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Publisher Correction: The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

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Genome biology 2025年第1期26卷 31页

作者： Jacob Matthew Schreiber Carles A Boix Jin Wook Lee Hongyang Li Yuanfang Guan Chun-Chieh Chang Jen-Chien Chang Alex Hawkins-Hooker Bernhard Schölkopf Gabriele Schweikert Mateo Rojas Carulla Arif Canakoglu Francesco Guzzo Luca Nanni Marco Masseroli Mark James Carman Pietro Pinoli Chenyang Hong Kevin Y Yip Jefrey P Spence Sanjit Singh Batra Yun S Song Shaun Mahony Zheng Zhang Wuwei Tan Yang Shen Yuanfei Sun Minyi Shi Jessika Adrian Richard S Sandstrom Nina P Farrell Jessica M Halow Kristen Lee Lixia Jiang Xinqiong Yang Charles B Epstein J Seth Strattan Bradley E Bernstein Michael P Snyder Manolis Kellis William S Noble Anshul Bharat Kundaje Department of Genetics Stanford University Stanford CA USA. jmschreiber91@***. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA. Department of Genetics Stanford University Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. Department of Research and Development DeepSeq.AI San Francisco CA USA. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Department of Empirical Inference Max Planck Institute for Intelligent Systems Stuttgart Germany. School of Life Sciences University of Dundee Dundee UK. Department of Electronics Information and Bioengineering Politecnico Di Milano Milan Italy. Department of Computational Biology University of Lausanne Lausanne Switzerland. Department of Computer Science and Engineering The Chinese University of Hong Kong Sha Tin Hong Kong. Sanford Burnham Prebys Medical Discovery Institute San Diego CA USA. Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA. Department of Statistics University of California Berkeley Berkeley CA USA. Department of Biochemistry & Molecular Biology Center for Eukaryotic Gene Regulation Pennsylvania State University University Park PA USA. Department of Statistics Pennsylvania State University University Park PA USA. Department of Electrical and Computer Engineering Texas A&M University College Station TX USA. Altius Institute Seattle WA USA. Epigenomics Program The Broad Institute of MIT and Harvard Cambridge MA USA. Department of Genome Sciences University of Washington Seattle WA USA. Department of Computer Science Stanford University Stanford CA USA.

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Transient thermo-mechanical analysis for bimorph soft robot based on thermally responsive liquid crystal elastomers

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Applied Mathematics and Mechanics(English Edition) 2019年第7期40卷 943-952页

作者： Yun CUI Yafei YIN Chengjun WANG K. SIM Yuhang LI Cunjiang YU Jizhou SONG Institute of Solid Mechanics Beihang University Beijing 100191 China Department of Engineering Mechanics Soft Matter Research Center Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Zhejiang University Hangzhou 310027 China Materials Science and Engineering Program University of Houston Houston TX 77204 U. S.A. State Key Laboratory of Strength and Vibration of Mechanical Structures School of Aerospace Engineering Xi’an Jiaotong University Xi’an 710049 China Department of Mechanical Engineering University of Houston Houston TX 77204 U. S.A. Department of Electrical and Computer Engineering University of Houston Houston TX 77204 U. S.A. Department of Biomedical Engineering Texas Center for Superconductivity University of Houston Houston TX 77204 U. S.A.

Thermally responsive liquid crystal elastomers (LCEs) hold great promise in applications of soft robots and actuators because of the induced size and shape change with temperature. Experiments have successfully demonstrated that the LCE based bimorphs can be effective soft robots once integrated with soft sensors and thermal actuators. Here, we present an analytical transient thermo-mechanical model for a bimorph structure based soft robot, which consists of a strip of LCE and a thermal inert polymer actuated by an ultra-thin stretchable open-mesh shaped heater to mimic the unique locomotion behaviors of an inchworm. The coupled mechanical and thermal analysis based on the thermo-mechanical theory is carried out to underpin the transient bending behavior, and a systematic understanding is therefore achieved. The key analytical results reveal that the thickness and the modulus ratio of the LCE and the inert polymer layer dominate the transient bending deformation. The analytical results will not only render fundamental understanding of the actuation of bimorph structures, but also facilitate the rational design of soft robotics.

