Communications engineering最新文献_第4页

An energy-resolving photon-counting X-ray detector for computed tomography combining silicon-photomultiplier arrays and scintillation crystals 用于计算机断层扫描的能量分辨光子计数 X 射线探测器，结合了硅光电倍增管阵列和闪烁晶体。

Communications engineering Pub Date : 2024-11-25 DOI: 10.1038/s44172-024-00313-1

Kenji Shimazoe, Donghwan Kim, Moh Hamdan, Yosuke Kobayashi, Kei Kamada, Masao Yoshino, Yasuhiro Shoji, Kyohei Sakamoto, Fabio Acerbi, Alberto Gola

{"title":"An energy-resolving photon-counting X-ray detector for computed tomography combining silicon-photomultiplier arrays and scintillation crystals","authors":"Kenji Shimazoe, Donghwan Kim, Moh Hamdan, Yosuke Kobayashi, Kei Kamada, Masao Yoshino, Yasuhiro Shoji, Kyohei Sakamoto, Fabio Acerbi, Alberto Gola","doi":"10.1038/s44172-024-00313-1","DOIUrl":"10.1038/s44172-024-00313-1","url":null,"abstract":"X-ray photon-counting computed tomography (PCCT) has garnered considerable interest owing to its low-dose administration, high-quality imaging, and material decomposition characteristics. Current commercial PCCT systems employ compound semiconductor photon-counting X-ray detectors, which offer good energy resolution. However, the choice of materials is limited, and cadmium telluride or cadmium zinc telluride is mostly used. Although indirect radiation detectors can be used as alternatives to compound semiconductor detectors, implementing fine-pitch segmentation in such detectors is challenging. Here we designed an indirect fine-pitch X-ray photon-counting detector by combining miniaturized silicon photomultiplier arrays and fast scintillation crystals, with a pixel size of 250 µm, for future indirect PCCT. The fabricated array detector has the potential to discriminate photon energies with a 27% resolution at 122 keV, 296 µm spatial resolution, and charge-sharing inhibition ability. Kenji Shimazoe and co-authors present an indirect fine-pitch X-ray photon-counting detector by combining silicon photomultiplier arrays and fast scintillation crystals. The detector is capable of detecting the photons and differentiating them by the energy level.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00313-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142717947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ultra-lightweight rechargeable battery with enhanced gravimetric energy densities >750 Wh kg−1 in lithium–sulfur pouch cell 超轻型充电电池，锂硫袋电池的重力能量密度大于 750 Wh kg-1。

Communications engineering Pub Date : 2024-11-25 DOI: 10.1038/s44172-024-00321-1

Kenji Kakiage, Toru Yano, Hiroki Uehara, Masaki Kakiage

{"title":"Ultra-lightweight rechargeable battery with enhanced gravimetric energy densities >750 Wh kg−1 in lithium–sulfur pouch cell","authors":"Kenji Kakiage, Toru Yano, Hiroki Uehara, Masaki Kakiage","doi":"10.1038/s44172-024-00321-1","DOIUrl":"10.1038/s44172-024-00321-1","url":null,"abstract":"Lithium–sulfur (Li–S) rechargeable batteries have been expected to be lightweight energy storage devices with the highest gravimetric energy density at the single-cell level reaching up to 695 Wh kg(cell)−1, having also an ultralow rate of 0.005 C only in the first discharge. Sulfurized polyacrylonitrile (SPAN) is one of the sulfur-based active materials, which allows more freedom in the Li–S cell design because it shows no undesirable reactions with electrolyte solutions. Here we present an original Li–S pouch cell construction, ADEKA’s Lithium–Sulfur/Pouch Cell (ALIS-PC). It is an ultra-lightweight rechargeable battery cell, which is designed by combining the SPAN cathode and effective ten technologies involving chemical engineering. As a result, the highest gravimetric energy densities of 713 and 761 Wh kg(cell)−1 after some charge-and-discharge cycles, which were based on the total mass of all cell components, were achieved with successful operating at 0.1 and 0.05C-rates, respectively, significantly exceeding those of commercial lithium-ion and next-generation rechargeable batteries in development. Kenji Kakiage and colleagues report an ultra-lightweight Li-S pouch cell with a gravimetric energy density of 761 Wh/kg. They use sulfurized polyacrylonitrile as a cathode active material, combining ten technologies for rechargeable batteries.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00321-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142717165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Synergetic pyrolysis of lithium-ion battery cathodes with polyethylene terephthalate for efficient metal recovery and battery regeneration 利用聚对苯二甲酸乙二醇酯协同热解锂离子电池正极，实现高效金属回收和电池再生。

