Influence of Y~(3+),Yb~(3+),Gd~(3+) cations on structural and electromagnetic properties of CuFe_2O_4 nanoferrites prepared via one step sol-gel method

Rare earths(REs) play a key role in distorting spinel structure by creating some defects at the lattice sites and make them suitable for magnetodielectric applications.In the present study,the nanoferrites of CuRE_(0.02)Fe_(1.98)O_4,where REs=Y~(3+),Yb~(3+),Gd~(3+),were prepared using one step sol-gel method.The prepared samples are copper ferrite(CFO),yttrium doped copper ferrite(Y-CFO),ytterbium doped copper ferrite(Yb-CFO) and gadolinium doped copper ferrite(Gd-CFO),respectively.The single-phase structure of all the REs doped nanoferrites was determined by X-ray diffraction(XRD) analysis.The porosity,agglomerations and grain size of the REs doped copper ferrite were examined using field emission scanning electron microscopy(FESEM) analysis.Fourier transform infrared spectroscopy(FTIR)elaborates the phase formation and environmental effects on the REs doped nanoparticles(NPs).The recorded room temperature M-H loops from a vibrating sample magnetometer(VSM) elucidate the magnetic properties of the REs doped spinel nanoferrites.The magnetic saturation(M_s) was calculated in the range of 23.08 to 51.78 emu/g.The calculated coercivity values(272.6 to 705.60 Oe) confirm the soft magnetic behavior of REs doped copper ferrites.Furthermore,the electromagnetic and dielectric properties were assessed using a Vector network analyzer(VNA) from 1 to 6 GHz.The permeability,permittivity,dielectric tangent loss and electric modulus of the REs doped spinel ferrites illustrate that the prepared NPs may be suitable for microwave and high frequency applications.     

Investigation of structural,optical, magnetic,and dielectric properties of calcium hexaferrite synthesized in presence of Azadirachta indica and Murraya koenigii leaves extract

M-type calcium hexaferrite- CaFe₁₂O₁₉ (CaM) has been prepared in presence of Azadirachta indica, and Murraya koenigii leaves extracts, followed by calcination at 650 °C for 3h. It was observed that the presence of phytochemicals in both leaves extract plays a vital role in deciding the structural, optical, microstructural, magnetic, and dielectric properties of prepared CaM hexaferrites. Prepared samples were characterized using FT-IR, XRD, UV–Vis, SEM, VSM, and dielectric measurements. FTIR, UV– Vis, and antioxidant assay confirmed the presence of phenolic content and antioxidant property of plant extract. This further resulted in the formation of a pure hexagonal phase as revealed by the XRD analysis. The surface morphology of prepared ferrites modified through this greener route was illustrated by the spongy appearance of ferrites in SEM micrographs.The saturation magnetization for the CaM powder prepared using Murraya koenigii leaves extract is 11.78 Am²/kg, while that prepared from Azadirachta indica leaves extract is 3.56 Am²/kg. Both samples show a magnetically soft nature, with a multidomain structure. The energy bandgap was also observed to be 2.01 eV. Moreover, the calcium ferrite synthesized by Murraya koenigii leaves had ε’_max ～ 25 and that synthesized in presence of Azadirachta indica leaves had ε’_max ～ 200 at ～20 Hz.     

Preparations and characterizations of Ca doped Ni–Mg–Mn nanocrystalline ferrites for switching field high-frequency applications

Ca-doped Ni–Mg–Mn spinel ferrites with compositions of Ni_0·5Mg_0·3Mn_0.2Ca_xFe_2-xO₄ (x = 0, 0.1, 0.2, 0.3, 0.4, 0.5) were prepared via sol-gel auto-ignition technique. TGA/DTA, FTIR, XRD, FESEM, and VSM were employed to evaluate the thermal, spectral, structural, morphological, and magnetic features of Ca-doped Ni–Mg–Mn spinel ferrites. TGA/DTA curves show the weight loss in the sample. This weight loss was attributed to the oxidation and decomposition of the sample contents at a temperature of 500 °C. XRD reveals a single-phase structure of the Ni–Mg–Mn nano ferrites. A single-phase orthorhombic structure was confirmed for Ca-doped Ni–Mg–Mn ferrites. Structural parameters such as lattice parameter, ‘da’, ‘db’, ‘dc’, and ‘dv’ were evaluated using unit cell software. The absorption peaks at 427 to 538 cm^?1 confirmed the spinel structure, which was evaluated using FTIR. FESEM analyses showed that the agglomerations increased with the doping of Ca in Ni–Mg–Mn ferrites. Remanence, Y–K angles, saturation, coercive force, magnetic squareness, magnetic moment, and anisotropy constant were determined for Ca-doped Ni–Mg–Mn spinel ferrite samples. It is noticed that saturation increases from 29.157 to 51.322 emu/g, whereas remanence increased from 5.34 to 9.40 emu/g, respectively. The permeability, anisotropy constant, and magnetic moments were also found to increase with Ca doping. However, the Y–K angles increased with Ca concentration in Ni–Mg–Mn nano ferrites. In addition, the switching field distribution (SFD) and high-frequency response of all the Ca-doped Ni–Mg–Mn samples were also evaluated. Ca-doped Ni–Mg–Mn samples are suggested to be suitable for switching, filters, inductors, and microwave absorption applications because of the superparamagnetic nature of the prepared spinel ferrites.     

