近红外荧光磁性复合载药脂质体的制备及应用

拟建立以近红外荧光磁性复合脂质体(NFMSLs)为模型药物载体、盐酸多柔比星(DOX)为包封药物的药物输送系统,研究了近红外荧光磁性载药复合脂质体(DOX-NFMSLs)的制备、性质及初步应用。采用共沉淀法制备Fe3O4磁流体,CdTe掺杂Se制备近红外量子点CdSeTe,薄膜分散法制备DOX-NFMSLs。用DOX荧光分光光度法测定DOX-NFMSLs的包封率和体外药物释放率;用DOX-NFMSLs与HepG2肝癌细胞共孵育来进行细胞成像和细胞毒性实验。结果表明,近红外CdSeTe量子点粒径约为5nm,闪锌矿结构,发射波长824nm。磷脂与胆固醇质量比为8∶1,药脂比为1∶20的DOX-NFMSLs平均粒径为252.9nm,Zeta电位为-48.6mV,理想释放药物温度为41℃,平均包封率为(74.84±0.89)%。DOX-NFMSLs对HepG2肝癌细胞有一定的抗癌效果。得到了具有良好磁响应、释药温度T=41℃、可近红外成像的载药脂质体。     

横磁处理对1K107纳米晶铁芯磁性能的影响及高频损耗分析

对Fe73.5Cu1Nb3Si13.5B9纳米晶铁芯分别进行普通热处理与横磁处理,并检测铁芯经两种热处理后的各项磁性能。获得了各频率下损耗与幅值磁密的关系。结果表明,横磁处理降低损耗效果较明显,该方法在高频电力电子变压器铁芯领域具有潜在应用前景。损耗分离结果表明,横磁处理后,磁滞损耗、涡流损耗、剩余损耗在总损耗中所占比例分别为不变、升高、降低。横磁处理后,磁畴结构改变,以磁矩旋转磁化为主要磁化方式,降低畴壁共振造成的异常损耗是除降低磁滞损耗外,降低总损耗的另一原因。     

溶液燃烧法合成Co3O4纳米粉体及热处理研究

分别以氨基乙酸、柠檬酸、葡萄糖为燃料,Co(NO_3)_2·6H_2O为氧化剂,采用溶液燃烧法合成Co_3O_4粉体,并对氨基乙酸为燃料合成的Co_3O_4粉体在500℃、600℃和700℃热处理,研究其结构、微观形貌和磁学性能。研究表明各燃料配制的前驱体溶液在300℃均可发生燃烧反应合成Co_3O_4粉体,以氨基乙酸为燃料时,合成粉体的颗粒较大,中间有气孔,分散性好,残留少量的氨基乙酸。n(氨基乙酸)∶n(硝酸钴)=1.11∶1时合成的Co_3O_4粉体600℃热处理后得到了高纯度、分散性好、平均径向尺寸80nm的Co_3O_4纳米粉体。以氨基乙酸为燃料合成的Co_3O_4产物在600℃和700℃热处理后,其矫顽力和剩磁值都比500℃热处理后的要小。     

Co掺杂CeO2稀磁氧化物陶瓷和纳米颗粒的铁磁性能

分别采用固相烧结法及激光液相烧蚀(LAL)技术,成功制备出Co掺杂CeO_2稀磁氧化物陶瓷块体和纳米颗粒。XRD和SEM研究发现所制备的材料具有良好的结晶性和形貌。Co掺杂CeO_2稀磁氧化物陶瓷块体和纳米颗粒均为多晶立方结构,与纯立方相的CeO_2结构相同,说明Co掺杂未形成其他结构和杂相。磁性测量表明固相烧结法和激光烧蚀液相法制备的Co掺杂CeO_2样品均具有较高的室温铁磁性,且远高于文献中报道的结果。将陶瓷块材经激光烧蚀成纳米颗粒后,纳米颗粒的铁磁性与陶瓷块材保持一致。这说明激光烧蚀法制备的纳米材料可以很好地保持母材的特性,是一种很好的纳米颗粒制备方法。根据XRD和SEM研究结果,笔者认为Co掺杂CeO_2陶瓷块材及纳米颗粒的室温铁磁性是内禀性质;磁性产生的机理源于氧空位诱导的铁磁性耦合。     

