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An ionic compound is stable because of the electrostatic attraction between its positive and negative ions. The lattice energy of a compound is a measure of the strength of this attraction. The lattice energy (ΔHlattice) of an ionic compound is defined as the energy required to separate one mole of the solid into its component gaseous ions. For the ionic solid sodium chloride, the lattice energy is the enthalpy change of the process:
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The relative amount of a given solution component is known as its concentration. Often, though not always, a solution contains one component with a concentration that is significantly greater than that of all other components. This component is called the solvent and may be viewed as the medium in which the other components are dispersed or dissolved. Solutions in which water is the solvent are, of course, very common on our planet. A solution in which water is the solvent is called an aqueous...
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Brain Slice Stimulation Using a Microfluidic Network and Standard Perfusion Chamber
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Single-Layered Microfluidic Network-Based Combinatorial Dilution for Standard Simplex Lattice Design.

Kangsun Lee1,2, Choong Kim3, Kwang W Oh4,5

  • 1SMALL (Sensors and MicroActuators Learning Lab), Department of Electrical Engineering, The State University of New York at Buffalo, Buffalo, NY 14260, USA. kslee1230@gmail.com.

Micromachines
|November 15, 2018
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Summary
This summary is machine-generated.

This study presents a novel microfluidic device for generating sample combinations. The fabricated devices demonstrate high accuracy in mixing and liquid handling for high-throughput screening.

Keywords:
3D simplex lattice designcombinatorial dilutiondesign of experiment (DOE)microfluidic networkmicrofluidic spotting system

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Area of Science:

  • Microfluidics
  • Chemical Engineering
  • Biotechnology

Background:

  • Generating precise sample combinations is crucial for high-throughput screening and assays.
  • Traditional methods can be time-consuming and require large sample volumes.
  • Microfluidic devices offer a potential solution for miniaturized and efficient sample handling.

Purpose of the Study:

  • To develop a straightforward strategy for generating multiple sample combinations using a single-layer microfluidic network.
  • To evaluate the performance of different microfluidic device designs through computational simulation and experimental validation.
  • To assess the suitability of two liquid handling methods for high-throughput applications.

Main Methods:

  • Utilized an experimental simplex lattice design to define 15 sample combinations.
  • Employed computational fluid dynamics (CFD-ACE+) simulations to compare plain and groove structural microfluidic devices.
  • Fabricated polydimethylsiloxane (PDMS) devices using soft lithography and validated mixing performance with fluorescent dye.

Main Results:

  • Simulated output concentrations closely matched desired values with <1% absolute error.
  • Experimental results showed good mixing performance for 15 combinations with <4% absolute error.
  • Both bottom-up and top-down liquid handling methods were successfully implemented using structured groove sets.

Conclusions:

  • The developed single-layer microfluidic network provides an efficient and accurate method for generating sample combinations.
  • The groove structural design enhances mixing performance compared to plain structures.
  • The integrated liquid handling methods support high-throughput screening and assay applications.