Three-Dimensional Analysis of Particle Distribution on Filter Layers inside N95 Respirators by Deep Learning
- Hye Ryoung LeeHye Ryoung LeeGeballe Laboratory for Advanced Materials, Stanford University, Stanford, California 94305, United StatesStanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United StatesMore by Hye Ryoung Lee,
- Lei Liao ,
- Wang Xiao ,
- Arturas VailionisArturas VailionisStanford Nano Shared Facility, Stanford University, Stanford, California 94305, United StatesDepartment of Physics, Kaunas University of Technology, LT-51368 Kaunas, LithuaniaMore by Arturas Vailionis,
- Antonio J. RiccoAntonio J. RiccoDepartment of Electrical Engineering, Stanford University, Stanford, California 94305, United StatesMore by Antonio J. Ricco,
- Robin WhiteRobin WhiteCarl Zeiss X-ray Microscopy, Inc., Pleasanton, California 94588, United StatesMore by Robin White,
- Yoshio NishiYoshio NishiDepartment of Electrical Engineering, Stanford University, Stanford, California 94305, United StatesMore by Yoshio Nishi,
- Wah ChiuWah ChiuDepartment of Bioengineering, James H. Clark Center, Stanford University, Stanford, California 94305, United StatesDivision of CryoEM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United StatesMore by Wah Chiu,
- Steven ChuSteven ChuDepartment of Physics, Stanford University, Stanford, California 94305, United StatesDepartment of Molecular and Cellular Physiology, Stanford University, Stanford, California 94305, United StatesMore by Steven Chu, and
- Yi Cui*Yi Cui*Email: [email protected]Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United StatesDepartment of Materials Science and Engineering, Stanford University, Stanford, California 94305, United StatesMore by Yi Cui
Abstract
The global COVID-19 pandemic has changed many aspects of daily lives. Wearing personal protective equipment, especially respirators (face masks), has become common for both the public and medical professionals, proving to be effective in preventing spread of the virus. Nevertheless, a detailed understanding of respirator filtration-layer internal structures and their physical configurations is lacking. Here, we report three-dimensional (3D) internal analysis of N95 filtration layers via X-ray tomography. Using deep learning methods, we uncover how the distribution and diameters of fibers within these layers directly affect contaminant particle filtration. The average porosity of the filter layers is found to be 89.1%. Contaminants are more efficiently captured by denser fiber regions, with fibers <1.8 μm in diameter being particularly effective, presumably because of the stronger electric field gradient on smaller diameter fibers. This study provides critical information for further development of N95-type respirators that combine high efficiency with good breathability.
Note
Due to a production error, this paper was published ASAP on December, 7, 2020, with missing information from the Associated Content section. The corrected version was reposted on December 7, 2020.
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This paper contains enhanced objects.
Note
This article is made available via the ACS COVID-19 subset for unrestricted RESEARCH re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
The zoonotic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes COVID-19 has led to a global pandemic, raising significant societal challenges worldwide in healthcare, economics, education, and governance, all as wealth disparities worsen. Many countries have adopted moderate to drastic measures to prevent rapid spread of the virus by mandating periods of quarantine, social distancing, wearing of respirators, and recommending improved general hygiene including frequent hand washing. Despite such efforts, thousands of new cases are diagnosed worldwide each day, with rapid increases recorded whenever lockdown orders are eased and schools and face-to-face businesses reopen.
Human coronavirus (HCoV) infection can cause respiratory diseases, with two major outbreaks in the recent past: severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). (1) SARS-CoV-2 has similarities to the other HCoVs in size, structure, and chemical properties, yet significant portions of this virus along with COVID-19 disease itself are poorly understood, including critical details of transmission range, periods of contagiousness, and mechanisms of infection. (2,3) As new symptoms and post-recovery conditions evolve, the Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) have updated symptom lists to inform the public. In this dynamic and urgent situation, the combined academic, government, and commercial science community has focused major research effort on uncovering essential details of SARS-CoV-2 and COVID-19 in addition to seeking vaccines and improved medical treatments.
Studies show that virus-containing aerosols can linger in the air for many hours and travel more than 6 to 8 m. (4−6) Thus, as the pandemic developed, the importance of personal protective equipment (PPE) increased, fueling worldwide demand for N95 respirators, which filter out at least 95% of particles around 0.3 μm in diameter (particle size with maximum filter penetration, i.e., the transition from impaction-based capture to diffusion-based capture). Equivalents of the United States N95 designation include FFP2, P2, PFF2, KF94, KN95, and DS/DL2 in the European Union, Australia/New Zealand, Brazil, South Korea, China, and Japan, respectively; all filter at least 94% or 95% of particles. Given the short supply of N95 respirators, disinfection methods for mask reuse as well as alternative mask types have been actively studied. (7−10) Most countries mandate the wearing of respirators in public spaces. Protection of first responders and medical professionals is understandably emphasized, as is, to varying degrees, safeguarding teachers and workers with high risk from repeated or prolonged exposure to large numbers of people.
The efficacy of N95 respirators is usually measured by overall filtration efficiency for a defined incident flow of particles carried by a stream of air forced through the filtration layers while the pressure differentials are measured. Nonetheless, neither the internal structures of these layers nor the particle penetration and capture mechanisms have been well studied. In this study, we used X-ray microscopy (XRM) to nondestructively visualize, analyze, and understand the three-dimensional internal configurations and adhesion of particles to polymer fibers inside N95 respirators. Such analysis is critical to assess the effectiveness of these respirators, which were not originally studied and designed for protection from HCoV viruses, including SARS-CoV-2. Artificial intelligence (A.I.), specifically deep learning (DL), was used to segment and analyze 3D data sets obtained by XRM to efficiently evaluate the large volume of data, consisting of over a thousand slices for each sample. We investigated key physical features of N95 respirators, quantifying large data sets for porosity, fiber distribution, and particle distribution in multiple samples prepared by varying quantities of particle deposition.
Wearing respirators is one of the most effective ways to protect people from pathogen-related diseases including COVID-19. Typical commercial respirators consist of multiple layers to accommodate both good filtration efficiency and reasonable breathability, as shown in Figure 1a (SARS-CoV-2 image: by courtesy of the CDC/Alissa Eckert, MSMI, and Dan Higgins, MAMS). The meltblown filter fabric in such respirators, the key component for providing high filtration efficiency, is usually made of polypropylene polymer fibers, which are shown in the SEM of Figure 1b.
Figure 1
Figure 1. (a) Schematic images of wearing a respirator against SARS-CoV-2 and multilayer of N95 respirators, (b) an SEM image of N95 filter fabric layer, (c) a simplified illustration of the X-ray microscope (XRM), (d) an example of a 3D X-ray image of N95 filter fabric layer obtained by XRM, and (e) data process flow of 3D X-ray data sets analyzed by deep learning techniques with input/output image examples.
We measured the 3D structure of the meltblown polypropylene filter fabric layer of N95 respirators by XRM (Carl Zeiss X-ray Microscopy Inc., Xradia Versa 520 and 810 Microscopes). A functional schematic of XRM is shown in Figure 1c. XRM nondestructively images a wide range of materials with varying degrees of densities, including “soft materials,” such as polymers, which are typically damaged by electron-beam irradiation during acquisition of scanning-electron micrographs; therefore, the materials often require additional sample processing such as surface coatings or must be imaged using a low-temperature process. In contrast, XRM, in particular, X-ray computed tomography (XCT), provides detailed geometrical and relative density information from a volume measurement of the samples without damaging or altering the internal structures. Multiple projection images are the basis of 3D structural images reconstructed after drift correction.
Unlike medical CT, materials-research-based XCT generally acquires data as the sample stage rotates as shown in Figure 1c. We used two XRM systems: the Xradia 520 (micro-XRM or micro-CT) offers a relatively large field of view with pixel size of 0.5–0.7 μm; the Xradia a 810 (nano-XRM or nano-CT) provides high-resolution imaging with spatial resolution down to 50 nm, similar to synchrotron X-ray spatial resolution. Figure 1d is an exemplary 3D image of an N95 filter layer measured by Xradia 520 and then reconstructed by XMReconstructor (Carl Zeiss X-ray Microscopy Inc.). Reconstructed X-ray images were processed by deep learning algorithms as summarized by Figure 1e.