基于统计学习的自适应文本聚类

针对文本数据的高维性和稀疏性从而使传统的聚类算法在文本聚类应用中的表现不能让人满意的问题,通过计算文档相似度矩阵,在聚类过程中动态地统计学习已划分和未划分文本集合的相关信息,探测剩余未划分的数据集中的与已划分类簇覆盖度较小的最大密集区域,逐步生成预定数目的初始聚类中心集合,最后将剩余文档划分到最相似的初始聚类中心集合完成聚类,从而有效地减小了划分聚类算法对初始聚类中心的敏感性。算法中的一些阈值参数均通过在聚类过程中动态地对数据集进行统计学习得到,避免了多数聚类算法通过经验或实验设定阈值参数的盲目性,在不同     

基于向量空间模型和专利文献特征的相似专利确定方法

为了确定专利文献的相似性,帮助企业进行专利申请、保护和利用,提出基于向量空间模型（VSM）和专利文献特征的相似专利确定方法.依据专利文献的信息特征构建专利模型树,定义了专利模型树和专利模型树的节点.通过分析专利模型树的节点属性值,采用基于向量空间模型的文本分类技术,以专利名称和专利摘要的加权相似度作为专利文献分类的依据,对专利文献进行分类,然后在类内根据专利文献特征的相似性确定相似专利,并根据企业的实际应用需求,分析专利文献要素权重确定的几种方法.应用示例验证了该方法能够有效地进行专利分类和相似专利检索.     

基于RSS的层次结构用户兴趣模型的分析与设计

在网络迅速普及的今天,对用户的网络访问行为进行分析并建立用户兴趣模型是经实践证明的有效方法．提出了基于RSS的层次结构用户兴趣模型的建立与动态更新方案,成功地解决了兴趣模型的提取、表示和更新等问题．     

利用PROTEUS VSM建立单片机虚拟实验室

介绍单片机系统仿真工具PROTEUS VSM及其在单片机系统仿真中的应用,并详细阐述采用PROTEUS VSM建立单片机虚拟实验室的方法.     

基于聚团词的大规模文本转载识别算法

文本转载识别是指从大规模文本库中检测出内容相同或相近的文档集合,在热门话题检测、搜索引擎结果凝练、学术文章抄袭识别等诸多应用上,存在普遍的需求。为适应网络文本转载形式的日趋多样化,并进一步提升实用系统效率,对各种文本特征及比较算法进行了研究分析,提出了基于聚团词的大规模文本转载识别算法,即:依据词语的分布属性,识别并提取高得分聚团词用于表征文本,之后通过对文本集进行扩展线性比较与多维比较两次操作,最终筛选出转载识别结果。对比实验表明:该算法在准确率、召回率与效率上有较高的综合性能。     

面向基础教育阶段的教学资源推荐系统

针对传统推荐方法应用于教学场景存在数据稀疏、缺乏对课程内容和教师上下文环境分析的问题,设计了一种面向基础教育阶段的网络教学资源推荐系统。该系统由数据层、离线层和在线层组成:1)数据层基于马尔可夫链和向量空间模型构建课程模型和资源模型,综合教师个人注册信息和课程模型的节点构建教师模型;2)离线层使用词频逆向文件频率(TF-IDF)算法提取课程和资源的内容特征,初始化并优化数据层的课程模型和资源模型,进一步应用关联规则挖掘和相似度量方法,计算任意两资源或课程与资源之间的关系,并结合课程模型推理教师模型,产生用于推荐的中间结果;3)在线层采用加权混合的方式产生推荐资源列表。该系统现已应用于某教育资源共享平台中,可实现对其中60万条资源的个性化推荐。     

基于支持向量机的隐含语意特征选择方法

隐含语意索引(LSI)是一个能有效捕获文档中词的隐含语意特征的方法。然而,用该方法选择的特征空间对文本分类来说可能不是最适合的,因为这种方法按照词的变化排序特征,而没有考虑到分类能力。支持向量机(SVM)高度的泛化能力使它特别适用于高维数据例如文档的分类。为此提出基于支持向量机的特征提取方法用于选择适于分类的LSI特征。该方法利用SVM高度泛化的分类能力, 通过使用在每一个规则下训练的分类器的参数对第k个特征对反向平方分解面的贡献w2k的值进行估计。实验表明当需要比LSI更少的训练和测试时间时,该方法能够以更为紧凑的表示方式提高分类性能。