Using d-limonene as the non-aromatic and non-chlorinated solvent for the fabrications of high performance polymer light-emitting diodes and field-effect transistors

Aiming to environment protection, green solvents are crucial for commercialization of solution-processed optoelectronic devices. In this work, d-limonene, a natural product, was introduced as the non-aromatic and non-chlorinated solvent for processing of polymer light-emitting diodes (PLEDs) and organic field effect transistors (OFETs). It was found that d-limonene could be a good solvent for a blue-emitting polyfluorene-based random copolymer for PLEDs and an alternating copolymer FBT-Th₄(1,4) with high hole mobility (μ_h) for OFETs. In comparisons to routine solvent-casted films of the two conjugated polymers, the resulting d-limonene-deposited films could show comparable film qualities, based on UV–vis absorption spectra and observations by atomic force microscopy (AFM). With d-limonene as the processing solvent, efficient blue PLEDs with CIE coordinates of (0.16, 0.16), maximum external quantum efficiency of 3.57%, and luminous efficiency of 3.66 cd/A, and OFETs with outstanding μ_h of 1.06 cm² (V s)⁻¹ were demonstrated. Our results suggest that d-limonene would be a promising non-aromatic and non-chlorinated solvent for solution processing of conjugated polymers and molecules for optoelectronic device applications.     

Data monitoring and sports injury prediction model based on embedded system and machine learning algorithm

Managing sports performance is very important in the sports industry. Performance, the executives, centers on boosting competitor execution and decreasing the danger of injury. Several factors contribute to these goals, including player health, emotional status, exercise load and physical intensity requirements. Generally speaking, injury prediction is an essential component of injury prevention, and successful identification of injury prediction is a primary indicator for effective prevention. The proposed Artificial Neural Network (ANN) objective is to develop and use early-doing ability and exercise load data to validate a hierarchical machine learning prediction system with accurate detection of player injuries. The physical and workload that requires detection of this early personalized damage can be avoided with specific help. The framework is used to test 21 soccer players’ sports information from various sources, including gathered and inside burden information, outside burden information, and review information. The entirety of this information is fused into the proposed framework to improve the exactness of harm expectation. This calculation distinguishes competitors in danger of injury, with their early intervention available.     

Phase transition of lithiated-spinel Li2Mn2O4 at high temperature

1 Introduction Lithium manganese oxides are the most attractive cathode materials for rechargeable lithium-ion batteries because of their low-cost and less toxicity when compared with either cobaltates or nickelates[1?3]. Among these oxides, the spinel-fr…     

A software tool for translating deterministic model results into probabilistic assessments of water quality standard compliance

Most assessments of whether a water body will comply with pollutant standards after modification of land use, loading, or climate change are based on the results of deterministic simulation models. These models, including those used to support the United States Environmental Protection Agency (USEPA) total maximum daily load (TMDL) program, typically do not account for common sources of assessment uncertainty. Instead, model results are typically represented by a time series of predicted pollutant concentration values or the parameters of a frequency-based distribution of these values over a specified time period. The rate of exceedance of relevant pollutant limits is then assessed directly from this time series or distribution to determine standard compliance. In this way, sampling and analysis-based variability and model uncertainty are typically ignored, although they may substantially influence the probability of non-compliance. To help address this problem, we introduce ProVAsT (Probabilistic Water Quality Standard Violation Assessment Tool), a software tool encoded in the graphical model-based package Analytica^®. Here, we present a version of ProVAsT which translates model-predicted in situ fecal indicator bacteria (FIB) pollutant concentrations into the expected frequency of water quality standard violations and provides a Bayesian measure of the degree of confidence in this assessment. We call this version ProVAsT-FIB. Along with inputting their own simulation model results, users can specify the particular water quality analysis methods employed (e.g. the analytic procedure used and the number and volume of sample aliquots) as well as the numeric limits pertaining to local water quality standards. It is our hope that ProVAsT will encourage the rational consideration of uncertainty and variability in water quality assessments by reducing the burden of complex statistical calculations.     

