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Nanofluidic chips are becoming increasingly important for biological and chemical applications due to the special phenomena only occur in nanochannels. In this study, a simple and low-cost method for fabricating SU-8 nanofluidic chips is proposed based on traditional ultraviolet (UV) photolithography. The thermal-induced cracks (nanochannels) can be automatically formed by thermal stress release during the postbake process. The influence of pattern-shape, pattern-angle and pattern-distance on the maximum stress of the SU-8 patterns was investigated. And the effect of postbake temperature on the maximum stress of the SU-8 patterns was also analyzed. The numerical and experimental results show that when the triangle SU-8 patterns with pattern-angle of 60° is postbaked at 125 °C, the nanocracks can be easily formed between two patterns. With this newly developed technology, simple, low-cost and large scale nanofluidic chips can be fabricated only by traditional UV photolighography, which allows a commercially manufacturing of nano-components. [ABSTRACT FROM AUTHOR]

To prevent corrosion of PZT (Lead zirconate titanate) and electrodes in printheads as they are exposed to ink, Parylene C was selected as the protective layer. The deposition thickness, patterning method, and TGA (thermogravimetric analysis) of Parylene C, as well as its adhesive strength on the substrate and its aging property were studied experimentally. The results of the experiments showed that when the deposition thickness of Parylene C was 450 nm, it fully met the voltage-tolerance requirement, with a reduction in vibration displacement of only 12.89%. To pattern the Parylene C, the oxygen plasma etching method was employed, and the dependence of the etch rate on chamber power and chamber pressure were investigated. TGA experiments with Parylene C indicate that it can withstand the working temperature of a wire-bonding process. Pretreating the substrate with silane coupling agent improved the adhesive strength between Parylene C and the substrate significantly. The dielectric constant of Parylene C was reduced only 10% after 23 days in ink, which indicates the excellent aging resistance of Parylene C. [ABSTRACT FROM AUTHOR]

Environmental Microorganism Data Set Fifth Version (EMDS-5) is a microscopic image dataset including original Environmental Microorganism (EM) images and two sets of Ground Truth (GT) images. The GT image sets include a single-object GT image set and a multi-object GT image set. EMDS-5 has 21 types of EMs, each of which contains 20 original EM images, 20 single-object GT images and 20 multi-object GT images. EMDS-5 can realize to evaluate image preprocessing, image segmentation, feature extraction, image classification and image retrieval functions. In order to prove the effectiveness of EMDS-5, for each function, we select the most representative algorithms and price indicators for testing and evaluation. The image preprocessing functions contain two parts: image denoising and image edge detection. Image denoising uses nine kinds of filters to denoise 13 kinds of noises, respectively. In the aspect of edge detection, six edge detection operators are used to detect the edges of the images, and two evaluation indicators, peak-signal to noise ratio and mean structural similarity, are used for evaluation. Image segmentation includes single-object image segmentation and multi-object image segmentation. Six methods are used for single-object image segmentation, while k-means and U-net are used for multi-object segmentation. We extract nine features from the images in EMDS-5 and use the Support Vector Machine (SVM) classifier for testing. In terms of image classification, we select the VGG16 feature to test SVM, k-Nearest Neighbors, Random Forests. We test two types of retrieval approaches: texture feature retrieval and deep learning feature retrieval. We select the last layer of features of VGG16 network and ResNet50 network as feature vectors. We use mean average precision as the evaluation index for retrieval. EMDS-5 is available at the URL:https://github.com/NEUZihan/EMDS-5.git. [ABSTRACT FROM AUTHOR]