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SSRN 2024年

作者： Lamm, Erik D. Babler, Kristina M. Sharkey, Mark E. Amirali, Ayaaz Beaver, Cynthia Boone, Melinda M. Comerford, Samuel Cooper, Daniel Currall, Benjamin B. Grills, George S. Kobetz, Erin Kumar, Naresh Laine, Jennifer Lamar, Walter E. Lyu, Jiangnan Kennedy, Althea Perritano, Stefan Mason, Christopher E. Reding, Brian D. Roca, Matthew A. Schürer, Stephan C. Shukla, Bhavarth S. Solle, Natasha Schaefer Tallon, John J. Thomas, Collette Tierney, Braden T. Torres, Belkis Venkatpuram, Sreeharsha Vidovic, Dusica Williams, Sion L. Yin, Xue Zarnegarnia, Yalda Solo-Gabriele, Helena M. Department of Chemical Environmental and Materials Engineering University of Miami Coral GablesFL United States Department of Medicine University of Miami Miller School of Medicine MiamiFL United States Sylvester Comprehensive Cancer Center University of Miami Miller School of Medicine MiamiFL United States DataGrade Solutions LLC MiamiFL United States Department of Public Health Sciences University of Miami Miller School of Medicine MiamiFL United States Environmental Health and Safety University of Miami MiamiFL United States Division of Occupational Health Safety & Compliance University of Miami Health System MiamiFL United States Department of Physiology and Biophysics Weill Cornell Medical College New York CityNY United States The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine Weill Cornell Medicine New YorkNY United States The WorldQuant Initiative for Quantitative Prediction Weill Cornell Medicine New YorkNY United States Department of Molecular & Cellular Pharmacology University of Miami Miller School of Medicines MiamiFL United States Institute for Data Science & Computing University of Miami Coral GablesFL United States Facilities and Operations University of Miami Coral GablesFL United States

Measurements of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in wastewater can be used to understand the prevalence of COVID-19 cases within a community. Environmental conditions inclusive of water quality are known to impact wastewater SARS-CoV-2 signals, but they are rarely measured in areas upstream of wastewater treatment plants. The objectives of this study were to report on continuous measurements of environmental parameters (water quality and flow) in the upper reaches of a sewershed and to evaluate whether the inclusion of these environmental parameters improves associations between SARS-CoV-2 RNA levels in wastewater, and COVID-19 prevalence in the sewershed community. Measurements of environmental parameters and SARS-CoV-2 RNA in wastewater spanned different time scales (minutes, hours and weeks) and population scales (building, campus, community). Spearman correlations and stepwise regressions were used to evaluate associations. When comparing SARS-CoV-2 RNA levels directly to environmental parameters from instantaneous measurements or grab samples, results showed inconsistent correlations likely due to short term (minute-scale) variations observed in flow and water quality. For the analysis involving COVID-19 prevalence data which required averaging over weekly time scales, regressions showed that inclusion of environmental parameters, in particular pH, improved correlations between RNA and COVID-19 prevalence. The significance of pH in improving correlations of SARS-CoV-2 with prevalence data was greater for the cluster sewershed scale (populations of a few thousand) than at the community scale (populations of several 100,000). The significance of pH is believed to be related to the properties of the tap water characterized by high pH and low alkalinity, with pH serving as a proxy of fecal waste allowing for an adjustment of the SARS-CoV-2 signal to account for human waste dilution within the sewer system. © 2024, The Authors. All rights

关键词： Coronavirus

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The effects of different travel modes and travel destinations on COVID-19 transmission in global cities

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Science Bulletin 2022年第6期67卷 588-592,M0003页

