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The anode‐cathode interplay is an important but rarely considered factor that initiates the degradation of aqueous zinc ion batteries (AZIBs). Herein, to address the limited cyclability issue of V‐based AZIBs, Al2(SO4)3 is proposed as decent electrolyte additive to manipulate OH−‐mediated cross‐communication between Zn anode and NaV3O8 ⋅ 1.5H2O (NVO) cathode. The hydrolysis of Al3+ creates a pH≈0.9 strong acidic environment, which unexpectedly prolongs the anode lifespan from 200 to 1000 h. Such impressive improvement is assigned to the alleviation of interfacial OH− accumulation by Al3+ adsorption and solid electrolyte interphase formation. Accordingly, the strongly acidified electrolyte, associated with the sedated crossover of anodic OH− toward NVO, remarkably mitigate its undesired dissolution and phase transition. The interrupted OH−‐mediated communication between the two electrodes endows Zn||NVO batteries with superb cycling stability, at both low and high scan rates. [ABSTRACT FROM AUTHOR]

Recent advancements in machine learning and communication technologies have enabled new approaches to automated fault diagnosis and detection in industrial systems. Given wide variation in occurrence frequencies of different classes of faults, the class distribution of real-world industrial fault data is usually imbalanced. However, most prior machine learning-based classification methods do not take this imbalance into consideration, and thus tend to be biased toward recognizing the majority classes and result in poor accuracy for minority ones. To solve such problems, we propose a k-means clustering generative adversarial network (KM-GAN)-based fault diagnosis approach able to reduce imbalance in fault data and improve diagnostic accuracy for minority classes. First, we design a new k-means clustering algorithm and GAN-based oversampling method to generate diverse minority-class samples obeying the similar distribution to the original minority data. The k-means clustering algorithm is adopted to divide minority-class samples into k clusters, while a GAN is applied to learn the data distribution of the resulting clusters and generate a given number of minority-class samples as a supplement to the original dataset. Then, we construct a deep neural network (DNN) and deep belief network (DBN)-based heterogeneous ensemble model as a fault classifier to improve generalization, in which DNN and DBN models are trained separately on the resulting dataset, and then the outputs from both are averaged as the final diagnostic result. A series of comparative experiments are conducted to verify the effectiveness of our proposed method, and the experimental results show that our method can improve diagnostic accuracy for minority-class samples. [ABSTRACT FROM AUTHOR]

The anode-cathode interplay is an important but rarely considered factor that initiates the degradation of aqueous zinc ion batteries (AZIBs). Herein, to address the limited cyclability issue of V-based AZIBs, Al 2 (SO 4 ) 3 is proposed as decent electrolyte additive to manipulate OH - -mediated cross-communication between Zn anode and NaV 3 O 8  ⋅ 1.5H 2 O (NVO) cathode. The hydrolysis of Al 3+ creates a pH≈0.9 strong acidic environment, which unexpectedly prolongs the anode lifespan from 200 to 1000 h. Such impressive improvement is assigned to the alleviation of interfacial OH - accumulation by Al 3+ adsorption and solid electrolyte interphase formation. Accordingly, the strongly acidified electrolyte, associated with the sedated crossover of anodic OH - toward NVO, remarkably mitigate its undesired dissolution and phase transition. The interrupted OH - -mediated communication between the two electrodes endows Zn||NVO batteries with superb cycling stability, at both low and high scan rates., (© 2022 Wiley-VCH GmbH.)

Colorimetry is widely used in chemical sensing due to its high sensitivity and high selectivity. However, most colorimetric sensors are one-time use because the color-producing reactions or bindings are usually irreversible. In addition, traditional colorimetric sensors like the detection tubes are bulky and packed individually, making parallel sensing of multiple analytes difficult. Here, we demonstrate a gradient-based colorimetric array sensor (GCAS) to overcome these limitations. Different colorimetric sensing elements are inkjet-printed as parallel straight lines on a porous substrate. Lateral transport of analytes across the substrate creates color gradients on the sensing elements. The color gradients shift along the transport direction over time, and GCAS tracks the gradient shifts and converts them into analyte concentrations in real time. Using a low-cost complementary metal-oxide semiconductor imager, we show detection of three air pollutants using a single GCAS chip and 24 h continuous monitoring of ambient ozone.

An integrated and miniaturized Micro-Gas Chromatography with real-time imaging capability for simultaneous chemical separation and detection was developed. Surface Plasmon Resonance imaging (SPRi) was used as a sensitive and real-time imaging based detector for various gaseous chemical mixtures and good gas chromatographs were obtained. The system integrated a home-made miniaturized molecular sieve packed spiral micro-channel column with the SPRi imaging chip and real-time chemical separation and detection were demonstrated using alkanes. The chemical separation processes were simulated using COMSOL and matched well with experimental results. The system enabled the study of chemical separation processes in real-time by miniaturizing and integrating the Micro-GC separation and detection units. This approach can be expanded to multidimensional GC development.