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Multicolor upconversion luminescence (UCL) plays an essential role in anti-fake and information encipher. Here, we report a strategy to expand the color-adjustable emission range by regulating the doping concentration of Yb3+ in core@shell (C@S) upconversion nanoparticles (UCNPs) under the excitation of near-infrared (NIR) light at 980 nm and promoting the energy-transfer process. Subsequently, multicolor UCNPs were combined with acrylamide hydrogel to construct a three-dimensional (3D) information code with an adjustable emission color. The encoded information is readable only under 980 nm excitation light and disappears as soon as the excitation light is removed. We adopted three strategies to improve the security: multiple permutations and combinations of different emission light hydrogel blocks, the combination of rich optical information and binary code, and the shape coding of hydrogel blocks. Constructing a complex and changeable coding library significantly guarantees that the data can be enciphered and deciphered by simple light irradiation, and the content can be edited by permutation and combination combined with information coding rules. Therefore, it is expected to provide ideas for developing intelligent anti-counterfeiting materials and devices. [ABSTRACT FROM AUTHOR]

Nanothermometers that can reflect temperature in real‐time are crucial for photothermal therapy. Near‐infrared (NIR) fluorescent probes that conform to Boltzmann distribution will play an important role if they have the above characteristics. Here, the LiLuF4: Nd3+/Yb3+ nanocrystals were designed and synthesized by control, exhibiting multiple bright near‐infrared emission peaks at 925–1080 nm region at 280–360 K under the excitation of an 808 nm laser. The feasibility of being used as a nanothermometer was confirmed by testing the spectral variation with temperature and analysis. [ABSTRACT FROM AUTHOR]

Compounding carbon substrate materials with pseudocapacitive materials is an important strategy for making high‐performance supercapacitor materials. Nano carbon black (CB) is widely used as the conductive additive in energy storage devices owing to its low cost, high conductivity, good spreadability, and excellent ability of adsorbing electrolyte, reducing the binder consumption, adjusting the pore size and fraction of the electrode, and high‐voltage operation. However, very little attention has been paid to applying CB as the substrate materials for producing composite active materials. Herein, a surface‐modified CB is developed by growing vertical graphene nanosheets on the surface of CB by chemical vapor deposition. After growing MnO2 nanosheets on the modified CB, the obtained nanocomposites deliver high specific capacitance, rate performance, and cycling stability. The assembled symmetric supercapacitor obtains a high energy density of 54 Wh kg−1 at a power density of 181 W kg−1. Systematic experimental research reveals the superior performance benefits from the surface modification of CB by vertical graphene nanosheets, which improves the electrical conductivity, thermal stability, and pore structure of the whole electrodes. This method is of great promise for the development of new materials for high‐performance supercapacitors. [ABSTRACT FROM AUTHOR]

Surface and interface play critical roles in energy storage devices, calling for operando characterization techniques to probe the electrified surfaces/interfaces. In this work, surface science methodology, including electron spectroscopy and scanning probe microscopy, has been successfully applied to visualize electrochemical processes at operating electrode surfaces in an Al/graphite model battery. Intercalation of anions together with cations is directly observed in the surface region of a graphite electrode with tens of nanometers thickness, the concentration of which is one order higher than that in bulk. An intercalation pseudocapacitance mechanism and a double specific capacity in the electrode surface region are expected based on the super-dense intercalants and anion/cation co-intercalation, which are in sharp contrast to the battery-like mechanism in the electrode bulk. The distinct electrochemical mechanism at the electrode surface is verified by performance tests of real battery devices, showing that a surface-dominant, nanometer-thick graphite cathode outperforms a bulk-dominant, micrometer-thick graphite cathode. Our findings highlight the important surface effect of working electrodes in charge storage systems. [ABSTRACT FROM AUTHOR]

