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arXiv 2019年

作者： Pretto, Alberto Aravecchia, Stéphanie Burgard, Wolfram Chebrolu, Nived Dornhege, Christian Falck, Tillmann Fleckenstein, Freya Fontenla, Alessandra Imperoli, Marco Khanna, Raghav Liebisch, Frank Lottes, Philipp Milioto, Andres Nardi, Daniele Nardi, Sandro Pfeifer, Johannes Popović, Marija Potena, Ciro Pradalier, Cédric Rothacker-Feder, Elisa Sa, Inkyu Schaefer, Alexander Siegwart, Roland Stachniss, Cyrill Walter, Achim Winterhalter, Wera Wu, Xiaolong Nieto, Juan Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy International Research Lab 2958 Georgia Tech-CNRS Metz France Department of Computer Science University of Freiburg Germany Toyota Research Institute Los Altos United States Photogrammetry & Robotics Lab University of Bonn Germany Robert Bosch GmbH Corporate Research Renningen Germany Osimo Italy Autonomous Systems Lab. Department of Mechanical and Process Engineering ETH Zurich Zürich Switzerland Department of Environmental Systems Science Institute of Agricultural Sciences ETH Zurich Zürich Switzerland IT+Robotics Srl Padova Italy Smart Robotics Lab. Department of Computing Imperial College London London United Kingdom CSIRO Data61 Australia

The application of autonomous robots in agriculture is gaining increasing popularity thanks to the high impact it may have on food security, sustainability, resource use efficiency, reduction of chemical treatments, and the optimization of human effort and yield. With this vision, the Flourish research project aimed to develop an adaptable robotic solution for precision farming that combines the aerial survey capabilities of small autonomous unmanned aerial vehicles (UAVs) with targeted intervention performed by multi-purpose unmanned ground vehicles (UGVs). This paper presents an overview of the scientific and technological advances and outcomes obtained in the project. We introduce multi-spectral perception algorithms and aerial and ground-based systems developed for monitoring crop density, weed pressure, crop nitrogen nutrition status, and to accurately classify and locate weeds. We then introduce the navigation and mapping systems tailored to our robots in the agricultural environment, as well as the modules for collaborative mapping. We finally present the ground intervention hardware, software solutions, and interfaces we implemented and tested in different field conditions and with different crops. We describe a real use case in which a UAV collaborates with a UGV to monitor the field and to perform selective spraying without human intervention. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping

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Sustainability in the Internet of Production: Interdisciplinary Opportunities and Challenges

Sustainability in the Internet of Production: Interdisciplin...

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International Symposium on Technology and Society (ISTAS)

作者： Sebastian Bernhard Sebastian Pütz Calvin Röhl Ralph Baier Philipp Brauner Ester Christou Hannah Dammers Roman Flaig Leon M. Gorißen Jan-Christoph Heilinger Christian Hinke István Koren Dirk Lüttgens Michael Millan Kai Müller Alexander Schollemann Luisa Vervier Thomas Gries Alexander Mertens Saskia K. Nagel Frank T. Piller Günther Schuh Martina Ziefle Verena Nitsch Carmen Leicht-Scholten Gender and Diversity in Engineering RWTH Aachen University Aachen Germany Institute of Industrial Engineering and Ergonomics RWTH Aachen University Aachen Germany Human-Computer Interaction Center RWTH Aachen University Aachen Germany Institute for Technology and Innovation Management RWTH Aachen University Aachen Germany Institut für Textiltechnik RWTH Aachen University Aachen Germany Chair for Laser Technology RWTH Aachen University Aachen Germany Human Technology Center/Applied Ethics RWTH Aachen University Aachen Germany Chair of Process and Data Science RWTH Aachen University Aachen Germany Laboratory for Machine Tools and Production Engineering RWTH Aachen University Aachen Germany

The vision of the Internet of Production (loP) is focused on optimizing manufacturing processes, with the help of Industry 4.0 technologies (14Ts). However, considering global megatrends such as climate change and the need to achieve the Sustainable Development Goals (SDGs), it is a growing imperative for the manufacturing industry to become more sustainable. This opens the door to transforming the IoP into an Internet of Sustainable Production (loSP). Accordingly, this paper proposes a novel four-step loSP-framework that establishes an Information System (IS) allowing researchers and practitioners to acknowledge and adapt to the interconnected nature of sustainability. Further, to test its applicability, an inter- and cross-disciplinary perspective is adopted to illustrate case-related challenges, opportunities, and pathways - revealed by the proposed framework - in the example of a digital economy for sustainability data, strategic design of global production networks, human-robot collaboration, digital photonic production, and the textile industry. Together, the framework demonstrates the usefulness of generating holistic information on the interconnected nature of sustainability derived from process-specific and contextualized data, while simultaneously assessing the framework's utilization for sustainability, as well as the sustainability of its use.