Deep learning (DL), a technique inspired by the human nervous system and structurally mimicking the human brain, (11) was used in this study to identify features in the tomography images obtained from XRM. DL is a subset of machine learning, which is a subfield of artificial intelligence (Figure S1a). DL is particularly important when traditional machine learning fails to scale in “big data” applications (Figure S1b); therefore, it is utilized in many everyday applications including Google Translate, photo sorting in smart phones, medical imaging, self-driving cars, and face and speech recognition. (11,12) We exploit DL’s demonstrated image-recognition efficacy by implementing it via U-net convolutional neural networks for the structural analysis of N95 filter fabric. U-net convolutional neural networks are one of the popular DL architectures for image processing. Originally developed for bioimaging applications, the U-net network is a very efficient image-segmentation method that requires very few training images. (13−15) Image segmentation is the process of assigning image data components to sets of groups, also called classes, that have common properties. (16) U-net architecture has proven to be very appropriate for our 3D X-ray image segmentation: each pixel can be separately classified rather than assigning the entire output to a single class.
DL is particularly helpful to resolve adjacent features from materials with low Z-contrast (similar atomic number): relative to carbon and oxygen that comprise the polymer fibers, the contaminating NaCl particles used in this study have only moderate Z-contrast. Quantitative analysis of the sample was especially challenging as many particles are near or less than the resolution limit of the XRM. With DL training, the models distinguished key features and, with repeated training, enabled extraction of quantitative details from large data sets.
Initially, reconstructed X-ray tomographic images were processed by several deep-learning models, with U-Net architecture being ultimately selected because it provides the best feature recognition for our data sets. Training data were collected through manual segmentation in representative local areas at various locations throughout the data set. Training was continued iteratively using an Adadelta optimization algorithm, while monitoring model performance. Up to the point at which the model showed good agreement with the original data sets, iterative training and model modification continued. After training, all segmented image slices were visually inspected before quantitative analysis was performed.
Figure 2a shows the reconstructed image, while Figure 2b,c shows the DL-processed 2D images obtained from 3D X-ray tomography. As training continues, the DL model recognizes the fiber features more accurately. Figure 2d,e and Figure S2a–d display 3D X-ray images of the filter fabric layers from various viewing angles. The left (gray) results are reconstructed X-ray images and the right (yellow) ones are DL-segmented images. A 3D video of DL-processed propylene filter layer is in Movie 1.
Figure 2
Figure 2. (a) Reconstructed XRM image of N95 filter fabric layer. (b,c) Deep-learning-assisted image segmention of X-ray slices captured from 3D XCT images; the yellow color indicates segmented polymer fibers and the black background is empty space. (d,e) 3D perspective views of the filter fabric layer; the gray image on the left is the reconstructed 3D X-ray tomographic image, and the yellow one on the right is a segmented 3D image from deep learning models. Each grid box is 100 μm × 100 μm in size. (f) Porosity of single N95 meltblown filter fabric as a function of depth into the layer. (g) Statistical distributions of fiber diameters of meltblown polypropylene fibers within filters of N95 respirators measured from SEM.
N95 respirators consist of multiple layers to optimize filtration efficiency and breathability; the key filtration layer usually has meltblown polypropylene fibers that are low cost, chemically robust, and readily scaled to high-volume manufacturing. (17−19) The typical meltblown layer thickness used in commercially available N95 respirators is about 200–400 μm. The average layer thickness studied here is 345 μm with porosity of the meltblown fabric of 78.5–99.8% as shown in Figure 2f. Though most manufacturers use two or more layers of meltblown filter fabric inside N95 respirators, we studied a single layer of the filter fabric to analyze one layer’s characteristics and understand the filtration mechanism. The fibers, characterized by SEM and XRM, were found to be randomly distributed in the meltblown filter fabric, as shown in Figure 1b,d and Figure 2d,e. A randomized network is favorable for effective filtration compared with aligned or regular structures that could include facile channeling flow-through paths for droplets or aerosols. The distribution-weighted average fiber diameter measured from SEM images is 4.2 ± 3.9 μm, as seen in Figure 2g.
Sodium chloride (NaCl) particle-decorated N95 meltblown fabric was tested for filtration efficiency and internal contaminant capture morphology and mechanism; Figure 3a–d shows NaCl particle-decorated fabric morphology acquired by micro-XRM and the segmented results obtained by DL models. A 3D video of DL-processed NaCl particle-decorated polypropylene filter layer is in Movie 2. The count median diameter of NaCl particles used in this study is 75 ± 20 nm, which is similar to virus particle sizes. The mass loading of NaCl particles in the sample was 1.02 mg/cm2. Not surprisingly, Figure 3 reveals that particles accumulate more on the outer surface, upon which they are first incident, than on the innermost surface, closest to the wearer’s face. The overall porosity of the NaCl particle-decorated filter layer is reduced to the range of 75.2–99.8% (average: 87.3%) from its value prior to particle deposition of 78.5–99.8% (average: 89.1%). NaCl particle loading uniformity was also characterized as a function of depth into the filtration layer for three mass-loading conditions: 0.17, 0.51, and 1.02 mg/cm2 (Figure S4(a-f)).
Figure 3
Figure 3. Images of NaCl particle-decorated meltblown filter fabric: (a) reconstructed X-ray tomographic volume, (b) deep-learning-assisted segmented particles only, (c) deep-learning-assisted segmented particles overlaid on reconstructed X-ray tomographic image, and (d) deep-learning-assisted segmented particles on deep-learning-assisted segmented fabric fibers; blue color in (b), (c), and (d) indicates NaCl particles. (e) Porosity of the filter fabric and (f) areal occupancies of fibers and NaCl particles vs depth into the meltblown layer. NaCl mass loading was 1.02 mg/cm2.
In addition to the loading-dependent concentration gradient of contaminants across the depth of the filter layer, the second factor that significantly affects contaminant particle concentration distribution is uniformity of fiber distribution. While the meltblown fabric has (pseudo-) random networks, there exists in-plane and out-of-plane nonuniformity throughout the depth, resulting in contaminant distribution variations as dense areas catch particles more efficiently. The red curves in Figure 3f and Figure S4b,d, f indicate the areal occupancy of the fibers as a function of depth into the fabric layer; the black curves indicate the particle areal densities. (An aerial occupancy of 100% corresponds to no voids between fibers or particles at a given depth into the filter; the total area analyzed was 100 cm2 of fabric.) Figure 3f and Figure S4b,d,f show that the trend of the particle occupancy (black curves) follows that of the fiber density (red curves). In other words, more particles can be captured in denser fiber areas. Also, it is likely that nonuniformity may be caused by dendritic particle accumulation, i.e., particles “piling up” on those previously captured. In addition, particle buildup at a given depth is influenced by the preceding fiber density along the particles’ paths, creating complex dynamics that may affect each particle’s deposition location.
Filtration efficiency tests used the Automated Filter Tester 8130A (TSI, Inc.) with 85 L/min flow rate and NaCl solution (2 wt % in water) to generate aerosol particles. The experimental configuration leads to the complete evaporation of water from each aerosol droplet before it reaches the respirator, such that solid NaCl particles with 0.26 μm median diameter are incident on the material being tested. Each sample was cut to approximately 15 cm × 15 cm. This process, including use of NaCl particles, is the standard testing protocol for N95 respirators for certification and assessment by CDC’s National Institute for Occupational Safety and Health (NIOSH). The loading-and-efficiency test was done using a single meltblown fabric filter layer, even though N95 respirators usually include two or more layers depending on the manufacturer and intended application(s). The additional layers are often made of different materials that provide moisture-repellency, preventing transmission of large droplets and aerosols.
Dependence of the particle capture efficiency on total particle loading was investigated to understand the efficiency changes during filtration. Figure 4d reveals that meltblown layer particle capture efficiency starts at close to the expected 92.6% but decreases to a minimum of ∼71.3% at ∼0.183 mg/cm2 of loading; this may result from the decay or neutralization of electrostatic charge initially present on the filter layer. As NaCl particle loading proceeds further, capture efficiency increases, reaching ∼99.5% at loading of ∼0.60 mg/cm2, likely because the accumulated particles start to block voids between fibers. While better filtration efficiency is desirable, blocking filter voids hampers air permeability, directly related to user breathability. The balance and trade-off of filtration efficiency and respirator breathability are critical for efficient respirator design while protecting the wearer’s health.
