When does gradient Ricci soliton have one end?

Annals of Global Analysis and Geometry - In this paper, we study a problem of finding compact hypersurfaces of prescribed mean curvature with free boundary in a ball. We transform the problem to an...     

废旧聚酯（PET）的化学循环利用

废旧PET 聚酯可通过化学解聚来实现其循环利用。本文总结了废旧PET 聚酯化学循环利用领域的研究现状, 介绍了目前国内外开发的主要化学解聚工艺方法, 包括水解法、甲醇解聚法、乙二醇解聚法等, 同时对各种工艺过程的优缺点进行了综合比较。     

Visually precise,low-damage,single-cell spatial manipulation with single-pixel resolution

The analysis of single living cells, including intracellular delivery and extraction, is essential for monitoring their dynamic biochemical processes and exploring intracellular heterogeneity. However, owing to the 2D view in bright-field microscopy and optical distortions caused by the cell shape and the variation in the refractive index both inside and around the cells, achieving spatially undistorted imaging for high-precision manipulation within a cell is challenging. Here, an accurate and visual system is developed for single-cell spatial manipulation by correcting the aberration for simultaneous bright-field triple-view imaging. Stereo information from the triple view enables higher spatial resolution that facilitates the precise manipulation of single cells. In the bright field, we resolved the spatial locations of subcellular structures of a single cell suspended in a medium and measured the random spatial rotation angle of the cell with a precision of ±5°. Furthermore, we demonstrated the visual manipulation of a probe to an arbitrary spatial point of a cell with an accuracy of <1 pixel. This novel system is more accurate and less destructive for subcellular content extraction and drug delivery.

We achieved the low-damage spatial puncture of single cells at specific visual points with an accuracy of <65 nm.     

Mass spectrometry-based chemical mapping and profiling toward molecular understanding of diseases in precision medicine

Precision medicine has been strongly promoted in recent years. It is used in clinical management for classifying diseases at the molecular level and for selecting the most appropriate drugs or treatments to maximize efficacy and minimize adverse effects. In precision medicine, an in-depth molecular understanding of diseases is of great importance. Therefore, in the last few years, much attention has been given to translating data generated at the molecular level into clinically relevant information. However, current developments in this field lack orderly implementation. For example, high-quality chemical research is not well integrated into clinical practice, especially in the early phase, leading to a lack of understanding in the clinic of the chemistry underlying diseases. In recent years, mass spectrometry (MS) has enabled significant innovations and advances in chemical research. As reported, this technique has shown promise in chemical mapping and profiling for answering “what”, “where”, “how many” and “whose” chemicals underlie the clinical phenotypes, which are assessed by biochemical profiling, MS imaging, molecular targeting and probing, biomarker grading disease classification, etc. These features can potentially enhance the precision of disease diagnosis, monitoring and treatment and thus further transform medicine. For instance, comprehensive MS-based biochemical profiling of ovarian tumors was performed, and the results revealed a number of molecular insights into the pathways and processes that drive ovarian cancer biology and the ways that these pathways are altered in correspondence with clinical phenotypes. Another study demonstrated that quantitative biomarker mapping can be predictive of responses to immunotherapy and of survival in the supposedly homogeneous group of breast cancer patients, allowing for stratification of patients. In this context, our article attempts to provide an overview of MS-based chemical mapping and profiling, and a perspective on their clinical utility to improve the molecular understanding of diseases for advancing precision medicine.

An overview of MS-based chemical mapping and profiling, indicating its contributions to the molecular understanding of diseases in precision medicine by answering "what", "where", "how many" and "whose” chemicals underlying clinical phenotypes.     

Structural and process controls of AIEgens for NIR-II theranostics

Aggregation-induced emission (AIE) is a cutting-edge fluorescence technology, giving highly-efficient solid-state photoluminescence. Particularly, AIE luminogens (AIEgens) with emission in the range of second near-infrared window (NIR-II, 1000–1700 nm) have displayed salient advantages for biomedical imaging and therapy. However, the molecular design strategy and underlying mechanism for regulating the balance between fluorescence (radiative pathway) and photothermal effect (non-radiative pathway) in these narrow bandgap materials remain obscure. In this review, we outline the latest achievements in the molecular guidelines and photophysical process control for developing highly efficient NIR-II emitters or photothermal agents with aggregation-induced emission (AIE) attributes. We provide insights to optimize fluorescence efficiency by regulating multi-hierarchical structures from single molecules (flexibilization) to molecular aggregates (rigidification). We also discuss the crucial role of intramolecular motions in molecular aggregates for balancing the functions of fluorescence imaging and photothermal therapy. The superiority of the NIR-II region is demonstrated by fluorescence/photoacoustic imaging of blood vessels and the brain as well as photothermal ablation of the tumor. Finally, a summary of the challenges and perspectives of NIR-II AIEgens for in vivo theranostics is given.

