Theoretical Design of Biodegradable Phthalic Acid Ester Derivatives in Marine and Freshwater Environments

The biodegradability of phtalic acid esters in marine and freshwater environments was characterized by their binding free energy with corresponding degrading enzymes. According to comprehensive biodegradation effects weights, the binding free energy values were converted into dimensionless efficacy coefficient using ratio normalization method. Then, considering comprehensive dual biodegradation effects value and the structural parameters of PAEs in both marine and freshwater environments, a 3D-QSAR pharmacophore model was constructed, five PAE derivatives (DBP−COOH, DBP−CHO, DBP−OH, DINP−NH₂, and DINP−NO₂) were screened out based on their environmental friendliness, functionality and stability. The prediction of biodegradation effects on five PAE derivatives by biodegradation models in marine and freshwater environment increased by 15.90 %, 15.84 %, 27.21 %, 12.33 %, and 8.32 %, and 21.57 %, 15.21 %, 20.99 %, 15.10 %, and 9.74 %, respectively. By simulating the photodegradation path of the PAE derivative molecular, it was found that DBP−OH can generate ^.OH and provides free radicals for the photodegradation of microplastics in the environment.     

Semiotic Aggregation in Deep Learning

Convolutional neural networks utilize a hierarchy of neural network layers. The statistical aspects of information concentration in successive layers can bring an insight into the feature abstraction process. We analyze the saliency maps of these layers from the perspective of semiotics, also known as the study of signs and sign-using behavior. In computational semiotics, this aggregation operation (known as superization) is accompanied by a decrease of spatial entropy: signs are aggregated into supersign. Using spatial entropy, we compute the information content of the saliency maps and study the superization processes which take place between successive layers of the network. In our experiments, we visualize the superization process and show how the obtained knowledge can be used to explain the neural decision model. In addition, we attempt to optimize the architecture of the neural model employing a semiotic greedy technique. To the extent of our knowledge, this is the first application of computational semiotics in the analysis and interpretation of deep neural networks.     

Identifying the viscoelastic properties of soft matter from the indentation response of a hard film-soft substrate system

The indentation technique is widely used in measuring the mechanical properties of soft matter at the microscale or nanoscale,but still faces challenges by these unique properties as well as the consequent strong surface adhesion, including the strong nonlinear effect, unclear judgment of the contact point, difficulties in estimating the contact area, and the risk of the indenter piercing the sample. Here we propose a two-step method to solve these problems: lay a hard film on a soft matter, and obtain the viscoelastic properties of this soft matter through the indentation response of this composite structure. We first establish a theoretical indentation model of the hard film-soft substrate system based on the theory of plates, elastic-viscoelastic correspondence principle and Boltzmann superposition principle. To verify the correctness of this method, we measure the mechanical properties of the methyl vinyl silicone rubber(MVSR) covered by a Cu nanofilm. Finally, we test the effectiveness and error sensitivity of this method with the finite element method(FEM). The results show that our method can accurately measure the mechanical properties of soft matter, while effectively circumventing the problems of the traditional indentation technique.     

An Update on Molecularly Imprinted Polymer Design through a Computational Approach to Produce Molecular Recognition Material with Enhanced Analytical Performance

Molecularly imprinted polymer (MIP) computational design is expected to become a routine technique prior to synthesis to produce polymers with high affinity and selectivity towards target molecules. Furthermore, using these simulations reduces the cost of optimizing polymerization composition. There are several computational methods used in MIP fabrication and each requires a comprehensive study in order to select a process with results that are most similar to properties exhibited by polymers synthesized through laboratory experiments. Until now, no review has linked computational strategies with experimental results, which are needed to determine the method that is most appropriate for use in designing MIP with high molecular recognition. This review will present an update of the computational approaches started from 2016 until now on quantum mechanics, molecular mechanics and molecular dynamics that have been widely used. It will also discuss the linear correlation between computational results and the polymer performance tests through laboratory experiments to examine to what extent these methods can be relied upon to obtain polymers with high molecular recognition. Based on the literature search, density functional theory (DFT) with various hybrid functions and basis sets is most often used as a theoretical method to provide a shorter MIP manufacturing process as well as good analytical performance as recognition material.     

