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Data on flow cell optimization for membrane-based electrokinetic energy conversion.

ABSTRACT: This article elaborates on the design and optimization of a specialized flow cell for the measurement of direct conversion of pressure into electrical energy (Electrokinetic Energy Conversion, EKEC) which has been presented in Østedgaard-Munck et al. (2017) [1]. Two main flow cell parameters have been monitored and optimized: A) the hydraulic pressure profile on each side of the membrane introduced by pumps recirculating the electrolyte solution through the flow fields and B) the electrical resistance between the current collectors across the combined flow cell. The latter parameter has been measured using four-point Electrochemical Impedance spectroscopy (EIS) for different flow rates and concentrations. The total cell resistance consists of contributions from different components: the membrane [Formula: see text], anode charge transfer [Formula: see text], cathode charge transfer [Formula: see text], and ion diffusion in the porous electrodes [Formula: see text]. The intrinsic membrane properties of Nafion 117 has been investigated experimentally in LiI/I2 solutions with concentrations ranging between 0.06 and 0.96 M and used to identify the preferred LiI/I2 solution concentration. This was achieved by measuring the solution uptake, internal solution concentration and ion exchange capacity. The membrane properties were further used to calculate the transport coefficients and electrokinetic Figure of merit in terms of the Uniform potential and Space charge models. Special attention has been put on the streaming potential coefficient which is an intrinsic property.

SUBMITTER: Ostedgaard-Munck DN

PROVIDER: S-EPMC5712061 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Data on flow cell optimization for membrane-based electrokinetic energy conversion.

Østedgaard-Munck David Nicolas DN Catalano Jacopo J Birch Kristensen Mette M Bentien Anders A

Data in brief 20170901

This article elaborates on the design and optimization of a specialized flow cell for the measurement of direct conversion of pressure into electrical energy (Electrokinetic Energy Conversion, EKEC) which has been presented in Østedgaard-Munck et al. (2017) [1]. Two main flow cell parameters have been monitored and optimized: A) the hydraulic pressure profile on each side of the membrane introduced by pumps recirculating the electrolyte solution through the flow fields and B) the electrical resi ...[more]

PMID: 29214189

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Project description:Detection of analytes using streaming current has previously been explored using both experimental approaches and theoretical analyses of such data. However, further developments are needed for establishing a viable microchip that can be exploited to deliver a sensitive, robust, and scalable biosensor device. In this study, we demonstrated the fabrication of such a device on silicon wafer using a scalable silicon microfabrication technology followed by characterization and optimization of this sensor for detection of small extracellular vesicles (sEVs) with sizes in the range of 30 to 200 nm, as determined by nanoparticle tracking analyses. We showed that the sensitivity of the devices, assessed by a common protein-ligand pair and sEVs, significantly outperforms previous approaches using the same principle. Two versions of the microchips, denoted as enclosed and removable-top microchips, were developed and compared, aiming to discern the importance of high-pressure measurement versus easier and better surface preparation capacity. A custom-built chip manifold allowing easy interfacing with standard microfluidic connections was also constructed. By investigating different electrical, fluidic, morphological, and fluorescence measurements, we show that while the enclosed microchip with its robust glass-silicon bonding can withstand higher pressure and thus generate higher streaming current, the removable-top configuration offers several practical benefits, including easy surface preparation, uniform probe conjugation, and improvement in the limit of detection (LoD). We further compared two common surface functionalization strategies and showed that the developed microchip can achieve both high sensitivity for membrane protein profiling and low LoD for detection of sEV detection. At the optimum working condition, we demonstrated that the microchip could detect sEVs reaching an LoD of 104 sEVs/mL (when captured by membrane-sensing peptide (MSP) probes), which is among the lowest in the so far reported microchip-based methods.

Project description:BackgroundRecent advances in bioimaging and automated analysis methods have enabled the large-scale systematic analysis of cellular dynamics during the embryonic development of Caenorhabditis elegans. Most of these analyses have focused on cell lineage tracing rather than cell shape dynamics. Cell shape analysis requires cell membrane segmentation, which is challenging because of insufficient resolution and image quality. This problem is currently solved by complicated segmentation methods requiring laborious and time consuming parameter adjustments.ResultsOur new framework BCOMS (Biologically Constrained Optimization based cell Membrane Segmentation) automates the extraction of the cell shape of C. elegans embryos. Both the segmentation and evaluation processes are automated. To automate the evaluation, we solve an optimization problem under biological constraints. The performance of BCOMS was validated against a manually created ground truth of the 24-cell stage embryo. The average deviation of 25 cell shape features was 5.6%. The deviation was mainly caused by membranes parallel to the focal planes, which either contact the surfaces of adjacent cells or make no contact with other cells. Because segmentation of these membranes was difficult even by manual inspection, the automated segmentation was sufficiently accurate for cell shape analysis. As the number of manually created ground truths is necessarily limited, we compared the segmentation results between two adjacent time points. Across all cells and all cell cycles, the average deviation of the 25 cell shape features was 4.3%, smaller than that between the automated segmentation result and ground truth.ConclusionsBCOMS automated the accurate extraction of cell shapes in developing C. elegans embryos. By replacing image processing parameters with easily adjustable biological constraints, BCOMS provides a user-friendly framework. The framework is also applicable to other model organisms. Creating the biological constraints is a critical step requiring collaboration between an experimentalist and a software developer.

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Data on flow cell optimization for membrane-based electrokinetic energy conversion.

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Data on flow cell optimization for membrane-based electrokinetic energy conversion.

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