Figure 4
Figure 4. (a,b) High-resolution nano-XRM images of several fibers with NaCl particles adhered show more captured particles per unit length as fiber diameter decreases. (c) An SEM image of polypropylene meltblown fabric with adherent NaCl particles. (d) Filtration efficiency graph of the meltblown fabric as a function of the deposited mass density of NaCl particles. (e) Dependence of measured particle capture area per unit fiber area on fiber diameter.
The diameter distribution in the meltblown polypropylene layer in Figure 2g raises the question of which fiber size would provide the greatest filtration effect. Interestingly, NaCl particles accumulate more on the thinnest fibers (observed consistently by both SEM and XRM). The XRM images of several isolated fibers in Figure 4a,b and SEM image in Figure 4c clearly demonstrate this phenomenon: DL-assisted analysis of 3D XRM tomographic images shows that the effect persists throughout the thickness of the filtration layer. Quantification of captured particle area per fiber area, Figure 4e, shows that fibers <1.8 μm in diameter are significantly more effective at particle capture than larger fibers. This may stem from the magnitude of the electric field gradient associated with the static charge, which is generated by the corona discharge process step, utilized by most manufacturers to increase particle capture efficiency. The better the capture efficiency, particularly if it is associated with small fiber diameters, the better breathability can be for a given overall filtration efficiency. Further aspects of the corona discharge mechanism are explained in previous studies; the mechanistic details of particle capture as a function of fiber type and diameter are under active investigation. (20−24)
In summary, we used X-ray microscopy to shed light on the 3D microstructure and morphology of N95 respirator filtration layers, and to understand the adhesion distribution of contaminant particles to polymer fibers. This analysis will aid evaluations of the efficacy of respirators against a variety of contaminants, including SARS-CoV-2. Deep-learning algorithms were proven to be essential to segment and analyze a large number of 3D tomography data sets through trained models. Fiber diameters, density distributions, and overall fabric porosity were characterized, as were particle distributions in various samples prepared with different amounts of particle deposition. We found that the fiber diameter plays a crucial role, with capture efficiency drastically improved for fibers <1.8 μm in diameter, likely related to the electric field gradient. We also observed that fiber distribution directly affects contaminant-capture efficiency: dense fiber areas trap more particles. We conclude that appropriate design of fiber density gradients in combination with well-chosen fiber diameter distributions can, in future N95 respirators, lead to high particle-capture efficiencies without sacrificing good breathability over a larger total particle loading range relative to current filter characteristics.
Methods
ARTICLE SECTIONS
Sample Preparation
TM95 meltblown fabric (Guangdong Meltblown Technology Co. Ltd.) samples were used for filtration testing. Each sample was cut to approximately 15 cm × 15 cm.
SEM Analysis
Scanning electron microscope images were taken using a Phenom Pro SEM and Thermofisher Helios at 10 kV.
X-ray Microscope Analysis
Zeiss Xradia 520 and Zeiss Xradia Ultra 810 X-ray Microscopes (XRMs) (Carl Zeiss X-ray Microscopy Inc., Pleasanton, CA) were used to obtain X-ray tomographic images and ORS Dragonfly software (version 4.1 and 2020.1) and ImageJ (25) software were used for the data analysis. Deep Learning Module in Dragonfly software (26,27) was used for image segmentation via 15–20 slices for training each data set. Data augmentation of factor 10 with rotation, horizontal and vertical flipping, shearing (of 2 degrees) and scaling (0.9 to 1.1) was implemented to reduce the processing time. Dice loss and AdaDelta optimization method were used to train images.
Fiber and particle occupancy results were quantified by full-depth 3D filter fabric data collected using the Zeiss Xradia 520 system. Particle adhesion dependence on fiber diameter was confirmed by the data obtained from the Zeiss Xradia Ultra 810 system. Reconstruction was performed using commercial software package XMReconstructor (version 14.0.1, Carl Zeiss X-ray Microscopy Inc., Pleasanton, CA).
Supporting Information
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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.nanolett.0c04230.
The scope of deep learning, reconstructed and DL-processed 3D X-ray images, porosity and areal occupancy graphs (PDF)
Terms & Conditions
Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.
Author Information
ARTICLE SECTIONS
- Yi Cui - Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States; Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States;
http://orcid.org/0000-0002-6103-6352;
- Hye Ryoung Lee - Geballe Laboratory for Advanced Materials, Stanford University, Stanford, California 94305, United States; Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States;http://orcid.org/0000-0003-4546-4880
- Wah Chiu - Department of Bioengineering, James H. Clark Center, Stanford University, Stanford, California 94305, United States; Division of CryoEM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States;http://orcid.org/0000-0002-8910-3078
Acknowledgments
ARTICLE SECTIONS
This research was funded by DOE Office of Science through the National Virtual Biotechnology Laboratory, a consortium of DOE national laboratories focused on response to COVID-19, with funding provided by the Coronavirus CARES Act (to W.C.). Part of this work was performed at the Stanford Nano Shared Facilities (SNSF), supported by the National Science Foundation under award ECCS-1542152. Authors thank Prof. Piero A Pianetta, Dr. Johanna Nelson, Dr. Yijin Liu for valuable discussion. The authors also would like to thank the ORS support team, the ImageJ team, and CDC/Alissa Eckert, MSMI, and Dan Higgins, MAMS.
References
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- 5Setti, L.; Passarini, F.; De Gennaro, G.; Barbieri, P.; Perrone, M. G.; Borelli, M.; Palmisani, J.; Di Gilio, A.; Piscitelli, P.; Miani, A. Airborne Transmission Route of Covid-19: Why 2 Meters/6 Feet of Inter-Personal Distance Could Not Be Enough. Int. J. Environ. Res. Public Health 2020, 17 (8), 2932, DOI: 10.3390/ijerph17082932[Crossref], [CAS], Google Scholar5https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhvVaqsL%252FL&md5=0d567a2e90708d5d07da072ed328cdfeAirborne transmission route of COVID-19: why 2 meters/6 feet of inter-personal distance could not be enoughSetti, Leonardo; Passarini, Fabrizio; De Gennaro, Gianluigi; Barbieri, Pierluigi; Perrone, Maria Grazia; Borelli, Massimo; Palmisani, Jolanda; Di Gilio, Alessia; Piscitelli, Prisco; Miani, AlessandroInternational Journal of Environmental Research and Public Health (2020), 17 (8), 2932CODEN: IJERGQ; ISSN:1660-4601. (MDPI AG)The COVID-19 pandemic caused the shutdown of entire nations all over the world. In addn. to mobility restrictions of people, the World Health Organization and the Governments have prescribed maintaining an inter-personal distance of 1.5 or 2 m (∼6 ft) from each other to minimize the risk of contagion through the droplets that we usually disseminate around us from nose and mouth. However, recently published studies support the hypothesis of virus transmission over a distance of 2 m from an infected person. Researchers have proved the higher aerosol and surface stability of SARS-COV-2 as compared with SARS-COV-1 (with the virus remaining viable and infectious in aerosol for hours) and that airborne transmission of SARS-CoV can occur besides close-distance contacts. Indeed, there is reasonable evidence about the possibility of SARS-COV-2 airborne transmission due to its persistence into aerosol droplets in a viable and infectious form. Based on the available knowledge and epidemiol. observations, it is plausible that small particles contg. the virus may diffuse in indoor environments covering distances ≤10 m from the emission sources, thus representing a kind of aerosol transmission. On-field studies carried out inside Wuhan Hospitals showed the presence of SARS-COV-2 RNA in air samples collected in the hospitals and also in the surroundings, leading to the conclusion that the airborne route has to be considered an important pathway for viral diffusion. Similar findings are reported in analyses concerning air samples collected at the Nebraska University Hospital. On March 16th, we have released a Position Paper emphasizing the airborne route as a possible addnl. factor for interpreting the anomalous COVID-19 outbreaks in northern Italy, ranked as 1 of the most polluted areas in Europe and characterized by high particulate matter (PM) concns. The available information on the SARS-COV-2 spreading supports the hypothesis of airborne diffusion of infected droplets from person to person at a distance >2 m (6 ft). The inter-personal distance of 2 m can be reasonably considered as an effective protection only if everybody wears face masks in daily life activities.
