A built‐in electric field in electrocatalyst can significantly accumulate higher concentration of NO3− ions near electrocatalyst surface region, thus facilitating mass transfer for efficient nitrate ...removal at ultra‐low concentration and electroreduction reaction (NO3RR). A model electrocatalyst is created by stacking CuCl (111) and rutile TiO2 (110) layers together, in which a built‐in electric field induced from the electron transfer from TiO2 to CuCl (CuCl_BEF) is successfully formed . This built‐in electric field effectively triggers interfacial accumulation of NO3− ions around the electrocatalyst. The electric field also raises the energy of key reaction intermediate *NO to lower the energy barrier of the rate determining step. A NH3 product selectivity of 98.6 %, a low NO2− production of <0.6 %, and mass‐specific ammonia production rate of 64.4 h−1 is achieved, which are all the best among studies reported at 100 mg L−1 of nitrate concentration to date.
An electrocatalyst is created by stacking CuCl (111) and rutile TiO2 (110) layers together. A built‐in electric field induced from the electron transfer from TiO2 to CuCl (CuCl_BEF) is thus formed, which triggers interfacial accumulation of NO3− ions around the electrocatalyst. A NH3 product selectivity of 98.6 %, a low NO2− production of <0.6 %, and mass‐specific ammonia production rate of 64.4 h−1 is achieved.
Nitrate electrocatalytic reduction (NO3RR) for ammonia production is a promising strategy to close the N‐cycle from nitration contamination, as well as an alternative to the Haber–Bosch process with ...less energy consumption and carbon dioxide release. However, current long‐term stability of NO3RR catalysts is usually tens of hours, far from the requirements for industrialization. Here, symmetry‐broken Cusingle‐atom catalysts are designed, and the catalytic activity is retained after operation for more than 2000 h, while an average ammonia production rate of 27.84 mg h−1 cm−2 at an industrial level current density of 366 mA cm−2 is achieved, obtaining a good balance between catalytic activity and long‐term stability. Coordination symmetry breaking is achieved by embedding one Cu atom in graphene nanosheets with two N and two O atoms in the cis‐configuration, effectively lowering the coordination symmetry, rendering the active site more polar, and accumulating more NO3− near the electrocatalyst surface. Additionally, the cis‐coordination splits the Cu 3d orbitals, which generates an orbital‐symmetry‐matched π‐complex of the key intermediate *ONH and reduces the energy barrier, compared with the σ‐complex generated with other catalysts. These results reveal the critical role of coordination symmetry in single‐atom catalysts, prompting the design of more coordination‐symmetry‐broken electrocatalysts toward possible industrialization.
A coordination‐symmetry‐breaking Cusingle‐atom catalyst enables a good balance between catalytic activity and long‐term stability in nitrate electroreduction to ammonia. The catalytic activity is retained after operation for more than 2000 h, while an average ammonia production rate of 27.84 mg h−1 cm−2 at an industrial level current density of 366 mA cm−2 is achieved.
•An online RUL estimation method for lithium-ion battery is proposed.•RUL is described by the difference among battery terminal voltage curves.•A feed forward neural network is employed for RUL ...estimation.•Importance sampling is utilized to select feed forward neural network inputs.
An accurate battery remaining useful life (RUL) estimation can facilitate the design of a reliable battery system as well as the safety and reliability of actual operation. A reasonable definition and an effective prediction algorithm are indispensable for the achievement of an accurate RUL estimation result. In this paper, the analysis of battery terminal voltage curves under different cycle numbers during charge process is utilized for RUL definition. Moreover, the relationship between RUL and charge curve is simulated by feed forward neural network (FFNN) for its simplicity and effectiveness. Considering the nonlinearity of lithium-ion charge curve, importance sampling (IS) is employed for FFNN input selection. Based on these results, an online approach using FFNN and IS is presented to estimate lithium-ion battery RUL in this paper. Experiments and numerical comparisons are conducted to validate the proposed method. The results show that the FFNN with IS is an accurate estimation method for actual operation.
The remaining useful life (RUL) prediction plays a pivotal role in the predictive maintenance of industrial manufacturing systems. However, one major problem with the existing RUL estimation ...algorithms is the assumption of a single health degradation trend for different machine health stages. To improve the RUL prediction accuracy with various degradation trends, this article proposes an algorithm dubbed degradation-aware long short-term memory (LSTM) autoencoder (AE) (DELTA). First, the Hilbert transform is adopted to evaluate the degradation stage and factor with the real-time sensory signal. Second, we adopt LSTM AE to predict RUL based on multisensor time-series data and the degradation factor. Distinct from the existing studies, the proposed framework is able to dynamically model the degradation factor and explore latent variables to improve RUL prediction accuracy. The performance of DELTA is evaluated with the open-source FEMTO bearing data set. Compared with the existing algorithms, DELTA achieves appreciable improvements in the RUL prediction accuracy.