关键词： transient thermo-mechanical analysis soft robot thermal-responsive liq- uid crystal elastomer (LCE)

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Fast, accurate, and versatile data analysis platform for the quantification of molecular spatiotemporal signals

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Cell 2025年第10期188卷 2794-2809.e21页

作者： Mi, Xuelong Chen, Alex Bo-Yuan Duarte, Daniela Carey, Erin Taylor, Charlotte R. Braaker, Philipp N. Bright, Mark Almeida, Rafael G. Lim, Jing-Xuan Ruetten, Virginia M.S. Wang, Yizhi Wang, Mengfan Zhang, Weizhan Zheng, Wei Reitman, Michael E. Huang, Yongkang Wang, Xiaoyu Li, Lei Deng, HanFei Shi, Song-Hai Poskanzer, Kira E. Lyons, David A. Nimmerjahn, Axel Ahrens, Misha B. Yu, Guoqiang Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Arlington 22203 VA United States Janelia Research Campus Howard Hughes Medical Institute Ashburn 20147 VA United States Department of Molecular and Cellular Biology Harvard University Cambridge 02138 MA United States Graduate Program in Neuroscience Harvard Medical School Boston 02115 MA United States Waitt Advanced Biophotonics Center The Salk Institute for Biological Studies La Jolla 92037 CA United States Department of Biochemistry & Biophysics University of California San Francisco San Francisco CA United States Neuroscience Graduate Program University of California San Francisco San Francisco CA United States Centre for Discovery Brain Sciences University of Edinburgh Edinburgh BioQuarter Edinburgh EH16 4SB United Kingdom Gatsby Computational Neuroscience Unit UCL London W1T 4JG United Kingdom Kavli Institute for Fundamental Neuroscience San Francisco CA United States Department of Automation Tsinghua University Beijing 100084 China IDG/McGovern Institute for Brain Research Tsinghua University Beijing 100084 China New Cornerstone Science Laboratory Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological Structure School of Life Sciences Tsinghua University Beijing 100084 China State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University Shanghai 200032 China Beijing National Research Center for Information Science and Technology Beijing 100084 China

Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful spatiotemporal patterns embedded within complex and rich data sources, many of which cannot be captured with existing methods. Here, we introduce activity quantification and analysis (AQuA2), a fast, accurate, and versatile data analysis platform built upon advanced machine-learning techniques. It decomposes complex live-imaging-based datasets into elementary signaling events, allowing accurate and unbiased quantification of molecular activities and identification of consensus functional units. We demonstrate applications across a wide range of biosensors, cell types, organs, animal models, microscopy techniques, and imaging approaches. As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, as well as distinct sensorimotor signal propagation patterns in the mouse spinal cord. © 2025 The Author(s)

关键词： AQuA2 astrocytes cell interaction analysis functional units glial cells image analysis machine learning molecular spatiotemporal signals neurons time-lapse imaging

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Training of Physical Neural Networks

arXiv

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arXiv 2024年

作者： Momeni, Ali Rahmani, Babak Scellier, Benjamin Wright, Logan G. McMahon, Peter L. Wanjura, Clara C. Li, Yuhang Skalli, Anas Berloff, Natalia G. Onodera, Tatsuhiro Oguz, Ilker Morichetti, Francesco del Hougne, Philipp Le Gallo, Manuel Sebastian, Abu Mirhoseini, Azalia Zhang, Cheng Marković, Danijela Brunner, Daniel Moser, Christophe Gigan, Sylvain Marquardt, Florian Ozcan, Aydogan Grollier, Julie Liu, Andrea J. Psaltis, Demetri Alù, Andrea Fleury, Romain Lausanne Switzerland Microsoft Research 198 Cambridge Science Park CambridgeCB4 0AB United Kingdom Rain AI San Francisco United States Department of Applied Physics Yale University CT United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States FEMTO-ST Institute Optics Department CNRS University Bourgogne Franche-Comté Besançon25030 Cedex France Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale United States Lausanne Switzerland Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Univ Rennes CNRS IETR UMR 6164 RennesF-35000 France IBM Research Europe– Zurich Rüschlikon8803 Switzerland Department of Computer Science Stanford University United States Google DeepMind 1600 Amphitheatre Parkway Mountain ViewCA94043 United States Unité Mixte de Physique CNRS/Thales CNRS Thales Université Paris-Saclay Palaiseau France Laboratoire Kastler Brossel Sorbonne Université École Normale Supérieure Collège de France CNRS UMR 8552 Paris France Laboratoire Albert Fert CNRS Thales UniversitéParis-Saclay Palaiseau91767 France Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Lausanne Switzerland Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern artificial intelligence (AI). Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "textityes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained – primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Enantiospecificity in NMR Enabled by Chirality-Induced Spin Selectivity

