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Nonaqueous flow batteries hold promise given their high cell voltage and energy density, but their performance is often plagued by the crossover of redox compounds. In this study, we used permselective lithium superionic conducting (LiSICON) ceramic membranes to enable reliable long-term use of organic redox molecules in nonaqueous flow cells. With different solvents on each side, enhanced cell voltages were obtained for a flow battery using viologen-based negolyte and TEMPO-based posolyte molecules. The thermoplastic assembly of the LiSICON membrane realized leakless cell sealing, thus overcoming the mechanical brittleness challenge. As a result, stable cycling was achieved in the flow cells, which showed good capacity retention over an extended test time.

Abstract The cavitation in axial piston pumps threatens the reliability and safety of the overall hydraulic system. Vibration signal can reflect the cavitation conditions in axial piston pumps and it has been combined with machine learning to detect the pump cavitation. However, the vibration signal usually contains noise in real working conditions, which raises concerns about accurate recognition of cavitation in noisy environment. This paper presents an intelligent method to recognise the cavitation in axial piston pumps in noisy environment. First, we train a convolutional neural network (CNN) using the spectrogram images transformed from raw vibration data under different cavitation conditions. Second, we employ the technique of gradient‐weighted class activation mapping (Grad‐CAM) to visualise class‐discriminative regions in the spectrogram image. Finally, we propose a novel image processing method based on Grad‐CAM heatmap to automatically remove entrained noise and enhance class features in the spectrogram image. The experimental results show that the proposed method greatly improves the diagnostic performance of the CNN model in noisy environments. The classification accuracy of cavitation conditions increases from 0.50 to 0.89 and from 0.80 to 0.92 at signal‐to‐noise ratios of 4 and 6 dB, respectively.

Redox-active organic molecules that store positive charge in aqueous redox flow cells (catholyte redoxmers) frequently exhibit poor chemical stability for reasons that are not entirely understood. While for some catholyte molecules, deprotonation in their charged state is resposible for shortening the lifetime, for well designed molecules that avoid this common fate, it is seldom known what causes their eventual decomposition as it appears to be energetically prohibitive. Here, a highly soluble (1.6 M) phenoxazine molecule with a redox potential of 0.48 V vs. Ag/AgCl has been examined in flow cells. While this molecule has highly reversible redox chemistry, during cycling the capacity fades in a matter of hours. Our analyses suggest a cooperative decomposition pathway involving disproportionation of two charged molecules followed by anion substitution and deprotonation. This example suggests that cooperative reactions can be responsible for unexpectedly low chemical instability in the catholyte redoxmers and that researchers need to be keenly aware of such reactions and methods for their mitigation.

Desalination of saline water is becoming an increasingly critical strategy to overcome the global challenge of drinkable water shortage, but current desalination methods are often plagued with major drawbacks of high energy consumption, high capital cost, or low desalination capacity. To address these drawbacks, we have developed a unique continuous-mode redox flow desalination approach capitalizing on the characteristics of redox flow batteries. The operation is based on shuttled redox cycles of very dilute Fe2+/Fe3+ chelate redoxmers with ultralow cell overpotentials. The air instability of Fe2+ chelate is naturally compensated for by its in situ electrochemical generation, making the desalination system capable of operations with electrolytes at any specified state of charge. Under unoptimized conditions, fast desalination rates up to 404.4 mmol·m−2·h−1 and specific energy consumptions as low as 7.9 Wh·molNaCl−1 have been successfully achieved. Interestingly, this desalination method has offered an opportunity of sustainable, distributed drinkable water supplies through direct integration with renewable energy sources such as solar power. Therefore, our redox flow desalination design has demonstrated competitive desalination performance, promising to provide an energy-saving, high-capacity, robust, cost-effective desalination solution.