Experimental investigation and thermodynamic assessment of the Zn–Cu–Sr ternary system

Zn–Cu–Sr alloys play a crucial role in the development of biodegradable implant materials based on zinc. The current study aimed to investigate the phase equilibria of the Zn–Cu–Sr ternary system in the Cu–Zn-rich region, through experimental analysis. For this purpose, fifteen and fourteen samples were respectively prepared and equilibrated at 350 and 400 °C, to determine the isothermal sections. The equilibrated alloys were then subjected to various analytical techniques such as scanning electron microscopy (SEM) equipped with energy dispersive spectrometry analysis (EDS), electron probe microanalysis (EPMA), and powder X-ray diffraction analysis (XRD). The analysis revealed the presence of five three-phase equilibria and ten two-phase equilibria in the two isothermal sections. Differential scanning calorimetry (DSC) was used to investigate the phase transformation temperature with constant values of 8 at. % Sr and 30 at. % Cu. The obtained experimental results were used to perform a thermodynamic assessment of the Zn–Cu–Sr system especial in Zn-rich region using the calculation of phase diagrams (CALPHAD) method. The modified quasi-chemical model (MQM) was used to model the liquid solution, while the compound energy formalism (CEF) was used to represent Gibbs free energies of the solid phases. The present obtained thermodynamic parameters were found to accurately reproduce the experimentally measured phase relationships in the Zn–Cu–Sr ternary system.     

Phase equilibria and microstructure development in Mg-rich Mg-Gd-Sr alloys: Experiments and CALPHAD assessment

Mg-Sr alloys are promising to fabricate orthopedic implants. The alloying of rare earth elements such as Gd may improve the comprehensive mechanical properties of Mg-Sr alloys. The information on the phase diagram and the microstructure development are required to design chemical composition and microstructure of Gd alloyed Mg-Sr alloys. The phase equilibria and the microstructure development in Mg-rich Mg-Gd-Sr alloys (Gd, Sr < 30 at. %) are experimentally investigated via phase identification, chemical analysis, and microstructure observation with respect to the annealed ternary alloys. The onset temperatures of liquid formation are measured by differential scanning calorimetry. A thermodynamic database of the Mg-rich Mg–Gd–Sr ternary system is developed for the first time via CALPHAD (CALculation of PHAse Diagram) approach assisted by First-Principles calculations. The thermodynamic calculations with the developed database enable a well reproduction of the experimental findings and the physical-metallurgical understanding of the microstructure formation in solidification and annealing.     

基于FPGA的WSNs低功耗节点设计与实现

基于FPGA结构灵活、可编程性强,具有强大的并行数据处理能力等优点,采用低功耗的Zig Bee技术对无线传感器网络(WSNs)节点进行软硬件设计。植入Nios系统和相关的IP核到芯片EP1C12Q240C8中,协同具有多种工作模式的CC1100射频芯片,实现节点的功能,并进行了功耗测试及比较。结果表明:节点功耗低,具有良好的可扩展性和移植性,达到了WSNs节点设计的要求,可用于工业生产。     

基于灰关联分析的KFCM-LSSVM蒸发过程软测量

针对氧化铝蒸发过程出口浓度在线检测问题,提出一种基于灰色关联分析和核模糊聚类相结合的多支持向量机蒸发过程建模方法.该方法采用灰色关联分析提取影响出口浓度较大的变量,再用核模糊聚类算法将样本划分成不同的子类,并对各子类构建的支持向量机模型的输出综合加权得到最终输出.对蒸发过程生产数据进行实验,并与核模糊聚类最小二乘支持向量机(KFCM-LSSVM)相比较,结果表明新模型鲁棒性较强、精度较高、泛化性更好,可以用于蒸发过程的优化控制.