Microfluidic mixing becomes a necessity when thorough sample homogenization is required in small volumes of fluid, such as in lab-on-a-chip devices. For example, efficient mixing is extraordinarily challenging in capillary-filling microfluidic devices and in microchambers with stagnant fluids. To address this issue, specifically designed geometrical features can enhance the effect of diffusion and provide efficient mixing by inducing chaotic fluid flow. This scheme is known as "passive" mixing. In addition, when rapid and global mixing is essential, "active" mixing can be applied by exploiting an external source. In particular, magnetic mixing (where a magnetic field acts to stimulate mixing) shows great potential for high mixing efficiency. This method generally involves magnetic beads and external (or integrated) magnets for the creation of chaotic motion in the device. However, there is still plenty of room for exploiting the potential of magnetic beads for mixing applications. Therefore, this review article focuses on the advantages of magnetic bead mixing along with recommendations on improving mixing in low Reynolds number flows ( ≤ 1) and in stagnant fluids.
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http://dx.doi.org/10.3390/mi10110731 | DOI Listing |
Food Chem
December 2024
Xinjiang Key Laboratory of Biological Resources and Genetic Engineering, College of Life Science and Technology, Xinjiang University, Urumqi 830017, China. Electronic address:
This study developed antimicrobial peptides (AMPs) from quinoa with high antibacterial activity and stability by mixed-bacteria fermentation. Furthermore, among 9 peptide fractions purified by membrane separation and chromatography, F1 could effectively inhibit the growth and propagation of bacterial microorganisms in apple juice. Subsequently, F1 identified LC-MS/MS as 95 peptides, molecular weights 494.
View Article and Find Full Text PDFCornea
October 2024
Center for Translational Ocular Immunology, Department of Ophthalmology, Tufts Medical Center and Tufts University School of Medicine, Boston, MA; and.
Purpose: Neuropathic corneal pain (NCP) has been recognized as a distinct disease, yet treatment options remain limited. The aim of this pilot study was to explore the effectiveness of extranasal neurostimulation (EXNS) as a potential pain relief strategy for individuals with the peripheral component of NCP.
Methods: A retrospective study was performed to identify patients who were diagnosed with refractory peripheral or mixed NCP and subsequently underwent a single session of EXNS.
MCN Am J Matern Child Nurs
December 2024
Purpose: To describe the relationship between experiencing traumatic childbirth events and burnout.
Study Designs And Methods: This descriptive cross-sectional study used an anonymous online survey to assess traumatic childbirth event exposure and the three independent constructs of burnout: emotional exhaustion, depersonalization, and personal accomplishment. Participants were a convenience sample of registered nurses, obstetric residents, family medicine residents, and attending obstetricians across five hospitals from December 2020 through June 2021.
PLoS One
December 2024
Department of Family Medicine, McMaster University, Hamilton, Canada.
Objective: Gestational diabetes mellitus (GDM) is a common medical complication of pregnancy that leads to adverse outcomes for both infants and pregnant people. Early detection and treatment can mitigate these negative outcomes. The COVID-19 pandemic strained healthcare and laboratory services, including GDM screening programs.
View Article and Find Full Text PDFPLoS One
December 2024
Department of Life Sciences, Centre for Clinical and Cognitive Neuroscience, Brunel University London, Kingston Lane, Uxbridge, Middlesex, United Kingdom.
Multitasking (MT)-performing more than one task at a time-has become ubiquitous in everyday life. Understanding of how MT is learned could enable optimizing learning regimes for tasks and occupations that necessitate frequent MT. Previous research has distinguished between MT learning regimes in which all tasks are learned in parallel, single-task (ST) learning regimes in which all tasks are learned individually, and mixed learning regimes (Mix) in which MT and ST regimes are mixed.
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