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Prediction of Activated Sludge Settling Characteristics from Microscopy Images with Deep Convolutional Neural Networks and Transfer Learning

SSRN

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SSRN 2024年

作者： Borzooei, Sina Scabini, Leonardo F.S. Miranda, Gisele Daneshgar, Saba Deblieck, Lukas De Langhe, Piet Bruno, Odemir De Baets, Bernard Nopens, Ingmar Torfs, Elena IVL Swedish Environmental Research Institute PO Box 210 60 StockholmSE-100 31 Sweden Department of Data Analysis and Mathematical Modelling Faculty of Bioscience Engineering Ghent University Coupure links 653 Gent9000 Belgium São Carlos Institute of Physics University of São Paulo SP São Carlos13560-970 Brazil KERMIT Department of Data Analysis and Mathematical Modelling Ghent University Coupure links 653 Gent9000 Belgium Division of Computational Science and Technology KTH Royal Institute of Technology Stockholm10044 Sweden CAPTURE Centre for Advanced Process Technology for Urban Resource Recovery Frieda Saeysstraat 1 Gent9000 Belgium Pantarein Water Egide Walschaertsstraat 22L Mechelen2800 Belgium Université Laval 1065 avenue de la Médecine QuébecQCG1V 0A6 Canada

Timely assessment and prediction of changes in microbial compositions leading to activated sludge settling problems, such as filamentous bulking (FB), can reduce water resource recovery facilities (WRRFs) upsets, operational challenges, and negative environmental impacts. This study presents a computer vision approach to assess activated sludge-settling characteristics based on Microscopy Images (MIs). We utilize MIs to train deep convolutional neural networks (CNN) using transfer learning to investigate the morphological properties of flocs and filaments. The methodology was tested on the offline MI dataset collected over two years at a full-scale industrial WRRF in Belgium. Various CNN architectures were tested, including Inception v3, ResNet18, ResNet152, ConvNeXt-nano, and ConvNeXt-S. The sludge volume index (SVI) was used as the final prediction variable, but the method can be easily adjusted to predict any other settling metric of choice. The best-performing CNN, ConvNeXt-nano, could predict SVI values with MAE (37.51 ± 4.02), MTD (11.65 ± 1.94), MAPE (0.18 ± 0.02), and R2 (0.75 ± 0.05). The model was tested in real-life FB events, where it identified early indicators of bulking by predictive surges in SVI values. We also use an explainable AI technique, Eigen-CAM, to discover key morphological indicators of sludge bulking transitions. The findings highlight the SVI multimodality issue, where SVI readings as a unidimensional metric could not capture delicate shifts from good to poor sludge settling, while the model detected these subtle changes. The key morphological attributes of threshold conditions leading to FB were identified, which can provide actionable insight for preemptive WRRF management. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Evaluation of Activated Sludge Settling Characteristics from Microscopy Images with Deep Convolutional Neural Networks and Transfer Learning

arXiv

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arXiv 2024年

作者： Borzooei, Sina Scabini, Leonardo Miranda, Gisele Daneshgar, Saba Deblieck, Lukas De Langhe, Piet Bruno, Odemir De Baets, Bernard Nopens, Ingmar Torfs, Elena IVL Swedish Environmental Research Institute P.O. Box 210 60 StockholmSE-100 31 Sweden BIOMATH Department of Data Analysis and Mathematical Modelling Faculty of Bioscience Engineering Ghent University Coupure links 653 Gent9000 Belgium São Carlos Institute of Physics University of São Paulo SP São Carlos13560-970 Brazil KERMIT Department of Data Analysis and Mathematical Modelling Ghent University Coupure links 653 Gent9000 Belgium Division of Computational Science and Technology KTH Royal Institute of Technology Stockholm10044 Sweden CAPTURE Centre for Advanced Process Technology for Urban Resource Recovery Frieda Saeysstraat 1 Gent9000 Belgium Pantarein Water Egide Walschaertsstraat 22L Mechelen2800 Belgium Université Laval 1065 avenue de la Médecine QuébecQCG1V 0A6 Canada