作者： Rui Zhu Luc Anselin Michael Batty Mei-Po Kwan Min Chen Wei Luo Tao Cheng Che Kang Lim Paolo Santi Cheng Cheng Qiushi Gu Man Sing Wong Kai Zhang Guonian Lü Carlo Ratti Department of Land Surveying and Geo-Informatics the Hong Kong Polytechnic UniversityHong KongChina Key Laboratory of Virtual Geographic Environment(Ministry of Education) Nanjing Normal UniversityNanjing 210023China Senseable City Laboratory Future Urban Mobility IRGSingapore-MIT Alliance for Research and TechnologySingapore 138602Singapore Center for Spatial Data Science Division of the Social SciencesUniversity of ChicagoChicago IL 60637USA School of Geography Science Nanjing Normal UniversityNanjing 210023China Centre for Advanced Spatial Analysis Faculty of the Built EnvironmentUniversity College LondonLondon WC1E 6BTUK Department of Geography and Resource Management the Chinese University of Hong KongHong KongChina Institute of Space and Earth Information Science the Chinese University of Hong KongHong KongChina Department of Human Geography and Spatial Planning Utrecht UniversityUtrecht 3584 CSNetherlands State Key Laboratory Cultivation Base of Geographical Environment Evolution Nanjing 210023China Department of Geography National University of SingaporeSingapore 117570Singapore SpaceTimeLab Department of CivilEnvironmental and Geomatic EngineeringUniversity College LondonLondon WC1E 6BTUK Department of Clinical Translational Research Singapore General HospitalSingapore 169856Singapore Division of Clinical Immunology and Transfusion Medicine Department of Laboratory MedicineKarolinska InstitutetStockholm 17177Sweden Senseable City Laboratory Department of Urban Studies and PlanningMassachusetts Institute of TechnologyCambridgeMA 02139USA Istituto di Informatica e Telematica del CNR Pisa 56124Italy Department of System Science Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore Department of Tourism Studies School of HumanitiesSoutheast UniversityNanjing 210096China

The international community has made significant efforts to flatten the COVID-19 curve,including predicting transmission[1,2],executing unprecedented global lockdowns and social distancing[3,4],promoting the wearing of facemasks and social distancing measures[5],and isolating confirmed cases and contacts[6].Because of the adverse consequences of these lockdown measures[7],many cities have reopened so they can rebuild their ***,as mobility has gradually returned towards normal,imported cases from unknown sources have disrupted the recovery situation,and cities are continually at high risk of new waves of infection[8,9]since airborne transmission is the dominant transmission route[10].

关键词：多变量时间序列疫情传播出行方式谷歌公司交通出行病毒传播出行行为大城市

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LO-mode phonon of KCl and NaCl at 300 K by inelastic X ray scattering measurements and first principles calculations

arXiv

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arXiv 2022年

作者： Togo, Atsushi Hayashi, Hiroyuki Tadano, Terumasa Tsutsui, Satoshi Tanaka, Isao Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Ibaraki Tsukuba305-0047 Japan Center for Elements Strategy Initiative for Structural Materials Kyoto University Sakyo Kyoto606-8501 Japan Department of Materials Science and Engineering Kyoto University Sakyo Kyoto606-8501 Japan Research Center for Magnetic and Spintronic Materials National Institute for Materials Science Ibaraki Tsukuba305-0047 Japan Japan Synchrotron Radiation Research Institute Hyogo Sayo-Cho679-5198 Japan Institute of Quantum Beam Science Graduate School of Science and Engineering Ibaraki University Ibaraki Hitachi316-8511 Japan Nanostructures Research Laboratory Japan Fine Ceramics Center Atsuta Nagoya456-8587 Japan

Longitudinal-optical (LO) mode phonon branches of KCl and NaCl were measured using inelastic X-ray scattering (IXS) at 300 K and calculated by the first-principles phonon calculation with the stochastic self-consistent harmonic approximation. Spectral shapes of the IXS measurements and calculated spectral functions agreed well. We analyzed the calculated spectral functions that provide higher resolutions of the spectra than the IXS measurements. Due to strong anharmonicity, the spectral functions of these phonon branches have several peaks and the LO modes along Γ-L paths are disconnected. Copyright © 2022, The Authors. All rights reserved.

