{"title":"Energy-efficient wireless communications","authors":"Lishu Wu","doi":"10.1038/s44287-024-00027-8","DOIUrl":"10.1038/s44287-024-00027-8","url":null,"abstract":"An article in IEEE Journal on Selected Areas in Communications presents an approach that leverages cell-free massive MIMO technology for optimal network performance with minimal energy consumption.","PeriodicalId":501701,"journal":{"name":"Nature Reviews Electrical Engineering","volume":"1 2","pages":"77-77"},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139750162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Controlling limbless locomotors with mechanical intelligence","authors":"Silvia Conti","doi":"10.1038/s44287-024-00026-9","DOIUrl":"10.1038/s44287-024-00026-9","url":null,"abstract":"An article in Science Robotics reports the role of mechanical intelligence in terrestrial limbless locomotion.","PeriodicalId":501701,"journal":{"name":"Nature Reviews Electrical Engineering","volume":"1 2","pages":"78-78"},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139750167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The road to climate change mitigation via methane emissions monitoring","authors":"Binbin Weng","doi":"10.1038/s44287-023-00014-5","DOIUrl":"10.1038/s44287-023-00014-5","url":null,"abstract":"At the University of Oklahoma, we developed a high-performance mid-infrared photonic sensing solution for the deployment of a scalable, continuous monitoring network for methane emissions in the Anadarko Basin, one of the largest oil and gas production basins in the USA.","PeriodicalId":501701,"journal":{"name":"Nature Reviews Electrical Engineering","volume":"1 2","pages":"69-70"},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139750170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Wafer-to-wafer hybrid bonding at 400-nm interconnect pitch","authors":"Soon Aik Chew, Joeri De Vos, Eric Beyne","doi":"10.1038/s44287-024-00019-8","DOIUrl":"10.1038/s44287-024-00019-8","url":null,"abstract":"Wafer-to-wafer hybrid bonding is an attractive 3D integration technology for stacking multiple heterogeneous chips with high 3D interconnect density. We highlight recent design and technology innovations that enable hybrid Cu, SiCN-to-Cu and SiCN bonding with interconnect pitches down to an unprecedented 400 nm.","PeriodicalId":501701,"journal":{"name":"Nature Reviews Electrical Engineering","volume":"1 2","pages":"71-72"},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139750171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dai-Ming Tang, Ovidiu Cretu, Shinsuke Ishihara, Yongjia Zheng, Keigo Otsuka, Rong Xiang, Shigeo Maruyama, Hui-Ming Cheng, Chang Liu, Dmitri Golberg
{"title":"Chirality engineering for carbon nanotube electronics","authors":"Dai-Ming Tang, Ovidiu Cretu, Shinsuke Ishihara, Yongjia Zheng, Keigo Otsuka, Rong Xiang, Shigeo Maruyama, Hui-Ming Cheng, Chang Liu, Dmitri Golberg","doi":"10.1038/s44287-023-00011-8","DOIUrl":"10.1038/s44287-023-00011-8","url":null,"abstract":"Carbon nanotubes (CNTs), tubular nanostructures consisting of rolled-up graphene, are promising materials for electronic devices at the nanometre and molecular regimes. Fundamentally, the electronic properties of CNTs and their junctions depend on global and local chiralities, as defined by quantum boundary conditions along the circumferential and longitudinal directions. As such, CNTs can behave as a metal, a semiconductor or a quantum dot in an electronic device. Much of the progress in CNT electronics, going from single resistors and transistors to complex functional logic and communication devices, thin films and flexible electronics, sensors and intelligent systems, has been achieved through control over the ‘global chirality’ of CNTs — the distribution of chiralities at the macroscale. In this Review, we summarize approaches to control global and local CNT chiralities by growth, separation and transformation strategies. We then discuss opportunities and challenges for chirality engineering towards surpassing the performance of conventional electronic devices, and development of unconventional CNT quantum electronics including coherent quantum transistors and quantum sensors. Chirality fundamentally determines the electrical properties of CNTs and is therefore critical for the performance of CNT electronics. This Review summarizes approaches in controlling the global chirality distribution and local chirality junctions and discusses the progress in CNT electronics.","PeriodicalId":501701,"journal":{"name":"Nature Reviews Electrical Engineering","volume":"1 3","pages":"149-162"},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139778787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Artificial intelligence-based methods for renewable power system operation","authors":"Yuanzheng Li, Yizhou Ding, Shangyang He, Fei Hu, Juntao Duan, Guanghui Wen, Hua Geng, Zhengguang Wu, Hoay Beng Gooi, Yong Zhao, Chenghui Zhang, Shengwei Mei, Zhigang Zeng","doi":"10.1038/s44287-024-00018-9","DOIUrl":"10.1038/s44287-024-00018-9","url":null,"abstract":"Carbon neutrality goals are driving the increased use of renewable energy (RE). Large-scale use of RE requires accurate energy generation forecasts; optimized power dispatch, which minimizes costs while satisfying operational constraints; effective system control to ensure a stable power supply; and electricity markets that support bidding and trading decisions associated with RE. However, the uncertainties in RE generation make renewable power systems challenging to operate. For example, the intermittent nature of wind power can make it difficult to balance the supply and demand of electricity in real time; therefore, traditional power sources could be needed to meet the demand, which