Microbial communities play a key role in biological wastewater treatment processes. Activated sludge settling characteristics, for example, are affected by microbial community composition, varying by changes in operating conditions and influent characteristics of wastewater treatment plants (WWTPs). Timely assessment and prediction of changes in microbial composition leading to settling problems, such as filamentous bulking (FB), can prevent operational challenges, reduction of treatment efficiency, and adverse environmental impacts. This study presents an innovative computer vision-based approach to assess activated sludge-settling characteristics based on the morphological properties of flocs and filaments in microscopy images. Implementing the transfer learning of deep convolutional neural network (CNN) models, this approach aims to overcome the limitations of existing quantitative image analysis techniques. The offline microscopy image dataset was collected over two years, with weekly sampling at a full-scale industrial WWTP in Belgium. Multiple data augmentation techniques were employed to enhance the generalizability of the CNN models. Various CNN architectures, including Inception v3, ResNet18, ResNet152, ConvNeXt-nano, and ConvNeXt-S, were tested to evaluate their performance in predicting sludge settling characteristics. The sludge volume index (SVI) was used as the final prediction variable, but the method can easily be adjusted to predict any other settling metric of choice. The results showed that the suggested CNN-based approach provides less labourintensive, objective, and consistent assessments while transfer learning notably minimises the training phase, resulting in a generalizable system that can be employed in real-time applications. The model's capability to signal early indicators of FB events makes it a valuable tool for effective monitoring and preemptive management of WWTPs facing activated sludge settling problems. © 2024, CC BY-NC-ND.

关键词： Convolutional neural networks

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Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates

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Heliyon 2024年第19期10卷 e38791页

作者： Arefi, Arman Sturm, Barbara Babor, Majharulislam Horf, Michael Hoffmann, Thomas Höhne, Marina Friedrich, Kathleen Schroedter, Linda Venus, Joachim Olszewska-Widdrat, Agata Department of Systems Process Engineering Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) Max-Eyth Allee 100 Potsdam 14469 Germany Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) Max-Eyth Allee 100 Potsdam 14469 Germany Albrecht Daniel Thaer-Institute of Agricultural and Horticultural Sciences Humboldt University of Berlin Hinter der Reinhardtstr Germany Department of Data Science in Bioeconomy Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) Max-Eyth Allee 100 Potsdam 14469 Germany Department of Agromechatronics Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) Max-Eyth Allee 100 Potsdam 14469 Germany University of Potsdam Potsdam Germany Department of Microbiome Biotechnology Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) Max-Eyth Allee 100 Potsdam 14469 Germany

As concerns about the environmental impacts of biowaste disposal increase, lactic acid bacterial fermentation is becoming increasingly popular. Current academic research is aimed at the process optimization by developing digital bioreactors. The primary focus is to develop a digital model mimicking the biochemical reactions. In the light of this, this paper intended to build a digital model of biochemical reactions during the fermentation process of both glucose and biowaste substrates, including white pasta and organic municipal waste. For this purpose, near-infrared (NIR) and mid-infrared (MIR) spectroscopy techniques were used to collect spectral information during the fermentation process. Next, the samples were analyzed by High Pressure Liquid Chromatography (HPLC) to measure their glucose, fructose, arabinose, xylose, disaccharide, lactic acid, and acetic acid contents. The results showed that learning algorithms trained on MIR spectra accurately estimated the biochemical reactions for both glucose and biowaste substrates. For the glucose substrate, the results showed R-squared of 0.97 and RMSE of 4.69 g/L for glucose, and R-squared of 0.98 and RMSE of 2.74 g/L for lactic acid. In the case of biowaste substrate, estimations included glucose (R-squared = 0.97, RMSE = 4.69 g/L), fructose (R-squared = 0.88, RMSE = 1.47 g/L), arabinose (R-squared = 0.98, RMSE = 0.55 g/L), xylose (R-squared = 0.93, RMSE = 1.11 g/L), disaccharide (R-squared = 0.90, RMSE = 0.55 g/L), total sugar (R-squared = 0.98, RMSE = 3.79 g/L), lactic acid (R-squared = 0.98, RMSE = 2.74 g/L), and acetic acid (R-squared = 0.97, RMSE = 0.36 g/L). Regarding NIR spectral data, the predictive models were accurate when the substrate was glucose, however, they failed to accurately estimate the chemical reactions in the case of biowaste substrate. The findings of this study can be used to fulfill the requirements for a continuous fermentation process with the objective of maximizing lactic acid productio

