Study on the removal of olefins from naphtha by clay loaded with trifluoromethanesulfonic acid

Journal of Porous Materials - In this work, a trifluoromethanesulfonic acid (TFOH) modified clay (TFOH-Clay) was developed for the removal of trace olefins in heavy naphtha. 5%TFOH-Clay can...     

增材制造设计(DfAM)下的金刚石晶格结构研究

基于机械零件的增材制造设计,用仿自然式方法设计了一种金刚石晶格结构,对晶胞相对密度和等效弹性模量建立了数学模型,并拟合得到二者关系式,一系列的仿真验证结果显示理论研究所得关系式具有一定的正确性;对金刚石晶格填充结构提出变密度假设,并用仿真和压缩试验的方法进行了验证,结果显示,填充单元密度以受力点为中心渐渐减小的变密度结构拥有更好的抗压性能,同时验证了本文得出的等效弹性模量数学模型具有一定的正确性和适用性.本研究结果可应用于承压机械零件的实体夹层填充,对机械零件进行应力匹配变密度设计,以达到机械结构轻量化的目标.     

基于现代审美下的赣傩艺术元素分析与设计应用

目的 赣傩艺术元素内容丰富并保存完整,是研究我国古代赣傩地区文化与艺术的珍贵史料.研究赣傩艺术元素的提取及衍生图案的设计方法,在设计应用中表达赣傩傩面具、选取傩舞形和构筑傩庙魂,能给当代赣傩艺术元素的产品设计以指引.方法 通过直接引用、形意结合和局部强化的设计方法,增厚其丰富内涵、设计个性和主题意蕴.通过现代化审美中"绿色化、感性化、地域化"的要求,遵循"以退为进,化繁为简""多元并存,个性体现"的原则,强化赣傩艺术元素的独特风格.结论 用现代的手法,将特色的赣傩艺术元素植入到理性的产品设计中去,使赣傩艺术元素与现代设计思想有机结合.在继承传统文化与现代流行中找到契合点,深入挖掘赣傩艺术元素蕴含的文化精神、文化胸怀和文化自信,使中华传统文化在现代审美下进行创新再现.     

洋葱蛋白及多肽的制备及其体外抗氧化活性评价

对洋葱蛋白、4种不同水解度的粗洋葱多肽、4种分子量范围的纯化多肽组分的水解度、分子量、氨基酸组成、DPPH·清除能力进行了研究。结果表明,洋葱蛋白酶解后分子量大幅降低,必需氨基酸组分和支链氨基酸组分均有所提高,DPPH·清除能力大幅提高,且与水解度成正比;在4种水解度的粗洋葱多肽的纯化组分中,P-III(分子量为1~5kDa)的DPPH·清除能力均为最强,这归功于其合适的分子量分布。该研究为洋葱的高值化利用提供了理论依据。     

纳米Co/rGO磁性复合吸附材料的制备及对Cu2+的吸附性能

以改进Hummer法制备的薄片状氧化石墨烯(GO)为载体和模板负载钴离子，然后采用原位还原法制得纳米金属Co/石墨烯磁性复合吸附材料(Co/rGO)，并将其应用于对Cu2+的吸附和脱除，以期为高效可复用的铜离子脱除剂的合成与应用提供指导。实验结果证实，Co/rGO复合材料具有超顺磁性，能够很方便的使用磁铁进行分离并在无磁场情况下振荡分散。Co/rGO复合材料对Cu2+具有稳定的吸附/脱附性能，实验条件下对Cu2+的最大吸附容量达到117.5 mg/g且5 min内实现吸附平衡，远优于其原料GO的60 min吸附容量27.6 mg/g。本工作系统考察了NaOH加入量、络合剂种类、溶剂种类等关键因素对Co粒子在rGO载体上形貌和分布特性的影响，比较了不同合成条件下的复合材料对Cu2+吸附效果的影响，并对优选条件下制备的Co/rGO复合材料进行了FT-IR, XRD, SEM表征。研究结果表明，纳米Co/rGO磁性材料对Cu2+的吸附过程更符合Freundlich模型，属于多层吸附。室温下吸附焓ΔH=17.81 kJ/mol，吸附反应平衡常数Kθ=3.65。当初始Cu2+浓度为39.22 mg/L时，对Cu2+的吸附率为93.47%，五次吸附/脱附循环后吸附容量仍保持在初始值的94%，每次吸附后溶液中残余Cu2+浓度均满足钴电解液对杂质铜离子的浓度去除要求(5 mg/L)或GB 8978-1996污水综合排放标准3级(2 mg/L)，有望在相关领域发挥作用。     

环境设计专业学习空间设计探析--以教室改造的参与式设计为例

学习空间的设计必须基于教育理念、学习模式、技术设施与精神氛围等因素展开,以更开放、灵活及多样化的有趣形态来吸引使用者。对于已掌握基本设计方法并具有一定设计思维的环境设计专业学生而言,设计与自身学习行为关联的学习空间,能体现"参与式设计"的特殊意义:即一方面以有着直观体验感受的熟悉空间作为训练案例来有效地整合设计能力;另一方面则通过用户与设计者身份的叠加,从使用者体验一维到对空间发展的专业预设考虑一维共同实现学习空间设计理念的更新。     

A Polyallylamine Anchored Amine‐Rich Laser‐Ablated Graphene Platform for Facile and Highly Selective Electrochemical IgG Biomarker Detection

Current immunosensors have an insufficient number of binding sites for the recognition of biomolecules, which leads to false positive or negative results. In this research, a facile, cost‐effective, disposable, and highly selective electrochemical immunosensing platform is developed based on cationic polyelectrolyte polyallylamine (PAAMI) anchored laser‐ablated graphene (LAG). Here, for the first time, PAAMI is introduced to stabilize LAG flakes, while retaining the intrinsic thermal and electronic properties of the substrate by noncovalent π–π interaction and electrostatic physical absorption. The sensing platform offers a suitable number of anchoring sites for the immobilized antibodies by providing ? NH₂ functional groups. The proper grafting of PAAMI is confirmed through X‐ray photoelectron spectroscopy and Raman spectroscopy. The immunosensing platform is applied to detect immunoglobulin (IgG) biomarkers as a proof of concept. Under optimized conditions, the sensing platform exhibits a linear range of 0.012–15 and 15–352 ng mL^?1 with a limit of detection of 6 pg mL^?1 for IgG detection with high selectivity. Based on the analysis, the developed immunosensing platform can be used for point‐of‐care detection of IgG in clinical diagnostic centers. Furthermore, the developed strategy is well suited for the detection of other cancer biomarkers after immobilizing the relevant antibodies.     

EGGS: Sparsity-Specific Code Generation

Sparse matrix computations are among the most important computational patterns, commonly used in geometry processing, physical simulation, graph algorithms, and other situations where sparse data arises. In many cases, the structure of a sparse matrix is known a priori, but the values may change or depend on inputs to the algorithm. We propose a new methodology for compile-time specialization of algorithms relying on mixing sparse and dense linear algebra operations, using an extension to the widely-used open source Eigen package. In contrast to library approaches optimizing individual building blocks of a computation (such as sparse matrix product), we generate reusable sparsity-specific implementations for a given algorithm, utilizing vector intrinsics and reducing unnecessary scanning through matrix structures. We demonstrate the effectiveness of our technique on a benchmark of artificial expressions to quantitatively evaluate the benefit of our approach over the state-of-the-art library Intel MKL. To further demonstrate the practical applicability of our technique we show that our technique can improve performance, with minimal code changes, for mesh smoothing, mesh parametrization, volumetric deformation, optical flow, and computation of the Laplace operator.