- 6Jones, N. R.; Qureshi, Z. U.; Temple, R. J.; Larwood, J. P. J.; Greenhalgh, T.; Bourouiba, L. Two Metres or One: What Is the Evidence for Physical Distancing in Covid-19?. BMJ. 2020, 370, m3223, DOI: 10.1136/bmj.m3223
- 7Liao, L.; Xiao, W.; Zhao, M.; Yu, X.; Wang, H.; Wang, Q.; Chu, S.; Cui, Y. Can. N95 Respirators Be Reused after Disinfection? How Many Times?. ACS Nano 2020, 14 (5), 6348– 6356, DOI: 10.1021/acsnano.0c03597[ACS Full Text
], [CAS], Google Scholar7https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXosVCmt7o%253D&md5=5fc1b3a9b67826faa66784801f5cf1d8Can N95 Respirators Be Reused after Disinfection? How Many Times?Liao, Lei; Xiao, Wang; Zhao, Mervin; Yu, Xuanze; Wang, Haotian; Wang, Qiqi; Chu, Steven; Cui, YiACS Nano (2020), 14 (5), 6348-6356CODEN: ANCAC3; ISSN:1936-0851. (American Chemical Society)The coronavirus disease 2019 (COVID-19) pandemic has led to a major shortage of N95 respirators, which are essential for protecting healthcare professionals and the general public who may come into contact with the virus. Thus, it is essential to det. how we can reuse respirators and other personal protective equipment in these urgent times. We investigated multiple commonly used disinfection schemes on media with particle filtration efficiency of 95%. Heating was recently found to inactivate the virus in soln. within 5 min at 70°C and is among the most scalable, user-friendly methods for viral disinfection. We found that heat (≤85°C) under various humidities (≤100% relative humidity, RH) was the most promising, nondestructive method for the preservation of filtration properties in meltblown fabrics as well as N95-grade respirators. At 85°C, 30% RH, we were able to perform 50 cycles of heat treatment without significant changes in the filtration efficiency. At low humidity or dry conditions, temps. up to 100°C were not found to alter the filtration efficiency significantly within 20 cycles of treatment. UV irradn. was a secondary choice, which was able to withstand 10 cycles of treatment and showed small degrdn. by 20 cycles. However, UV can potentially impact the material strength and subsequent sealing of respirators. Finally, treatments involving liqs. and vapors require caution, as steam, alc., and household bleach all may lead to degrdn. of the filtration efficiency, leaving the user vulnerable to the viral aerosols. - 8Zhao, M.; Liao, L.; Xiao, W.; Yu, X.; Wang, H.; Wang, Q.; Lin, Y. L.; Kilinc-Balci, F. S.; Price, A.; Chu, L.; Chu, M. C.; Chu, S.; Cui, Y. Household Materials Selection for Homemade Cloth Face Coverings and Their Filtration Efficiency Enhancement with Triboelectric Charging. Nano Lett. 2020, 20 (7), 5544– 5552, DOI: 10.1021/acs.nanolett.0c02211[ACS Full Text], [CAS], Google Scholar8https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhtVOmtLbJ&md5=27921343d002cc18bed5f34b01f0c29fHousehold Materials Selection for Homemade Cloth Face Coverings and Their Filtration Efficiency Enhancement with Triboelectric ChargingZhao, Mervin; Liao, Lei; Xiao, Wang; Yu, Xuanze; Wang, Haotian; Wang, Qiqi; Lin, Ying Ling; Kilinc-Balci, F. Selcen; Price, Amy; Chu, Larry; Chu, May C.; Chu, Steven; Cui, YiNano Letters (2020), 20 (7), 5544-5552CODEN: NALEFD; ISSN:1530-6984. (American Chemical Society)The COVID-19 pandemic is currently causing a severe disruption and shortage in the global supply chain of necessary personal protective equipment (e.g., N95 respirators). The U.S. CDC has recommended use of household cloth by the general public to make cloth face coverings as a method of source control. We evaluated the filtration properties of natural and synthetic materials using a modified procedure for N95 respirator approval. Common fabrics of cotton, polyester, nylon, and silk had filtration efficiency of 5-25%, polypropylene spunbond had filtration efficiency 6-10%, and paper-based products had filtration efficiency of 10-20%. An advantage of polypropylene spunbond is that it can be simply triboelec. charged to enhance the filtration efficiency (from 6 to >10%) without any increase in pressure (stable overnight and in humid environments). Using the filtration quality factor, fabric microstructure, and charging ability, we are able to provide an assessment of suggested fabric materials for homemade facial coverings.
- 9Lustig, S. R.; Biswakarma, J. J. H.; Rana, D.; Tilford, S. H.; Hu, W.; Su, M.; Rosenblatt, M. S. Effectiveness of Common Fabrics to Block Aqueous Aerosols of Virus-like Nanoparticles. ACS Nano 2020, 14 (6), 7651– 7658, DOI: 10.1021/acsnano.0c03972[ACS Full Text], [CAS], Google Scholar9https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXpvVGrtr8%253D&md5=1a365f37e89311b0793d98f9ff08c0daEffectiveness of Common Fabrics to Block Aqueous Aerosols of Virus-like NanoparticlesLustig, Steven R.; Biswakarma, John J. H.; Rana, Devyesh; Tilford, Susan H.; Hu, Weike; Su, Ming; Rosenblatt, Michael S.ACS Nano (2020), 14 (6), 7651-7658CODEN: ANCAC3; ISSN:1936-0851. (American Chemical Society)Layered systems of commonly available fabric materials can be used by the public and healthcare providers in face masks to reduce the risk of inhaling viruses with protection that is about equiv. to or better than the filtration and adsorption offered by 5-layer N95 respirators. Over 70 different common fabric combinations and masks were evaluated under steady-state, forced convection air flux with pulsed aerosols that simulate forceful respiration. The aerosols contain fluorescent virus-like nanoparticles to track transmission through materials that greatly assist the accuracy of detection, thus avoiding artifacts including pore flooding and the loss of aerosol due to evapn. and droplet breakup. Effective materials comprise both absorbent, hydrophilic layers and barrier, hydrophobic layers. Although the hydrophobic layers can adhere virus-like nanoparticles, they may also repel droplets from adjacent absorbent layers and prevent wicking transport across the fabric system. Effective designs are noted with absorbent layers comprising terry cloth towel, quilting cotton, and flannel. Effective designs are noted with barrier layers comprising nonwoven polypropylene, polyester, and polyaramid.
- 10Konda, A.; Prakash, A.; Moss, G. A.; Schmoldt, M.; Grant, G. D.; Guha, S. Aerosol Filtration Efficiency of Common Fabrics Used in Respiratory Cloth Masks. ACS Nano 2020, 14 (5), 6339– 6347, DOI: 10.1021/acsnano.0c03252[ACS Full Text], [CAS], Google Scholar10https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXnslChsr4%253D&md5=961b798525395cbb0bc53e325418e39dAerosol Filtration Efficiency of Common Fabrics Used in Respiratory Cloth MasksKonda, Abhiteja; Prakash, Abhinav; Moss, Gregory A.; Schmoldt, Michael; Grant, Gregory D.; Guha, SupratikACS Nano (2020), 14 (5), 6339-6347CODEN: ANCAC3; ISSN:1936-0851. (American Chemical Society)The emergence of a pandemic affecting the respiratory system can result in a significant demand for face masks. This includes the use of cloth masks by large sections of the public, as can be seen during the current global spread of COVID-19. However, there is limited knowledge available on the performance of various commonly available fabrics used in cloth masks. Importantly, there is a need to evaluate filtration efficiencies as a function of aerosol particulate sizes in the 10 nm to 10μm range, which is particularly relevant for respiratory virus transmission. We have carried out these studies for several common fabrics including cotton, silk, chiffon, flannel, various synthetics, and their combinations. Although the filtration efficiencies for various fabrics when a single layer was used ranged from 5 to 80% and 5 to 95% for particle sizes of <300 nm and >300 nm, resp., the efficiencies improved when multiple layers were used and when using a specific combination of different fabrics. Filtration efficiencies of the hybrids (such as cotton-silk, cotton-chiffon, cotton-flannel) was >80% (for particles <300 nm) and >90% (for particles >300 nm). We speculate that the enhanced performance of the hybrids is likely due to the combined effect of mech. and electrostatic-based filtration. Cotton, the most widely used material for cloth masks performs better at higher weave densities (i.e., thread count) and can make a significant difference in filtration efficiencies. Our studies also imply that gaps (as caused by an improper fit of the mask) can result in over a 60% decrease in the filtration efficiency, implying the need for future cloth mask design studies to take into account issues of "fit" and leakage, while allowing the exhaled air to vent efficiently. Overall, we find that combinations of various commonly available fabrics used in cloth masks can potentially provide significant protection against the transmission of aerosol particles.