关键词： Phonons

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Design and development of industrial prototype of spot specific orchard sprayer using ultrasonic sensing system and advanced pressure control mechanism

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Computers and Electronics in Agriculture 2025年 237卷

作者： Arshed Ahmed Shoaib Rashid Saleem Muhammad Naveed Tahir Shahram Hamza Manzoor Qamar Zaman Zhao Zhang Rashed Ahmed School of Information and Electrical Engineering China Agricultural University Beijing China Center for Precision Agriculture Faculty of Agricultural Engineering and Technology PMAS- Arid Agriculture University Rawalpindi Pakistan Key Laboratory of Smart Agriculture System Integration Ministry of Education Beijing 100083 China Key Laboratory of Agricultural Information Acquisition Technology Ministry of Agriculture and Rural Affairs China Agricultural University Beijing 100083 China Data Driven Smart Decision Platform DDSDP PMAS- Arid Agriculture University Rawalpindi Pakistan Institute of Geo-Information & Earth Observation PMAS- Arid Agriculture University Rawalpindi Pakistan Department of Agronomy PMAS- Arid Agriculture University Rawalpindi Pakistan College of Engineering China Agricultural University Beijing China Department of Engineering Faculty of Agriculture Dalhousie University Truro Canada

Variable rate technologies (VRT) and spot-specific applications are one of the key research areas of precision agriculture with the ability to facilitate the farmers by automatically targeting only plant areas to avoid unnecessary losses of agrochemicals. Spot-specific spraying not only saves costly agrochemicals, but it can also help to protect environment. The primary objective of this study was to develop a spot specific orchard sprayer (SSOS) system based on tree detection through ultrasonic sensors. For this purpose, a traditional two vertical booms horticulture sprayer with six nozzles on each boom was modified to a spot specific spraying using automatic control of individual nozzle. Two sensor booms were installed, one on each side of the SSOS, in front of the sprayer booms. Six ultrasonic sensors were mounted, three on each boom. The developed sprayer was tested for fast switching of electronic valves using power transistors and relay-based operation, and testing results showed a switching time of only 28 ms. Similarly, the sprayer was tested for accurate sensor spacing mount position and sensor mount angle to minimize the miss detection of the target tree canopy and testing results showed that a mount angle of 0° worked best during lab testing. Furthermore, we have developed a Machine Learning (ML) based Decision Tree model for a pressure control mechanism to protect the hydraulic piping system in the traditional sprayers. The rule-based decision tree algorithm precisely controls the Overflow Connections (OFCs) through accurate predictions for switching of such connections, resulting in 94 % accuracy. The developed SSOS was tested in different citrus orchards at University Research Farms, Rawalpindi, Pakistan. The test results showed that spot specific orchard spraying system saved 38 % agrochemicals compared to traditional spraying for citrus orchards.

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care system for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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Bridging the gap between prostate radiology and pathology through machine learning

arXiv

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arXiv 2021年

作者： Bhattacharya, Indrani Lim, David S. Aung, Han Lin Liu, Xingchen Seetharaman, Arun Kunder, Christian A. Shao, Wei Soerensen, Simon J.C. Fan, Richard E. Ghanouni, Pejman To'o, Katherine J. Brooks, James D. Sonn, Geoffrey A. Rusu, Mirabela Department of Radiology Stanford University School of Medicine StanfordCA94305 United States Department of Urology Stanford University School of Medicine StanfordCA94305 United States Department of Computer Science Stanford University StanfordCA94305 United States Department of Biomedical Data Science Stanford University School of Medicine StanfordCA94305 United States Department of Electrical Engineering Stanford University StanfordCA94305 United States Department of Pathology Stanford University School of Medicine StanfordCA94305 United States Department of Epidemiology and Population Health Stanford University School of Medicine StanfordCA94305 United States Department of Radiology VA Palo Alto Health Care System Palo AltoCA94304 United States