This paper proposes an enterprise performance management model based on the CPS evaluation system to improve the efficiency and efficiency of enterprises. First, this paper explains the connotation ...of the CPS evaluation system and constructs an enterprise performance evaluation model. The index data is normalized, and the index weights are calculated using Delphi and entropy methodsthod so that the indexes are scored comprehensively. Then the enterprise performance management system was established, and the assessment criteria for each department of the enterprise, including the finance and production departments, were established. Finally, the management effect of this performance management model was analyzed in terms of enterprise profit and return rate. In terms of enterprise profit, the enterprise’s net profit was between 700,000 and 800,000 in the five months before the adoption of this method, while the profit of the enterprise was basically above 800,000 and increased month by month after the adoption of this system. In terms of payback rate, the payback rate fluctuated around 60% before the adoption of the evaluation system, but after the adoption, the payback rate of the enterprise was above 70% and showed an increasing trend. The enterprise performance management mode based on the CPS evaluation system can improve the efficiency of the enterprise, which proves the reliability of the method of this paper.
This paper investigates whether retail investor attention promotes or inhibits corporate green innovation. Using Chinese nonfinancial public listed firms from 2011 to 2020, we find that retail ...investor attention significantly positively impacts corporate green innovation. This finding still holds after a series of robustness tests for possible endogeneity concerns, alternative explanatory variables, and regression methods. We further verify that retail investor attention increases corporate green innovation by increasing information transparency, alleviating financing constraints and deterring agency costs. Cross-sectional heterogeneity analysis further supports our channel test, in which our results are pronounced for firms with less information asymmetry, higher reputation capital and better corporate governance characteristics. Our results shed essential insight into sustainable and green growth from a micro enterprise perspective in the digital economic era.
•The impact of retail investor attention on corporate green innovation is explored in China.•Retail investor attention significantly positively impacts corporate green innovation.•This effect is achieved by increasing information transparency, alleviating financing constraints and deterring agency costs.•This effect is pronounced for firms with less information asymmetry, higher reputation capital and better corporate governance.
Fusing the advantages of multiple acoustic features is important for the robustness of voice activity detection (VAD). Recently, the machine-learning-based VADs have shown a superiority to ...traditional VADs on multiple feature fusion tasks. However, existing machine-learning-based VADs only utilize shallow models, which cannot explore the underlying manifold of the features. In this paper, we propose to fuse multiple features via a deep model, called deep belief network (DBN). DBN is a powerful hierarchical generative model for feature extraction. It can describe highly variant functions and discover the manifold of the features. We take the multiple serially-concatenated features as the input layer of DBN, and then extract a new feature by transferring these features through multiple nonlinear hidden layers. Finally, we predict the class of the new feature by a linear classifier. We further analyze that even a single-hidden-layer-based belief network is as powerful as the state-of-the-art models in the machine-learning-based VADs. In our empirical comparison, ten common features are used for performance analysis. Extensive experimental results on the AURORA2 corpus show that the DBN-based VAD not only outperforms eleven referenced VADs, but also can meet the real-time detection demand of VAD. The results also show that the DBN-based VAD can fuse the advantages of multiple features effectively.
•The equivalent circuit model is estimated for battery states estimation.•Battery peak current is analyzed by multi-constrained conditions.•A novel multi-time-scale observer is used to estimate SOE ...and SOP concurrently.•The accuracy of the proposed method is verified under different conditions.
The battery state of energy and state of power are two important parameters in battery usage. The state of energy represents the residual energy storage in battery and the state of power represents the ability of battery discharge/charge. To estimate the two states with high accuracy, the characteristics of battery maximum available capacity and open-circuit voltage are analyzed under different working temperatures. Meanwhile, the equivalent circuit model of the battery is employed to embody the battery dynamic performance. To improve the accuracy of the battery states estimation, the multi-time-scale filter is applied in battery model parameters identification and battery states prediction. Besides, the state of power is analyzed by multi-constrained conditions to ensure battery work with safety. The proposed approach is verified by experiments operated on lithium-ion battery under new European driving cycle profiles and dynamic test profiles. The experimental results indicate the proposed method can estimate the battery states with high accuracy for actual application. In addition, the factors affecting the change of battery states are analyzed.
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•Dietary BPA uptake induces oxidative stress in the colon and liver.•Dietary BPA uptake induces local inflammation in the colon and liver.•Dietary BPA uptake induces mitochondrial ...dysfunction in the colon and liver.•Dietary BPA uptake induces apoptosis in the colon and liver.
Bisphenol A (BPA), a widely used industrial compound worldwide, was recently classified as an environmental toxicant. The intestines and liver are responsible for detoxification in humans and animals, and functional damage to these organs adversely affects the health of the body. However, the effect of BPA on intestinal and liver function remains unclear. In this study, we investigated the effects of dietary BPA uptake on oxidative stress, inflammatory response, apoptosis and mitochondrial function in the colons and livers of mice. Dietary BPA uptake significantly reduced the body weights of mice as well as their colon and liver weights. Dietary BPA uptake increased the levels of oxidative stress indicators such as reactive oxygen species, reactive nitrogen species, malondialdehyde and hydrogen peroxide in mouse serum, colon and liver tissues. Antioxidant indicators, such as superoxide dismutase, glutathione peroxidase, catalase and total antioxidant capacity, as well as proinflammatory cytokines (interleukin-1β, interleukin-6, interleukin-8 and tumor necrosis factor-α) were also significantly reduced in the serum, colon, and liver tissues in the BPA group. Moreover, mitochondria-encoded genes and mitochondrial copy numbers were significantly reduced in the colon and liver tissues of the BPA mice. Dietary BPA uptake also increased gene abundance and enzyme activity of caspase-3, -8, -9 and -10. Our study found that dietary BPA induced oxidative stress, inflammatory response, apoptosis and mitochondrial dysfunction in mouse colons and livers.
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