共查询到20条相似文献,搜索用时 140 毫秒
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Binbin Chen Nicolas Gauquelin Daen Jannis Daniel M. Cunha Ufuk Halisdemir Cinthia Piamonteze Jin Hong Lee Jamal Belhadi Felix Eltes Stefan Abel Zoran Jovanović Matjaž Spreitzer Jean Fompeyrine Johan Verbeeck Manuel Bibes Mark Huijben Guus Rijnders Gertjan Koster 《Advanced materials (Deerfield Beach, Fla.)》2020,32(50):2004995
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Yuanmiao Sun Shengnan Sun Haitao Yang Shibo Xi Jose Gracia Zhichuan J. Xu 《Advanced materials (Deerfield Beach, Fla.)》2020,32(39):2003297
Oxygen evolution and reduction reactions play a critical role in determining the efficiency of the water cycling (H2O ⇔ H2 + O2), in which the hydrogen serves as the energy carrier. That calls for a comprehensive understanding of oxygen electrocatalysis for efficient catalyst design. Current opinions on oxygen electrocatalysis have been focused on the thermodynamics of the reactant/intermediate adsorption on the catalysts. Because the oxygen molecule is paramagnetic, its production from or its reduction to diamagnetic hydroxide/water involves spin-related electron transfer. Both electron transfer and orbital interactions between the catalyst and the reactant/intermediate show spin-dependent character, making the reaction kinetics and thermodynamics sensitive to the spin configurations. Herein, a brief introduction on the spintronic explanation of the catalytic phenomena on oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) is given. The local spin configurations and orbital interactions in the benchmark transition-metal-based catalysts for OER and ORR are analyzed as examples. To further understand the spintronic oxygen electrocatalysis and to develop more efficient spintronic catalysts, the challenges are summarized and future opportunities proposed. Spin electrocatalysis may emerge as an important topic in the near future and help integrate a comprehensive understanding of oxygen electrocatalysis. 相似文献
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Jun Wu Yaxin Zhang Zhitao Xiao Fang Zhang Lei Geng 《International journal of imaging systems and technology》2023,33(1):299-311
Diabetic macular edema (DME) is a typical fundus disease that can cause blindness in severe cases. The morphology of the inner limiting membrane (ILM) to the retinal pigment epithelium (RPE) layer in the retina and the macular edema (ME) area are important features for the diagnosis of DME. Doctors usually use non-invasive and high-resolution optical coherence tomography (OCT) to examine the fundus of the patient. However, manual diagnosis has low efficiency and strong subjectivity. Realizing the automatic segmentation of the ILM-RPE layer and ME is extremely important for the early diagnosis of DME. In this paper, the attention mechanism based on residual convolution module U-Net (RCU-Net) is proposed for the automatic segmentation of the retinal layer and cystoid edema lesions. Through the fusion of the residual structure and CBAM for feature extraction, the useful features in the channel and space are effectively strengthened, and the network can better learn different levels of information. The proposed network is combined with the Lovász-softmax loss, which can better target the correlation between targets to obtain the optimal segmentation model during training. Finally, this paper compares the proposed method with several other segmentation methods. The experimental results show that the of the method in this model reaches 88.595%, and the reaches 99.171%. The RCU-Net proposed in this paper is used to segment the ILM-RPE layer and ME region in the retina OCT B-scan images, and its overall performance is better than other networks. 相似文献
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Shobha Jose Thomas George Selvaraj Kenneth Samuel Jobin T. Philip Sairamya Nanjappan Jothiraj Subathra Muthu Swamy Pandian Vikram Shenoy Handiru Easter S. Suviseshamuthu 《International journal of imaging systems and technology》2023,33(2):659-669
This article presents an automatic diagnostic system to classify intramuscular electromyography (iEMG) signals, thereby detecting neuromuscular disorders. To this end, we tailored the center symmetric local binary pattern (CSLBP) to analyze one-dimensional ( -D) signals. In this approach, the -D CSLBP feature extracted from a decimated iEMG signal is fed to a combination of classifiers, which in turn assigns a set of labels to the signal, and ultimately the signal category is determined by the Boyer-Moore majority voting (BMMV) algorithm. The proposed framework was investigated with a benchmark iEMG dataset that contains signals recorded from three different muscles: biceps brachii (BB), deltoideus (DE), and vastus medialis (VM). In a repeated -fold cross-validation, CSLBP-Combined-Classifiers-BMMV (CSLBP-CC-BMMV) achieved an average classification accuracy of %, %, and % for the iEMG signals recorded from BB, DE, and VM muscle, respectively. Interestingly, the performance of CSLBP-CC-BMMV surpassed the other published approaches and ensemble learning methods that are akin to our scheme in terms of classification accuracy and computational time. 相似文献
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Jin Song Biao Li Yuyang Chen Yuxuan Zuo Fanghua Ning Huaifang Shang Guang Feng Na Liu Chongheng Shen Xinping Ai Dingguo Xia 《Advanced materials (Deerfield Beach, Fla.)》2020,32(16):2000190
The search for new high-performance and low-cost cathode materials for Li-ion batteries is a challenging issue in materials research. Commonly used cobalt- or nickel-based cathodes suffer from limited resources and safety problems that greatly restrict their large-scale application, especially for electric vehicles and large-scale energy storage. Here, a novel Li–Mn–O Li-rich cathode material with symmetry is developed via intralayer Li/Mn disordering in the Mn-layer. Due to the special atomic arrangement and higher symmetry with respect to the C2/m symmetry, the oxygen redox activity is modulated and the Li in the Li-layer is preferentially thermodynamically extracted from the crystal structure instead of Li in the Mn-layer. The as-obtained material delivers a reversible capacity of over 300 mAh g−1 at 25 mA g−1 and rate capability of up to 260 mAh g−1 at 250 mA g−1 within 2.0–4.8 V. The excellent performance is attributed to its highly structural reversibility, mitigation of Jahn–Teller distortion, lower bandgap, and faster Li-ion 2D channels during the lithium-ion de/intercalation process. This material is not only a promising cathode material candidate but also raises new possibilities for the design of low-cost and high-performance cathode materials. 相似文献
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Ludwik Kranz Samuel Keith Gorman Brandur Thorgrimsson Yu He Daniel Keith Joris Gerhard Keizer Michelle Yvonne Simmons 《Advanced materials (Deerfield Beach, Fla.)》2020,32(40):2003361
Electron spins in silicon offer a competitive, scalable quantum-computing platform with excellent single-qubit properties. However, the two-qubit gate fidelities achieved so far have fallen short of the 99% threshold required for large-scale error-corrected quantum computing architectures. In the past few years, there has been a growing realization that the critical obstacle in meeting this threshold in semiconductor qubits is charge noise arising from the qubit environment. In this work, a notably low level of charge noise of S0 = 0.0088 ± 0.0004 μeV2 Hz−1 is demonstrated using atom qubits in crystalline silicon, achieved by separating the qubits from surfaces and interface states. The charge noise is measured using both a single electron transistor and an exchange-coupled qubit pair that collectively provide a consistent charge noise spectrum over four frequency decades, with the noise level S0 being an order of magnitude lower than previously reported. Low-frequency detuning noise, set by the total measurement time, is shown to be the dominant dephasing source of two-qubit exchange oscillations. With recent advances in fast (≈μs) single-shot readout, it is shown that by reducing the total measurement time to ≈1 s, 99.99% two-qubit gate fidelities can be achieved in single-crystal atom qubits in silicon. 相似文献
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