- 11Shrestha, A.; Mahmood, A. Review of Deep Learning Algorithms and Architectures. IEEE Access 2019, 7, 53040– 53065, DOI: 10.1109/ACCESS.2019.2912200
- 12Lecun, Y.; Bengio, Y.; Hinton, G. Deep Learning. Nature 2015, 521 (7553), 436– 444, DOI: 10.1038/nature14539[Crossref], [PubMed], [CAS], Google Scholar12https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXht1WlurzP&md5=8bc52a4d89944b3bc753cad905aba9e1Deep learningLeCun, Yann; Bengio, Yoshua; Hinton, GeoffreyNature (London, United Kingdom) (2015), 521 (7553), 436-444CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
- 13Sommer, C.; Straehle, C.; Kothe, U.; Hamprecht, F. A. Ilastik: Interactive Learning and Segmentation Toolkit. Proc. - Int. Symp. Biomed. Imaging 2011, 2014, 230– 233, DOI: 10.1109/ISBI.2011.5872394
- 14Ronneberger, O.; Fischer, P.; Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015; MICCAI 2015. Lecture Notes in Computer Science, vol 9351; Navab, N., Hornegger, J., Wells, W., Frangi, A., Eds.; Springer: Switzerland, 2015; pp 234– 241 DOI: 10.1007/978-3-319-24574-4_28 .
- 15Falk, T.; Mai, D.; Bensch, R.; Cicek, O.; Abdulkadir, A.; Marrakchi, Y.; Bohm, A.; Deubner, J.; Jackel, Z.; Seiwald, K.; Dovzhenko, A.; Tietz, O.; Dal Bosco, C.; Walsh, S.; Saltukoglu, D.; Tay, T. L.; Prinz, M.; Palme, K.; Simons, M.; Diester, I.; Brox, T.; Ronneberger, O. U-Net: Deep Learning for Cell Counting, Detection, and Morphometry. Nat. Methods 2019, 16 (1), 67– 70, DOI: 10.1038/s41592-018-0261-2[Crossref], [PubMed], [CAS], Google Scholar15https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXisFCitL%252FF&md5=deaf6c538787729ef17d07cf1b97b528U-Net deep learning for cell counting, detection, and morphometryFalk, Thorsten; Mai, Dominic; Bensch, Robert; Cicek, Oezguen; Abdulkadir, Ahmed; Marrakchi, Yassine; Boehm, Anton; Deubner, Jan; Jaeckel, Zoe; Seiwald, Katharina; Dovzhenko, Alexander; Tietz, Olaf; Dal Bosco, Cristina; Walsh, Sean; Saltukoglu, Deniz; Tay, Tuan Leng; Prinz, Marco; Palme, Klaus; Simons, Matias; Diester, Ilka; Brox, Thomas; Ronneberger, OlafNature Methods (2019), 16 (1), 67-70CODEN: NMAEA3; ISSN:1548-7091. (Nature Research)U-Net is a generic deep-learning soln. for frequently occurring quantification tasks such as cell detection and shape measurements in biomedical image data. We present an ImageJ plugin that enables non-machine-learning experts to analyze their data with U-Net on either a local computer or a remote server/cloud service. The plugin comes with pretrained models for single-cell segmentation and allows for U-Net to be adapted to new tasks on the basis of a few annotated samples.
- 16Smistad, E.; Falch, T. L.; Bozorgi, M.; Elster, A. C.; Lindseth, F. Medical Image Segmentation on GPUs - A Comprehensive Review. Med. Image Anal. 2015, 20 (1), 1– 18, DOI: 10.1016/j.media.2014.10.012[Crossref], [PubMed], [CAS], Google Scholar16https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2Mzps1WjtQ%253D%253D&md5=30a4d5a665de03cd3f438ba821e655f0Medical image segmentation on GPUs--a comprehensive reviewSmistad Erik; Falch Thomas L; Bozorgi Mohammadmehdi; Elster Anne C; Lindseth FrankMedical image analysis (2015), 20 (1), 1-18 ISSN:.Segmentation of anatomical structures, from modalities like computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound, is a key enabling technology for medical applications such as diagnostics, planning and guidance. More efficient implementations are necessary, as most segmentation methods are computationally expensive, and the amount of medical imaging data is growing. The increased programmability of graphic processing units (GPUs) in recent years have enabled their use in several areas. GPUs can solve large data parallel problems at a higher speed than the traditional CPU, while being more affordable and energy efficient than distributed systems. Furthermore, using a GPU enables concurrent visualization and interactive segmentation, where the user can help the algorithm to achieve a satisfactory result. This review investigates the use of GPUs to accelerate medical image segmentation methods. A set of criteria for efficient use of GPUs are defined and each segmentation method is rated accordingly. In addition, references to relevant GPU implementations and insight into GPU optimization are provided and discussed. The review concludes that most segmentation methods may benefit from GPU processing due to the methods' data parallel structure and high thread count. However, factors such as synchronization, branch divergence and memory usage can limit the speedup.
- 17Lee, Y.; Wadsworth, L. C. Structure and Filtration Properties of Melt Blown Polypropylene Webs. Polym. Eng. Sci. 1990, 30 (22), 1413– 1419, DOI: 10.1002/pen.760302202[Crossref], [CAS], Google Scholar17https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK3MXot1Si&md5=e42aa5d9b932b98919df54dfc96ebea5Structure and filtration properties of melt-brown polypropylene websLee, Youngchul; Wadsworth, Larry C.Polymer Engineering and Science (1990), 30 (22), 1413-19CODEN: PYESAZ; ISSN:0032-3888.Polypropylene melt-blown webs were studied in terms of the relationships among the processing conditions, structure, and filtration efficiency. The effects of the processing conditions on filtration efficiency to aerosolized latex particles, pore size, fiber diam., and air permeability were investigated. The melt-blowing process conditions investigated were die and air temps., die-to-collector distance, and attenuation air flow rate at the die. The filtration efficiency increased linearly as mean pore size decreased. The degree of fiber entanglement increased, therefore, the pore size and air permeability decreased with increasing processing temp., increasing air flow rate at the die, or decreasing die-to-collector distance. Av. fiber diam. appeared to change little with die-to-collector distance, but decreased with increasing die temp. or with increasing air flow rate.
- 18De Rovere, A.; Shambaugh, R. L.; O’Rear, E. Investigation of Gravity-Spun, Melt-Spun, and Melt-Blown Polypropylene Fibers Using Atomic Force Microscopy. J. Appl. Polym. Sci. 2000, 77, 1921– 1937, DOI: 10.1002/1097-4628(20000829)77:9<1921::AID-APP8>3.0.CO;2-1[Crossref], [CAS], Google Scholar18https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3cXks1Oiuro%253D&md5=48a8ed581ceab19e41da806d18fac9c8Investigation of gravity-spun, melt-spun, and melt-blown polypropylene fibers using atomic force microscopyDe Rovere, Anne; Shambaugh, Robert L.; O'Rear, Edgar A.Journal of Applied Polymer Science (2000), 77 (9), 1921-1937CODEN: JAPNAB; ISSN:0021-8995. (John Wiley & Sons, Inc.)The morphol. exhibited in a polymer depends on the particular process and processing conditions used to shape and modify the polymer. This morphol. has an important influence on the final polymer product (sheet, molded part, etc.). Ten years ago, at. force microscopy (AFM) was applied for the first time on polymer materials. Since then, AFM has been used extensively on polypropylene (PP) surfaces, but still very little has been reported on the use of AFM for analyzing PP fibers. The purpose of our work was to show the modifications of (a) the morphol. and (b) the microstiffness of PP fiber surfaces processed under different operating conditions. Three fiber prodn. processes were used: gravity spinning, melt spinning, and melt blowing.