arXiv

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arXiv 2024年

作者： Georgiou, T. Palma, J.L. Mujica, V. Varela, S. Galante, M. Santamaría García, V. Mboning, L. Schwartz, R.N. Cuniberti, G. Bouchard, L.-S. The Molecular Biology Institute University of California Los Angeles 611 Charles E. Young Drive East Los AngelesCA90095-1570 United States Department of Chemistry Penn State University 2201 University Drive Lemont FurnacePA15456 United States School of Molecular Sciences Arizona State University 551 E University Dr TempeAZ85281 United States Institute for Materials Science Max Bergmann Center of Biomaterials TU Dresden Dresden01062 Germany Department of Mechanical Engineering Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Tecnologico de Monterrey Escuela de Ingeniería y Ciencias Ave. Eugenio Garza Sada 2501 Monterrey64849 Mexico Department of Chemistry and Biochemistry University of California Los Angeles 607 Charles E. Young Dr. East Los AngelesCA90095-1569 United States Department of Electrical and Computer Engineering University of California Los Angeles 420 Westwood Plaza Los AngelesCA90095-1594 United States TU Dresden Dresden01062 Germany California NanoSystems Institute University of California Los Angeles 607 Charles E. Young Dr. East Los AngelesCA90095-1569 United States Department of Bioengineering University of California Los Angeles 607 Charles E. Young Dr. East Los AngelesCA90095-1569 United States

Spin polarization in chiral molecules is a magnetic molecular response associated with electron transport and enantioselective bond polarization that occurs even in the absence of an external magnetic field. An unexpected finding by Santos and co-workers reported enantiospecific NMR responses in solid-state cross-polarization (CP) experiments, suggesting a possible additional contribution to the indirect nuclear spin-spin coupling in chiral molecules induced by bond polarization in the presence of spin-orbit coupling. Herein we provide a theoretical treatment for this phenomenon, presenting an effective spin-Hamiltonian for helical molecules like DNA and density functional theory (DFT) results on amino acids that confirm the dependence of J-couplings on the choice of enantiomer. The connection between nuclear spin dynamics and chirality could offer insights for molecular sensing and quantum information sciences. These results establish NMR as a potential tool for chiral discrimination without external agents. © 2024, CC BY.

关键词： Nuclear magnetic resonance

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Development of activate capture and digital counting (AC+DC) assay on a self-powered microfluidic cartridge for protein biomarker detection

Development of activate capture and digital counting (AC+DC)...

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Optical Sensors, Sensors 2020 - Part of Optical Sensors and Sensing Congress 2020

作者： Che, Congnyu Li, Nantao Long, Kenneth D. Aguirre, Miguel Ángel Canady, Taylor D. Huang, Qinglan Demirci, Utkan Cunningham, Brian T. Department of Bioengineering University of Illinois at Urbana-Champaign 208 North Wright Street UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign 208 North Wright Street UrbanaIL61801 United States Department of Analytical Chemistry and Food Science University Institute of Materials Faculty of Science University of Alicante P.O. Box Alicante03080 Spain Carl R. Woese Institute for Genomic Biology University of Illinois at Urbana-Champaign 1206 West Gregory Drive UrbanaIL61801 United States Laboratory Canary Center at Stanford for Cancer Early Detection Department of Radiology School of Medicine Stanford University Palo AltoCA94304 United States

Activate capture and digital counting (AC + DC) is a rapid, 2-step, and ultrasensitive assay for protein quantification from a single droplet through the nanoparticle–photonic crystal coupling embedded in a self-powe... 详细信息

ISBN: (纸本)9781557528209

关键词： Proteins

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electrically-insulating flexible films with quasi-one-dimensional van-der-waals fillers as efficient electromagnetic shields

arXiv

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arXiv 2021年

作者： Barani, Zahra Kargar, Fariborz Ghafouri, Yassamin Ghosh, Subhajit Godziszewski, Konrad Seyedmahmoudbaraghani, Saba Yashchyshyn, Yevhen Cywiński, Grzegorz Rumyantsev, Sergey Salguero, Tina T. Balandin, Alexander A. Center Department of Electrical and Computer Engineering University of California RiversideCA92521 United States Department of Chemistry University of Georgia AthensGA30602 United States Institute of Radioelectronics and Multimedia Technology Warsaw University of Technology Warsaw00-665 Poland CENTERA Laboratories Institute of High-Pressure Physics Polish Academy of Sciences Warsaw01-142 Poland CEZAMAT Warsaw University of Technology Warsaw02-822 Poland Materials Science and Engineering Program University of California RiversideCA92521 United States

We report polymer composite films containing fillers comprised of quasi-one-dimensional (1D) van der Waals materials, specifically transition metal trichalcogenides containing 1D structural motifs that enable their exfoliation into bundles of atomic threads. These nanostructures are characterized by extremely large aspect ratios of up to ~106. The polymer composites with low loadings of quasi-1D TaSe3 fillers ( Copyright © 2021, The Authors. All rights reserved.