Communications engineering Pub Date : 2024-11-23 DOI: 10.1038/s44172-024-00317-x

Zhe Meng, Jinchuan Dai, Xiao-Ying Lu, Kehua Wu, Yonghong Deng, Jun Wang, Kaimin Shih, Yuanyuan Tang

{"title":"Synergetic pyrolysis of lithium-ion battery cathodes with polyethylene terephthalate for efficient metal recovery and battery regeneration","authors":"Zhe Meng, Jinchuan Dai, Xiao-Ying Lu, Kehua Wu, Yonghong Deng, Jun Wang, Kaimin Shih, Yuanyuan Tang","doi":"10.1038/s44172-024-00317-x","DOIUrl":"10.1038/s44172-024-00317-x","url":null,"abstract":"Spent LiNixCoyMnzO2 (x + y + z = 1) and polyethylene terephthalate are major solid wastes due to the growing Li-ion battery market and widespread plastic usage. Here we propose a synergistic pyrolysis strategy to recover valuable metals by thermally treating LiNi1/3Co1/3Mn1/3O2 and polyethylene terephthalate. With polyethylene terephthalate assistance, LiNi1/3Co1/3Mn1/3O2 decomposes at 400 °C, and fully converts to Li2CO3, MnO, and Ni-Co alloy at 550 °C within 30 min, using a 1.0:0.3 mass ratio of LiNi1/3Co1/3Mn1/3O2 to polyethylene terephthalate. Furthermore, density functional theory calculations confirm the preference for O-Li bonding. Surface adsorption and free radical/gaseous reduction reactions explain the role of polyethylene terephthalate in promoting lattice destruction. The complete decomposition facilitates efficient post-treatment, achieving over 99% recovery of Li, Ni, Co, and Mn via water washing. Regenerated LiNi1/3Co1/3Mn1/3O2 was synthesized by using recovered Li- and transition metal-containing products as feedstocks. This study provided a chemical-free, energy-saving, and scalable recovery strategy while addressing polyethylene terephthalate waste minimization. Zhe Meng and co-authors demonstrate the feasibility of synergetic pyrolysis of lithium-ion battery cathode materials with PET plastic for recovering Li and transition metals. They demonstrate a high recovery ratio and energy efficiency.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11585579/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Aerodynamics-guided machine learning for design optimization of electric vehicles 空气动力学引导的机器学习，用于电动汽车的优化设计。

Communications engineering Pub Date : 2024-11-20 DOI: 10.1038/s44172-024-00322-0

Jonathan Tran, Kai Fukami, Kenta Inada, Daisuke Umehara, Yoshimichi Ono, Kenta Ogawa, Kunihiko Taira

{"title":"Aerodynamics-guided machine learning for design optimization of electric vehicles","authors":"Jonathan Tran, Kai Fukami, Kenta Inada, Daisuke Umehara, Yoshimichi Ono, Kenta Ogawa, Kunihiko Taira","doi":"10.1038/s44172-024-00322-0","DOIUrl":"10.1038/s44172-024-00322-0","url":null,"abstract":"The transition to electric vehicles is driving a fundamental shift in the automobile design process. Changes in constraints afforded by the absence of a combustion engine create new opportunities for modifying vehicle geometries. Current approaches to optimizing vehicle aerodynamics require a vast amount of computational studies and physical experiments, which are expensive when performing parameter sweeps over conceivable geometric configurations, suggesting the need for more efficient surrogate models to assist analysis. Here we analyze a dataset of industry-quality automobile geometries with their associated aerodynamic performance obtained from experimentally validated, high-fidelity large-eddy simulations. We show that a relationship between these geometries and their respective aerodynamics can be extracted in a low-dimensional manner by leveraging a nonlinear autoencoder which is simultaneously trained to estimate the drag coefficient from the latent variables. We perform aerodynamic design optimization of vehicle designs by making use of the learned aerodynamic relationship in the low-order space obtained by the model. We demonstrate that the aerodynamic trends for the geometries produced from the optimization process show agreement with validation simulations. The findings of this work demonstrate the application of data-driven approaches to the analysis and design of vehicles in a production environment. Jonathan Tran and colleagues use aerodynamics-guided machine learning for the shape optimization of electric cars. Their approach saves computational time for high complexity engineering tasks, e.g., computational fluid dynamics-based design optimization.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The future of battery data and the state of health of lithium-ion batteries in automotive applications 电池数据的未来和汽车应用中锂离子电池的健康状况。

