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Ancient ceramics have profound influences on the living habits of human beings. The emergence of high-fired polychromic glazes during Tang period (618–907 AD) highlights a significant advancement in ceramic history that reflected inter-civilizational communications more than 1000 years ago. However, the understanding of colouration mechanisms in Tang glazes are still not satisfactory. In this work, we unravel the mysteries of colourful Tang glazes through a comparative study of Tang Sancai and Changsha kiln samples. Multiple chemical and structural factors, including colourants in varied concentrations and oxidation states as well as microstructures resulted from phase separation, impurity and corrosion, are found responsible for the glaze appearance. More importantly, the combination of different coloration ingredients brings about additional colours and optical effects that any individual factor can hardly generate. These fresh observations and comprehensive analyses provide an insightful assessment of the diversity of Tang glazes and promote the perception of ancient ceramics in a more scientific manner. The knowledge and methodology revealed here are expected to inspire more studies of heritage materials.

Nonnegative Matrix Factorization (NMF) is a popular technique for machine learning. Its power is that it can decompose a nonnegative matrix into two nonnegative factors whose product well approximates the nonnegative matrix. However, the nonnegative constraint of the data matrix limits its application. Additionally, the representations learned by NMF fail to respect the intrinsic geometric structure of the data. In this paper, we propose a novel unsupervised matrix factorization method, called Robust Structured Convex Nonnegative Matrix Factorization (RSCNMF). RSCNMF not only achieves meaningful factorizations of the mixed-sign data, but also learns a discriminative representation by leveraging local and global structures of the data. Moreover, it introduces the L2,1-norm loss function to deal with noise and outliers, and exploits the L2,1-norm feature regularizer to select discriminative features across all the samples. We develop an alternate iterative scheme to solve such a new model. The convergence of RSCNMF is proven theoretically and verified empirically. The experimental results on eight real-world data sets show that our RSCNMF algorithm matches or outperforms the state-of-the-art methods.

The sluggish conversion reaction of sulfur and poor interfacial contact restrict electrochemical performance and practical application of all-solid-state Li–S batteries. Herein, we present a ration...

A novel strategy has been proposed to produce in situ Li2S at the interfacial layer between lithium anode and the solid electrolyte, by using an amorphous-sulfide–LiTFSI–poly(vinylidene difluoride)...

Nonnegative matrix factorization (NMF) has attracted more and more attention due to its wide applications in computer vision, information retrieval, and machine learning. In contrast to the original NMF and its variants, this paper proposes a novel unsupervised learning framework, called robust structured nonnegative matrix factorization (RSNMF) which respects both global and local structures of the data space. Specifically, to learn a discriminative representation, RSNMF explores both the global structure via considering the data variance and the local structure via exploiting the data neighborhood. To well address the problem of noise and outliers, it imposes joint L2,1-norm minimization on both the loss function of NMF and the regularization of the basis matrix. The geometric structure and the joint L2,1-norm are formulated as an optimization model, which is solved by the proposed iterative algorithm. Finally, the convergence of RSNMF is analyzed theoretically and empirically. The experimental results on real-world data sets show the effectiveness of our proposed algorithm in comparison to state-of-the-art algorithms.

Early Chinese lead glaze has been recognized as an important artifact due to its multiple significances in history and science. Some efforts were made to explore its composition and degradation, but the chemical mechanisms of glaze transformation and incrustation formation, especially the evolution of lead and the roles of colorants, are still not clearly elucidated. In this work, an amorphous Si-rich structure and a layer of dendritic PbCO3 crystal were detected in the degraded glaze. Such characteristics of the incrustation help to attain insights into the chemistries governing glaze decomposition, re-crystallization of the lead compound and formation of the multi-layered structure. In addition, comparative studies on newly constructed lead-based glazes provide solid evidences for the lead transformation and highlight the crucial role of colorants in the glaze degradation. These fresh findings enable better understandings of the lead-silicate glass material and clarify long-term transformation of lead in burial environments.

Electrification is seen as one of the key strategies to mitigate the growing energy demands in areas like transportation. With electrification, a better and safer energy storage system becomes a pressing need. Therefore, Li-based batteries are gaining popularity due to their high theoretical capacities. However, the use of Li-based batteries had been fraught with safety concerns. Specifically, Li dendrite formation during Li-plating can cause shorting in cells and thermal runaway. To that end, much effort has been put into mitigating the growth of these dendrites. To tackle this issue, the mechanisms involved in the formation of different morphologies of the plated Li is highlighted, as it determines, to a large extent, the mechanical properties of the plated Li. In turn, the mechanical properties of the plated Li will affect the cyclability and the overall safety of the battery. However, the yield strength of most materials used in separators and solid electrolytes are usually not high enough to prevent penetration by Li dendrites. Hence, various strategies to control the growth and morphology of Li deposits that can form dendrites, has been highlighted here as these strategies are key research directions for the advancement of high energy density Li-based batteries.

An all-solid-state lithium-sulfur battery (ASSLSB) is a promising candidate for post-Li-ion battery technologies with high energy densities and good safety performance. However, the intrinsic insulating nature of sulfur requires triple-phase contact with an ionic conductor and an electronic conductor for electrochemical reactions, which decreases the amount of active surface and lowers the charge-transfer efficiency. In this work, a double-phase interface constructed from a mixed ionic/electronic conductor is proposed to enhance the solid-state electrochemical reaction of sulfur. By employing lithium lanthanum titanium oxide/carbon (LLTO/C) nanofibers with mixed ionic/electronic conductivity, enhanced charge-transfer behavior is realized at the sulfur-LLTO/C double-phase interface, compared to the traditional triple-phase interface. As a result, high sulfur utilization and excellent rate performance are achieved. And the facilitated charge transfer shows great potential to lower the operating temperature and improve the sulfur content for practical applications of ASSLSBs. Cycle performance is also enhanced due to the suppressed shuttle effect of polysulfides by the incorporation of the LLTO/C nanofibers.

In our study, we report the development of molybdenum disulfide (MoS2) nanosheet-supported poly(indole-6-carboxylic acid) (PIn6COOH) and its usage for the electrochemical determination of circulating tumor DNA (ctDNA) such as PIK3CA gene associated with breast cancer. The MoS2 nanosheets prepared by the solvent exfoliation method from commercial bulk MoS2 were first immobilized on the surface of a carbon paste electrode; then, PIn6COOH, a novel electroactive material, was electropolymerized on the MoS2 substrate by the potentiostatic method to form a unique nanocomposite structure. The physical adsorption between aromatic In6COOH monomers and MoS2 efficiently improved the electropolymerization efficiency, resulting in increased electrochemical response of PIn6COOH. Owing to the presence of abundant carboxyl groups, probe ssDNA was covalently immobilized on the carboxyl-terminated PIn6COOH/MoS2 nanocomposite through free amines of DNA sequences based on 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide and N-hydrosulfosuccinimide crosslinking reaction. The covalently immobilized probe ssDNA could selectively hybridize with its target DNA to form dsDNA on the surface of the PIn6COOH/MoS2 nanocomposite. The self-signal changes of PIn6COOH induced by DNA immobilization and hybridization could be sensitively recognized via cyclic voltammetry and electrochemical impedance spectroscopy. This developed biosensor showed high performance of DNA hybridization with a detection limit of 1.5 × 10−17 mol L−1 for the detection of PIK3CA gene. The easy construction of this biosensor besides its fine sensitivity and very low time consumption can make this PIn6COOH/MoS2 nanocomposite very highly competitive for further applications in carcinoma diagnostic and therapeutic fields.