within our computational health platform, which is built on open-source applications. Study-specific filters were developed in NiFi (Apache Software Foundation, Wakefield, MA, USA) to identify the study-appropriate specimens in real time. Specimen metadata were stored in Elasticsearch (Elastic N. V., Mountain View, CA, USA) for visualization and automated alerting. Results:Between June 2018 and December 2018, we identified 2992 unique specimens belonging to 2815 unique patients, split between two different use cases. Based on laboratory policy for specimen retention and study-specific stability requirements, secure E-mail notifications were sent to investigators to automatically notify of availability. The assessment of throughput on commodity hardware demonstrates the ability to scale to approximately 2000 results per second. Conclusion:This work demonstrates that real-world clinical data can be analyzed in real time to increase the efficiency of biospecimen identification with minimal overhead for the clinical laboratory. Future work will integrate additional data types, including the analysis of unstructured data, to enable more complex cases and biospecimen identification.

Project description:BackgroundUnderstanding how to connect habitat remnants to facilitate the movement of species is a critical task in an increasingly fragmented world impacted by human activities. The identification of dispersal routes and corridors through connectivity analysis requires measures of landscape resistance but there has been no consensus on how to calculate resistance from habitat characteristics, potentially leading to very different connectivity outcomes.MethodsWe propose a new model, called the Time-Explicit Habitat Selection (TEHS) model, that can be directly used for connectivity analysis. The TEHS model decomposes the movement process in a principled approach into a time and a selection component, providing complementary information regarding space use by separately assessing the drivers of time to traverse the landscape and the drivers of habitat selection. These models are illustrated using GPS-tracking data from giant anteaters (Myrmecophaga tridactyla) in the Pantanal wetlands of Brazil.ResultsThe time model revealed that the fastest movements tended to occur between 8 p.m. and 5 a.m., suggesting a crepuscular/nocturnal behavior. Giant anteaters moved faster over wetlands while moving much slower over forests and savannas, in comparison to grasslands. We also found that wetlands were consistently avoided whereas forest and savannas tended to be selected. Importantly, this model revealed that selection for forest increased with temperature, suggesting that forests may act as important thermal shelters when temperatures are high. Finally, using the spatial absorbing Markov chain framework, we show that the TEHS model results can be used to simulate movement and connectivity within a fragmented landscape, revealing that giant anteaters will often not use the shortest-distance path to the destination patch due to avoidance of certain habitats.ConclusionsThe proposed approach can be used to characterize how landscape features are perceived by individuals through the decomposition of movement patterns into a time and a habitat selection component. Additionally, this framework can help bridge the gap between movement-based models and connectivity analysis, enabling the generation of time-explicit connectivity results.

Dataset Information

Bridging the Gap: Rewritable Electronics Using Real-Time Light-Induced Dielectrophoresis on Lithium Niobate.

Publications

Bridging the Gap: Rewritable Electronics Using Real-Time Light-Induced Dielectrophoresis on Lithium Niobate.

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