Alloy anodes are promising anode materials for lithium-ion batteries due to their high-energy capacity and safety characteristics. However, the commercial use of alloy anodes has been hindered to ...date by their low cycle life and high initial capacity loss. This review highlights the recent progress in improving and understanding the electrochemical performance of various alloy anodes. The approaches used for performance improvement are summarized, and the causes of first-cycle irreversible capacity loss are discussed. The capacity retentions and irreversible capacity losses of various alloy anodes are compared. Several alloy anodes exhibited excellent cycle life (up to 300 cycles) with high initial coulombic efficiency (80–90%) and large reversible capacity (500–700
mAh
g
−1).
▶ The phase transformation and performance of LiFePO4 materials are reviewed. ▶ Carbon coating is more critical than doping and particle size control. ▶ Iron deposit on graphite anode leads to ...capacity degradation. ▶ Low temperature performance can be improved by electrolyte modification.
LiFePO4 has been considered a promising battery material in electric vehicles. However, there are still a number of technical challenges to overcome before its wide-spread applications. In this article, the structure and electrochemical performance of LiFePO4 are reviewed in light of the major technical requirements for EV batteries. The rate capability, capacity density, cyclic life and low-temperature performance of various LiFePO4 materials are described. The major factors affecting these properties are discussed, which include particle size, doping, carbon coating, conductive carbon loading and synthesis techniques. Important future research for science and engineering is suggested.
Measurement-device-independent quantum key distribution (MDI QKD) removes all detector side channels and enables secure QKD with an untrusted relay. It is suitable for building a star-type quantum ...access network, where the complicated and expensive measurement devices are placed in the central untrusted relay and each user requires only a low-cost transmitter, such as an integrated photonic chip. Here, we experimentally demonstrate a 1.25-GHz silicon photonic chip-based MDI QKD system using polarization encoding. The photonic chip transmitters integrate the necessary encoding components for a standard QKD source. We implement random modulations of polarization states and decoy intensities, and demonstrate a finite-key secret rate of31bit/sover 36-dB channel loss (or 180-km standard fiber). This key rate is higher than state-of-the-art MDI QKD experiments. The results show that silicon photonic chip-based MDI QKD, benefiting from miniaturization, low-cost manufacture, and compatibility with CMOS microelectronics, is a promising solution for future quantum secure networks.
Surface reactions constitute the foundation of various energy conversion/storage technologies, such as the lithium–sulfur (Li‐S) batteries. To expedite surface reactions for high‐rate battery ...applications demands in‐depth understanding of reaction kinetics and rational catalyst design. Now an in situ extrinsic‐metal etching strategy is used to activate an inert monometal nitride of hexagonal Ni3N through iron‐incorporated cubic Ni3FeN. In situ etched Ni3FeN regulates polysulfide‐involving surface reactions at high rates. Electron microscopy was used to unveil the mechanism of in situ catalyst transformation. The Li‐S batteries modified with Ni3FeN exhibited superb rate capability, remarkable cycling stability at a high sulfur loading of 4.8 mg cm−2, and lean‐electrolyte operability. This work opens up the exploration of multimetallic alloys and compounds as kinetic regulators for high‐rate Li‐S batteries and also elucidates catalytic surface reactions and the role of defect chemistry.
Inert hexagonal Ni3N can be activated by an extrinsic metal‐incorporating strategy with in situ etching that uses cubic Ni3FeN. Vacancy‐rich Ni3FeN catalysts kinetically regulate polysulfide‐involving reactions at high rates for use in advanced lithium–sulfur batteries.
The electrochemical performance of alloy anodes has been reviewed in a previous paper
1. In this work, the fundamental understanding of lithium-insertion/extraction mechanism in alloy anodes is ...discussed. The article summarizes the different types of lithium-reaction processes observed in Si, Sn, Sb, Al, Mg and their alloys, with particular emphasis on the characteristics unique to alloy anodes, including the sloping voltage profiles, lithiation amorphization, cycling hysteresis and reaction-potential depression. These unique characteristics are discussed with respect to the effect of interface and surface energies on the phase transformation and thermodynamic stability of fine alloy particles.
The basic principle of quantum mechanics1 guarantees the unconditional security of quantum key distribution (QKD)2–6 at the cost of forbidding the amplification of a quantum state. As a result, and ...despite remarkable progress in worldwide metropolitan QKD networks7,8 over the past decades, a long-haul fibre QKD network without a trusted relay has not yet been achieved. Here, through the sending-or-not-sending protocol9, we achieve twin-field QKD10 and distribute secure keys without any trusted repeater over a 511 km long-haul fibre trunk that links two distant metropolitan areas. The fibre trunk contains 12 fibres in the cable, three of which are used for the quantum channel, optical synchronization and frequency locking, respectively. The remaining nine are used for classical fibre communication. Our secure key rate is around three orders of magnitude greater than that expected if the previous QKD field-test system was applied over the same length. Efficient quantum-state transmission and stable single-photon interference over such a long-haul deployed fibre pave the way to large-scale fibre quantum networks.A field test of twin-field quantum key distribution was implemented through a 511 km optical fibre. To this end, precise wavelength control of remote independent laser sources and fast time- and phase-compensation systems are developed.
