All-solid-state lithium batteries are promising to overcome the safety issues and limited energy density concern of commercial Li-ion batteries (LIBs). In this study, cubic-phase Li 1.4 Al 0.4 Ti 1.6 (PO 4 ) 3 (LATP) powders are prepared and filled into biodegradable polycaprolactone (PCL) matrixes to form a flexible PCL−LiClO 4 −LATP hybrid solid electrolytes (HSEs). Owing to the excellent electrochemical stability of the PCL matrix, the HSEs offer a wide electrochemical potential window of 5 V (vs Li/Li + ), an ionic conductivity of 3.64 × 10 −5 S cm −1 at 55 °C, and a high Li-ion transference number of 0.58. In addition, the fabricated allsolid-state lithium batteries exhibit an outstanding electrochemical performance with a high initial discharge specific capacity of 136.6 mAh g −1 and a good capacity retention of 75% after 200 cycles at 0.3 C. The superior performance indicates that the HSEs with the PCL as the polymer matrix provide an inspiring approach to develop high-performance flexible and safe all-solid-state lithium batteries.

All‐solid‐state lithium batteries have received extensive attention due to their high safety and promising energy density and are considered as the next‐generation electrochemical energy storage system. However, exploring solid‐state electrolytes in customized geometries without sacrificing the ionic transport is significant yet challenging. Herein, various 3D printable Li1.3Al0.3Ti1.7(PO4)3 (LATP)‐based inks are developed to construct ceramic and hybrid solid‐state electrolytes with arbitrary shapes as well as high conductivities. The obtained inks show suitable rheological behaviors and can be successfully extruded into solid‐state electrolytes using the direct ink writing (DIW) method. As‐printed free‐standing LATP ceramic solid‐state electrolytes deliver high ionic conductivity up to 4.24 × 10−4 S cm−1 and different shapes such as “L”, “T,” and “+” can be easily realized without sacrificing high ionic transport properties. Moreover, using this printing method, LATP‐based hybrid solid‐state electrolytes can be directly printed on LiFePO4 cathodes for solid‐state lithium batteries, where a high discharge capacity of 150 mAh g−1 at 0.5 C is obtained. The DIW strategy for solid‐state electrolytes demonstrates a new way toward advanced solid‐state energy storage with the high ionic transport and customized manufacturing ability.

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Food storage security is critical to the national economy and people's lives. The environmental parameters of a granary should be accurately monitored in order to provide a better preservation environment for food storage. In this paper, we use temperature sensors to measure and collect grain temperature data for a period of 423 days from a real world granary, and collect the corresponding meteorological data from China Meteorological Data Network. We propose to leverage meteorological data to predict the average temperature of the grain pile with machine learning algorithms: a support vector regression (SVR) approach and an adaptive boosting (AdaBoost) approach. We incorporate different kernel functions in the SVR model and choose the appropriate base-estimator and the number of estimators in the AdaBoost model. We then analyze the correlation between a large amount of historical data from the granary and the corresponding meteorological forecast data based on the Pearson correlation coefficient. We find that there are strong correlations between some meteorological factors. In order to eliminate redundant information, we reduce the dimension of data by principal components analysis (PCA), and compare the prediction models before and after PCA dimension reduction. The results show that the proposed approaches can achieve a high accuracy and the Adboost method after PCA dimension reduction achieves the best performance.

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