4,050 research outputs found
Stochastic Yield Catastrophes and Robustness in Self-Assembly
A guiding principle in self-assembly is that, for high production yield,
nucleation of structures must be significantly slower than their growth.
However, details of the mechanism that impedes nucleation are broadly
considered irrelevant. Here, we analyze self-assembly into finite-sized target
structures employing mathematical modeling. We investigate two key scenarios to
delay nucleation: (i) by introducing a slow activation step for the assembling
constituents and, (ii) by decreasing the dimerization rate. These scenarios
have widely different characteristics. While the dimerization scenario exhibits
robust behavior, the activation scenario is highly sensitive to demographic
fluctuations. These demographic fluctuations ultimately disfavor growth
compared to nucleation and can suppress yield completely. The occurrence of
this stochastic yield catastrophe does not depend on model details but is
generic as soon as number fluctuations between constituents are taken into
account. On a broader perspective, our results reveal that stochasticity is an
important limiting factor for self-assembly and that the specific
implementation of the nucleation process plays a significant role in
determining the yield
Polarons in semiconductor quantum-dots and their role in the quantum kinetics of carrier relaxation
While time-dependent perturbation theory shows inefficient carrier-phonon
scattering in semiconductor quantum dots, we demonstrate that a quantum kinetic
description of carrier-phonon interaction predicts fast carrier capture and
relaxation. The considered processes do not fulfill energy conservation in
terms of free-carrier energies because polar coupling of localized quantum-dot
states strongly modifies this picture.Comment: 6 pages, 6 figures, accepted for publication in Phys.Rev.
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UK Research Information Shared Service (UKRISS) Final Report, July 2014
The reporting of research information is a complex and expensive activity for research organisations (ROs). There is little alignment between funders of the reporting requests made to institutions and requests made to individual researchers about their research outputs and outcomes. This inevitably results in duplication and increased costs across the sector, whilst limiting the potential sharing and reuse of the information. The UK Research Information Shared Service (UKRISS) project conducted a feasibility and scoping study for the reporting of research information at a national level based on CERIF (Common European Research Information Format), with the objective of increasing efficiency, productivity and quality across the sector. The aim was to define and prototype solutions which are compelling, easy to use, have a low entry barrier, and support innovative information sharing and benchmarking. CERIF has emerged as the preferred format for expressing research information across Europe. To date, CERIF has been piloted for specific applications, but not as a format for reporting requirements across all UK ROs. The final report presents the work carried out by the UKRISS project, including requirements gathering, modelling and prototyping, as well as recommendation for sustainability. UKRISS was divided into two phases. Phase 1, mapping the reporting landscape, ran from March 2012 to December 2012. Phase 2, exploring delivery of potential solutions, began in February 2013 and ended in December 2013
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Linked optical and gene expression profiling of single cells at high-throughput.
Single-cell RNA sequencing has emerged as a powerful tool for characterizing cells, but not all phenotypes of interest can be observed through changes in gene expression. Linking sequencing with optical analysis has provided insight into the molecular basis of cellular function, but current approaches have limited throughput. Here, we present a high-throughput platform for linked optical and gene expression profiling of single cells. We demonstrate accurate fluorescence and gene expression measurements on thousands of cells in a single experiment. We use the platform to characterize DNA and RNA changes through the cell cycle and correlate antibody fluorescence with gene expression. The platform's ability to isolate rare cell subsets and perform multiple measurements, including fluorescence and sequencing-based analysis, holds potential for scalable multi-modal single-cell analysis
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