关键词： Biowaste fermentation Deep learning Lactic acid fermentation Near and mid infrared spectroscopy Non-invasive measurements

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Overview of the South China Sea Monsoon Experiment

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Advances in Atmospheric sciences 2004年第3期21卷 343-360页

作者：丁一汇李崇银柳艳菊 National Climate Center China Meteorological Administration Beijing 100081 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 National Climate Center China Meteorological Administration Beijing 100081 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid DynamicsInstitute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029he present paper gives an overview of the key project " South China Sea Monsoon Experiment (SCSMEX)" operated by the Ministry of Science and Technology of China during the period of 1996-2001. The SCSMEX is a joint atmospheric and oceanic field experiment which aims to better understand the onset maintenance and variability of the summer monsoon over the South China Sea (SCS). It is a large-scale international effort with many participating countries and regions cooperatively involved in this experiment. With the field observation in May-August 1998 a large amount of meteorological and oceanic data was acquired which provides excellent datasets for the study of the SCS monsoon and the East Asian monsoon and their interaction with the ocean. The preliminary research achievements are as follows. (1) The earliest onset of the Asian monsoon over the SCS and Indo-China Peninsula has been well documented. Prom the viewpoint of the synoptic process its onset is closely related to the early rapid development of a twin cyclone to the east of Sri Lanka. The conceptual modei of the SCS monsoon onset in 1998 was put forward. The 50-year time series of the SCS monsoon onset date was also made. (2) Two major modes namely the 30-60-day and 10-20-day oscillations were ascertained. The influences of the abnormal SCS monsoon on the precipitation over eastern China and its modes were identifled. A strong (weak) monsoon over the SCS usually leads to less (more) precipitation over the middle and lower reaches of the Yangtze River

The present paper gives an overview of the key project ' South China Sea Monsoon Experiment (SCSMEX)' operated by the Ministry of science and Technology of China during the period of 1996-2001. The SCSMEX is a joint atmospheric and oceanic field experiment which aims to better understand the onset, maintenance, and variability of the summer monsoon over the South China Sea (SCS). It is a large-scale international effort with many participating countries and regions cooperatively involved in this experiment. With the field observation in May-August 1998, a large amount of meteorological and oceanic data was acquired, which provides excellent datasets for the study of the SCS monsoon and the East Asian monsoon and their interaction with the ocean. The preliminary research achievements are as follows. (1) The earliest onset of the Asian monsoon over the SCS and Indo-China Peninsula has been well documented. Prom the viewpoint of the synoptic process, its onset is closely related to the early rapid development of a twin cyclone to the east of Sri Lanka. The conceptual modei of the SCS monsoon onset in 1998 was put forward. The 50-year time series of the SCS monsoon onset date was also made. (2) Two major modes, namely the 30-60-day and 10-20-day oscillations were ascertained. The influences of the abnormal SCS monsoon on the precipitation over eastern China and its modes were identifled. A strong (weak) monsoon over the SCS usually leads to less (more) precipitation over the middle and lower reaches of the Yangtze River basin, and more (less) precipitation in North China. (3) During the monsoon onset over the SCS, a wide variety of organized mesoscale convective systems (MCSs) were observed by a Doppler radar array deployed over the northern SCS. The relationship between large-scale circulations and MCSs during the monsoon onset process in 1998 was clearly revealed. It was suggested that there is a kind of positive feedback mechanism between large-scale circulations and

关键词： field experiment South China Sea monsoon low frequency oscillation (LFO) meso scale convective systems (MSCs) air-sea interaction numerical simulation

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Exploring outliers in crowdsourced ranking for QoE

arXiv

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arXiv 2017年

作者： Xu, Qianqian Yan, Ming Huang, Chendi Xiong, Jiechao Huang, Qingming Yao, Yuan Institute of Information Engineering Cas Beijing100093 Department of Computational Mathematics Michigan State University East LansingMI48824 United States BICMR-LMAM-LMEQF-LMP School of Mathematical Sciences Peking University Beijing100871 Tencent Ai Lab Shenzhen518057 University of Chinese Academy of Sciences Beijing100049 China Key Lab of Intell. Info. Process. Inst. of Comput. Tech. Cas Beijing100190 Key Lab of Big Data Mining and Knowledge Management Cas Beijing100190 Department of Mathematics Hong Kong University of Science and Technology 100871 Hong Kong

