An off-the-shelf microfluidic device for the controllable fabrication of multiple-holed hollow particles and their cell culture application

The microfluidic droplet method has an excellent promise for preparing hollow particles because it can be versatile in realizing multi-level size distribution and scalable in production. However, the application of...

Rapid ESKAPE Pathogens Detection Method using Tapered Dielectrophoresis Electrodes via Crossover Frequency Analysis

This paper introduces the versatile of an electrokinetic technique by using the non-uniform electric field for dielectrophoresis (DEP) application. This technique is defined as electromicrofluidics. The potential application for portable and real time detection method of Enterococcus faecium (EF), Staphylococcus aureus (SA), Klebsiella pneumoniae (KP), Acinetobacter baumannii (AB), Pseudomonas aeruginosa (PA) and Enterobacter aerogenes (EA), which are the (ESKAPE) bacteria. The MATLAB analytical modelling was used in simulating the polarisation factor and velocities of bacteria based on Clausius-Mossotti factor (CMF). The validation of CMF simulation through the DEP experimental can be quantified based on the response of alternating current (AC) voltage applied using 6 voltage peak to peak (Vp-p) to their input frequencies from 100 to 15000 kHz. The droplet method was deployed to place properly 0.2 μL of sample onto DEP microelectrode. The velocities and crossover frequency (fxo) ranges of bacteria were determined through bacteria trajectory in specific time interval monitored by microscope attached with eyepiece camera. The applied range of input frequencies from 100 to 15000 kHz at 6 Vp-p for each bacteria were successfully identified the unique ranges of frequencies response for detection application. The advantages of this works are selective with rapid capability for multidrug resistant (MDR) bacteria detection application.

Predicting the morphology of ice particles in deep convection using the super-droplet method: development and evaluation of SCALE-SDM 0.2.5-2.2.0, -2.2.1, and -2.2.2

The super-droplet method (SDM) is a particle-based numerical scheme that enables accurate cloud microphysics simulation with lower computational demand than multi-dimensional bin schemes. Using SDM, a detailed numerical model of mixed-phase clouds is developed in which ice morphologies are explicitly predicted without assuming ice categories or mass–dimension relationships. Ice particles are approximated using porous spheroids. The elementary cloud microphysics processes considered are advection and sedimentation; immersion/condensation and homogeneous freezing; melting; condensation and evaporation including cloud condensation nuclei activation and deactivation; deposition and sublimation; and coalescence, riming, and aggregation. To evaluate the model's performance, a 2-D large-eddy simulation of a cumulonimbus was conducted, and the life cycle of a cumulonimbus typically observed in nature was successfully reproduced. The mass–dimension and velocity–dimension relationships the model predicted show a reasonable agreement with existing formulas. Numerical convergence is achieved at a super-particle number concentration as low as 128 per cell, which consumes 30 times more computational time than a two-moment bulk model. Although the model still has room for improvement, these results strongly support the efficacy of the particle-based modeling methodology to simulate mixed-phase clouds.

Comparison of Multiple Displacement Amplification (MDA) and Multiple Annealing and Looping-Based Amplification Cycles (MALBAC) in Limited DNA Sequencing Based on Tube and Droplet

Whole genome amplification (WGA) is crucial for whole genome sequencing to investigate complex genomic alteration at the single-cell or even single-molecule level. Multiple displacement amplification (MDA) and multiple annealing and looping based amplification cycles (MALBAC) are two most widely applied WGA methods, which have different advantages and disadvantages, dependent on research objectives. Herein, we compared the MDA and MALBAC to provide more information on their performance in droplets and tubes. We observed that the droplet method could dramatically reduce the amplification bias and retain the high accuracy of replication than the conventional tube method. Furthermore, the droplet method exhibited higher efficiency and sensitivity for both homozygous and heterozygous single nucleotide variants (SNVs) at the low sequencing depth. In addition, we also found that MALBAC offered a greater uniformity and reproducibility and MDA showed a better efficiency of genomic coverage and SNV detection. Our results provided insights that will allow future decision making.

Predicting the morphology of ice particles in deep convection using the super-droplet method

<p>In this presentation, we summarize the main results of Shima et al. (2019). The super-droplet method (SDM) is a particle-based numerical algorithm that enables accurate cloud microphysics simulation with lower computational demand than multi-dimensional bin schemes. Using SDM, we developed a detailed numerical model of mixed-phase clouds in which ice morphologies are explicitly predicted without assuming ice categories or mass-dimension relationships. Ice particles are approximated as porous spheroids. The elementary cloud microphysics processes considered are advection and sedimentation; immersion/condensation and homogeneous freezing; melting; condensation and evaporation including cloud condensation nuclei activation and deactivation; deposition and sublimation; collision-coalescence, -riming, and -aggregation. To evaluate the model's performance, we conducted a 2D large-eddy simulation of a cumulonimbus. The results well capture characteristics of a real cumulonimbus. The mass-dimension and velocity-dimension relationships the model predicted show a reasonable agreement with existing formulas. Numerical convergence is achieved at a super-particle number concentration as low as 128/cell, which consumes 30 times more computational time than a two-moment bulk model. Although the model still has room for improvement, these results strongly support the efficacy of the particle-based modeling methodology to simulate mixed-phase clouds. </p><p>Shima, S., Sato, Y., Hashimoto, A., and Misumi, R.: Predicting the morphology of ice particles in deep convection using the super-droplet method: development and evaluation of SCALE-SDM 0.2.5-2.2.0/2.2.1, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-294, 1-83, 2019.</p>