Effect of structure on the properties of polyurethanes based on aromatic cardanol-based polyols prepared by thiol-ene coupling

Industrial-grade cardanol and 2-mercaptoethanol were reacted to generate hydroxyl-functionalized cardanol by UV, free-radical-initiated thiol-ene coupling between the double bond moieties of the cardanol long carbon side chain and thiol functional groups. The average hydroxyl number of the hydroxyl-functionalized cardanol was controlled by reaction time, with the hydroxyl values of this ranging within 168–201 mg KOH g⁻¹. This cardanol was then used as a polyol to prepare cardanol-based polyurethane with hexamethylene diisocyanate and a NCO/OH ratio of 1. To compare the effect of cardanol-based polyols with the properties of cardanol-based polyurethane, cardanol modified with 10-undecylenate was used as a raw material to prepare cardanol-based polyols, including the long carbon chain of 10-undecylenate. All properties were examined, and data revealed that cardanol-based polyols including this long carbon chain can improve the hydrophobic and mechanic properties of the cardanol-based polyurethane.     

Solvent-free synthesis of C9 and C10 branched alkanes with furfural and 3-pentanone from lignocellulose

Jet fuel range branched alkanes were first synthesized under solvent-free conditions by the aldol condensation of furfural and 3-pentanone from lignocellulose followed by the one-step hydrodeoxygenation (HDO). Among the investigated solid base catalysts, CaO and KF/Al₂O₃ demonstrated the highest activity for the aldol condensation reaction of furfural and 3-pentanone. The aldol condensation product of furfural and 3-pentanone (liquid at room temperature) was directly hydrodeoxygenated to 4-methyl-nonane and 4-methyl-octane. These alkanes have low freezing points (174.1 K and 159.8 K) and can be blended into jet fuel without hydroisomerization. Among the investigated HDO catalysts, the Ni-Cu/SiO₂ exhibited the best performance.     

Adsorption Mechanism and Electrochemical Properties of Methyl Blue onto Magnetic CoxCu(1–x)Fe2O4 Nanoparticles Prepared via an Alcohol Solution of Nitrate Combustion and Calcination Process

Journal of Inorganic and Organometallic Polymers and Materials - Magnetic CoxCu(1–x)Fe2O4 nanoparticles were prepared via an alcohol solution of nitrate combustion and calcination process....     

读屏时代纸质书籍设计策略初探

文章通过比较纸质书籍与电子书在多个方面的异同点,分析纸质书籍在数字阅读迅速发展的读屏时代所扮演的独特角色,并以此为基础探讨纸质书籍的设计发展的方向即“心”的方向。纸质书籍在数字化时代不但不会消亡,而且会以新的身份出现并被赋予新的意义。     

无烟煤熔融盐催化气化反应特性研究

根据煤催化气化原理及熔融盐特性,设计了一套小型熔融盐催化煤气化实验装置。针对无烟煤燃烧特性差的特点,在实验中研究了温度和二氧化碳流量对气化结果的影响,并对影响机理进行分析。     

空间可变有限混合模型

目的 有限混合模型是一种无监督学习方法,它被广泛的应用到数据分类任务中。然而,在图像分割过程中,由于有限混合模型没有引入邻域像素间的空间关系,导致了图像分割结果对噪声非常敏感。为了增强有限混合模型的抗噪性,提出一种新的空间可变有限混合模型。方法 该模型通过在像素的先验分布中引入一种新的空间关系来降低噪声对图像分割结果的干扰。在构建空间关系的过程中,利用形态学膨胀原理将空间邻域内特征值出现的概率而不是特征值本身进行膨胀操作,然后通过根据具有最大概率的分类标记在高斯混合模型迭代地计算过程中进行局部像素空间平滑,从而起到抑制噪声干扰的作用。结果 本文实验包含了人工合成图像和医学CT图像的图像分割实验。在人工合成图像分割实验中,对人工合成图像添加了不同程度的噪声来测试本文模型和对比模型对噪声抑制能力的高低;对医学CT图像进行图像分割实验,以是比较本文模型与对比模型之间在实际图像分割中的效果。结论 实验数据显示,本文提出的模型在噪声抑制能力上,图像分割精度和计算效率上均有更优的性能。     

引入动态内存分析的微信撤回消息恢复方法

作为广受用户青睐的即时通信系统,微信在给人们生活带来极大便利的同时,也给不法分子提供了违法犯罪的新手段、新工具。微信聊天记录作为我国法律中明确列出的电子证据类型,其有效性引起广泛关注,使得微信聊天记录的恢复成为相关领域的研究热点。针对现有的聊天记录恢复研究多集中于删除消息的恢复,撤回消息的恢复尚未取得有效进展,通过研究PC版微信运行过程中的动态内存管理机制,分析撤回消息在动态内存中的特征字符及字段结构,对比文本、表情、图片等不同类型的消息在内存中的存储原理,提出一种基于动态内存分析的微信撤回消息恢复方法。利用Python语言编写的工具实现了对微信撤回消息原文、撤回状态、撤回方微信ID等内容的批量恢复,验证了该方法的有效性。     

国内外大学车辆工程专业人才培养情况分析

国外多所大学与国内高校的专业和课程设置情况有所不同,国外车辆工程专业人才培养既注重学生综合素质培养又满足学生个体发展需求,人才培养紧密结合企业与社会需求和科学研究,课程成绩评价方式多样化,人才培养服务体系比较完善。研究国外人才培养方案能为我国高校车辆工程专业以及其他工科专业的人才培养体系的改革和创新提供借鉴。     

正交法在“W型”锅炉燃烧调整中的应用

采用正交试验法设计燃烧调整试验,寻找W型燃烧锅炉高效低污染燃烧方式。试验发现:正交法设计的试验结果可对比性强,能有效降低试验次数,节约资源;预备性试验对于正交试验方案设计至关重要;氧量对炉效、NOx生成量及主汽压均有显著影响;燃尽风对抑制NOx生成效果明显,但燃尽风过量时会降低炉效并影响主汽压稳定;二次风箱压力对NOx生成量有轻微影响,对主汽压有一定影响;配风方式对各项指标影响不明显;通过试验否定了早期"二次风门实际开度左大右小,需对风门进行检修"的判断,节约了人力物力;发现低氮燃烧器改造时所设计的燃尽风喷口过大,风门可利用行程区间短等问题。     

Improving classification-based diagnosis of batch processes through data selection and appropriate pretreatment

This work considers the application of classification algorithms for data-driven fault diagnosis of batch processes. A novel data selection methodology is proposed which enables online classification of detected disturbances without requiring the estimation of unknown (future) process behavior, as is the case in previously reported approaches.The proposed method is benchmarked in two case studies using the Pensim process model of Birol et al. (2002) implemented in RAYMOND. Both a simple k Nearest Neighbors (k-NN) and complex Least Squares Support Vector Machine (LS-SVM) are employed for classification to demonstrate the generic nature of the proposed approach. In addition, the influence of different data pretreatment methods on the classification performance is discussed, together with a motivation for selecting the correct pretreatment steps. Finally, the influence of the number of available training batches is studied.The results demonstrate that a good classification performance can be achieved with the proposed data selection method even with a low number of faulty training batches by exploiting knowledge on the nature of the to-be-diagnosed faults in the data pretreatment. This provides a proof of concept for classification-based batch diagnosis and demonstrates the importance of incorporating process insight in the construction of data-driven process monitoring and diagnosis tools.