BackgroundQuarantine as one of the most effective epidemic prevention measures, significantly increased people's stress levels. Ongoing monitoring of the stress status of people under quarantine during the pandemic is an important part of assessing the long-term impact of COVID-19 on mental health. This study aimed to gain a more comprehensive understanding of the stress status of people under quarantine, including perceived stress and stress responses, during the third year of the COVID-19 pandemic in China.MethodsAn anonymous online survey was conducted among 464 participants from 39 cities in China from March 31 to April 12, 2022. The survey included three questionnaires: a self-designed questionnaire collecting demographic information and quarantine characteristics, the Perceived Stress Scale (PSS-10) and the Stress Response Questionnaire (SRQ). The t-test or one-way ANOVA or the Welch F-test were used to examine the differences among demographic and quarantine variables of perceived stress and stress responses, then multiple linear regressions were performed to identify the predictors of perceived stress and stress responses.Results428 valid respondents were finally included. The average scores of perceived stress, total stress response, emotional response, physical response, and behavioral response were 14.70 ± 7.02, 50.24 ± 22.48, 20.35 ± 9.99, 15.23 ± 7.25, and 11.39 ± 5.27, respectively. The regression analysis showed that the degree of financial worries and days of continuous quarantine were the predictors of perceived stress. The degree of financial worries was a vital factor in predicting total stress response, emotional response, physical response and behavioral response, and in predicting emotional response, age was also a significant predictor.ConclusionThe stress status of individuals under quarantine was generally stable but still needs further attention during the third year of the COVID-19 pandemic. People who are young, have a high degree of financial worries and have been quarantined for a long time may be at a higher risk of perceived stress and stress responses. Relevant authorities should pay closer attention to the risk groups, and additional support and assistance might be required for those mostly worried about their financial situations under quarantine.

The total organic carbon (TOC) content of organic-rich shale is a key parameter in screening for potential source rocks and sweet spots of shale oil/gas. Traditional methods of determining the TOC content, such as the geochemical experiments and the empirical mathematical regression method, are either high cost and low-efficiency, or universally non-applicable and low-accuracy. In this study, we propose three machine learning models of random forest (RF), support vector regression (SVR), and XGBoost to predict the TOC content using well logs, and the performance of each model are compared with the traditional empirical methods. First, the decision tree algorithm is used to identify the optimal set of well logs from a total of 15. Then, 816 data points of well logs and the TOC content data collected from five different shale formations are used to train and test these three models. Finally, the accuracy of three models is validated by predicting the unknown TOC content data from a shale oil well. The results show that the RF model provides the best prediction for the TOC content, with R2 = 0.915, MSE = 0.108, and MAE = 0.252, followed by the XGBoost, while the SVR gives the lowest predictive accuracy. Nevertheless, all three machine learning models outperform the traditional empirical methods such as Schmoker gamma-ray log method, multiple linear regression method and ΔlgR method. Overall, the proposed machine learning models are powerful tools for predicting the TOC content of shale and improving the oil/gas exploration efficiency in a different formation or a different basin.

Drilling has demonstrated that oil can be produced from the lacustrine shale in the upper part of the fourth member of the Shahejie Formation (Es4s) in Dongying Depression. These wells are often drilled in multiple lithofacies combinations, rather than a single lithofacies. Thus, it is necessary to classify the shale lithofacies combinations to optimize favorable exploration targets. The basic characteristics of carbonate mineral-rich shales in the Es4s were determined by core description, optical microscopy of thin sections, mineral analysis by X-ray diffraction, and scanning electron microscope analysis of argon ion-polished samples, combined with organic geochemical analysis and reservoir property analysis. The depositional setting was determined, and the lithofacies combinations of the shale were divided based on the depositional setting and vertical changes in lithofacies combination. Lithofacies combinations include 1) laminated argillaceous limestone interbedded with the calcareous mudstone (LLM); 2) laminated argillaceous limestone interbedded with dolomite (LLD); and 3) calcareous mudstone intercalated with argillaceous limestone (BML). LLM formed in a semi-humid climate, with limited detrital provenance, while LLD was mainly the product of an arid climate, little detrital input, semi-deep water, and a strongly reducing saltwater environment. In addition to the basal strata in the center of the depression, the LLD lithofacies combination was also widely developed on the gentle slope and the slope area transitional to deep depression in the south of the basin. BML was mainly formed in a semi-humid climate, brackish, deep water, and a reducing environment with many detrital provenances. The porosity in LLM and LLD consists of various types. Intercrystalline pores in clay minerals, intercrystalline pores and dissolution pores in carbonate minerals, interbedded fractures, and ultra-pressure fractures are well developed. The pore width distribution range of both LLM and LLD is large. The porosity of LLM is slightly higher than that of LLD. Interlayers with higher dolomite content have higher permeability. The types of pores in BML with no significant laminae characteristics are less abundant, and it is difficult to form an effective reservoir porosity network from the pores and microfractures, and hence, the reservoir physical properties are relatively poor. Oil saturation index indicates that both LLD and LLM of medium–low degree of evolution have good oil content.