静电纺丝法制备的Co0.6Ni0.3Cu0.1Fe2O4和Co0.6Ni0.3Zn0.1Fe2O4纳米纤维的结构及磁性能

采用静电纺丝技术制备出表面光滑、直径均匀的Co_(0.6)Ni_(0.3)Cu_(0.1)Fe_2O_4/PVP和Co_(0.6)Ni_(0.3)Zn_(0.1)Fe_2O_4/PVP纳米纤维前驱丝,经500~900℃煅烧后得到Co_(0.6)Ni_(0.3)Cu_(0.1)Fe_2O_4和Co_(0.6)Ni_(0.3)Zn_(0.1)Fe_2O_4纳米纤维。用TG-DSC、XRD、SEM及VSM现代测试分析手段对Co_(0.6)Ni_(0.3)Cu_(0.1)Fe_2O_4和Co_(0.6)Ni_(0.3)Zn_(0.1)Fe_2O_4纳米纤维的结构、形貌及磁学性能进行测试表征。结果表明:在空气气氛中经500~900℃煅烧后可得到纯尖晶石相、结晶度良好的纳米纤维或短纤维;当温度为700℃时,Co_(0.6)Ni_(0.3)Cu_(0.1)Fe_2O_4和Co_(0.6)Ni_(0.3)Zn_(0.1)Fe_2O_4纳米纤维的形貌细长而光滑且直径相对均匀,大约为80nm;此时Co_(0.6)Ni_(0.3)Cu_(0.1)Fe_2O_4纳米纤维则保有较高的剩磁比(M_r/M_s)及矫顽力,分别为0.56和1 088.87Oe。在500℃、600℃、700℃、800℃、900℃煅烧后,Co_(0.6)Ni_(0.3)Zn_(0.1)Fe_2O_4纳米纤维的饱和磁化强度分别比Co_(0.6)Ni_(0.3)Cu_(0.1)Fe_2O_4纳米纤维增大了14.5%、7%、16%、10.7%、8%,而矫顽力则分别降低了38%、51%、50%、46%、46.7%。两种纳米纤维的饱和磁化强度及矫顽力存在差异,为CoNi铁氧体在电磁方面的应用提供了很好的参考。     

近红外荧光磁性复合载药脂质体的制备及应用

拟建立以近红外荧光磁性复合脂质体(NFMSLs)为模型药物载体、盐酸多柔比星(DOX)为包封药物的药物输送系统,研究了近红外荧光磁性载药复合脂质体(DOX-NFMSLs)的制备、性质及初步应用.采用共沉淀法制备FeO4磁流体,CdTe掺杂Se制备近红外量子点CdSeTe,薄膜分散法制备DOX-NFMSLs.用DOX荧光分光光度法测定DOX-NFMSLs的包封率和体外药物释放率;用DOX-NFMSLs与HepG2肝癌细胞共孵育来进行细胞成像和细胞毒性实验.结果表明,近红外CdSeTe量子点粒径约为5nm,闪锌矿结构,发射波长824 nm.磷脂与胆固醇质量比为8∶1,药脂比为1∶20的DOX-NFMSLs平均粒径为252.9 nm,Zeta电位为-48.6 mV,理想释放药物温度为41℃,平均包封率为(74.84±0.89)％.DOX-NFMSLs对HepG2肝癌细胞有一定的抗癌效果.得到了具有良好磁响应、释药温度T=41℃、可近红外成像的载药脂质体.     

Energy coupled to matter for magnetic alignment of rare earth–doped alumina

Energy coupled to matter (ECM) concepts such as magnetic field–assisted processing were used to align rare earth–doped alumina ceramics in the presence of applied fields. The addition of gadolinium and ytterbium dopants to alumina increased the magnetic susceptibility anisotropies, and induced magnetic torques that led to significant alignment of ceramic particles under the application of magnetic fields as low as 1.8?T. In comparison, undoped alumina materials showed minimal alignment under applied field strengths as high as 9?T. Density function theory modeling indicated that the specific dopant type dictated changes in the magnetic properties of different rare earth–doped alumina systems by directly affecting the magnetic moment localization and magnetocrystalline anisotropy.     

Investigation of magnetic field-assisted EDM of composites

The Electrical Discharge Machining (EDM) technique was performed under the magnetic field influence to determine the material removal mechanism as well as surface roughness (SR) of nonmagnetic material. This study presents an exploration of the hybrid EDM technique assisted by magnetic field, with an aim to improve process performance. Herein, magnetic field intensity, peak current, duration of pulse-on/off, tool electrode material, and SiC percentage distribution were opted as the machining parameters. The chosen parameters were analyzed for their effects on the material removal rate (MRR) and SR while machining of SiC-reinforced aluminum-based metal matrix composites. Taguchi methodology was adopted for optimization of process parameters to achieve better MRR and lower SR. The experimental results witnessed improved surface finish and enhanced material removal ability of the process and also inferred that the magnetic field-assisted EDM facilitated the process stability.     

An adaptive method based on the improved LPA-ICI algorithm for MRI enhancement

Various diseases are diagnosed using medical imaging used for analysing internal anatomical structures. However, medical images are susceptible to noise introduced in both acquisition and transmission processes. We propose an adaptive data-driven image denoising algorithm based on an improvement of the intersection of confidence intervals (ICI), called relative ICI (RICI) algorithm. The 2D mask of the adaptive size and shape is calculated for each image pixel independently, and utilized in the design of the 2D local polynomial approximation (LPA) filters. Denoising performances, in terms of the PSNR, are compared to the original ICI-based method, as well as to the fixed sized filtering. The proposed adaptive RICI-based denoising outperformed the original ICI-based method by up to 1.32 dB, and the fixed size filtering by up to 6.48 dB. Furthermore, since the denoising of each image pixel is done locally and independently, the method is easy to parallelize.     

Computer-aided detection and characterization of stroke lesion – a short review on the current state-of-the art methods

The fast advancements in the field of computer vision, progress in radiology, image processing, modelling and simulation have changed the medical science to diagnose people in an efficient way. To be exact, the headways in medical imaging have prompted better diagnostic planning and accuracy in surgical methodology with little human–machine intervention. Stroke remains the third driving reason for death, after heart attack and cancer. Automatic computer-aided diagnosis of brain diseases has been gaining significant attention in the last two decades. The aim of this work is to review the current state-of-the-art techniques employed for segmentation, classification and detection of stroke lesion and present the key challenges in it. By investigating the advanced aspects and significant pitfalls of the different surveyed techniques, an overview on the performance of these methods is presented in this work.