Size-Tunable Elasto-Inertial Sorting of Haematococcus pluvialis in the Ultrastretchable Microchannel.

Haematococcus pluvialis is a good source of astaxanthin, which reduces oxidation in the human body, treats inflammation, and slows the growth of breast and skin cancer cells. Since the size of H. pluvialis is often closely related to astaxanthin yield, size-based microalgal separation has far-reaching significance for high-value algae extraction and algal directed evolution. In this work, we report a novel size-tunable elasto-inertial sorting of H. pluvialis in the Ecoflex ultrastretchable microfluidic devices. Ecoflex microfluidic chips can deform and be flexible, bringing flexibility and stretchability to microchannels as well as new possibilities for large-scale modulation of channel geometry. Here, the effects of velocity, channel elongation, and particle size on the elasto-inertial migration of particles are systematically studied. We found that channel elongation has a strong regulating effect on particle focusing. In addition, we verified the continuous regulation of the sorting threshold of microalgal cells by stretching the channel, providing technical support for the extraction and directed evolution of high-yield microalgae.

Self-Adhesive, Biocompatible, Wearable Microfluidics with Erasable Liquid Metal Plasmonic Hotspots for Glucose Detection in Sweat.

Sweat is a noninvasive metabolite that can provide clinically meaningful information about physical conditions without harming the body. Glucose, a vital component in sweat, is closely related to blood glucose levels, and changes in its concentration can reflect the health status of diabetics. We introduce a self-adhesive, wearable microfluidic chip with erasable liquid metal plasmonic hotspots for the precise detection of glucose concentration in sweat. The self-adhesive, wearable microfluidic chip is made from modified polydimethylsiloxane (PDMS) with enhanced stickiness, enabling conformal contact with the skin, and can collect, deliver, and store sweat. The plasmonic hotspots are located inside the microfluidic channel, are generated by synthesizing silver nanostructures on liquid metal, and can be removed in the alkaline solution. It indicates the erasable and reproducible nature of the plasmonic hotspots. The detection method is based on surface-enhanced Raman spectroscopy (SERS), which allows for accurate detection of the glucose concentration. To enhance the sensitive detection of glucose, the SERS substrate is modified by 4-mercaptophenylboronic acid to achieve the limit of detection of 1 ng/L glucose, which is much lower than the physiological conditions (7.2-25.2 µg/L). The developed microfluidic chip is soft, stretchable, and nontoxic, bringing new possibilities to wearable sweat-sensing devices.

A free-standing, phase-change liquid metal mold for 3D flexible microfluidics.

This paper describes a method to fabricate the 3D microfluidic channel using the free-standing, phase-change gallium mold. Three approaches to prepare the free-standing gallium molds are described. The solid metal framework is strong enough to stand against the gravity. After casting, the embedded gallium molds are melted from solid to liquid and then extracted from the encasing elastomer to form the 3D microfluidic channel due to the phase change property. Since this method is compatible with many encasing materials (e.g., elastomers, gels, resins, ceramics), the encasing materials will bring novel functionalities to the microfluidic chip. Two proof-of-concept experiments have been demonstrated. Firstly, a soft, sticky, on-skin microfluidic cooler is developed based on this method to deliver the focused, minimal invasive cooling power at arbitrary skins of human body with temperature control. Secondly, an ultra-stretchable viscoelastic microchannel with the ultra-soft base is fabricated to continuously tune the viscoelastic particle focusing with a large dynamic range. This proposed technique suggests the new possibilities for the development of lab-on-a-chip applications.

Automatic segmentation of intracranial hematoma and volume measurement.

In this paper, a two-step segmentation method is developed for segmenting the hematoma area from brain CT images. The volume of hematoma area is calculated after the segmentation. During the second segmentation process, the method of two-dimensional entropy is introduced to separate hematoma. In using the method of two-dimensional entropy, most important is to find the optional threshold which can be achieved by an improved genetic algorithm (GA) i.e. hierarchical genetic algorithm (HGA). HGA is more efficient than simple GA in overcoming the shortcoming of standard GA in local optimal solution and low precision convergence. An experiment is designed to test the effectiveness of automatic segmentation. The results prove that the precision of automatic segmentation is better than artificial segmentation, and the clinical needs are met.