Nearing 30 years since its introduction, 3D printing technology is set to revolutionize research and teaching laboratories. This feature encompasses the history of 3D printing, reviews various printing methods, and presents current applications. The authors offer an appraisal of the future direction and impact this technology will have on laboratory settings as 3D printers become more accessible.

A mini-review with 79 references. In this review, the most recent trends in 3D-printed microfluidic devices are discussed. In addition, a focus is given to the fabrication aspects of these devices, with the supplemental information containing detailed instructions for designing a variety of structures including: a microfluidic channel, threads to accommodate commercial fluidic fittings, a flow splitter; a well plate, a mold for PDMS channel casting; and how to combine multiple designs into a single device. The advantages and limitations of 3D-printed microfluidic devices are thoroughly discussed, as are some future directions for the field.

In this paper, we explore the role of Instance Normalization in low-level vision tasks. Specifically, we present a novel block: Half Instance Normalization Block (HIN Block), to boost the performance of image restoration networks. Based on HIN Block, we design a simple and powerful multi-stage network named HINet, which consists of two subnetworks. With the help of HIN Block, HINet surpasses the state-of-the-art (SOTA) on various image restoration tasks. For image denoising, we exceed it 0.11dB and 0.28 dB in PSNR on SIDD dataset, with only 7.5% and 30% of its multiplier-accumulator operations (MACs), 6.8× and 2.9× speedup respectively. For image deblurring, we get comparable performance with 22.5% of its MACs and 3.3× speedup on REDS and GoPro datasets. For image deraining, we exceed it by 0.3 dB in PSNR on the average result of multiple datasets with 1.4× speedup. With HINet, we won the 1st place on the NTIRE 2021 Image Deblurring Challenge -Track2. JPEG Artifacts, with a PSNR of 29.70. The code is available at https://github.com/megviimodel/HINet.

A fluidic device constructed with a 3D-printer can be used to investigate stored blood components with subsequent high-throughput calibration and readout with a standard plate reader.