Poly(1,3-dioxolane)(PDOL)-based solid electrolytes hold great potential for solid-state lithium(Li)metal batteries due to their superior ionic conductivity at room ***,traditional PDOL electrolytes suffer from inferio...
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Poly(1,3-dioxolane)(PDOL)-based solid electrolytes hold great potential for solid-state lithium(Li)metal batteries due to their superior ionic conductivity at room ***,traditional PDOL electrolytes suffer from inferior thermal stability,which has hampered their practical *** this work,a competitive coordination mechanism is proposed to strengthen vulnerable ether oxygen bonds in PDOL chains,thereby improving the thermal stability of PDOL *** strong coordination of Lewis base ligands on Li_(6.75)La_(3)Zr_(1.75)Ta_(0.25)O_(12)(LLZTO)surface with Li ions weakens the ionic-dipolar interactions between PDOL chains and Li ions,conversely reinforcing the bond energy of ether oxygen *** LLZTO into PDOL electrolytes effectively enhances the thermal decomposition temperature from 110 to 302℃.Li||LiFePO_(4)full cell with a 12μm ultrathin PDOL hybrid electrolyte delivers enhanced discharge capacity and extended cycling life for 100 cycles at an elevated temperature of 60℃.This work provides critical insights into the development of thermally stable PDOL electrolytes for safe solid-state Li metal batteries.
Dear Editor,H∞This letter develops a new framework for the robust stability and performance conditions as well as the relevant controller synthesis with respect to uncertain robot *** often exist model uncertainties ...
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Dear Editor,H∞This letter develops a new framework for the robust stability and performance conditions as well as the relevant controller synthesis with respect to uncertain robot *** often exist model uncertainties between the nominal model and the real robot manipulator and disturbances. Hence, dealing with their effects plays a crucial role in leading to high tracking performances, as discussed in [1]–[5].
The agricultural sector contributes significantly to greenhouse gas emissions, which cause global warming and climate change. Numerous mathematical models have been developed to predict the greenhouse gas emissions fr...
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Construction and demolition (C&D) waste management is challenging in urban areas due to the high volume of waste generated and widespread illegal dumping. City authorities are struggling with environmental, econom...
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All the software products developed will need testing to ensure the quality and accuracy of the product. It makes the life of testers much easier when they can optimize on the effort spent and predict defects for the ...
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Chatbots use artificial intelligence (AI) and natural language processing (NLP) algorithms to construct a clever system. By copying human connections in the most helpful way possi-ble, chatbots emulate individuals and...
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Millions of people die from lung illness each year as a result of its rise in recent years. CXR imaging is one of the most widely used and reasonably priced diagnostic techniques for the diagnosis of many illnesses. U...
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Millions of people die from lung illness each year as a result of its rise in recent years. CXR imaging is one of the most widely used and reasonably priced diagnostic techniques for the diagnosis of many illnesses. Unfortunately, even for seasoned radiologists, accurately diagnosing sickness from Chest X-Rays (CXR) samples is challenging. To combat the pandemic, a reliable, affordable, and efficient way to diagnose lung disease has become essential. Consequently, a unique optimized Auto Encod-BI Long-Short Term Memory (Bi-LSTM) model is proposed in this research work. Pre-processing, segmentation, feature extraction, and multiple types of lung illness diagnosis are the four main stages of the suggested model. First, Laplacian filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to pre-process the gathered CXR pictures. Next, the Region of Interest (ROI) from the previously processed images are recognized by means of the newly enhanced MobileNetV2. The new Self-Improved Slime Mould Algorithm (SI-SMA) is used to fine-tune the hyper-parameters of MobileNetV2 in order to precisely identify the afflicted locations. Based on the phenomenon of slime mould oscillation, the conventional Slime Mould Algorithm (SMA) model has been modified with the creation of the SI-SMA model. Next, characteristics like the Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG) are taken out. Finally, a unique AutoEncod-BiLSTM Framework—which is divided into three categories—is shown to automate the process of identifying illnesses in CXR pictures: pneumonia, COVID-19, and normal. The autoencoder and Bi-LSTM are combined to create the suggested AutoEncod-BiLSTM model. The retrieved features are used to train the AutoEncod-BiLSTM Framework. Moreover, the proposed model enhanced the disease detection efficiency than the existing models and the disease detection accuracy of the proposed model is about 99.1%. Furthermore, the suggested model attains better
In Taiwan, the current electricity prices for residential users remain relatively low. This results in a diminished incentive for these users to invest in energy-saving improvements. Consequently, devising strategies ...
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Weather variability significantly impacts crop yield, posing challenges for large-scale agricultural operations. This study introduces a deep learning-based approach to enhance crop yield prediction accuracy. A Multi-...
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Emotion recognition is crucial in human-computer interaction and psychological research, utilizing modalities such as facial expressions, voice intonations, and EEG signals. This research investigates AI-driven techni...
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