Te溶剂Bridgman法CdMnTe晶体核辐射探测器的制备和表征

碲锰镉(CdMnTe)作为性能优异的室温核辐射探测器材料,可用于环境监测和工业无损检测领域。本文中采用Te溶剂Bridgman法生长In掺杂Cd_0.9Mn_0.1Te晶体,制备成10 mm×10 mm×2 mm大小的室温单平面探测器,研究了该探测器对²⁴¹Am@59.5 keV γ射线源的能谱响应。通过表征红外透过率、电阻率以及探测器能谱响应等参数,综合评定了探测器用CdMnTe晶体的质量、电学和探测器性能。结果表明,晶片的红外透过率均在55%以上,最好可达到60%。采用湿法钝化,100 V偏压下的漏电流由钝化前的9.48 nA降为钝化后的7.90 nA,钝化后的电阻率为2.832×10¹⁰ Ω·cm。在-400 V反向偏压下,CdMnTe探测器对²⁴¹Am@59.5 keV γ射线源的能量分辨率在钝化前后分别为13.53%和12.51%,钝化后的电子迁移率寿命积为1.049×10^-3 cm²/V。研究了探测器的能量分辨率随电压的变化特性,当偏压≤400 V时,探测器的能量分辨率主要由载流子的收集效率决定,而当偏压>400 V时,能量分辨率由漏电流决定。本文研究结果表明,Te溶剂Bridgman法生长的CdMnTe晶体质量较好,电阻率和电子迁移率寿命积满足探测器制备需求。     

近紫外激发Gd2ZnTiO6∶Eu3+,Li+红色荧光粉的制备及荧光性能

采用溶胶-凝胶法成功制备出系列Eu³⁺掺杂和Li⁺、Eu³⁺共掺杂Gd₂ZnTiO₆红色荧光粉,并研究Li⁺、Eu³⁺掺杂对样品的晶体结构、微观形貌及发光性能的影响。结果显示,所制备的Gd₂ZnTiO₆∶Eu³⁺,Li⁺(GZT∶Eu³⁺,Li⁺) 荧光粉为双钙钛矿结构,属于单斜晶系(空间群:P2₁/n),大小为10 μm的无规则形状的颗粒。在395 nm近紫外光的激发下,GZT∶Eu³⁺的发射光谱展示出典型的Eu³⁺线状特征光谱,发射峰中心位于615 nm处,归属于Eu³⁺的⁵D₀→⁷F₂跃迁。Eu³⁺的最佳掺杂浓度为0.07(摩尔分数),样品显示明显的浓度猝灭效应,其机制为电偶极子-电偶极子(d-d)相互作用。此外,研究还发现,Li⁺掺杂对样品的晶体结构、微观形貌没有影响,但是一定量的Li⁺掺杂可以显著增强样品的荧光强度。当Li⁺浓度为0.05时,荧光粉发射主峰强度增强程度最大,提高至原来的4.3倍,说明通过Li⁺、Eu³⁺共掺杂可以获得高亮度的GZT红色荧光粉。GZT∶0.14Eu³⁺,0.05Li⁺荧光粉的CIE色坐标为(0.631 1,0.375 3)与标准红光色坐标(0.670,0.330)较为接近,是一种潜在的LED用红色荧光粉。     

DMF体系中Cu2O球晶的制备及形貌分析

以N,N-二甲基甲酰胺(DMF)为溶剂,通过改变铜源和表面活性剂,调控反应参数,溶剂热条件下制备了三维十字形、空心及实心的Cu₂O球晶。利用XRD、SEM等表征手段,分析探讨了工艺条件变化对Cu₂O球晶形貌的影响。研究表明,随着DMF浓度的增大,体系的还原能力增强,Cu⁺增多,溶液的过饱和度增大,Cu₂O晶体集合体形态由晶体结构控制的各向异性与对称性的球晶逐渐向各向同性球晶演变。十二烷基硫酸钠(SDS)、十六烷基三甲基溴化铵(CTAB)、聚乙烯吡咯烷酮(PVP)等表面活性剂有助于降低溶液的过饱和度,增加结晶质的表面扩散能力,有利于规则形态Cu₂O晶粒的形成。反应体系中,Cu(Ac)₂·H₂O水解生成的羧基与DMF中的甲酰基在高温下发生脱羧反应产生CO₂气体以及SDS发泡作用产生的气体是形成空心Cu₂O球晶的重要原因。     

ZnO超细纳米线阵列的制备及其电化学性能

以Zn(NO₃)₂· 6H₂O和C₆H₁₂N₄为原材料,采用二步水热法在碳纤维布上合成了形貌尺寸均匀的ZnO超细纳米线阵列。用 X 射线衍射(XRD)和扫描电镜(SEM)对其晶体结构和形貌进行了表征,利用恒流充放电测试等手段对其进行电化学性能测试。测试结果表明,材料表现出优异的电化学性能。在200 mA/g的电流密度下循环150次后,ZnO超细纳米线阵列仍然约有730 mAh/g的充放电比容量,库伦效率保持在95%以上。在1 200 mA/g的大倍率条件下,材料的充放电比容量依旧可达481 mAh/g左右,表现出十分良好的循环稳定性和可逆性能,是一种较为理想的锂离子电池负极复合材料。