Ultrahigh Piezoelectric Performance through Synergistic Compositional and Microstructural Engineering

Piezoelectric materials enable the conversion of mechanical energy into electrical energy and vice-versa. Ultrahigh piezoelectricity has been only observed in single crystals. Realization of piezoelectric ceramics with longitudinal piezoelectric constant (d33) close to 2000 pC N–1, which combines single crystal-like high properties and ceramic-like cost effectiveness, large-scale manufacturing, and machinability will be a milestone in advancement of piezoelectric ceramic materials. Here, guided by phenomenological models and phase-field simulations that provide conditions for flattening the energy landscape of polarization, a synergistic design strategy is demonstrated that exploits compositionally driven local structural heterogeneity and microstructural grain orientation/texturing to provide record piezoelectricity in ceramics. This strategy is demonstrated on [001]PC-textured and Eu3+-doped Pb(Mg1/3Nb2/3)O3-PbTiO3 (PMN-PT) ceramics that exhibit the highest piezoelectric coefficient (small-signal d33 of up to 1950 pC N–1 and large-signal d33* of ≈2100 pm V–1) among all the reported piezoelectric ceramics. Extensive characterization conducted using high-resolution microscopy and diffraction techniques in conjunction with the computational models reveals the underlying mechanisms governing the piezoelectric performance. Further, the impact of losses on the electromechanical coupling is identified, which plays major role in suppressing the percentage of piezoelectricity enhancement, and the fundamental understanding of loss in this study sheds light on further enhancement of piezoelectricity. These results on cost-effective and record performance piezoelectric ceramics will launch a new generation of piezoelectric applications

Batch Fermentation Options for High Titer Bioethanol Production from a SPORL Pretreated Douglas-Fir Forest Residue without Detoxification

This study evaluated batch fermentation modes, namely, separate hydrolysis and fermentation (SHF), quasi-simultaneous saccharification and fermentation (Q-SSF), and simultaneous saccharification and fermentation (SSF), and fermentation conditions, i.e., enzyme and yeast loadings, nutrient supplementation and sterilization, on high titer bioethanol production from SPORL-pretreated Douglas-fir forest residue without detoxification. The results indicated that Q-SSF and SSF were obviously superior to SHF operation in terms of ethanol yield. Enzyme loading had a strong positive correlation with ethanol yield in the range studied. Nutrient supplementation and sterility were not necessary for ethanol production from SPORL-pretreated Douglas-fir. Yeast loading had no substantial influence on ethanol yield for typical SSF conditions. After 96 h fermentation at 38 °C on shake flask at 150 rpm, terminal ethanol titer of 43.2 g/L, or 75.1% theoretical based on untreated feedstock glucan, mannan, and xylan content was achieved, when SSF was conducted at whole slurry solids loading of 15% with enzyme and yeast loading of 20 FPU/g glucan and 1.8 g/kg (wet), respectively, without nutrition supplementation and sterilization. It is believed that with mechanical mixing, enzyme loading can be reduced without reducing ethanol yield with extended fermentation duration

SBNN: A Searched Binary Neural Network for SAR Ship Classification

The synthetic aperture radar (SAR) for ocean surveillance missions requires low latency and light weight inference. This paper proposes a novel small-size Searched Binary Network (SBNN), with network architecture search (NAS) for ship classification with SAR. In SBNN, convolution operations are modified by binarization technologies. Both input feature maps and weights are quantized into 1-bit in most of the convolution computation, which significantly decreases the overall computational complexity. In addition, we propose a patch shift processing, which can adjust feature maps with learnable parameters at spatial level. This process enhances the performance by reducing the information irrelevant to the targets. Experimental results on the OpenSARShip dataset show the proposed SBNN outperforms both binary neural networks from computer vision and CNN-based SAR ship classification methods. In particular, SBNN shows a great advantage in computational complexity

Chem. Eng. J.

CuO/TiO2 catalysts prepared by an impregnation method were studied to determine their efficiencies of mercury oxidation in the simulated flue gas. In this study, 7% CuO/TiO2 was found to be an optimal catalyst with an oxidation efficiency of over 98% at temperatures in the range of 50-300 degrees C. X-ray diffracto-grams (XRD), Brunauer-Emmet-Teller (BET) measurements, transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS) were used to characterize the catalysts; the results indicated the CuO was well-dispersed on the surface of TiO2 and that Cu2+ was the primary Cu species contributing to Hg-0 oxidation. HCl was the most effective flue gas component responsible for the Hg-0 oxidation. Also, 5-ppm HCl resulted in 100% Hg-0 oxidation under the experimental conditions. The mechanism of Hg-0 oxidation was also investigated. (C) 2013 Elsevier B.V. All rights reserved.CuO/TiO2 catalysts prepared by an impregnation method were studied to determine their efficiencies of mercury oxidation in the simulated flue gas. In this study, 7% CuO/TiO2 was found to be an optimal catalyst with an oxidation efficiency of over 98% at temperatures in the range of 50-300 degrees C. X-ray diffracto-grams (XRD), Brunauer-Emmet-Teller (BET) measurements, transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS) were used to characterize the catalysts; the results indicated the CuO was well-dispersed on the surface of TiO2 and that Cu2+ was the primary Cu species contributing to Hg-0 oxidation. HCl was the most effective flue gas component responsible for the Hg-0 oxidation. Also, 5-ppm HCl resulted in 100% Hg-0 oxidation under the experimental conditions. The mechanism of Hg-0 oxidation was also investigated. (C) 2013 Elsevier B.V. All rights reserved