Outlier detection is a crucial part of robust evaluation for crowd-sourceable assessment of Quality of Experience (QoE) and has attracted much attention in recent years. In this paper, we propose some simple and fast algorithms for outlier detection and robust QoE evaluation based on the nonconvex optimization principle. Several iterative procedures are designed with or without knowing the number of outliers in samples. Theoretical analysis is given to show that such procedures can reach statistically good estimates under mild conditions. Finally, experimental results with simulated and real-world crowdsourcing datasets show that the proposed algorithms could produce similar performance to Huber-LASSO approach in robust ranking, yet with nearly 8 or 90 times speed-up, without or with a prior knowledge on the sparsity size of outliers, respectively. Therefore the proposed methodology provides us a set of helpful tools for robust QoE evaluation with crowdsourcing data. Copyright © 2017, The Authors. All rights reserved.

关键词： Crowdsourcing

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Exploiting dynamic nonlinearity in upconversion nanoparticles for super-resolution imaging

arXiv

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arXiv 2022年

作者： Chen, Chaohao Ding, Lei Liu, Baolei Du, Ziqin Liu, Yongtao Di, Xiangjun Shan, Xuchen Lin, Chenxiao Zhang, Min Xu, Xiaoxue Zhong, Xiaolan Wang, Jianfeng Chang, Lingqian Halkon, Ben J. Chen, Xin Cheng, Faliang Wang, Fan Guangdong Engineering and Technology Research Center for Advanced Nanomaterials School of Environment and Civil Engineering Dongguan University of Technology Dongguan523808 China Department of Chemical Engineering Shaanxi Key Laboratory of Energy Chemical Process Intensification Institute of Polymer Science in Chemical Engineering School of Chemical Engineering and Technology Xi'an Jiaotong University Xi'An710049 China School of Mathematical and Physical Sciences Faculty of Science University of Technology SydneyNSW2007 Australia School of Physics Beihang University Beijing100191 China Smart Computational Imaging Laboratory Nanjing University of Science and Technology Jiangsu Province Nanjing210094 China Department for Electrochemical Energy Storage Helmholtz-Zentrum Berlin für Materialien und Energie Hahn-Meitner-Platz Berlin14109 Germany School of Biomedical Engineering Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing100191 China Centre for Audio Acoustics & Vibration Faculty of Engineering & IT University of Technology Sydney UltimoNSW2007 Australia School of Electrical and Data Engineering Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia

Single-beam super-resolution microscopy, also known as superlinear microscopy, exploits the nonlinear response of fluorescent probes in confocal microscopy. The technique requires no complex purpose-built system, light field modulation, or beam shaping. Here, we present a strategy to enhance this technique's spatial resolution by modulating excitation intensity during image acquisition. This modulation induces dynamic optical nonlinearity in upconversion nanoparticles (UCNPs), resulting in variations of higher spatial-frequency information in the obtained images. The high-order information can be extracted with a proposed weighted finite difference imaging algorithm from raw fluorescence images, to generate an image with a higher resolution than superlinear microscopy images. We apply this approach to resolve two adjacent nanoparticles within a diffraction-limited area, improving the resolution to 130 nm. This work suggests a new scope for developing dynamic nonlinear fluorescent probes in super-resolution nanoscopy. © 2022, CC BY.

关键词： Nanoparticles

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Model bias in calculating factor importance of climate on vegetation growth

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Global and Planetary Change 2023年 228卷

作者： Liang, Boyi Liu, Hongyan Wang, Shaopeng Cressey, Elizabeth L. Dahlsjö, Cecilia A.L. Xu, Chongyang Wang, Jia Wang, Zong Liu, Feng Feng, Siwen Shi, Liang Dai, Jingyu Cao, Jing Li, Fufu Quine, Timothy A. College of Forestry Precision Forestry Key Laboratory of Beijing Beijing Forestry University Beijing100083 China College of Urban and Environmental Sciences and MOE Laboratory for Earth Surface Processes Peking University Beijing100871 China Geography College of Life and Environmental Sciences University of Exeter ExeterEX4 4RJ United Kingdom School of Geography and the Environment University of Oxford OxfordOX1 3QY United Kingdom Faculty of Agriculture Food and Environment The Hebrew University of Jerusalem Rehovot7610001 Israel Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China National Ecosystem Science Data Center Beijing100101 China State Key Laboratory of Environmental Criteria and Risk Assessment State Environmental Protection Key Laboratory of Regional Eco-process and Function Assessment Chinese Research Academy of Environmental Sciences Beijing100012 China