Constraining the distribution of water in different regions of the mantle remains one of the significant challenges to comprehend the global deep water cycle. Geomagnetic depth soundings can provide such constraint through the electrical conductivity structure. Hence, this study aims to propose a regularization technique that can estimate previously unavailable C -response. In the method, the objective function comprised an L 1-norm measured data prediction error and a spectral smoothness constraint term. We used the data error of C -response to weight the predicted error. The L-BFGS method was introduced to determine the minimum point of the objective function, and the regularization parameter decreased adaptively during inversion. Thus, the geomagnetic data processed yielded high-quality C -responses in 31 stations in Eastern China. In addition, we obtained 1-D electrical conductivity profiles in the mantle transition zone (MTZ) beneath Eastern China from C -responses using the L-BFGS method. Compared with the global 1-D model, the conductivity–depth profiles revealed that the MTZ beneath Eastern China is more conductive in the east but more resistive in the west. The conversion of these conductivities to water content based on the mineral physics suggested that the MTZ beneath Eastern China is characterized by a high water concentration, approximately 0.2 and 1 wt per cent in the upper and lower MTZ, respectively. Owing to the inclusion of more stations, the water-rich region could be constrained roughly to the east of the North–South Gravity Lineament (NSGL). Further considering seismic images in the same area, this water content distribution pattern suggested that the front of the stagnant Pacific Plate in the lower MTZ might have reached the NSGL. However, the dehydration reactions in the stagnant slab were more active in the eastern part. Perhaps, some of these fluids migrated into the upper MTZ and could be the source of the trapped water found in the xenoliths from the deep upper mantle beneath Eastern China. [ABSTRACT FROM AUTHOR]

[Display omitted] • We designed an innovative Raman spectroscopy system that enables rapid nondestructive detection of food adulteration. • We constructed the first quantitative analysis model for rice flour adulteration with Rongalite. • We propose an innovative model SARNet based on residual structure combined with autoencoder for real-time detection of Rongalite content based on Raman spectroscopy. Rice flour is a raw material for various foods and is used as a substitute for wheat flour. However, some merchants adulterate rice flour with the illegal additive Rongalite to extend the shelf life and earn illegal profits. Rongalite is highly carcinogenic, and ingestion of more than 10 g can even cause death. high-performance liquid chromatography (HPLC) and mass spectrometry (MS) are currently the main methods for detecting food adulteration, however, the existing methods have many limitations, complex operation, expensive instrumentation, etc. Raman spectroscopy has the advantages of convenience and non-destructive samples, but Raman spectroscopy can be affected by interference such as fluorescence background that affects detection, in addition to the problem of difficult quantitative analysis due to nonlinear bias. In this article, we used the preprocessing method of Savitzky-Golay smoothing filtering and VTPspline to improve the quality of the spectra and proposed the SARNet, which combines autoencoder and residual network to achieve the quantitative analysis of Rongalite content in rice flour. The new model combines a linear model with a nonlinear model, which can solve the nonlinear problem effectively. Experiments showed that the new SARNet model achieved state-of-the-art results, achieving the best R2 of 0.9703 and RMSEP of 0.0075. The lowest Rongalite concentration detected by the portable Raman spectrometer was 0.49%. In summary, the proposed method using portable Raman spectroscopy combined with machine learning has low detection bias and high accuracy, which can realize quantitative analyses of adulterated Rongalite in rice flour quickly. The method provides an accurate and nondestructive analytical tool in the field of food detection. [ABSTRACT FROM AUTHOR]

[Display omitted] • A Raman spectroscopic system is designed to collect Raman spectra of the froth layer during the antimony flotation process. • We constructed a quantitative concentrate grade quantification model based on Raman spectra of mixed minerals in froth layers. • A model based on 1D-CNN combined with GRU (CNN-GRU) is proposed for the online prediction of concentrate grade. Froth flotation is the most critical process for separating stibnite from raw ore. Concentrate grade is a vital production indicator in the antimony flotation process. It is a direct reflection of the product quality of the flotation process and an essential basis for the dynamic adjustment of its operating parameters. Existing methods of measuring concentrate grades suffer from expensive measurement equipment, difficult maintenance of complex sampling systems, and extended testing times. This paper presents a nondestructive and fast methodology to quantify the concentrate grade in the antimony flotation process based on in situ Raman spectroscopy. A particular Raman spectroscopic measuring system is designed for on-line measurement of the Raman spectra of the mixed minerals from the froth layer during the antimony flotation process. To obtain representative Raman spectra that better characterize the concentrate grades, a traditional Raman spectroscopic system has been redesigned to account for the different interferences during actual flotation field acquisition. A one-dimensional convolutional neural network (1D-CNN) is combined with a gated recurrent unit (GRU) and applied to construct a model for online prediction of concentrate grades based on continuously collected Raman spectra of mixed minerals in the froth layer. With an average prediction error of 4.37% and a maximum prediction deviation of 10.56%, the quantitative analysis of concentrate grade by the model demonstrates that our method is distinguished by high accuracy, low deviation, and in situ analysis, and it essentially satisfies the requirements for online quantitative determination of concentrate grade in the antimony flotation site. [ABSTRACT FROM AUTHOR]