双枝芳醚树枝形聚合物构象研究

合成了外围只以一个芘基团修饰、核心为苯胺的双枝芳醚树枝形聚合物Py-[Gn]2-NPh (n＝1～2), 利用分子内电子转移和激基复合物的形成对其折叠构象和折叠程度进行了研究. 二氯甲烷溶液中选择性激发芘基团, 树枝形聚合物Py-[Gn]2-NPh分子内发生从苯胺到芘基团之间的电子转移, 观察到了分子内外围芘基团和核心苯胺基团之间形成激基复合物的发光, 为芳醚树枝形聚合物折叠构象的存在给出了直接实验观察. 二氯甲烷溶液中1～2代Py-[Gn]2-NPh分子内电子转移效率分别为0.87和0.81, 速率常数分别为2.3×108和1.5×108 s－1. 利用电子转移速率常数估算得到1～2代Py-[Gn]2-NPh分子内给、受体之间的距离分别为0.79和0.81 nm, 说明双枝芳醚树枝形聚合物与单枝结构类似, 其外围基团也可以折叠到达分子内部接近核心的位置.     

3-乙氧基-4-羟基苯乙醇酸在聚邻甲苯胺-Cu2+膜电极上的电氧化

采用循环伏安法制备了聚邻甲苯胺(POT)膜电极,再以浸泡吸附法在此膜中嵌入Cu2+,制成POT-Cu2+膜电极.研究了POT-Cu2+膜电极对3-乙氧基-4-羟基苯乙醇酸的电氧化行为,并讨论了影响电氧化活性的主要因素.结果表明,50℃下,在含有0.5mol.L-13-乙氧基-4-羟基苯乙醇酸的1.5mol.L-1氢氧化钠溶液中,POT-Cu2+膜电极具有高的电氧化活性和良好的稳定性.将该电极用于3-乙氧基-4-羟基苯乙醇酸电解氧化脱羧制备乙基香兰素,在0.55V(vsSCE)下恒电位电解1h,乙基香兰素的产率可达89%以上,电流效率达97%以上.     

恒定磁场(0.4T)对聚苯胺微观取向结构的影响

在恒定磁场(0.4 T)条件下, 采用乳液聚合法合成了十二烷基苯磺酸掺杂聚苯胺(PAn), 并通过透射电子显微镜(TEM)、X射线衍射(XRD)、傅立叶红外光谱(FT-IR)、电导率、产物复合膜电阻率的各向异性, 以及产物间甲酚(m-cresol)溶液的电导率在外电场作用下变化的分析与表征, 系统研究了磁场对PAn的微观取向结构及其性能的影响. 实验结果表明, 与无磁场条件下制备的聚苯胺(PAn_-O)相比, 磁场条件下制备的聚苯胺(PAn_-M)具有更高的结晶度和明显的微观取向结构, 其微粒有序排列和堆砌成了许多具有一定长/径比的条状聚集体; PAn_-M/PVA(聚乙烯醇)复合膜经磁化成膜, 可以表现出明显的导电性能的各向异性; PAn_-M/m-cresol溶液的电导率, 在有、无外场(电场)作用下存在明显的突变. 分析认为: 由于磁场条件下制备的PAn粉末存在着微观取向和显著的抗磁磁化率的各向异性, 在外磁场作用下其堆砌方式会进一步演变成宏观的取向, 随之表现出物理性能上的各向异性.     

含2,6-二甲酰胺基吡啶单元的大环合成、结构及其对四面体构型阴离子的络合性质

采用前体二胺N,N′-(3-氨基苯基)-2,6-二甲酰亚胺吡啶(1)和5,5′-亚甲基双水杨醛(2)进行缩合反应得到含酚羟基的[1+1]席夫碱大环L¹,将其中席夫碱C=N双键还原得到环状骨架更具柔性的饱和大环L²。通过¹H NMR、FABMS和元素分析等对大环组成进行了表征,并通过X射线单晶衍射解析了两个大环分子的晶体结构。采用UV-Vis光谱滴定技术对大环与系列阴离子的络合作用进行了考察,结果表明,席夫碱大环L¹对四面体构型的阴离子H₂PO^-₄、HP₂O₇^3-和H₂P₂O₇^2-有明显的选择性识别作用,进一步通过UV-Vis光谱、核磁滴定等技术获得了识别反应的配位比及平衡常数(K),L¹对3个磷酸阴离子的络合能力依H₂PO^-     