SBNN: A Searched Binary Neural Network for SAR Ship Classification

The synthetic aperture radar (SAR) for ocean surveillance missions requires low latency and light weight inference. This paper proposes a novel small-size Searched Binary Network (SBNN), with network architecture search (NAS) for ship classification with SAR. In SBNN, convolution operations are modified by binarization technologies. Both input feature maps and weights are quantized into 1-bit in most of the convolution computation, which significantly decreases the overall computational complexity. In addition, we propose a patch shift processing, which can adjust feature maps with learnable parameters at spatial level. This process enhances the performance by reducing the information irrelevant to the targets. Experimental results on the OpenSARShip dataset show the proposed SBNN outperforms both binary neural networks from computer vision and CNN-based SAR ship classification methods. In particular, SBNN shows a great advantage in computational complexity

Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Improved Mode Decomposition and Time Series

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries holds significant importance for their health management. Due to the capacity regeneration phenomenon and random interference during the operation of lithium-ion batteries, a single model may exhibit poor prediction accuracy and generalization performance under a single scale signal. This paper proposes a method for predicting the RUL of lithium-ion batteries. The method is based on the improved sparrow search algorithm (ISSA), which optimizes the variational mode decomposition (VMD) and long- and short-term time-series network (LSTNet). First, this study utilized the ISSA-optimized VMD method to decompose the capacity degradation sequence of lithium-ion batteries, acquiring global degradation trend components and local capacity recovery components, then the ISSA–LSTNet–Attention model and ISSA–LSTNet–Skip model were employed to predict the trend component and capacity recovery component, respectively. Finally, the prediction results of these different models were integrated to accurately estimate the RUL of lithium-ion batteries. The proposed model was tested on two public lithium-ion battery datasets; the results indicate a root mean square error (RMSE) under 2%, a mean absolute error (MAE) under 1.5%, and an absolute correlation coefficient (R2) and Nash–Sutcliffe efficiency index (NSE) both above 92.9%, implying high prediction accuracy and superior performance compared to other models. Moreover, the model significantly reduces the complexity of the series

Enhanced Whale Optimization Algorithm with Wavelet Decomposition for Lithium Battery Health Estimation in Deep Extreme Learning Machines

Lithium battery health state estimation can help optimize battery usage and management strategies. In response to the challenges faced by traditional battery management systems in accurately estimating the State of Health of lithium-ion batteries and addressing issues such as capacity recovery and noise interference, this paper proposes a method based on wavelet decomposition and an improved whale optimization algorithm optimized deep extreme learning machine for estimating the SOH of lithium-ion batteries. Firstly, the lithium-ion battery capacity degradation sequence is extracted, and the wavelet decomposition method is used to decompose the battery capacity into global and local degradation trends. Next, the non-linear convergence factor and the whale optimization algorithm with adaptive weights are employed to optimize the deep extreme learning machine for predicting each trend component. Finally, the prediction results are effectively integrated to obtain the lithium-ion battery SOH. This experimental method is validated using NASA and CALCE datasets, and the results indicate that the root mean square error and mean absolute percentage error are both below 0.95%, with relative accuracy and absolute correlation coefficients exceeding 98%. This demonstrates the method’s excellent accuracy and robustness

Mercury removal from coal combustion flue gas by modified fly ash

Fly ash is a potential alternative to activated carbon for mercury adsorption. The effects of physicochemical properties on the mercury adsorption performance of three fly ash samples were investigated. X-ray fluorescence spectroscopy, X-ray photoelectron spectroscopy, and other methods were used to characterize the samples. Results indicate that mercury adsorption on fly ash is primarily physisorption and chemisorption. High specific surface areas and small pore diameters are beneficial to efficient mercury removal. Incompletely burned carbon is also an important factor for the improvement of mercury removal efficiency, in particular. The C M bond, which is formed by the reaction of C and Ti, Si and other elements, may improve mercury oxidation. The samples modified with CuBr2, CuCl2 and FeCl3 showed excellent performance for Hg removal, because the chlorine in metal chlorides acts as an oxidant that promotes the conversion of elemental mercury (Hg-0) into its oxidized form (Hg2+). Cu2+ and Fe3+ can also promote Hg-0 oxidation as catalysts. HCl and O-2 promote the adsorption of Hg by modified fly ash, whereas SO2 inhibits the Hg adsorption because of competitive adsorption for active sites. Fly ash samples modified with CuBr2, CuCl2 and FeCl3 are therefore promising materials for controlling mercury emissions

HiVTac: A High-Speed Vision-Based Tactile Sensor for Precise and Real-Time Force Reconstruction with Fewer Markers

Although they have been under development for years and are attracting a lot of attention, vision-based tactile sensors still have common defects—the use of such devices to infer the direction of external forces is poorly investigated, and the operating frequency is too low for them to be applied in practical scenarios. Moreover, discussion of the deformation of elastomers used in vision-based tactile sensors remains insufficient. This research focuses on analyzing the deformation of a thin elastic layer on a vision-based tactile sensor by establishing a simplified deformation model, which is cross-validated using the finite element method. Further, this model suggests a reduction in the number of markers required by a vision-based tactile sensor. In subsequent testing, a prototype HiVTac is fabricated, and it demonstrates superior accuracy to its vision-based tactile sensor counterparts in reconstructing an external force. The average error of inferring the direction of external force is 0.32∘, and the root mean squared error of inferring the magnitude of the external force is 0.0098 N. The prototype was capable of working at a sampling rate of 100 Hz and a processing frequency of 1.3 kHz, even on a general PC, allowing for real-time reconstructions of not only the direction but also the magnitude of an external force