can increase electricity prices. This Review outlines the potential of artificial intelligence-based methods for supporting renewable power system operation. We discuss the ability of machine learning, deep learning and reinforcement learning methods to facilitate power system forecasts, dispatch, control and markets to support the use of RE. We also emphasize the applicability of these techniques to different operational problems. Finally, we discuss potential trends in renewable power system development and approaches to address the associated operational challenges such as the increasingly distributed nature of RE installations, diversification of energy storage systems and growing market complexity. The increasing integration of renewable energy technologies into power systems poses challenges owing to the large uncertainties associated with renewable energy production. This Review investigates the ability of artificial intelligence-based methods to improve forecasts, dispatch, control and electricity markets in renewable power systems.","PeriodicalId":501701,"journal":{"name":"Nature Reviews Electrical Engineering","volume":"1 3","pages":"163-179"},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139790849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cagla Deniz Bahadir, Mohamed Omar, Jacob Rosenthal, Luigi Marchionni, Benjamin Liechty, David J. Pisapia, Mert R. Sabuncu
{"title":"Artificial intelligence applications in histopathology","authors":"Cagla Deniz Bahadir, Mohamed Omar, Jacob Rosenthal, Luigi Marchionni, Benjamin Liechty, David J. Pisapia, Mert R. Sabuncu","doi":"10.1038/s44287-023-00012-7","DOIUrl":"10.1038/s44287-023-00012-7","url":null,"abstract":"Histopathology is a vital diagnostic discipline in medicine, fundamental to our understanding, detection, assessment and treatment of conditions such as cancer, dementia and heart disease. Traditionally, the standard workflow in histopathology has primarily relied on the visual interpretation of tissue samples carried out by human experts under a light microscope. Since the 2000s, thanks to advances in scanning technologies such as whole-slide imaging, histopathology is undergoing a digital transformation. The rapid increase in digital data is fuelling the development and application of artificial intelligence (AI) methods. In this Review, we delve into the latest progress in AI methods for histopathology, which promise to yield accurate, scalable, useful and affordable support tools for clinical decision. We examine the challenges and opportunities in this domain, exploring historically important approaches and problems that have shaped the field, while also highlighting recent technological breakthroughs that are poised to redefine its future. Furthermore, we offer an overview of publicly available datasets that have been instrumental in propelling the development of AI methods in histopathology. Increase in clinical digital data is propelling the development and application of artificial intelligence methods in histopathology. In this Review, machine learning algorithms and models and their clinical use cases are discussed, highlighting the computational and operational challenges in the field.","PeriodicalId":501701,"journal":{"name":"Nature Reviews Electrical Engineering","volume":"1 2","pages":"93-108"},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139750166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Geon Gug Yang, Hee Jae Choi, Sheng Li, Jang Hwan Kim, Kyeongha Kwon, Hyeong Min Jin, Bong Hoon Kim, Sang Ouk Kim
{"title":"Intelligent block copolymer self-assembly towards IoT hardware components","authors":"Geon Gug Yang, Hee Jae Choi, Sheng Li, Jang Hwan Kim, Kyeongha Kwon, Hyeong Min Jin, Bong Hoon Kim, Sang Ouk Kim","doi":"10.1038/s44287-024-00017-w","DOIUrl":"10.1038/s44287-024-00017-w","url":null,"abstract":"The Internet of Things (IoT) has emerged as the principal element for hyperconnectivity in the era of the fourth industrial revolution, in which low-power and self-sustainable operation, miniaturization and communication are the main requirements for advanced systems. Highly functional nanoscale structures, together with fabrication processes on the sub-100-nm scale, can be useful for the development of versatile miniaturized IoT devices. In this Perspective, we introduce block copolymer (BCP) self-assembly as a tool for the fabrication of high-performance IoT hardware components. Tailored material design of BCPs in terms of chemical diversity and molecular architectures enables the dense integration of physical and chemical functionalities below the tens of nanometres scale. BCPs can be used as nanoscale templates for surface nanopatterning, as soft 3D nanoporous structures or as nanopatterned substrates for spatially selective chemical functionalities. We summarize advances in technological areas relevant to the IoT, such as sensing, energy harvesting, user interfaces and information security systems. We also consider the limitations and open challenges that must be addressed, and we outline future research directions towards the use of BCP assembly for the next generation of IoT systems. Block copolymer self-assembly provides sub-10-nm periodic nanopatterned structures to fabricate Internet of Things (IoT) hardware components with a cost-effective, large-area approach. This Perspective focuses on how nanostructuring can improve the performance and introduce versatile functionalities to IoT components.","PeriodicalId":501701,"journal":{"name":"Nature Reviews Electrical Engineering","volume":"1 2","pages":"124-138"},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139750169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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