以5-(3-吡啶基)-1H-吡唑-3-羧酸为配体的钴共晶配合物的合成、晶体结构及电化学性质

采用溶剂热法合成了一种新型的钴（Ⅱ）基配合物,即{[Co（Hppc）₂][Co₂（4,4''-bipy）（H₂O）₄]（SO₄）₂·2H₂O}_n （1）,其中H₂ppc=5-（3-吡啶基）-1H-吡唑-3-羧酸,4,4''-bipy=4, 4''-联吡啶。配体H₂ppc是由吡啶环、吡唑环和羧基共同组成,同时兼具了刚性和柔性。通过单晶X射线衍射对配合物1进行了结构测定。结果显示所合成的配合物1结晶在单斜晶系C2/c空间群,包括2个晶体学独立部分：二维层状[Co（Hppc）₂]和一维链状[Co₂（4,4''-bipy）（H₂O）₄]^2-,并形成具有{4⁴·6²}{4}₂拓扑网络结构的共晶化合物。此外,配合物1呈现出良好的电化学发光（ECL）性能以及良好的超级电容器性能。     

一维钴(Ⅱ)/镍(Ⅱ)配位聚合物的合成、结构、磁性及荧光传感性质

采用水热法合成了2个新的配位聚合物[Co(L)_0.5(1,4-bib)(H₂O)₃]_n(1)和[Ni(L)_0.5(1,4-bib)(H₂O)₃]_n(2)(H₄L=1,4-二(2,6-二甲基-3,5-二羧基吡啶基)苯,1,4-bib=1,4-双(1-咪唑基)苯),通过X射线单晶及粉末衍射、元素分析、红外光谱分析和热重分析对其结构进行表征。结构分析表明配合物1和2为异质同构,均为一维结构。通过变温磁化率测量发现,配合物1和2中的Co(Ⅱ)/Ni(Ⅱ)离子之间存在反铁磁相互作用。此外,配合物1可通过荧光猝灭法检测头孢克肟(CEF),配合物2可用来检测四环素(TET),该方法灵敏度高、选择性好,其检出限分别为0.86 μmol·L^-1(CEF)和0.49 μmol·L^-1(TET)。     

A Cyanine-based Liposomal Nanophotosensitizer for Enhanced Cancer Chemo-Photodynamic Therapy

Currently, chemotherapy is one of the most important treatment modalities for malignant tumors in the clinic, however, it exhibits some shortcomings, such as poor selectivity, limited efficacy and serious adverse effects. Therefore, synergistic therapy and accurate drug delivery at tumor sites become a promising strategy for achieving tumor eradication. Herein, a smart NIR fluorescence imaging-guided nanoliposome was fabricated by encapsulating a chemotherapeutic drug(doxorubicin, DOX), liposomes(L) and a near-infrared(NIR) photosensitizer(CY) to form L@CY@DOX, which could realize enhanced therapeutic efficacy of chemo-PDT in cancer therapy(PDT=photodynamic therapy). L@CY@DOX can induce mitochondrial apoptosis and produce severe toxicity at the cellular level, and L@CY@DOX can enrich in the tumor site, which significantly induces tumor death. In vitro and in vivo studies demonstrated that L@CY@DOX exhibited great antitumor efficacy compared with each one of these monotherapies, indicating that the combination of chemotherapy and PDT possessed potential development prospects and is anticipated in clinical application.     

A Spiking Neural Network Based on Retinal Ganglion Cells for Automatic Burn Image Segmentation

Burn is a common traumatic disease. After severe burn injury, the human body will increase catabolism, and burn wounds lead to a large amount of body fluid loss, with a high mortality rate. Therefore, in the early treatment for burn patients, it is essential to calculate the patient’s water requirement based on the percentage of the burn wound area in the total body surface area (TBSA%). However, burn wounds are so complex that there is observer variability by the clinicians, making it challenging to locate the burn wounds accurately. Therefore, an objective, accurate location method of burn wounds is very necessary and meaningful. Convolutional neural networks (CNNs) provide feasible means for this requirement. However, although the CNNs continue to improve the accuracy in the semantic segmentation task, they are often limited by the computing resources of edge hardware. For this purpose, a lightweight burn wounds segmentation model is required. In our work, we constructed a burn image dataset and proposed a U-type spiking neural networks (SNNs) based on retinal ganglion cells (RGC) for segmenting burn and non-burn areas. Moreover, a module with cross-layer skip concatenation structure was introduced. Experimental results showed that the pixel accuracy of the proposed reached 92.89%, and our network parameter only needed 16.6 Mbytes. The results showed our model achieved remarkable accuracy while achieving edge hardware affinity.     

Entropy Sources Based on Silicon Chips: True Random Number Generator and Physical Unclonable Function

Entropy is a measure of uncertainty or randomness. It is the foundation for almost all cryptographic systems. True random number generators (TRNGs) and physical unclonable functions (PUFs) are the silicon primitives to respectively harvest dynamic and static entropy to generate random bit streams. In this survey paper, we present a systematic and comprehensive review of different state-of-the-art methods to harvest entropy from silicon-based devices, including the implementations, applications, and the security of the designs. Furthermore, we conclude the trends of the entropy source design to point out the current spots of entropy harvesting.