Paper capillary force driven hollow channel as a platform for multiphase flows bioassays

AbstractThis paper develops a simple, inexpensive, and portable diagnostic assays that may be useful in remote settings, and in particular, in less industrialized countries where simple assays are becoming increasingly important for detecting disease and monitoring health. In this assays, the paper capillary force is first used to transport complex fluids such as whole blood or colloidal suspensions that contain particulates in a new type channel - paper capillary driven hollow channel, which offset the disadvantages of current paper microfluidic technologies. To demonstrate the various applications of the paper capillary force driven hollow channel, several devices are design and made to complete the purpose of exhibiting laminar flow in a T-junction microchannel, sheath a core stream in a three-inlet channel and transportation whole blood

Fractal approach to calculate the thermal conductivity of moist soil

The ground heat exchanger (GHE) is a key component in the design of a GSHP system and the effective thermal conductivity is one of the most important parameters that determine the heat transfer underground. In this paper, the effect of particle sizes and distributions on the sand thermal conductivity were studied both experimentally and analytically. Fractal method was considered for simulating the thermal conductivity of both dry and moist, unsaturated sand. Seven types of dry sand samples and six types of moist, unsaturated sand were selected in the experiments and results showed that both porosity, fractal dimension and particle size ratio affect the sand thermal conductivity. Based on the fractal theory, the fractal models were applied to predict the sand thermal conductivity under both dry and wet conditions. By comparing to the experimental findings, the proposed model was able to predict the variation on the sand thermal conductivity. However, the contact thermal resistance and water distribution pattern are two key impacts on the soil behaviors and need to be further studied

Structural Engineering of Hierarchical Micro‐nanostructured Ge-C Framework by Controlling the Nucleation for Ultralong Life Li Storage

The rational design of a proper electrode structure with high energy and power densities, long cycling lifespan, and low cost still remains a significant challenge for developing advanced energy storage systems. Germanium is a highly promising anode material for high-performance lithium ion batteries due to its large specific capacity and remarkable rate capability. Nevertheless, poor cycling stability and high price significantly limit its practical application. Herein, a facile and scalable structural engineering strategy is proposed by controlling the nucleation to fabricate a unique hierarchical micro-nanostructured Ge-C framework, featuring high tap density, reduced Ge content, superb structural stability, and a 3D conductive network. The constructed architecture has demonstrated outstanding reversible capacity of 1541.1 mA h g −1 after 3000 cycles at 1000 mA g −1 (with 99.6% capacity retention), markedly exceeding all the reported Ge-C electrodes regarding long cycling stability. Notably, the assembled full cell exhibits superior performance as well. The work paves the way to constructing novel metal-carbon materials with high performance and low cost for energy-related applications

The Effect of Spiritual Leadership on Employee Effectiveness: An Intrinsic Motivation Perspective

Drawing on spiritual leadership theory and intrinsic motivation theory, we proposed a homologous multilevel model to explore the effectiveness of spiritual leadership on employees’ task performance, knowledge sharing behaviors and innovation behaviors at the individual level. With questionnaires rated by 306 pairs of employees and their supervisors in 26 teams from the energy industry in mainland China, we conduct multilevel analysis to examine our hypotheses. The results show that spiritual leadership was positively related to employee task performance, knowledge sharing behaviors and innovation behavior, when we controlled for possible confounding effects of moral leadership and benevolent leadership, and ruled out alternative explanation of ethical leadership. The theoretical and practical implications are discussed

A Primary Analysis on the Food Recall System: A Hard Mountain for China to Climb

Food safety concerns people's lives and property safety, the repeated food scandals of Chinese food safety brings a great shock at home and abroad. Chinese government has put forward many policies and schemes to solve the repeated food problems in China in recent years. There are several important measures in the process of dealing with the food safety problem. Among them, food recall is the most important and linking one that should be taken when the unsafe food has already flowed into the market. The establishment of Chinese food recall system has a great significance and shows a good start and a positive attitude of China in its long fight of food safety. However, in China, the food recall system is still in the bud and dose not get enough attention, many flaws and imperfection of Chinese food recall system has been revealed in its practical implementation. This paper attempts to find the flaws existing in Chinese food recall system and analyzes the complexity and diversity of Chinese present condition from the perspective of the theory of regulation. It’s concluded that when the food safety supervisory authorities formulate the detailed plans or regulations of food recall system, they should explore a new path of food recall system and consider more the current condition and the specialty of the country rather than just put other countries’ experience rigidly.

Strong enhancement of photoresponsivity with shrinking the electrodes spacing in few layer GaSe photodetectors

A critical challenge for the integration of the optoelectronics is that photodetectors have relatively poor sensitivities at the nanometer scale. It is generally believed that a large electrodes spacing in photodetectors is required to absorb sufficient light to maintain high photoresponsivity and reduce the dark current. However, this will limit the optoelectronic integration density. Through spatially resolved photocurrent investigation, we find that the photocurrent in metal-semiconductor-metal (MSM) photodetectors based on layered GaSe is mainly generated from the photoexcited carriers close to the metal-GaSe interface and the photocurrent active region is always close to the Schottky barrier with higher electrical potential. The photoresponsivity monotonically increases with shrinking the spacing distance before the direct tunneling happen, which was significantly enhanced up to 5,000 AW-1 for the bottom contacted device at bias voltage 8 V and wavelength of 410 nm. It is more than 1,700-fold improvement over the previously reported results. Besides the systematically experimental investigation of the dependence of the photoresponsivity on the spacing distance for both the bottom and top contacted MSM photodetectors, a theoretical model has also been developed to well explain the photoresponsivity for these two types of device configurations. Our findings realize shrinking the spacing distance and improving the performance of 2D semiconductor based MSM photodetectors simultaneously, which could pave the way for future high density integration of 2D semiconductor optoelectronics with high performances.Comment: 25 pages, 4 figure

Learning to Denoise Unreliable Interactions for Link Prediction on Biomedical Knowledge Graph

Link prediction in biomedical knowledge graphs (KGs) aims at predicting unknown interactions between entities, including drug-target interaction (DTI) and drug-drug interaction (DDI), which is critical for drug discovery and therapeutics. Previous methods prefer to utilize the rich semantic relations and topological structure of the KG to predict missing links, yielding promising outcomes. However, all these works only focus on improving the predictive performance without considering the inevitable noise and unreliable interactions existing in the KGs, which limits the development of KG-based computational methods. To address these limitations, we propose a Denoised Link Prediction framework, called DenoisedLP. DenoisedLP obtains reliable interactions based on the local subgraph by denoising noisy links in a learnable way, providing a universal module for mining underlying task-relevant relations. To collaborate with the smoothed semantic information, DenoisedLP introduces the semantic subgraph by blurring conflict relations around the predicted link. By maximizing the mutual information between the reliable structure and smoothed semantic relations, DenoisedLP emphasizes the informative interactions for predicting relation-specific links. Experimental results on real-world datasets demonstrate that DenoisedLP outperforms state-of-the-art methods on DTI and DDI prediction tasks, and verify the effectiveness and robustness of denoising unreliable interactions on the contaminated KGs