Communications engineering Pub Date : 2024-11-19 DOI: 10.1038/s44172-024-00299-w

Friedrich von Bülow, Felix Heinrich, William Arthur Paxton

引用次数: 0

Thermal imaging through hot emissive windows 通过热辐射窗进行热成像。

Communications engineering Pub Date : 2024-11-16 DOI: 10.1038/s44172-024-00316-y

Ciril Samuel Prasad, Henry O. Everitt, Gururaj V. Naik

引用次数: 0

A compact X-ray source via fast microparticle streams 通过快速微粒子流实现紧凑型 X 射线源。

Communications engineering Pub Date : 2024-11-15 DOI: 10.1038/s44172-024-00323-z

Rolf Behling, Christopher Hulme, Gavin Poludniowski, Panagiotis Tolias, Mats Danielsson

{"title":"A compact X-ray source via fast microparticle streams","authors":"Rolf Behling, Christopher Hulme, Gavin Poludniowski, Panagiotis Tolias, Mats Danielsson","doi":"10.1038/s44172-024-00323-z","DOIUrl":"10.1038/s44172-024-00323-z","url":null,"abstract":"The spatiotemporal resolution of diagnostic X-ray images is limited by the erosion and rupture of conventional stationary and rotating anodes of X-ray tubes from extreme density of input power and thermal cycling of the anode material. Conversely, detector technology has developed rapidly. Finer detector pixels demand improved output from brilliant keV-type X-ray sources with smaller X-ray focal spots than today and would be available to improve the efficacy of medical imaging. In addition, novel cancer therapy demands for greatly improved output from X-ray sources. However, since its advent in 1929, the technology of high-output compact X-ray tubes has relied upon focused electrons hitting a spinning rigid rotating anode; a technology that, despite of substantial investment in material technology, has become the primary bottleneck of further improvement. In the current study, an alternative target concept employing a stream of fast discrete metallic microparticles that intersect with the electron beam is explored by simulations that cover the most critical uncertainties. The concept is expected to have far-reaching impact in diagnostic imaging, radiation cancer therapy and non-destructive testing. We outline technical implementations that may become the basis of future X-ray source developments based on the suggested paradigm shift. Rolf Behling and colleagues propose a new X-ray source concept to improve the resolution of X-ray computed tomography and non-destructive testing and the efficacy of radiation cancer therapy by replacing the rotary anode with a fast stream of microparticles in the electron beam.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11568210/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142640119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Precision sampling of discrete sites identified during in-vivo functional testing in the mammalian heart 对哺乳动物心脏体内功能测试中确定的离散部位进行精确采样。

Communications engineering Pub Date : 2024-11-14 DOI: 10.1038/s44172-024-00307-z

Dylan Vermoortele, Camilla Olianti, Matthew Amoni, Francesco Giardini, Stijn De Buck, Chandan K. Nagaraju, Rik Willems, H. Llewellyn Roderick, Karin R. Sipido, Leonardo Sacconi, Piet Claus

