桑白皮中21种无机元素的测定及其质量评价

为准确了解不同产区桑白皮中多种元素分布及含量情况,用65%硝酸溶液对其样品进行微波消解处理,后采用电感耦合等离子体质谱（ICP-MS/MS）法测定桑白皮样品中21种元素的含量。得到21种元素的线性关系良好,R2 > 0.999,方法学考察试验的RSD均小于3%,加样回收率在91.32%~108.84%之间,RSD均小于4%;Al、Ga、Sr、Ba相比而言含量较大,且不同产区的特异性较明显;主成分分析中17批样品得到4个主成分,累计方差贡献率达 87.380%,确定V、Co、Al、Li、As、Se、Pb、U、Zn、Cr、Cd、Ga、Ba、Cs、Ni等15种元素为桑白皮药材的特征元素;相关性分析可知,元素间的相关性较强, P≤0.05的元素与主成分分析的特征元素基本一致。参照2020年版《中国药典》中对中药材中有害元素的限量规定,发现桑白皮样品中Pb、Cd、As、Cu有害元素的含量均未超过限量要求。ICP-MS/MS能准确快速测定桑白皮中21种元素含量情况,这对评价不同产区桑白皮质量,完善桑白皮质量标准具有一定参考价值。     

粉末压片-X荧光光谱法测定过磷酸钙中硫含量

硫是过磷酸钙中重要营养指标之一,为准确快速测定过磷酸钙中硫的含量,试验采用粉末压片-X荧光光谱法,将过磷酸钙试样充分干燥后研磨至粒度小于74 μm,采用硼酸镶边,在压力18 Mpa条件下保压30 s,制成样片。通过在过磷酸钙样品中添加不同质量的纯物质硫酸钙（质量分数范围1.52 %～17.21 %）,经过专用混匀设备混合均匀后,与试样压片相同条件下压制标准样片,作为过磷酸钙中硫的标准样品,建立硫标准曲线,曲线线性相关系数R2为0.9995,采用经验系数法校正干扰,建立了粉末压片-波长色散X射线荧光光谱法测定过磷酸钙中硫含量的方法, 方法检出限为0.002 %。对3个不同硫含量的过磷酸钙样品采用本实验方法重复测量7次,RSD在1.4 %~3.1 %,方法精密度性好,同时用高温燃烧红外光谱法和电感耦合等离子发射光谱法对比,三者测量结果相对极差小于2.0 %,测量结果无显著性差异。此方法不需要对样品进行熔融或溶解,样品制备简单,数据准确度和稳定性好,分析效率高,适合大批量样品中硫的测定。     

全自动高温水解-离子色谱法测定氧化镧铈中氟、氯

为探究稀土氧化物中氟(F)和氯(Cl)元素含量的快速检测方法,通过全自动高温水解仪对氧化镧铈样品进行前处理,并利用氢氧根体系离子色谱仪检测吸收液中F-、Cl-离子的含量,建立了基于全自动高温水解-离子色谱法测定氧化镧铈中的F、Cl元素含量的快速检测方法,该方法有效避免了传统前处理方法过程复杂、分析时间慢、极易受人为干扰的问题。称取0.3g氧化镧铈样品,在氧气流量为300 mL/min,1100 ℃高温下水解燃烧20 min,吸收定容为体积20mL的吸收液,以NAOH(15mmol/L)作为淋洗液,经色谱柱分离,测得F-与Cl-在质量浓度为1.00 mg/L-15.00 mg/L范围内,质量浓度与离子色谱峰面积呈线性关系,相关系数均为0.9999。检出限分别为0.003mg/L和0.12mg/L。全自动高温水解仪联用离子色谱仪检测系统对氧化镧铈中F-的平均加标回收率测定结果为98.4%,标准偏差RSD为0.94%;对Cl-的加标回收率测定结果为97.8%,RSD为2.86%。说明该方法较高准确度及精密度,测试结果准确可靠满足企业和检测机构的测试需求,为稀土氧化中氟、氯元素含量的研究及相关产品的开发提供了理论基础。     

Speckle characteristics of simulated deep Fresnel region under shadowing effect

After the three-dimensional self-affine fractal random surface simulation, we use the optical scattering theory to calculate the deep Fresnel region speckle(DFRS) under consideration of the more strict shadowing effect. The evolution of DFRS with the scattering distance and the intensity probability distribution are studied. It is found that the morphology of the scatterer has an antisymmetric relationship with the intensity distribution of DFRS, and the effect of micro-lenses on the scattering surface causes the intensity probability distribution of DFRS to deviate from the Gaussian speckle in the high light intensity area.     