Quantum computers promise to perform certain tasks that are believed to be intractable to classical computers. Boson sampling is such a task and is considered a strong candidate to demonstrate the ...quantum computational advantage. We performed Gaussian boson sampling by sending 50 indistinguishable single-mode squeezed states into a 100-mode ultralow-loss interferometer with full connectivity and random matrix-the whole optical setup is phase-locked-and sampling the output using 100 high-efficiency single-photon detectors. The obtained samples were validated against plausible hypotheses exploiting thermal states, distinguishable photons, and uniform distribution. The photonic quantum computer,
, generates up to 76 output photon clicks, which yields an output state-space dimension of 10
and a sampling rate that is faster than using the state-of-the-art simulation strategy and supercomputers by a factor of ~10
.
Malayan pangolin SARS-CoV-2-related coronavirus (SARSr-CoV-2) is closely related to SARS-CoV-2. However, little is known about its pathogenicity in pangolins. Using CT scans we show that SARSr-CoV-2 ...positive Malayan pangolins are characterized by bilateral ground-glass opacities in lungs in a similar manner to COVID-19 patients. Histological examination and blood gas tests are indicative of dyspnea. SARSr-CoV-2 infected multiple organs in pangolins, with the lungs the major target, and histological expression data revealed that ACE2 and TMPRSS2 were co-expressed with viral RNA. Transcriptome analysis indicated that virus-positive pangolins were likely to have inadequate interferon responses, with relative greater cytokine and chemokine activity in the lung and spleen. Notably, both viral RNA and viral proteins were detected in three pangolin fetuses, providing initial evidence for vertical virus transmission. In sum, our study outlines the biological framework of SARSr-CoV-2 in pangolins, revealing striking similarities to COVID-19 in humans.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background
Computed tomography (CT) is essential for pulmonary nodule detection in diagnosing lung cancer. As deep learning algorithms have recently been regarded as a promising technique in medical ...fields, we attempt to integrate a well‐trained deep learning algorithm to detect and classify pulmonary nodules derived from clinical CT images.
Materials and Methods
Open‐source data sets and multicenter data sets have been used in this study. A three‐dimensional convolutional neural network (CNN) was designed to detect pulmonary nodules and classify them into malignant or benign diseases based on pathologically and laboratory proven results.
Results
The sensitivity and specificity of this well‐trained model were found to be 84.4% (95% confidence interval CI, 80.5%–88.3%) and 83.0% (95% CI, 79.5%–86.5%), respectively. Subgroup analysis of smaller nodules (<10 mm) have demonstrated remarkable sensitivity and specificity, similar to that of larger nodules (10–30 mm). Additional model validation was implemented by comparing manual assessments done by different ranks of doctors with those performed by three‐dimensional CNN. The results show that the performance of the CNN model was superior to manual assessment.
Conclusion
Under the companion diagnostics, the three‐dimensional CNN with a deep learning algorithm may assist radiologists in the future by providing accurate and timely information for diagnosing pulmonary nodules in regular clinical practices.
Implications for Practice
The three‐dimensional convolutional neural network described in this article demonstrated both high sensitivity and high specificity in classifying pulmonary nodules regardless of diameters as well as superiority compared with manual assessment. Although it still warrants further improvement and validation in larger screening cohorts, its clinical application could definitely facilitate and assist doctors in clinical practice.
摘要
背景。在肺癌的诊断中，计算机断层扫描 (CT) 对于肺结节的检测必不可少。近几年，随着医学领域逐渐认识到深度学习算法这种技术的价值，本研究试图集成一种训练有素的深度学习算法，对临床 CT 图像中的肺结节进行检测和分类。
材料和方法。本研究使用了开源数据集和多中心数据集。本文设计了一种三维卷积神经网络 (CNN) 来检测肺结节，然后根据病理和实验室证实的结果，判断为恶性或良性结节。
结果。这种训练有素的模型敏感性和特异性分别为 84.4% 95% 可信区间 (CI)， 80.5%‐88.3%和83.0%(95% CI，79.5%‐86.5%)。小结节 (< 10mm) 亚组分析显示的敏感性和特异性显著，与大结节 (10‐30mm) 相似。对比不同级别医生的人工评估结果与三维 CNN 的评估结果，进行了额外的模型验证。结果表明，CNN 模型的表现优于人工评估。
结论。通过伴随诊断可知，加入深度学习算法的三维 CNN 能够提供准确、及时的信息，有助于放射科医生在常规临床实践中的肺结节诊断工作。
实践意义:在对各种直径的肺结节分类中，本文所述的三维卷积神经网络具有较高的敏感性和特异性，与人工评估结果相比具有优越性。虽然仍需在更大的筛选队列中进行进一步改进和验证，但可以肯定的是，临床应用三维卷积神经网络可以促进和协助医生的临床实践工作。
Interest in deep convolutional neural networks (CNN) is growing because of demonstrated accuracy with less manual intervention in computer vision tasks. This article describes efforts to use a pre‐trained CNN model integrating with multi‐centers datasets for detection and classification of pulmonary nodules.
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