Engineering solutions for biological studies of flow-exposed endothelial cells on orbital shakers v1

Shear stress is extremely important for endothelial cell (EC) function. The popularity of 6-well plates on orbital shakers to impose shear stress on ECs has increased among biologists due to their low cost and simplicity. One characteristic of such a platform is the heterogeneous flow profile within a well. While cells in the periphery are exposed to a laminar and high-velocity pulsatile flow that mimics physiological conditions, the flow in the center is disturbed and imposes low shear stress on the cells, which is characteristic of atheroprone regions. For studies where such heterogeneity is not desired, we present a simple cell-patterning technique to selectively prevent cell growth in the center of the well and facilitate the exclusive collection and analysis of cells in the periphery. This guarantees that cell phenotypes will not be influenced by secreted factors from cells exposed to other shear profiles nor that interesting results may be obscured by mixing cells from different regions. We also present a multi-staining platform that compartmentalizes each well into 5 smaller independent regions: four at the periphery and one in the center. This is ideal for studies that aim to grow cells on the whole well surface, for comparison with previous work and minimal interference in the cell culture, but require screening of markers by immunostaining afterwards. It allows to compare different regions of the well, reduces antibody-related costs, and allows the exploration of multiple markers essential for high-content screening of cell response. By increasing the versatility of the 6-well plate on an orbital shaker system, we hope that these two solutions motivate biologists to pursue studies on EC mechanobiology and beyond.

Nanopatterning of oxide 2-dimensional electron systems using low-temperature ion milling

We present a "top-down" patterning technique based on ion milling performed at low- temperature, for the realization of oxide two-dimensional electron system (2DES) devices with dimensions down to 160 nm. Using electrical transport and scanning SQUID measurements we demonstrate that the low-temperature ion milling process does not damage the 2DES properties nor creates oxygen vacancies-related conducting paths in the STO substrate. As opposed to other procedures used to realize oxide 2DES devices, the one we propose gives lateral access to the 2DES along the in-plane directions, finally opening the way to coupling with other materials, including superconductors.

Aspiration-mediated hydrogel micropatterning using rail-based open microfluidic devices for high-throughput 3D cell culture

Microfluidics offers promising methods for aligning cells in physiologically relevant configurations to recapitulate human organ functionality. Specifically, microstructures within microfluidic devices facilitate 3D cell culture by guiding hydrogel precursors containing cells. Conventional approaches utilize capillary forces of hydrogel precursors to guide fluid flow into desired areas of high wettability. These methods, however, require complicated fabrication processes and subtle loading protocols, thus limiting device throughput and experimental yield. Here, we present a swift and robust hydrogel patterning technique for 3D cell culture, where preloaded hydrogel solution in a microfluidic device is aspirated while only leaving a portion of the solution in desired channels. The device is designed such that differing critical capillary pressure conditions are established over the interfaces of the loaded hydrogel solution, which leads to controlled removal of the solution during aspiration. A proposed theoretical model of capillary pressure conditions provides physical insights to inform generalized design rules for device structures. We demonstrate formation of multiple, discontinuous hollow channels with a single aspiration. Then we test vasculogenic capacity of various cell types using a microfluidic device obtained by our technique to illustrate its capabilities as a viable micro-manufacturing scheme for high-throughput cellular co-culture.

Colloidal Lithography for Photovoltaics: An Attractive Route for Light Management

The pursuit of ever-more efficient, reliable, and affordable solar cells has pushed the development of nano/micro-technological solutions capable of boosting photovoltaic (PV) performance without significantly increasing costs. One of the most relevant solutions is based on light management via photonic wavelength-sized structures, as these enable pronounced efficiency improvements by reducing reflection and by trapping the light inside the devices. Furthermore, optimized microstructured coatings allow self-cleaning functionality via effective water repulsion, which reduces the accumulation of dust and particles that cause shading. Nevertheless, when it comes to market deployment, nano/micro-patterning strategies can only find application in the PV industry if their integration does not require high additional costs or delays in high-throughput solar cell manufacturing. As such, colloidal lithography (CL) is considered the preferential structuring method for PV, as it is an inexpensive and highly scalable soft-patterning technique allowing nanoscopic precision over indefinitely large areas. Tuning specific parameters, such as the size of colloids, shape, monodispersity, and final arrangement, CL enables the production of various templates/masks for different purposes and applications. This review intends to compile several recent high-profile works on this subject and how they can influence the future of solar electricity.

Fabrication and Characterization of Nanonet-Channel LTPS TFTs Using a Nanosphere-Assisted Patterning Technique

We present the fabrication and electrical characteristics of nanonet-channel (NET) low-temperature polysilicon channel (LTPS) thin-film transistors (TFTs) using a nanosphere-assisted patterning (NAP) technique. The NAP technique is introduced to form a nanonet-channel instead of the electron beam lithography (EBL) or conventional photolithography method. The size and space of the holes in the nanonet structure are well controlled by oxygen plasma treatment and a metal lift-off process. The nanonet-channel TFTs show improved electrical characteristics in terms of the ION/IOFF, threshold voltage, and subthreshold swing compared with conventional planar devices. The nanonet-channel devices also show a high immunity to hot-carrier injection and a lower variation of electrical characteristics. The standard deviation of VTH (σVTH) is reduced by 33% for a nanonet-channel device with a gate length of 3 μm, which is mainly attributed to the reduction of the grain boundary traps and enhanced gate controllability. These results suggest that the cost-effective NAP technique is promising for manufacturing high-performance nanonet-channel LTPS TFTs with lower electrical variations.

Solvent Vapor Annealing, Defect Analysis, and Optimization of Self-assembly of Block Copolymers Using Machine Learning Approaches

Self-assembly of block copolymers (BCP) is an alternative patterning technique that promises sublithographic resolution and density multiplication. Defectivity of the resulting nanopatterns remains too high for many applications in microelectronics, and is exacerbated by small variations of processing parameters, such as film thickness, and fluctuations of solvent vapour pressure and temperature, among others. In this work, a solvent vapor annealing (SVA) flow-controlled system is combined with Design of Experiments (DOE) and machine learning (ML) approaches.<br>

Solvent Vapor Annealing, Defect Analysis, and Optimization of Self-assembly of Block Copolymers Using Machine Learning Approaches

Self-assembly of block copolymers (BCP) is an alternative patterning technique that promises sublithographic resolution and density multiplication. Defectivity of the resulting nanopatterns remains too high for many applications in microelectronics, and is exacerbated by small variations of processing parameters, such as film thickness, and fluctuations of solvent vapour pressure and temperature, among others. In this work, a solvent vapor annealing (SVA) flow-controlled system is combined with Design of Experiments (DOE) and machine learning (ML) approaches.<br>