Enhanced dielectric and nonohmic properties of SrTiO3-modified CaCu3Ti4O12 ceramics

Dielectric and nonohmic properties of CaCu₃Ti₄O₁₂ (CCTO) ceramics can be modified by addition of SrTiO₃ (STO) in different molar proportions which were fabricated by a modified sol-gel method. XRD results indicated that all modified ceramics showed mixed phase consisting of both CCTO and STO. SEM images and grain size distribution probability also presented the change of microstructure with the addition of STO. The dielectric loss of the CCTO/0.4STO ceramics sintered at 1000 °C can be lower than 0.02 in a wide frequency (1 kHz–10 kHz), especially at 1 kHz, the dielectric loss of this sample is as low as 0.012. Furthermore, excellent nonlinear I–V electrical characteristic (high breakdown voltage to 54.15 kV/cm for CCTO/0.4STO sintered at 1000 °C) was observed as well. All the results indicated that the addition of STO does improve the dielectric properties and nonohmic characteristics of CCTO ceramics dramatically.     

Sentinel-2卫星的多光谱轻量级船舶目标检测算法

近年来深度卷积神经网络在可见光船舶检测方面取得了显著的进展，然而，大多数相关研究是通过改进大型的网络结构来提高检测性能，因此加大了对更高计算机性能的需求。此外，可见光图像难以在云、雾、海杂波、黑夜等复杂场景检测到船舶。针对以上问题，提出了一种融合红（red, R）、绿（green, G）、蓝（blue, B）和近红外（NIR）4个波段光谱信息的由粗到精细的轻量型船舶检测算法。与现有的方法中根据光谱特性利用水体检测算法提取水体区域不同之处是该算法是利用改进的水体检测算法来提取船舶候选区域。为获取更准确的候选区域，对船舶、厚云、薄云、平静海面、杂波海面5种场景中4个波段的像素值进行了统计分析，选取近红外大于阈值作为辅助判断，并以其中心点获取候选区域32×32大小的切片，并对切片进行非极大值抑制，由此获得了船舶粗检测结果。随后构建了轻量级LSGFNet网络对船舶候选区域切片进行精细识别。构建的网络融合了1×1卷积提取的波谱特征与3×3的提取几何特征，为防止光谱特征与几何特征的信息在融合时“信息不流通”，在LSGFNet网络中引入了ShuffleNet中的通道打乱机制，并减小了模型结构，与典型的轻量级网络相比具有更好的效果且模型较小。最后，利用Sentinel-2卫星多光谱10 m分辨率数据构建了512×512大小的1 120组数据进行粗检测，以及32×32大小的6 014组数据进行精细网络训练，其中候选区域粗提取的查全率为98.99%，精细识别网络精确度为96.04%，不同场景下的平均精确度为92.98%。实验表明该算法在抑制云层、海浪杂波等干扰的复杂背景下具有较高的检测效率，且训练时间短、计算机性能需求低。     

蛋白质-多糖复合体系在活性物质传递中的应用

蛋白质-多糖复合体系作为生物活性物质传递系统的壁材,有着人工合成聚合物或无机物等其他材料不可比拟的多重优势。本文就蛋白质和多糖之间的连接方式及蛋白质-多糖复合体系形成传递系统的多种形式进行了综述,以及对此领域的发展趋势进行了展望。结合蛋白质和多糖的结构特点,二者之间的链接方式分为非共价结合的物理共聚,和共价结合的美拉德偶联、化学交联、酶催化交联等方式,文中分别对各种连接方式的原理和机理,以及其影响因素做了深入阐述。以蛋白质-多糖复合体系为壁材对活性物质的传递形式大体上分成乳化系统、胶束、纳米凝胶、分子复合物以及壳核结构等系统。不同的活性物质的特征和传递需求,可针对性地选择合适结构的蛋白质和多糖种类以及二者的连接方式和传递系统的形式。并且,随着研究的逐步发展和推进,此领域的发展趋势朝着智能化和靶向性的方向进行。目前活性物质的蛋白质-多糖复合体系的传递系统,还依然面临着系统设计、评价和应用等多方面的挑战,这就要求我们在更全面更深入了解认识其对活性物质影响和功效的基础上,安全合理地设计和深入细致地评价活性成分的传递系统。     