{"title":"Precision sampling of discrete sites identified during in-vivo functional testing in the mammalian heart","authors":"Dylan Vermoortele, Camilla Olianti, Matthew Amoni, Francesco Giardini, Stijn De Buck, Chandan K. Nagaraju, Rik Willems, H. Llewellyn Roderick, Karin R. Sipido, Leonardo Sacconi, Piet Claus","doi":"10.1038/s44172-024-00307-z","DOIUrl":"10.1038/s44172-024-00307-z","url":null,"abstract":"Ventricular arrhythmias after myocardial infarction (MI) originate from discrete areas within the MI border zone (BZ), identified during functional electrophysiology tests. Accurate sampling of arrhythmogenic sites for ex-vivo study remains challenging, yet is critical to identify their tissue, cellular and molecular signature. In this study, we developed, validated, and applied a targeted sampling methodology based on individualized 3D prints of the human-sized pig heart. To this end, 3D anatomical models of the left ventricle were created from magnetic resonance imaging and fused with biplane fluoroscopy. Regions of interest for sampling were annotated on the anatomical models, from which we created a unique 3D printed cast with custom slits identifying the annotated regions for sampling. The methodology was validated by retrieving ablation lesions created at predefined locations on the anatomical model. We applied the methodology to sample arrhythmia-vulnerable regions after MI during adrenergic stimulation. A pipeline of imaging was developed to create a 3D high-resolution map of each sample, highlighting the complex interplay of cellular organization, and altered innervation in the BZ. Dr Piet Claus and colleagues report a method to extract tissue samples from a human-sized pig heart used for studying discrete arrhythmogenic sites. They determine locations for marking and sectioning by using a 3D printed model that is derived from MRI images, allowing them to correlate structural imaging with prior information obtained in vivo.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00307-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust neural networks using stochastic resonance neurons 使用随机共振神经元的鲁棒神经网络。

Communications engineering Pub Date : 2024-11-13 DOI: 10.1038/s44172-024-00314-0

Egor Manuylovich, Diego Argüello Ron, Morteza Kamalian-Kopae, Sergei K. Turitsyn

{"title":"Robust neural networks using stochastic resonance neurons","authors":"Egor Manuylovich, Diego Argüello Ron, Morteza Kamalian-Kopae, Sergei K. Turitsyn","doi":"10.1038/s44172-024-00314-0","DOIUrl":"10.1038/s44172-024-00314-0","url":null,"abstract":"Various successful applications of deep artificial neural networks are effectively facilitated by the possibility to increase the number of layers and neurons in the network at the expense of the growing computational complexity. Increasing computational complexity to improve performance makes hardware implementation more difficult and directly affects both power consumption and the accumulation of signal processing latency, which are critical issues in many applications. Power consumption can be potentially reduced using analog neural networks, the performance of which, however, is limited by noise aggregation. Following the idea of physics-inspired machine learning, we propose here a type of neural network using stochastic resonances as a dynamic nonlinear node and demonstrate the possibility of considerably reducing the number of neurons required for a given prediction accuracy. We also observe that the performance of such neural networks is more robust against the impact of noise in the training data compared to conventional networks. Manuylovich and colleagues propose the use of stochastic resonances in neural networks as dynamic nonlinear nodes. They demonstrate the possibility of reducing the number of neurons for a given prediction accuracy and observe that the performance of such neural networks can be more robust against the impact of noise in the training data compared to the conventional networks.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00314-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries 以领域知识为指导的锂离子电池健康状况评估机器学习框架。

Communications engineering Pub Date : 2024-11-12 DOI: 10.1038/s44172-024-00304-2

Andrea Lanubile, Pietro Bosoni, Gabriele Pozzato, Anirudh Allam, Matteo Acquarone, Simona Onori

{"title":"Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries","authors":"Andrea Lanubile, Pietro Bosoni, Gabriele Pozzato, Anirudh Allam, Matteo Acquarone, Simona Onori","doi":"10.1038/s44172-024-00304-2","DOIUrl":"10.1038/s44172-024-00304-2","url":null,"abstract":"Accurate estimation of battery state of health is crucial for effective electric vehicle battery management. Here, we propose five health indicators that can be extracted online from real-world electric vehicle operation and develop a machine learning-based method to estimate the battery state of health. The proposed indicators provide physical insights into the energy and power fade of the battery and enable accurate capacity estimation even with partially missing data. Moreover, they can be computed for portions of the charging profile and real-world driving discharging conditions, facilitating real-time battery degradation estimation. The indicators are computed using experimental data from five cells aged under electric vehicle conditions, and a linear regression model is used to estimate the state of health. The results show that models trained with power autocorrelation and energy-based features achieve capacity estimation with maximum absolute percentage error within 1.5% to 2.5%. Andrea Lanubile and colleagues develop a machine learning-based algorithm to estimate battery state of health during real world operations. The proposed method leads to highly accurate estimation even when partial battery data are missing.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11557864/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0