Structural and process controls of NIR-II AIEgens realize manipulating of radiative (R) and nonradiative (NR) decay for precise theranostics.     

Electrostatic Interactions Leading to Hierarchical Interpenetrating Electroconductive Networks in Silicon Anodes for Fast Lithium Storage

Recently, the frequency of combining MXene, which has unique properties such as metal-level conductivity and large specific surface area, with silicon to achieve excellent electrochemical performance has increased considerably. There is no doubt that the introduction of MXene can improve the conductivity of silicon and the cycling stability of electrodes after elaborate structure design. However, most exhaustive contacts can only improve the electrode conductivity on the plane. Herein, a MXene@Si/CNTs (HIEN-MSC) composite with hierarchical interpenetrating electroconductive networks has been synthesized by electrostatic self-assembly. In this process, the CNTs are first combined with silicon nanoparticles and then assembled with MXene nanosheets. Inserting CNTs into silicon nanoparticles can not only reduce the latter‘s agglomeration, but also immobilizes them on the three-dimensional conductive framework composed of CNTs and MXene nanosheets. Therefore, the HIEN-MSC electrode shows superior rate performance (high reversible capacity of 280 mA h⁻¹ even tested at 10 A g⁻¹), cycling stability (stable reversible capacity of 547 mA h g⁻¹ after 200 cycles at 1 A g⁻¹) and applicability (a high reversible capacity of 101 mA h g⁻¹ after 50 cycles when assembled with NCM622 into a full cell). These results may provide new insights for other electrodes with excellent rate performance and long-cycle stability.     

Analysis of financial stock markets through multidimensional scaling based on information measures

In this paper, a novel multidimensional scaling (MDS) based on information measures method is proposed to analyze financial stock markets. In order to examine the effectiveness of this method, we applied it to the classification of two types of artificial series, the logistic map model and the cubic map model, as well as stock time series. Moreover, the traditional MDS using Euclidean dissimilarity is also provided as a reference for comparisons. The results show that the MDS based on information measures can give us more detailed, exact and clearer information on the classification of simulation series and stock time series than the MDS using Euclidean dissimilarity. In addition, the proposed graphical method may also assist in the construction of multivariate econometric models.     

Boron-iron nanochains for selective electrocatalytic reduction of nitrate

The electrocatalysis of nitrate reduction reaction(NRR) has been considered to be a promising nitrate removal technology.Developing a highly effective iron-based electrocatalyst is an essential challenge for NRR.Herein,boron-iron nanochains(B-Fe NCs) as efficient NRR catalysts were prepared via a facile lowcost and scalable method.The Fe/B ratio of the B-Fe NCs-x can be elaborately adjusted to optimize the NRR catalytic performance.Due to the electron transfer from boron to metal,the metal-metal bonds are weakened and the electron density near the metal atom centers are rearranged,which are favor of the conversion from NO_3~-into N_2.Moreover,the well-crosslinked chain-like architectures benefit the mass/electron transport to boost the exposure of abundant catalytic active sites.Laboratory experiments demonstrated that the optimized B-Fe NCs catalyst exhibits superior intrinsic electrocatalytic NRR activity of high nitrate conversion(～80%),ultrahigh nitrogen selectivity(～99%) and excellent long-term reactivity in the mixed electrolyte system(0.02 mol/L NaCl and 0.02 mol/L Na_2 SO_4 mixed electrolyte),and the electrocatalytic activity of the material shows poor performance at low chloride ion concentration(Nitrate conversion of ～61 % and nitrogen selectivity of ～57% in 0.005 mol/L NaCl and 0.035 mol/L Na_2 SO_4 mixed electrolyte).This study provides a broad application prospect for further exploring the highefficiency and low-cost iron-based functional nanostructures for electrocatalytic nitrate reduction.     

关于完全完备分配格上矩阵相对于特征值的特征向量

借助于伪补和矩阵的幂序列研究了完全完备分配格上矩阵相对于特征值的特征向量的计算方法,利用特征向量的性质证明了最大特征向量的计算公式,并给出了一般特征向量的计算方法.