Designing amorphous formulations of polyphenols with naringin by spray-drying for enhanced solubility and permeability

Naringin (NAR), a major flavanone (FVA) glycoside, is a component of food mainly obtained from grapefruit. We used NAR as a food additive to improve the solubility and permeability of hydrophobic polyphenols used as supplements in the food industry. The spray-dried particles (SDPs) of NAR alone show an amorphous state with a glass transition temperature (T_g) at 93.2 °C. SDPs of hydrophobic polyphenols, such as flavone (FVO), quercetin (QCT), naringenin (NRG), and resveratrol (RVT) were prepared by adding varying amounts of NAR. All SDPs of hydrophobic polyphenols with added NAR were in an amorphous state with a single T_g, but SDPs of hydrophobic polyphenols without added NAR showed diffraction peaks derived from each crystal. The SDPs with NAR could keep an amorphous state after storage at a high humidity condition for one month, except for SDPs of RVT/NAR. SDPs with NAR enhanced the solubility of hydrophobic polyphenols, especially NRG solubility, which was enhanced more than 9 times compared to NRG crystal. The enhanced solubility resulted in the increased membrane permeability of NRG. The antioxidant effect of the hydrophobic NRG was also enhanced by the synergetic effect of NAR. The findings demonstrated that NAR could be used as a food additive to enhance the solubility and membrane permeability of hydrophobic polyphenols.     

Identifying catalyst layer compositions of proton exchange membrane fuel cells through machine-learning-based approach

Membrane electrode assembly (MEA) is considered a key component of a proton exchange membrane fuel cell (PEMFC). However, developing a new MEA to meet desired properties, such as operation under low-humidity conditions without a humidifier, is a time- and cost-consuming process. This study employs a machine-learning-based approach using K-nearest neighbor (KNN) and neural networks (NN) in the MEA development process by identifying a suitable catalyst layer (CL) recipe in MEA. Minimum redundancy maximum relevance and principal component analysis were implemented to specify the most important predictor and reduce the data dimension. The number of predictors was found to play an essential role in the accuracy of the KNN and NN models although the predictors have self-correlations. The KNN model with a K of 7 was found to minimize the model loss with a loss of 11.9%. The NN model constructed by three corresponding hidden layers with nine, eight, and nine nodes can achieve the lowest error of 0.1293 for the Pt catalyst and 0.031 for PVA as a good additive blending in the CL of the MEA. However, even if the error is low, the prediction of PVA seems to be inaccurate, regardless of the model structure. Therefore, the KNN model is more appropriate for CL recipe prediction.     

A Highly Sensitive,Reversible, and Bidirectional Humidity Actuator by Calcium Carbonate Ionic Oligomers Incorporated Poly(Vinylidene Fluoride)

Sensitivity and multi-directional motivation are major two factors for developing optimized humidity-response materials, which are promising for sensing, energy production, etc. Organic functional groups are commonly used as the water sensitive units through hydrogen bond interactions with water molecules in actuators. The multi-coordination ability of inorganic ions implies that the inorganic ionic compounds are potentially superior water sensitive units. However, the particle forms of inorganic ionic compounds produced by classical nucleation limit the number of exposed ions to interact with water. Recent progress on the inorganic ionic oligomers has broken through the limitation of classical nucleation, and realized the molecular-scaled incorporation of inorganic ionic compounds into an organic matrix. Here, the incorporation of hydrophilic calcium carbonate ionic oligomers into hydrophobic poly(vinylidene fluoride) (PVDF) is demonstrated. The ultra-small calcium carbonate oligomers within a PVDF film endow it with an ultra-sensitive, reversible, and bidirectional response. The motivation ability is superior to other bidirectional humidity-actuators at present, which realizes self-motivation on an ice surface, converting the chemical potential energy of the humidity gradient from ice to kinetic energy.     

High performance H2 sensor based on rGO-wrapped SnO2–Pd porous hollow spheres

Hydrogen (H₂) sensors based on metal oxide semiconductors (MOS) are promising for many applications such as a rocket propellant, industrial gas and the safety of storage. However, poor selectivity at low analyte concentrations, and independent response on high humidity limit the practical applications. Herein, we designed rGO-wrapped SnO₂–Pd porous hollow spheres composite (SnO₂–Pd@rGO) for high performance H₂ sensor. The porous hollow structure was from the carbon sphere template. The rGO wrapping was via self-assembly of GO on SnO₂-based spheres with subsequent thermal reduction in H₂ ambient. This sensor exhibited excellently selective H₂ sensing performances at 390 °C, linear response over a broad concentration range (0.1–1000 ppm) with recovery time of only 3 s, a high response of ～8 to 0.1 ppm H₂ in a minute, and acceptable stability under high humidity conditions (e. g. 80%). The calculated detection limit of 16.5 ppb opened up the possibility of trace H₂ monitoring. Furthermore, this sensor demonstrated certain response to H₂ at the minimum concentration of 50 ppm at 130 °C. These performances mainly benefited from the special hollow porous structure with abundant heterojunctions, the catalysis of the doped-PdO_x, the relative hydrophobic surface from rGO, and the deoxygenation after H₂ reduction.     