- 19Nayak, R.; Kyratzis, I. L.; Truong, Y. B.; Padhye, R.; Arnold, L.; Peeters, G.; O’Shea, M.; Nichols, L. Fabrication and Characterisation of Polypropylene Nanofibres by Meltblowing Process Using Different Fluids. J. Mater. Sci. 2013, 48 (1), 273– 281, DOI: 10.1007/s10853-012-6742-2[Crossref], [CAS], Google Scholar19https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhtFSqsb%252FO&md5=604c1c0f707669db59120c606e00abceFabrication and characterisation of polypropylene nanofibres by meltblowing process using different fluidsNayak, Rajkishore; Kyratzis, Ilias Louis; Truong, Yen Bach; Padhye, Rajiv; Arnold, Lyndon; Peeters, Gary; O'Shea, Mike; Nichols, LanceJournal of Materials Science (2013), 48 (1), 273-281CODEN: JMTSAS; ISSN:0022-2461. (Springer)In nonwoven industry, meltblowing was widely used as an important technique for the prodn. of nonwoven webs consisting of microfibres, suitable for various applications. Recently, great attention is being paid to fabricate nonwoven webs consisting of nanofibres, commonly known as nanowebs. In this paper, polypropylene was successfully used for the fabrication of nanowebs by meltblowing process with the injection of different fluids (such as air and water) at the vent port of com. meltblowing equipment. The lowest av. fiber diams. achieved were 755 and 438 nm by the use of air and water, resp. Differential scanning calorimetry results showed the presence of single melting peaks in the first heating cycle and double melting peaks in the second, due to the re-crystn. and re-organization by heating during the expts. The results obtained from thermo gravimetric anal. and intrinsic viscosity studies showed thermal degrdn. of the nanofibres during meltblowing. X-ray diffraction studies showed that all the meltblown polypropylene fibers produced with the injection of the fluids contained low degrees of crystallinity and monoclinic α-form crystals. The crystallinity was increased with annealing. Similar Fourier transform IR spectra of the polymer and the fibers indicated no change to the chem. functionality of the nanofibres by the application of the fluids and high temp. during meltblowing.
- 20Chang, J. S.; Lawless, P. A.; Yamamoto, T. Corona Discharge Processes. IEEE Trans. Plasma Sci. 1991, 19 (6), 1152– 1166, DOI: 10.1109/27.125038[Crossref], [CAS], Google Scholar20https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK38Xlt1ajt7o%253D&md5=7e9277aca31b7367c5f9ff1c7657182cCorona discharge processesChang, Jen Shih; Lawless, Phil A.; Yamamoto, ToshiakiIEEE Transactions on Plasma Science (1991), 19 (6), 1152-66CODEN: ITPSBD; ISSN:0093-3813.A review with 53 refs. of the elec. corona. Applications of corona discharge induced plasmas and unipolar ions. Current state-of-the-knowledge of ionized environments and the function of corona discharge processes are also discussed in detail.
- 21Overney, R. M.; Lüthi, R.; Haefke, H.; Frommer, J.; Meyer, E.; Güntherodt, H. J.; Hild, S.; Fuhrmann, J. An Atomic Force Microscopy Study of Corona-Treated Polypropylene Films. Appl. Surf. Sci. 1993, 64 (3), 197– 203, DOI: 10.1016/0169-4332(93)90025-7[Crossref], [CAS], Google Scholar21https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK3sXisFGnt78%253D&md5=cc75de86f57f664e82cd2fab5348058cAn atomic force microscopy study of corona-treated polypropylene filmsOverney, R. M.; Luethi, R.; Haefke, H.; Frommer, J.; Meyer, E.; Guentherodt, H. J.; Hild, S.; Fuhrmann, J.Applied Surface Science (1993), 64 (3), 197-203CODEN: ASUSEE; ISSN:0169-4332.The surfaces of corona-treated isotactic polypropylene films are investigated by at. force microscopy. The occurrence of droplets on the film surfaces is related to the energy dose of the corona discharge. The sizes of these droplets correlate with the corona dose. The loss of adhesive strength of self-adhered polypropylene films can be explained on the basis of morphol. changes during corona treatment. A comparative study of uniaxial and biaxial polypropylene films is presented.
- 22Kravtsov, A.; Brünig, H.; Zhandarov, S.; Beyreuther, R. Electret Effect in Polypropylene Fibers Treated in a Corona Discharge. Adv. Polym. Technol. 2000, 19 (4), 312– 316, DOI: 10.1002/1098-2329(200024)19:4<312::AID-ADV7>3.0.CO;2-X[Crossref], [CAS], Google Scholar22https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3cXnslKgsb4%253D&md5=7dec859941dc550477509c11828b56d7The electret effect in polypropylene fibers treated in a corona dischargeKravtsov, A.; Brunig, H.; Zhandarov, S.; Beyreuther, R.Advances in Polymer Technology (2000), 19 (4), 312-316CODEN: APTYD5; ISSN:0730-6679. (John Wiley & Sons, Inc.)Melt-spun polypropylene (PP) fibers were treated in an elec. field of a corona discharge, then characterized using thermally stimulated current (TSC) spectroscopy. The electret state of corona-treated PP fibers is a result of the combination of Maxwell-Wagner polarization and charge trapping. Activation energies and relaxation times for these processes were detd., and characteristics of trapping sites were calcd. The electret state induced in PP fibers by the corona discharge treatment is stable for several months. The effect of processing temp. and elec. field intensity on the characteristics of the electret state in melt-spun PP fibers were used allows to specify optimum technol. regimes for industrial prodn. of PP-based electret filter materials.
- 23Yovcheva, T. A.; Avramova, I. A.; Mekishev, G. A.; Marinova, T. S. Corona-Charged Polypropylene Electrets Analyzed by XPS. J. Electrost. 2007, 65 (10–11), 667– 671, DOI: 10.1016/j.elstat.2007.05.002[Crossref], [CAS], Google Scholar23https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXotVKksL0%253D&md5=879e62b118b7c46f1e681274c17f7538Corona-charged polypropylene electrets analyzed by XPSYovcheva, T. A.; Avramova, I. A.; Mekishev, G. A.; Marinova, T. S.Journal of Electrostatics (2007), 65 (10+11), 667-671CODEN: JOELDH; ISSN:0304-3886. (Elsevier B.V.)In the present study, we use XPS anal. to clarify how different polarities of corona initiate various changes in the surfaces of polypropylene (PP) electrets subject to corona discharge. The samples were charged in three-electrode corona discharge system using pos. and neg. corona polarities at both -20 and 75 °C temps. The tests were divided into four groups. The surface potentials of the electret samples were measured using the vibrating electrode method with compensation. XPS studies were carried out by means of a VG ESCALAB Mk II electronic spectrometer using an Al Kα excitation source (hν = 1486.6 eV). The spectra of C1s, O1s and N1s lines for all groups and for untreated samples were recorded and analyzed. The investigations that we carried out show that for neg.-corona-charged samples, the oxygen content is approx. 2.4 times higher than that in pos.-corona-charged samples. Based on the results we have obtained, we may assume that the changes in oxygen content in samples charged by different polarity coronas lead to the formation of different surface local levels. This assumption agrees well with the exptl. measurement made on the electrets.
- 24Ragoubi, M.; George, B.; Molina, S.; Bienaimé, D.; Merlin, A.; Hiver, J. M.; Dahoun, A. Effect of Corona Discharge Treatment on Mechanical and Thermal Properties of Composites Based on Miscanthus Fibres and Polylactic Acid or Polypropylene Matrix. Composites, Part A 2012, 43 (4), 675– 685, DOI: 10.1016/j.compositesa.2011.12.025[Crossref], [CAS], Google Scholar24https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XivVyqurY%253D&md5=7458853efaaf8e6be1963f1fa6160b1bEffect of corona discharge treatment on mechanical and thermal properties of composites based on miscanthus fibres and polylactic acid or polypropylene matrixRagoubi, M.; George, B.; Molina, S.; Bienaime, D.; Merlin, A.; Hiver, J.-M.; Dahoun, A.Composites, Part A: Applied Science and Manufacturing (2012), 43 (4), 675-685CODEN: CASMFJ; ISSN:1359-835X. (Elsevier Ltd.)In this study, we investigated the mech. and thermal properties of composites based on miscanthus fibers and poly lactic acid or polypropylene matrixes. The treatment of fibers by corona discharge which results in a surface oxidn. and an etching effect as shown by XPS and SEM, leads to an improvement of the interfacial compatibility between matrix and fillers. Hence the homogeneity of materials (checked by X-ray tomog. and fractog. evaluation) is better, the mech. properties measured by classical tensile tests are improved (Young moduli increase around 10-20%). Dynamic mech. anal. performed on samples leads to similar conclusions (higher modules and slight increase of glass transition temp. hence restricted mol. movement). The thermal stability of composites was investigated by thermogravimetric anal. While the incorporation of raw fibers leads to a slight decrease of decompn. temp., it is systematically increased as soon as fillers have been treated.
