Distance versus Capillary Flow Dynamics-Based Detection Methods on a Microfluidic Paper-Based Analytical Device (μPAD)

In recent years, there has been high interest in paper-based microfluidic sensors or microfluidic paper-based analytical devices (μPADs) towards low-cost, portable, and easy-to-use sensing for chemical and biological targets. μPAD allows spontaneous liquid flow without any external or internal pumping, as well as an innate filtration capability. Although both optical (colorimetric and fluorescent) and electrochemical detection have been demonstrated on μPADs, several limitations still remain, such as the need for additional equipment, vulnerability to ambient lighting perturbation, and inferior sensitivity. Herein, alternative detection methods on μPADs are reviewed to resolve these issues, including relatively well studied distance-based measurements and the newer capillary flow dynamics-based method. Detection principles, assay performance, strengths, and weaknesses are explained for these methods, along with their potential future applications towards point-of-care medical diagnostics and other field-based applications.     

Nonclassical Recrystallization

Applications in the fields of materials science and nanotechnology increasingly demand monodisperse nanoparticles in size and shape. Up to now, no general purification procedure exists to thoroughly narrow the size and shape distributions of nanoparticles. Here, we show by analytical ultracentrifugation (AUC) as an absolute and quantitative high-resolution method that multiple recrystallizations of nanocrystals to mesocrystals is a very efficient tool to generate nanocrystals with an excellent and so-far unsurpassed size-distribution (PDI_c=1.0001) and shape. Similar to the crystallization of molecular building blocks, nonclassical recrystallization removes “colloidal” impurities (i.e., nanoparticles, which are different in shape and size from the majority) by assembling them into a mesocrystal. In the case of nanocrystals, this assembly can be size- and shape-selective, since mesocrystals show both long-range packing ordering and preferable crystallographic orientation of nanocrystals. Besides the generation of highly monodisperse nanoparticles, these findings provide highly relevant insights into the crystallization of mesocrystals.     

Intra‐ and inter‐supramolecular complexation of poly(butyl methacrylate)‐co‐2‐(1,2,3‐triazol‐4‐yl)pyridine copolymers induced by CoII,FeII, and EuIII ions monitored by molecular hydrodynamics methods

Poly(butyl methacrylate) copolymers embedding bidentate 2‐(1,2,3‐triazol‐4‐yl)pyridine (trzpy) chelating units as comonomer in the side chains were synthesized by controlled radical addition‐fragmentation transfer (RAFT) polymerization. Intracomplexation and intercomplexation of the macromolecules of the poly(butyl methacrylate) copolymers containing 20 % mol of trzpy units induced by Co^II, Fe^II, and Eu^III ions were studied in the solutions by macromolecular hydrodynamics methods. The sedimentation velocity of extremely diluted copolymer solutions and the dynamic viscosity of moderately diluted solutions were studied in a wide range of the salts concentrations. Differences were observed with respect to the copolymer behavior in the presence of the Co²⁺, Fe²⁺, Eu³⁺ ions. These differences are namely due to the differences in the number of coordination bonds required for complex formation and not explicitly to the nature of the corresponding anions. © 2016 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2016 , 54, 2632–2639     

大豆油煎炸过程理化指标与LF-NMR弛豫特性的相关性研究

在对大豆油无料/薯条煎炸过程[温度:(180±5)℃,持续36 h]中的酸价(AV)、粘度(V)、吸光值(A)及总极性化合物(TPC)含量等及低场核磁共振(LF-NMR)弛豫特性(峰起始时间T21、T22、T23、相应的峰面积比例S21、S22、S23、单组份弛豫时间T2W)变化规律研究的基础上,利用多元回归分析建立了理化指标与其LF-NMR检测结果的相关性模型,并进行验证。结果表明:大豆油的酸价、TPC含量及S21峰面积均随煎炸时间的延长而线性增大,T21、T22峰起始时间及T2W则随煎炸时间的延长而线性减小(r2>0.90),粘度、吸光值随煎炸时间的延长逐渐增加并符合二项式关系(r2>0.90),而T23峰起始时间及S22、S23与煎炸时间之间无明显规律性变化。煎炸薯条后,油样的酸价、粘度、TPC含量、吸光值及S21均较无料煎炸显著增大(P<0.05),而T21、T22峰起始时间及T2W显著缩短。多元回归分析表明,酸价及TPC含量与T2W、T21,粘度与T2W,吸光值与S21间均可建立良好的相关性模型(R2>0.93)。模型验证合理可靠,可通过油样的LF-NMR检测结果有效预测其理化指标的变化。