Regulating Phase Junction and Oxygen Vacancies of TiO2 Nanoarrays for Boosted Photoelectrochemical Water Oxidation

In this study, we have provided a facile solution to synthesize well-aligned titanium dioxide nanorods by using hydrothermal reaction. By calcining the materials under different atmospheres and temperatures, a batch of titanium dioxides with excellent oxygen evolution reaction(OER) catalytic efficiency were obtained. This new structured TiO₂ photoanode material yields a high photocurrent density of 5.69 mA/cm² at 1.23 V vs. reversible hydrogen electrode(RHE) under simulated solar light(100 mW/cm²). Surface photovoltage techniques and other measurements were carried out to confirm that the enhanced photoelectrochemical performances were attributed to the synergistic effect of the phase junction and a certain content of surface states, which accelerate the separation and transmission of the photogenerated charges. This material with phase junction and surface states promises a potential application in the field of photoelectric catalysis under solar light.     

Cu/CuO-Graphene Foam with Laccase-like Activity for Identification of Phenolic Compounds and Detection of Epinephrine

Although great progress has been made in the advancement of nanozymes, most of the studies focus on mimicking peroxidase, oxidase, and catalase, while relatively few studies are used to mimic laccase. However, the use of nanomaterials to mimic laccase activity will have great potential in environmental and industrial catalysis. Herein, Cu/CuO-graphene foam with laccase-like activity was designed for the identification of phenolic compounds and the detection of epinephrine. In a typical experiment, the formation mechanism of Cu/CuO-graphene foam was investigated during the pyrolysis process by thermogravimetric-mass spectrometry. As a laccase mimic, Cu/CuO-graphene foam exhibited excellent catalytic activity with a Michaelis-Menten constant and a maximum initial velocity of 0.17 mmol/L and 0.012 mmol∙L^-1∙s^-1, respectively. Based on this principle, Cu/CuO-graphene foam nanozyme could differentially catalyze phenolic compounds and 4-aminoantipyrine for simultaneous identification of phenolic compounds. Furthermore, a colorimetric sensing platform was fabricated for the quantitative determination of epinephrine, showing linear responses to epinephrine in the range of 3 mg/mL to 20 mg/mL with the detection limit of 0.2 mg/mL. The proposed Cu/CuO-graphene foam nanozyme could be applied for the identification of phenolic compounds and the detection of epinephrine, showing great potential applications for environmental monitoring, biomedical sensing, and food detection fields.     

Metamorphic InAs(Sb)/InGaAs/InAlAs nanoheterostructures grown on GaAs for efficient mid-IR emitters

High-efficiency semiconductor lasers and light-emitting diodes operating in the 3–5?μm mid-infrared (mid-IR) spectral range are currently of great demand for a wide variety of applications, in particular, gas sensing, noninvasive medical tests, IR spectroscopy etc. III-V compounds with a lattice constant of about 6.1?Å are traditionally used for this spectral range. The attractive idea to fabricate such emitters on GaAs substrates by using In(Ga,Al)As compounds is restricted by either the minimum operating wavelength of ～8?μm in case of pseudomorphic AlGaAs-based quantum cascade lasers or requires utilization of thick metamorphic In_xAl_1-xAs buffer layers (MBLs) playing a key role in reducing the density of threading dislocations (TDs) in an active region, which otherwise result in a strong decay of the quantum efficiency of such mid-IR emitters. In this review we present the results of careful investigations of employing the convex-graded In_xAl_1-xAs MBLs for fabrication by molecular beam epitaxy on GaAs (001) substrates of In(Ga,Al)As heterostructures with a combined type-II/type-I InSb/InAs/InGaAs quantum well (QW) for efficient mid-IR emitters (3–3.6?μm). The issues of strain relaxation, elastic stress balance, efficiency of radiative and non-radiative recombination at T?=?10–300?K are discussed in relation to molecular beam epitaxy (MBE) growth conditions and designs of the structures. A wide complex of techniques including in-situ reflection high-energy electron diffraction, atomic force microscopy (AFM), scanning and transmission electron microscopies, X-ray diffractometry, reciprocal space mapping, selective area electron diffraction, as well as photoluminescence (PL) and Fourier-transformed infrared spectroscopy was used to study in detail structural and optical properties of the metamorphic QW structures. Optimization of the growth conditions (the substrate temperature, the As₄/III ratio) and elastic strain profiles governed by variation of an inverse step in the In content profile between the MBL and the InAlAs virtual substrate results in decrease in the TD density (down to 3?×?10⁷ cm^?2), increase of the thickness of the low-TD-density near-surface MBL region to 250–300?nm, the extremely low surface roughness with the RMS value of 1.6–2.4?nm, measured by AFM, as well as rather high 3.5?μm-PL intensity at temperatures up to 300?K in such structures. The obtained results indicate that the metamorphic InSb/In(Ga,Al)As QW heterostructures of proper design, grown under the optimum MBE conditions, are very promising for fabricating the efficient mid-IR emitters on a GaAs platform.     