低觉醒脑电识别与唤醒的可穿戴系统研究

为智能化地识别警戒作业人员出现的低觉醒、注意力下降的生理状态，本文介绍了一种基于FPGA和脑电信号处理的低觉醒状态检测与唤醒系统，系统通过传感器从大脑头皮采集脑电信号，转换为数字信号，经傅里叶变换获取了脑电信号的θ相对能量、α相对能量、重心频率、谱熵等4个特征量，由4个特征量表征低觉醒状态并运用支持向量机对低警戒状态进行识别，当识别出低觉醒状态时采用声音报警模块发出声音，唤醒警戒作业人员。设计系统能够较好地识别出低觉醒状态，识别率达90.8%，可为提高警戒作业工作绩效提供一种可穿戴的智能装备。     

1,5-己二烯交联烯烃聚合物的合成及其抗剪切稳定性

以金属配合物为催化剂、1,5-己二烯为交联剂,采用本体聚合法合成了交联超高相对分子质量烯烃类聚合物(简称交联聚合物)。用傅里叶变换红外光谱、核磁共振、凝胶渗透色潜表征交联聚合物的结构和相对分子质量。用旋转黏度计和超声波仪研究了1,5-己二烯用量对交联聚合物溶液表观黏度的影响及交联聚合物的相对分子质量对交联聚合物溶液抗剪切稳定性的影响。实验结果表明,在最佳聚合条件(单体80mL、助催化剂0．4mL、CS-1催化剂0．090g、1,5-己二烯0．40mL、0℃、24 b)下所合成的交联聚合物的重均相对分子质量为7．7×106,数均相对分子质量3．6×106。加入少量1,5-己二烯能提高交联聚合物的抗剪切能力,同时也能提高交联聚合物的相对分子质量。交联聚合物的相对分子质量越大,抗剪切稳定性越好。1,5-己二烯用量约为0．1 mL时(单体40 mL),交联聚合物溶液的表观黏度达到最大值(16．8 mPa·s)。     

重力坝模型的尺寸效应

利用两个不同比尺预制缝重力坝模型的试验成果，以有限元富集技术进行非线性断裂分析，建立了极限承载力和长度比尺的确定性尺寸效应公式。混凝土重力坝的断裂尺寸效应满足指数衰减关系，用三次指数衰减公式可以准确地预测原型的极限承载力。断裂过程区的相对长度是引起模型试验尺寸效应的主要原因。数值计算结果表明，断裂过程区相对长度不仅是与材料有关的参数，能否充分发展还取决于结构尺寸和几何形状；断裂过程区相对长度的尺寸效应不同于统计尺寸效应和断裂参数尺寸效应，具有尺寸范围效应。断裂过程区相对长度在小试件、大试件和相对无穷大试件的发展程度不同。     

凝胶渗透色谱法测定基础油PAO的相对分子质量及分布

聚α-烯烃是一种性能优良的润滑油基础油，其相对分子质量及分布直接影响宏观性能。本研究建立了一种用于表征1-癸烯齐聚物相对分子质量及分布的方法。采用带示差折光检测器的凝胶渗透色谱系统，综合考虑分离效果和测试可操作性，优化了流动相流速和凝胶色谱柱的组合方式。以四氢呋喃为流动相，流速为0.8 mL/min，色谱柱组合方式为Styragel HR 0.5,Styragel HR 1,PLGEL 3μm MIXED-E三柱串联，以聚苯乙烯作为标准样品，采用普适校正方法处理色谱流出曲线。此方法测得样品的质均相对分子质量与凝胶渗透色谱-光散射法所测定结果相比较，相对误差相对偏差在3%以内。     

RI/VI联用测定聚乙烯相对分子质量及其分布

150℃测试温度下，以1，2，4-三氯苯为溶剂，采用示差折光指数器，毛细管黏度计双检测器联用凝胶渗透色谱技术测定了聚乙烯的相对分子质量及其分布。与常规凝胶渗透色谱法对比，该方法不仅能直接得到聚乙烯较精确的相对分子质量和分布形态，同时可以得到聚乙烯的特性黏数分布。     

饼干炉湿度控制的改进方法

在大空间的温度控制系统中 ,采用简单的PID控制规律 ,对空间的温度、湿度进行解藕计算 ,将空间的相对湿度控制转化为送风含湿量的控制。该控制方式稳定、可靠 ,并且节约能源。本方法适用于所有的烘烤炉中