- 25Schneider, C. A.; Rasband, W. S.; Eliceiri, K. W. NIH Image to ImageJ: 25 Years of Image Analysis. Nat. Methods 2012, 9 (7), 671– 675, DOI: 10.1038/nmeth.2089[Crossref], [PubMed], [CAS], Google Scholar25https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhtVKntb7P&md5=85ab928cd79f1e2f2351c834c0c600f0NIH Image to ImageJ: 25 years of image analysisSchneider, Caroline A.; Rasband, Wayne S.; Eliceiri, Kevin W.Nature Methods (2012), 9 (7_part1), 671-675CODEN: NMAEA3; ISSN:1548-7091. (Nature Publishing Group)For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the anal. of scientific images. We discuss the origins, challenges and solns. of these two programs, and how their history can serve to advise and inform other software projects.
- 26Makovetsky, R.; Piche, N.; Marsh, M. Dragonfly as a Platform for Easy Image-Based Deep Learning Applications. Microsc. Microanal. 2018, 24 (S1), 532– 533, DOI: 10.1017/S143192761800315X
- 27Badran, A.; Marshall, D.; Legault, Z.; Makovetsky, R.; Provencher, B.; Piché, N.; Marsh, M. Automated Segmentation of Computed Tomography Images of Fiber-Reinforced Composites by Deep Learning. J. Mater. Sci. 2020, 55 (34), 16273– 16289, DOI: 10.1007/s10853-020-05148-7[Crossref], [CAS], Google Scholar27https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhslyntrnN&md5=47e44357e7fd40fe9a894fffef8dd187Automated segmentation of computed tomography images of fiber-reinforced composites by deep learningBadran, Aly; Marshall, David; Legault, Zacharie; Makovetsky, Ruslana; Provencher, Benjamin; Piche, Nicolas; Marsh, MikeJournal of Materials Science (2020), 55 (34), 16273-16289CODEN: JMTSAS; ISSN:0022-2461. (Springer)Abstr.: A deep learning procedure has been examd. for automatic segmentation of 3D tomog. images from fiber-reinforced ceramic composites consisting of fibers and matrix of the same material (SiC), and thus identical image intensities. The anal. uses a neural network to distinguish phases from shape and edge information rather than intensity differences. It was used successfully to segment phases in a unidirectional composite that also had a coating with similar image intensity. It was also used to segment matrix cracks generated during in situ tensile loading of the composite and thereby demonstrate the influence of nonuniform fiber distribution on the nature of matrix cracking. By avoiding the need for manual segmentation of thousands of image slices, the procedure overcomes a major impediment to the extn. of quant. information from such images. The anal. was performed using recently developed software that provides a general framework for executing both training and inference. Graphic abstr.: [graphic not available: see fulltext].
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Abstract
Figure 1
Figure 1. (a) Schematic images of wearing a respirator against SARS-CoV-2 and multilayer of N95 respirators, (b) an SEM image of N95 filter fabric layer, (c) a simplified illustration of the X-ray microscope (XRM), (d) an example of a 3D X-ray image of N95 filter fabric layer obtained by XRM, and (e) data process flow of 3D X-ray data sets analyzed by deep learning techniques with input/output image examples.
Figure 2
Figure 2. (a) Reconstructed XRM image of N95 filter fabric layer. (b,c) Deep-learning-assisted image segmention of X-ray slices captured from 3D XCT images; the yellow color indicates segmented polymer fibers and the black background is empty space. (d,e) 3D perspective views of the filter fabric layer; the gray image on the left is the reconstructed 3D X-ray tomographic image, and the yellow one on the right is a segmented 3D image from deep learning models. Each grid box is 100 μm × 100 μm in size. (f) Porosity of single N95 meltblown filter fabric as a function of depth into the layer. (g) Statistical distributions of fiber diameters of meltblown polypropylene fibers within filters of N95 respirators measured from SEM.
Figure 3
Figure 3. Images of NaCl particle-decorated meltblown filter fabric: (a) reconstructed X-ray tomographic volume, (b) deep-learning-assisted segmented particles only, (c) deep-learning-assisted segmented particles overlaid on reconstructed X-ray tomographic image, and (d) deep-learning-assisted segmented particles on deep-learning-assisted segmented fabric fibers; blue color in (b), (c), and (d) indicates NaCl particles. (e) Porosity of the filter fabric and (f) areal occupancies of fibers and NaCl particles vs depth into the meltblown layer. NaCl mass loading was 1.02 mg/cm2.
Figure 4
Figure 4. (a,b) High-resolution nano-XRM images of several fibers with NaCl particles adhered show more captured particles per unit length as fiber diameter decreases. (c) An SEM image of polypropylene meltblown fabric with adherent NaCl particles. (d) Filtration efficiency graph of the meltblown fabric as a function of the deposited mass density of NaCl particles. (e) Dependence of measured particle capture area per unit fiber area on fiber diameter.
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- 4Anfinrud, P.; Stadnytskyi, V.; Bax, C. E.; Bax, A. Visualizing Speech-Generated Oral Fluid Droplets with Laser Light Scattering. N. Engl. J. Med. 2020, 382 (21), 2061– 2062, DOI: 10.1056/NEJMc2007800[Crossref], [PubMed], [CAS], Google Scholar4https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB38zls1antg%253D%253D&md5=34b8dfa909b2e5e3fe2082b088dc4131Visualizing Speech-Generated Oral Fluid Droplets with Laser Light ScatteringAnfinrud Philip; Stadnytskyi Valentyn; Bax Adriaan; Bax Christina EThe New England journal of medicine (2020), 382 (21), 2061-2063 ISSN:.There is no expanded citation for this reference.
- 5Setti, L.; Passarini, F.; De Gennaro, G.; Barbieri, P.; Perrone, M. G.; Borelli, M.; Palmisani, J.; Di Gilio, A.; Piscitelli, P.; Miani, A. Airborne Transmission Route of Covid-19: Why 2 Meters/6 Feet of Inter-Personal Distance Could Not Be Enough. Int. J. Environ. Res. Public Health 2020, 17 (8), 2932, DOI: 10.3390/ijerph17082932[Crossref], [CAS], Google Scholar5https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhvVaqsL%252FL&md5=0d567a2e90708d5d07da072ed328cdfeAirborne transmission route of COVID-19: why 2 meters/6 feet of inter-personal distance could not be enoughSetti, Leonardo; Passarini, Fabrizio; De Gennaro, Gianluigi; Barbieri, Pierluigi; Perrone, Maria Grazia; Borelli, Massimo; Palmisani, Jolanda; Di Gilio, Alessia; Piscitelli, Prisco; Miani, AlessandroInternational Journal of Environmental Research and Public Health (2020), 17 (8), 2932CODEN: IJERGQ; ISSN:1660-4601. (MDPI AG)The COVID-19 pandemic caused the shutdown of entire nations all over the world. In addn. to mobility restrictions of people, the World Health Organization and the Governments have prescribed maintaining an inter-personal distance of 1.5 or 2 m (∼6 ft) from each other to minimize the risk of contagion through the droplets that we usually disseminate around us from nose and mouth. However, recently published studies support the hypothesis of virus transmission over a distance of 2 m from an infected person. Researchers have proved the higher aerosol and surface stability of SARS-COV-2 as compared with SARS-COV-1 (with the virus remaining viable and infectious in aerosol for hours) and that airborne transmission of SARS-CoV can occur besides close-distance contacts. Indeed, there is reasonable evidence about the possibility of SARS-COV-2 airborne transmission due to its persistence into aerosol droplets in a viable and infectious form. Based on the available knowledge and epidemiol. observations, it is plausible that small particles contg. the virus may diffuse in indoor environments covering distances ≤10 m from the emission sources, thus representing a kind of aerosol transmission. On-field studies carried out inside Wuhan Hospitals showed the presence of SARS-COV-2 RNA in air samples collected in the hospitals and also in the surroundings, leading to the conclusion that the airborne route has to be considered an important pathway for viral diffusion. Similar findings are reported in analyses concerning air samples collected at the Nebraska University Hospital. On March 16th, we have released a Position Paper emphasizing the airborne route as a possible addnl. factor for interpreting the anomalous COVID-19 outbreaks in northern Italy, ranked as 1 of the most polluted areas in Europe and characterized by high particulate matter (PM) concns. The available information on the SARS-COV-2 spreading supports the hypothesis of airborne diffusion of infected droplets from person to person at a distance >2 m (6 ft). The inter-personal distance of 2 m can be reasonably considered as an effective protection only if everybody wears face masks in daily life activities.