Background: Prostate cancer remains the second deadliest cancer for American men despite clinical advancements. While Magnetic Resonance Imaging (MRI) is increasingly used to guide targeted biopsies for prostate cancer diagnosis, its utility remains limited due to high rates of false positives and false negatives as well as low inter-reader agreements. Purpose: Machine learning methods to detect and localize cancer on prostate MRI can help standardize radiologist interpretations. However, existing machine learning methods vary not only in model architecture, but also in the ground truth labeling strategies used for model training. We compare different labeling strategies and the effects they have on the performance of different machine learning models for prostate cancer detection. Methods: Four different deep learning models (SPCNet, U-Net, branched U-Net, and DeepLabv3+) were trained using 75 patients with radical prostatectomy, and evaluated using 40 patients with radical prostatectomy and 275 patients with targeted biopsy. Each deep learning model was trained with four different label types: pathology-confirmed radiologist labels, pathologist labels on whole-mount histopathology images, and lesion-level and pixel-level digital pathologist labels (previously validated deep learning algorithm on histopathology images to predict pixel-level Gleason patterns) on whole-mount histopathology images. The pathologist and digital pathologist labels (collectively referred to as pathology labels) were mapped onto pre-operative MRI using an automated MRI-histopathology registration platform. Results: Radiologist labels missed cancers (ROC-AUC: 0.75 - 0.84), had lower lesion volumes (75% of pathology lesions), and lower Dice overlaps (0.24 - 0.28) when compared with pathology labels. Consequently, machine learning models trained with radiologist labels also showed inferior performance compared to models trained with pathology labels. Digital pathologist labels showed high con

关键词： Diseases

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Lunar orbiter platform-gateway: A clear use case for CCSDS MO services

Lunar orbiter platform-gateway: A clear use case for CCSDS M...

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AIAA Space and Astronautics Forum and Exposition, 2018

作者： Merri, Mario Sarkarati, Mehran Head of Mission Data Systems Division Ground System Engineering Department European Space Agency Darmstadt64293 Germany Ground System Engineering Department European Space Agency Darmstadt64293 Germany

ISBN: (数字)9781624105753

ISBN: (纸本)9781624105753

The Lunar Orbiter Platform-Gateway (LOP-G) is a concept for a crew-tended cislunar space station led by the International Space Station partners: ESA, NASA, Roscosmos, JAXA and CSA for construction in the 2020s. If approved, this program will open up the way for a new international exploration programme that most likely will consists of several space assets owned by different organizations that will have to interoperate. The current focus of the involved agencies is on several mission-critical technologies that are yet to be developed to succeed in this ambitious program. Capitalizing on the experience and lessons learnt from 20 years of ISS operations, we believe that it is equally important to start working on operational interoperability aspects and agree already at this stage among all international partners on common operational concepts to make the constituting LOP-G assets interoperable. We believe that the CCSDS Mission Operations (MO) Services standard provides the right framework to achieve the adequate and necessary level of interoperability. © 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Space stations

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Effect of temperature and fibre reinforcement on dynamic crush performance of glass-polyamide 6 composite tubes

Composites Part C: Open Access

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Composites Part C: Open Access 2025年 17卷

作者： Holger Böhm Jonas Richter Jinbong Kim Mungyu Jeong Andreas Hornig Maik Gude Institute of Lightweight Engineering and Polymer Technology TUD Dresden University of Technology Holbeinstraße 3 01307 Dresden Germany Composite Structures and System Laboratory Korea Institute of Materials Science 797 Changwondaero Seongsangu Changwon Gyeongnam 51508 South Korea Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig (ScaDS.AI) TUD Dresden University of Technology Strehlener Straße 12-14 01069 Dresden Germany Department of Engineering Science University of Oxford Parks Road Oxford OX1 3PJ United Kingdom