Machine learning is increasingly used to study vegetation growth, however, more often than not, predicting and simulating functions are prioritized over quantitative estimates of the drivers of vegetation growth such as climate. In this paper, we, for the first time, systematically investigate the model bias in calculating factor importance of climate on vegetation growth, especially when various kinds of machine learning models are considered. We undertake two case studies to simulate research in remote sensing and ground-based scenarios from which the difference in quantitative relationships between climate and vegetation is evaluated across multiple models. We found that model complexity increased the determination coefficient (R2) but reduced the absolute importance of the preselected independent variables. As the fitting accuracy increases, the absolute factor importance of dominant factor and all the other influencing factors decreases simultaneously, and factor importance calculated by different models tended to be more normally distributed across the study region. The reduction in factor importance was accompanied by the increased effect of model selection;e.g. the model that was used to estimate vegetation growth played a larger role in producing the factor importance (shown by variance analysis, remote sensing scenario, F-statistic = 555.2;ground based scenario, F-statistic = 30.8) than the climate variables (variance analysis, remote sensing scenario, F = 460.8;ground based scenario, F = 28.8). Critically, for those machine learning models with highest fitting accuracy, the resultant factor importance of climate factors had smaller difference with that of random factor. In contrast, the relative factor influence among the selected climate factor is more robust and reliable (in variation analysis, model was detected no significant impact on the resultant factor importance). For 5 of 8 models, the dominant factor (temperature) has relative influence over 0.

关键词： Machine learning

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Ultrathin Covalent Organic Framework Anchored on Graphene for Enhanced Organic Pollutant Removal

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Angewandte Chemie 2022年第35期134卷

作者： Dr. Changxia Li Patrick Guggenberger Dr. Seung Won Han Dr. Wei-Lu Ding Prof. Dr. Freddy Kleitz Department of Inorganic Chemistry—Functional Materials Faculty of Chemistry University of Vienna Währinger Straße 42 1090 Vienna Austria Contribution: Conceptualization (equal) Data curation (lead) Formal analysis (lead) Investigation (lead) Methodology (lead) Writing - original draft (lead) Writing - review & editing (equal) Contribution: Data curation (supporting) Formal analysis (supporting) Investigation (supporting) Writing - review & editing (supporting) Center for Nanomaterials and Chemical Reactions Institute for Basic Science (IBS) Daejeon 34141 South Korea Contribution: Data curation (supporting) Formal analysis (supporting) Investigation (supporting) Resources (supporting) Writing - review & editing (supporting) Beijing Key Laboratory of Ionic Liquids Clean Process CAS Key Laboratory of Green Process and Engineering Institute of Process Engineering Chinese Academy of Sciences Beijing 100190 China Contribution: Data curation (supporting) Formal analysis (supporting) Investigation (supporting) Methodology (supporting) Resources (supporting) Validation (supporting) Writing - review & editing (supporting) Contribution: Conceptualization (equal) Formal analysis (supporting) Funding acquisition (lead) Methodology (supporting) Project administration (lead) Resources (lead) Supervision (lead) Validation (lead) Writing - review & editing (equal)

Covalent organic frameworks (COFs) are of great potential as adsorbents owing to their tailorable functionalities, low density and high porosity. However, their intrinsically stacked two-dimensional (2D) structure limits the full use of their complete surface for sorption, especially the internal pores. The construction of ultrathin COFs could increase the exposure of active sites to the targeted molecules in a pollutant environment. Herein, an ultrathin COF with a uniform thickness of ca. 2 nm is prepared employing graphene as the surface template. The resulting hybrid aerogel with an ultralow density (7.1 mg cm −3 ) exhibits the ability to remove organic dye molecules of different sizes with high efficiency. The three-dimensional (3D) macroporous structure and well-exposed adsorption sites permit rapid diffusion of solution and efficient adsorption of organic pollutants, thereby, greatly contributing to its enhanced uptake capacity. This work highlights the effect of COF layer thickness on adsorption performance.

关键词： Aerogel Covalent Organic Frameworks Graphene Organic Pollutants Ultrathin Materials

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