关键词： Fillers

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Brain MRI before and at term equivalent age predicts motor and cognitive outcomes in very preterm infants

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NeuroImage: Reports 2025年第2期5卷 100262-100262页

作者： Pagnozzi, Alex M. Pannek, Kerstin Boyd, Roslyn N. van Eijk, Liza George, Joanne M. Bora, Samudragupta Bradford, DanaKai Fahey, Michael Ditchfield, Michael Malhotra, Atul Colditz, Paul B. Fripp, Jurgen The Australian e-Health Research Centre CSIRO Brisbane Australia The University of Queensland School of Electrical Engineering and Computer Science Brisbane Australia Child Health Research Centre Queensland Cerebral Palsy and Rehabilitation Research Centre Faculty of Medicine The University of Queensland Brisbane QLD Australia Department of Psychology James Cook University Townsville QLD Australia Physiotherapy Department Queensland Children's Hospital Children's Health Queensland Hospital and Health Service Brisbane Australia Mothers Babies and Women's Health Program Mater Research Institute Faculty of Medicine The University of Queensland Brisbane QLD Australia Monash Health Paediatric Neurology Unit and Department of Paediatrics School of Clinical Sciences Monash University Clayton Victoria Australia Monash Imaging Monash Health Melbourne Victoria Australia Department of Medicine Monash University Melbourne Victoria Australia Department of Paediatrics Monash University Melbourne Victoria Australia Monash Newborn Monash Children's Hospital Melbourne Victoria Australia Perinatal Research Centre The University of Queensland Centre for Clinical Research Faculty of Medicine The University of Queensland Brisbane QLD Australia

Brain Magnetic Resonance Imaging (MRI) of high-risk infants in the neonatal period (from 26 weeks postmenstrual age to Term Equivalent Age (TEA)) is increasingly being used for the detection of brain injuries, and the early prognostication of adverse outcomes such as Cerebral Palsy (CP). While most imaging is performed around TEA in clinical practice for infants born preterm (<37 weeks of gestation), this would often require families to return to hospital for imaging. In this work, we extract structural biomarkers from MRI acquired both before and at TEA in a cohort of very preterm infants from the PPREMO and PREBO studies (n = 100), to determine if either time-point, or both combined, are predictive of both Bayley Scales of Infant and Toddler Development – Third Edition (Bayley-III) and the Neuro-sensory Motor Developmental Assessment (NSMDA) at 2 years. Using multivariable regression, moderately strong and statistically significant associations were found between brain structure on both early and TEA MRIs with 2-year outcomes (r = 0.39–0.55 for early MRI, r = 0.37–0.49 for Term MRI, r = 0.37–0.56 for early and TEA MRI combined). Importantly, brain biomarkers associated with early childhood outcomes from MRIs were identified, including white and grey matter volumes, deep grey matter and cerebellar volumes, and gyrification and surface area across the whole cortex. Early MRI showed the best prognostic accuracy along with combining timepoints, indicating the potential clinical benefit of Early MRI in predicting adverse outcomes. © 2025

关键词： Biomarkers Cerebral palsy Magnetic resonance imaging Neurodevelopmental impairment Prediction Preterm infants Structural

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Project 1000 x 1000: Centrifugal melt spinning for distributed manufacturing of N95 filtering facepiece respirators

arXiv

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arXiv 2020年

作者： Molina, Anton Vyas, Pranav Khlystov, Nikita Kumar, Shailabh Kothari, Anesta Deriso, Dave Liu, Zhiru Banavar, Samhita Flaum, Eliott Prakash, Manu Department of Materials Science and Engineering Stanford University StanfordCA94305 United States Department of Bioengineering Stanford University StanfordCA94305 United States Department of Chemical Engineering Stanford University StanfordCA94305 United States Department of Electrical Engineering Stanford University StanfordCA94305 United States Department of Applied Physics Stanford University StanfordCA94305 United States Program in Biophysics Stanford University StanfordCA94305 United States

The COVID-19 pandemic has caused a global shortage of personal protective equipment. While existing supply chains are struggling to meet the surge in demand, the limited supply of N95 filtering facepiece respirators (FFRs) has placed healthcare workers at risk. This paper presents a method for scalable and distributed manufacturing of FFR filter material based on a combination of centrifugal melt spinning utilizing readily available cotton candy machines as an example. The proposed method produces nonwoven polypropylene fabric material with filtering efficiency of up to 96% for particles 0.30-0.49 µm in diameter. We additionally demonstrate a scalable means to test for filtration efficiency and pressure drop to ensure a standardized degree of quality in the output material. We perform preliminary optimization of relevant parameters for scale-up and propose that this is a viable method to rapidly produce up to one million N95 FFRs per day in distributed manner with just six machines per site operating across 200 locations. We share this work as a starting point for others to rapidly construct, replicate and develop their own affordable modular processes aimed at producing high quality filtration material to address the current FFR shortage globally. Copyright © 2020, The Authors. All rights reserved.

关键词： Melt spinning

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