In this study, the dynamic axial crushing response of tubular specimens made of glass-polyamide 6 composite material, with either mat or continuous bidirectional fibre reinforcement is experimentally investigated under three different temperature settings: −40 ° C , 23 ° C and 80 ° C . The assessment and evaluation of the dynamic crush performance are based on the measured global force–displacement response, application of typical crashworthiness criteria and a detailed examination of the existing damage and failure phenomena, which are responsible for energy absorption. At temperatures of 23 ° C and −40 ° C , all specimens showed a stable progressive crushing process by a pronounced splaying failure. Specimens with mat fibre reinforcement show a more stable and efficient crushing behaviour than specimens with continuous bidirectional fibre reinforcement. This behaviour changes for a temperature of 80 ° C where the continuous bidirectional reinforced specimens exhibit a higher crush efficiency, while specimens with mat fibre reinforcement show a very unstable crushing process, leading to local compressive kinking failure and a 22% decrease in crush efficiency.

关键词： Thermoplastic composite Crushing response Temperature dependency Splaying failure

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Unbalanced incomplete multi-view clustering via the scheme of view evolution: Weak views are meat;strong views do eat

arXiv

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arXiv 2020年

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Oliver The School of Computer Science and Technology Key Laboratory of Information Storage System Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China The Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous methods assume that all views have the same incompleteness, i.e., balanced incompleteness. However, different views often have distinct incompleteness, i.e., unbalanced incompleteness, which results in strong views (low-incompleteness views) and weak views (high-incompleteness views). The unbalanced incompleteness prevents us from directly using previous methods. In this paper, inspired by the effective biological evolution theory, we design the novel scheme of view evolution to cluster strong and weak views. Moreover, we propose an Unbalanced Incomplete Multi-view Clustering method (UIMC), which is the first effective method based on view evolution for unbalanced incomplete multi-view clustering. Compared with previous methods, UIMC has two unique advantages: 1) it proposes weighted multi-view subspace clustering to integrate unbalanced incomplete views, which effectively solves the unbalanced incomplete multi-view clustering problem;2) it designs the low-rank representation to recover the data, which diminishes the impact of the incompleteness and noises. Extensive experimental results demonstrate that UIMC improves the clustering performance by up to 40% on three evaluation metrics over other state-of-the-art methods. We provide codes for all of our experiments in https://***/ZeusDavide/TETCI_UIMC. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

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Rational Manipulation of Stacking Arrangements in Three-Dimensional Zeolites Built from Two-Dimensional Zeolitic Nanosheets

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Angewandte Chemie 2020年第45期132卷

作者： Xu L. Ma T. Shen Y. Wang Y. Han L. Chaittisilp W. Yokoi T. Sun J. Wakihara T. Okubo T. Department of Chemical System Engineering The University of Tokyo Tokyo 113-8656 Japan College of Chemistry and Molecular Engineering Peking University Beijing 100871 China Chemical Resources Laboratory Tokyo Institute of Technology Yokohama 226-8503 Japan School of Chemical Science and Engineering Tongji University Shanghai 200092 China Research and Services Division of Materials Data and Integrated System National Institute for Materials Sciences (NIMS) Ibaraki 305-0044 Japan

Unit-cell-thin zeolitic nanosheets have emerged as fascinating materials for catalysis and separation. The controllability of nanosheet stacking is extremely challenging in the chemistry of two-dimensional zeolitic materials. To date, the organization of zeolitic nanosheets in hydrothermal synthesis has been limited by the lack of tunable control over the guest–host interactions between organic structure-directing agents (OSDAs) and zeolitic nanosheets. A direct synthetic methodology is reported that enables systematic manipulation of the aluminosilicate MWW-type nanosheet stacking. Variable control of guest–host interactions is rationally achieved by synergistically altering the charge density of OSDAs and synthetic silica-to-alumina composition. These finely controlled interactions allow successful preparation of a series of three-dimensional (3D) zeolites, with MWW-layer stacking in wide ranges from variably disorder to fully ordered, leading to tunable catalytic activity in the cracking reaction. These results highlight unprecedented opportunities to modulate zeolitic nanosheets arrangement in 3D zeolites whose structure can be tailored for catalysis and separation. © 2020 Wiley-VCH GmbH

关键词： catalysis crystal growth layered compounds structure transformations

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