Flow-induced shear stress and deformation of a core–shell-structured microcapsule in a microchannel

A numerical model was developed and validated to investigate the fluid–structure interactions between fully developed pipe flow and core–shell-structured microcapsule in a microchannel. Different flow rates and microcapsule shell thicknesses were considered. A sixth-order rotational symmetric distribution of von Mises stress over the microcapsule shell can be observed on the microcapsule with a thinner shell configuration, especially at higher flow rate conditions. It is also observed that when being carried along in a fully developed pipe flow, the microcapsule with a thinner shell tends to accumulate stress at a higher rate compared to that with a thicker shell. In general, for the same microcapsule configuration, higher flow velocity would induce a higher stress level over the microcapsule shell. The deformation gradient was used to capture the microcapsule's deformation in the present study. The effect of Young's modulus on the microcapsule shell on the microcapsule deformation was investigated as well. Our findings will shed light on the understanding of the stability of core–shell-structured microcapsule when subjected to flow-induced shear stress in a microfluidic system, enabling a more exquisite control over the breakup dynamics of drug-loaded microcapsule for biomedical applications.     

Majorana Representation for a Composite System

International Journal of Theoretical Physics - The Majorana representation, which provides an intuitive way to represent the quantum state by stars on the Bloch sphere, has drawn considerable...     

Preparation and gas sensing activity of La and Y co-doped titania nanoparticles

Journal of Sol-Gel Science and Technology - A novel gas sensing material, La–Y co-doped TiO2 nanoparticles, was synthesized by sol–gel method and applied to detect organic pollutants...     

A New Approach to Develop a Standardized Method for Simultaneous Analysis of Astragaloside IV and Formononetin in Radix Astragali by High-Performance Thin-Layer Chromatography

JPC – Journal of Planar Chromatography – Modern TLC - A new high-performance thin-layer chromatographic (HPTLC) method has been developed for the simultaneous estimation of...     

Integral closure of a quartic extension

Let R be a Noetherian unique factorization domain such that 2 and 3 are units,and let A=R[α]be a quartic extension over R by adding a rootαof an irreducible quartic polynomial p(z)=z4+az2+bz+c over R.We will compute explicitly the integral closure of A in its fraction field,which is based on a proper factorization of the coefficients and the algebraic invariants of p(z).In fact,we get the factorization by resolving the singularities of a plane curve defined by z4+a(x)z2+b(x)z+c(x)=0.The integral closure is expressed as a syzygy module and the syzygy equations are given explicitly.We compute also the ramifications of the integral closure over R.     

A generalized super AKNS hierarchy associated with orthosymplectic Lie superalgebra OSP(2,2) and its super bi-Hamiltonian structures

For the orthosymplectic Lie superalgebra $\text{◂⋅▸}OSP(2,2)$, we choose a set of basis matrices. A linear combination of those basis matrices presents a spatial spectral matrix. The compatible condition of the spatial part and the corresponding temporal parts of the spectral problem leads to a generalized super AKNS (GSAKNS) hierarchy. By making use of the supertrace identity, the obtained GSAKNS hierarchy can be written as the super bi-Hamiltonian structures.     

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。     

Bimetallic Covalent Organic Frameworks for Constructing Multifunctional Electrocatalyst

Covalent organic frameworks (COFs) are a new class of crystalline porous polymers comprised mainly of carbon atoms, and are versatile for the integration of heteroatoms such as B, O, and N into the skeletons. The designable structure and abundant composition render COFs useful as precursors for heteroatom-doped porous carbons for energy storage and conversion. Herein, we describe a multifunctional electrochemical catalyst obtained through pyrolysis of a bimetallic COF. The catalyst possesses hierarchical pores and abundant iron and cobalt nanoparticles embedded with standing carbon layers. By integrating these features, the catalyst exhibits excellent electrochemical catalytic activity in the oxygen reduction reaction (ORR), with a 50 mV positive half-wave potential, a higher limited diffusion current density, and a much smaller Tafel slope than a Pt-C catalyst. Moreover, the catalyst displays superior electrochemical performance toward the hydrogen evolution reaction (HER), with overpotentials of −0.26 V and −0.33 V in acidic and alkaline aqueous solution, respectively, at a current density of 10 mA cm⁻². The overpotential in the catalysis of the oxygen evolution reaction (OER) was 1.59 V at the same current density.