Andrographolide Inhibits Biofilm and Virulence in Listeria monocytogenes as a Quorum-Sensing Inhibitor

Listeria monocytogenes is a major foodborne pathogen that can cause listeriosis in humans and animals. Andrographolide is known as a natural antibiotic and exhibits good antibacterial activity. We aimed to investigate the effect of andrographolide on two quorum-sensing (QS) systems, LuxS/AI-2 and Agr/AIP of L. monocytogenes, as well as QS-controlled phenotypes in this study. Our results showed that neither luxS expression nor AI-2 production was affected by andrographolide. Nevertheless, andrographolide significantly reduced the expression levels of the agr genes and the activity of the agr promoter P₂. Results from the crystal violet staining method, confocal laser scanning microscopy (CLSM), and field emission scanning electron microscopy (FE-SEM) demonstrated that andrographolide remarkably inhibited the biofilm-forming ability of L. monocytogenes 10403S. The preformed biofilms were eradicated when exposed to andrographolide, and reduced surviving cells were also observed in treated biofilms. L. monocytogenes treated with andrographolide exhibited decreased ability to secrete LLO and adhere to and invade Caco-2 cells. Therefore, andrographolide is a potential QS inhibitor by targeting the Agr QS system to reduce biofilm formation and virulence of L. monocytogenes.     

Space-Air-Ground Integrated Mobile Crowdsensing for Partially Observable Data Collection by Multi-Scale Convolutional Graph Reinforcement Learning

Mobile crowdsensing (MCS) is attracting considerable attention in the past few years as a new paradigm for large-scale information sensing. Unmanned aerial vehicles (UAVs) have played a significant role in MCS tasks and served as crucial nodes in the newly-proposed space-air-ground integrated network (SAGIN). In this paper, we incorporate SAGIN into MCS task and present a Space-Air-Ground integrated Mobile CrowdSensing (SAG-MCS) problem. Based on multi-source observations from embedded sensors and satellites, an aerial UAV swarm is required to carry out energy-efficient data collection and recharging tasks. Up to date, few studies have explored such multi-task MCS problem with the cooperation of UAV swarm and satellites. To address this multi-agent problem, we propose a novel deep reinforcement learning (DRL) based method called Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN). Our ms-SDRGN approach incorporates a multi-scale convolutional encoder to process multi-source raw observations for better feature exploitation. We also use a graph attention mechanism to model inter-UAV communications and aggregate extra neighboring information, and utilize a gated recurrent unit for long-term performance. In addition, a stochastic policy can be learned through a maximum-entropy method with an adjustable temperature parameter. Specifically, we design a heuristic reward function to encourage the agents to achieve global cooperation under partial observability. We train the model to convergence and conduct a series of case studies. Evaluation results show statistical significance and that ms-SDRGN outperforms three state-of-the-art DRL baselines in SAG-MCS. Compared with the best-performing baseline, ms-SDRGN improves 29.0% reward and 3.8% CFE score. We also investigate the scalability and robustness of ms-SDRGN towards DRL environments with diverse observation scales or demanding communication conditions.     

Synthesis of PNIPAAm-g-P4VP Microgel as Draw Agent in Forward Osmosis by RAFT Polymerization and Reverse Suspension Polymerization to Improve Water Flux

Microgels have unique and versatile properties allowing their use in forward osmosis areas as a draw agent. In this contribution, poly(4-vinylpyridine) (P4VP) was synthesized via RAFT polymerization and then grafted to a poly(N-Isopropylacrylamide) (PNIPAAm) crosslinking network by reverse suspension polymerization. P4VP was successfully obtained by the quasiliving polymerization with the result of nuclear magnetic resonance and gel permeation chromatography characterization. The particle size and particle size distribution of the PNIPAAm-g-P4VP microgels containing 0, 5, 10, 15 and 20 wt% P4VP were measured by means of a laser particle size analyzer. It was found that all the microgels were of micrometer scale and the particle size was increased with the P4VP load. Inter/intra-molecular-specific interactions, i.e., hydrogen bond interactions were then investigated by Fourier infrared spectroscopy. In addition, the water flux measurements showed that all the PNIPAAm-g-P4VP microgels can draw water more effectively than a blank PNIPAAm microgel. For the copolymer microgel incorporating 20 wt% P4VP, the water flux was measured to be 7.48 L∙m⁻²∙h⁻¹.     

Modified Wave Particle Duality and Black Hole Physics

de Broglie relation is revisited, in consideration of a generalization of canonical commuting relation. The possible effects on particle's localization and black hole physics are also discussed, in a heuristic manner.     

Optimal Energy-Storage Configuration for Microgrids Based on SOH Estimation and Deep Q-Network

Energy storage is an important adjustment method to improve the economy and reliability of a power system. Due to the complexity of the coupling relationship of elements such as the power source, load, and energy storage in the microgrid, there are problems of insufficient performance in terms of economic operation and efficient dispatching. In view of this, this paper proposes an energy storage configuration optimization model based on reinforcement learning and battery state of health assessment. Firstly, a quantitative assessment of battery health life loss based on deep learning was performed. Secondly, on the basis of considering comprehensive energy complementarity, a two-layer optimal configuration model was designed to optimize the capacity configuration and dispatch operation. Finally, the feasibility of the proposed method in microgrid energy storage planning and operation was verified by experimentation. By integrating reinforcement learning and traditional optimization methods, the proposed method did not rely on the accurate prediction of the power supply and load and can make decisions based only on the real-time information of the microgrid. In this paper, the advantages and disadvantages of the proposed method and existing methods were analyzed, and the results show that the proposed method can effectively improve the performance of dynamic planning for energy storage in microgrids.