化学法改性煤沥青脱除苯并芘的研究

在管式反应器中采用苯甲酸、聚乙二醇、固体古马隆树脂（S）、液体古马隆树脂（L）为添加剂来降低煤沥青中有害物质苯并芘的含量，以期使得煤沥青可绿色化应用。采用紫外-可见分光光度计分析煤沥青中苯并芘含量。考察了反应温度、反应时间、添加剂添加量、催化剂等工艺条件对添加剂脱除煤沥青中苯并芘的影响。结果表明，不同工艺条件能降低煤沥青中苯并芘的含量。在优化条件下，不同添加剂对苯并芘脱除率由高到低依次为：液体古马隆树脂、聚乙二醇、苯甲酸和固体古马隆树脂。分析其反应机理，这与催化剂的酸性相关，发生亲电取代反应。结果表明，液体古马隆树脂（L）在催化剂存在下对煤沥青中苯并芘脱除率可达73.0%，显示了良好的应用前景。     

A review of recent advances in catalytic combustion of VOCs on perovskite-type catalysts

Along with the rapid development of industry, VOCs gradually move into the spotlight, and now become a kind of harmful environmental pollutants that cannot be overlooked. This paper introduces the hazards of VOCs and the common catalytic combustion catalysts, noble metal catalysts and non-noble metal catalysts, for the elimination of VOCs. Perovskite catalysts, as one of the non-noble catalysts, play an important role in the field of catalytic combustion in recent years. According to the classification of elements doping in perovskites, the research achievements in the past five years were analyzed and reviewed. In addition, this paper also analyzes and elaborates the reaction kinetics and QSAR/QSPR models for the introduction of structural properties and reaction mechanisms.     

Prediction of drug-target interaction by integrating diverse heterogeneous information source with multiple kernel learning and clustering methods

BackgroundIdentification of potential drug-target interaction pairs is very important for pharmaceutical innovation and drug discovery. Numerous machine learning-based and network-based algorithms have been developed for predicting drug-target interactions. However, large-scale pharmacological, genomic and chemical datum emerged recently provide new opportunity for further heightening the accuracy of drug-target interactions prediction.ResultsIn this work, based on the assumption that similar drugs tend to interact with similar proteins and vice versa, we developed a novel computational method (namely MKLC-BiRW) to predict new drug-target interactions. MKLC-BiRW integrates diverse drug-related and target-related heterogeneous information source by using the multiple kernel learning and clustering methods to generate the drug and target similarity matrices, in which the low similarity elements are set to zero to build the drug and target similarity correction networks. By incorporating these drug and target similarity correction networks with known drug-target interaction bipartite graph, MKLC-BiRW constructs the heterogeneous network on which Bi-random walk algorithm is adopted to infer the potential drug-target interactions.ConclusionsCompared with other existing state-of-the-art methods, MKLC-BiRW achieves the best performance in terms of AUC and AUPR. MKLC-BiRW can effectively predict the potential drug-target interactions.