- 6Jones, N. R.; Qureshi, Z. U.; Temple, R. J.; Larwood, J. P. J.; Greenhalgh, T.; Bourouiba, L. Two Metres or One: What Is the Evidence for Physical Distancing in Covid-19?. BMJ. 2020, 370, m3223, DOI: 10.1136/bmj.m3223
- 7Liao, L.; Xiao, W.; Zhao, M.; Yu, X.; Wang, H.; Wang, Q.; Chu, S.; Cui, Y. Can. N95 Respirators Be Reused after Disinfection? How Many Times?. ACS Nano 2020, 14 (5), 6348– 6356, DOI: 10.1021/acsnano.0c03597[ACS Full Text], [CAS], Google Scholar7https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXosVCmt7o%253D&md5=5fc1b3a9b67826faa66784801f5cf1d8Can N95 Respirators Be Reused after Disinfection? How Many Times?Liao, Lei; Xiao, Wang; Zhao, Mervin; Yu, Xuanze; Wang, Haotian; Wang, Qiqi; Chu, Steven; Cui, YiACS Nano (2020), 14 (5), 6348-6356CODEN: ANCAC3; ISSN:1936-0851. (American Chemical Society)The coronavirus disease 2019 (COVID-19) pandemic has led to a major shortage of N95 respirators, which are essential for protecting healthcare professionals and the general public who may come into contact with the virus. Thus, it is essential to det. how we can reuse respirators and other personal protective equipment in these urgent times. We investigated multiple commonly used disinfection schemes on media with particle filtration efficiency of 95%. Heating was recently found to inactivate the virus in soln. within 5 min at 70°C and is among the most scalable, user-friendly methods for viral disinfection. We found that heat (≤85°C) under various humidities (≤100% relative humidity, RH) was the most promising, nondestructive method for the preservation of filtration properties in meltblown fabrics as well as N95-grade respirators. At 85°C, 30% RH, we were able to perform 50 cycles of heat treatment without significant changes in the filtration efficiency. At low humidity or dry conditions, temps. up to 100°C were not found to alter the filtration efficiency significantly within 20 cycles of treatment. UV irradn. was a secondary choice, which was able to withstand 10 cycles of treatment and showed small degrdn. by 20 cycles. However, UV can potentially impact the material strength and subsequent sealing of respirators. Finally, treatments involving liqs. and vapors require caution, as steam, alc., and household bleach all may lead to degrdn. of the filtration efficiency, leaving the user vulnerable to the viral aerosols.
- 8Zhao, M.; Liao, L.; Xiao, W.; Yu, X.; Wang, H.; Wang, Q.; Lin, Y. L.; Kilinc-Balci, F. S.; Price, A.; Chu, L.; Chu, M. C.; Chu, S.; Cui, Y. Household Materials Selection for Homemade Cloth Face Coverings and Their Filtration Efficiency Enhancement with Triboelectric Charging. Nano Lett. 2020, 20 (7), 5544– 5552, DOI: 10.1021/acs.nanolett.0c02211[ACS Full Text], [CAS], Google Scholar8https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhtVOmtLbJ&md5=27921343d002cc18bed5f34b01f0c29fHousehold Materials Selection for Homemade Cloth Face Coverings and Their Filtration Efficiency Enhancement with Triboelectric ChargingZhao, Mervin; Liao, Lei; Xiao, Wang; Yu, Xuanze; Wang, Haotian; Wang, Qiqi; Lin, Ying Ling; Kilinc-Balci, F. Selcen; Price, Amy; Chu, Larry; Chu, May C.; Chu, Steven; Cui, YiNano Letters (2020), 20 (7), 5544-5552CODEN: NALEFD; ISSN:1530-6984. (American Chemical Society)The COVID-19 pandemic is currently causing a severe disruption and shortage in the global supply chain of necessary personal protective equipment (e.g., N95 respirators). The U.S. CDC has recommended use of household cloth by the general public to make cloth face coverings as a method of source control. We evaluated the filtration properties of natural and synthetic materials using a modified procedure for N95 respirator approval. Common fabrics of cotton, polyester, nylon, and silk had filtration efficiency of 5-25%, polypropylene spunbond had filtration efficiency 6-10%, and paper-based products had filtration efficiency of 10-20%. An advantage of polypropylene spunbond is that it can be simply triboelec. charged to enhance the filtration efficiency (from 6 to >10%) without any increase in pressure (stable overnight and in humid environments). Using the filtration quality factor, fabric microstructure, and charging ability, we are able to provide an assessment of suggested fabric materials for homemade facial coverings.
- 9Lustig, S. R.; Biswakarma, J. J. H.; Rana, D.; Tilford, S. H.; Hu, W.; Su, M.; Rosenblatt, M. S. Effectiveness of Common Fabrics to Block Aqueous Aerosols of Virus-like Nanoparticles. ACS Nano 2020, 14 (6), 7651– 7658, DOI: 10.1021/acsnano.0c03972[ACS Full Text], [CAS], Google Scholar9https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXpvVGrtr8%253D&md5=1a365f37e89311b0793d98f9ff08c0daEffectiveness of Common Fabrics to Block Aqueous Aerosols of Virus-like NanoparticlesLustig, Steven R.; Biswakarma, John J. H.; Rana, Devyesh; Tilford, Susan H.; Hu, Weike; Su, Ming; Rosenblatt, Michael S.ACS Nano (2020), 14 (6), 7651-7658CODEN: ANCAC3; ISSN:1936-0851. (American Chemical Society)Layered systems of commonly available fabric materials can be used by the public and healthcare providers in face masks to reduce the risk of inhaling viruses with protection that is about equiv. to or better than the filtration and adsorption offered by 5-layer N95 respirators. Over 70 different common fabric combinations and masks were evaluated under steady-state, forced convection air flux with pulsed aerosols that simulate forceful respiration. The aerosols contain fluorescent virus-like nanoparticles to track transmission through materials that greatly assist the accuracy of detection, thus avoiding artifacts including pore flooding and the loss of aerosol due to evapn. and droplet breakup. Effective materials comprise both absorbent, hydrophilic layers and barrier, hydrophobic layers. Although the hydrophobic layers can adhere virus-like nanoparticles, they may also repel droplets from adjacent absorbent layers and prevent wicking transport across the fabric system. Effective designs are noted with absorbent layers comprising terry cloth towel, quilting cotton, and flannel. Effective designs are noted with barrier layers comprising nonwoven polypropylene, polyester, and polyaramid.
- 10Konda, A.; Prakash, A.; Moss, G. A.; Schmoldt, M.; Grant, G. D.; Guha, S. Aerosol Filtration Efficiency of Common Fabrics Used in Respiratory Cloth Masks. ACS Nano 2020, 14 (5), 6339– 6347, DOI: 10.1021/acsnano.0c03252[ACS Full Text], [CAS], Google Scholar10https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXnslChsr4%253D&md5=961b798525395cbb0bc53e325418e39dAerosol Filtration Efficiency of Common Fabrics Used in Respiratory Cloth MasksKonda, Abhiteja; Prakash, Abhinav; Moss, Gregory A.; Schmoldt, Michael; Grant, Gregory D.; Guha, SupratikACS Nano (2020), 14 (5), 6339-6347CODEN: ANCAC3; ISSN:1936-0851. (American Chemical Society)The emergence of a pandemic affecting the respiratory system can result in a significant demand for face masks. This includes the use of cloth masks by large sections of the public, as can be seen during the current global spread of COVID-19. However, there is limited knowledge available on the performance of various commonly available fabrics used in cloth masks. Importantly, there is a need to evaluate filtration efficiencies as a function of aerosol particulate sizes in the 10 nm to 10μm range, which is particularly relevant for respiratory virus transmission. We have carried out these studies for several common fabrics including cotton, silk, chiffon, flannel, various synthetics, and their combinations. Although the filtration efficiencies for various fabrics when a single layer was used ranged from 5 to 80% and 5 to 95% for particle sizes of <300 nm and >300 nm, resp., the efficiencies improved when multiple layers were used and when using a specific combination of different fabrics. Filtration efficiencies of the hybrids (such as cotton-silk, cotton-chiffon, cotton-flannel) was >80% (for particles <300 nm) and >90% (for particles >300 nm). We speculate that the enhanced performance of the hybrids is likely due to the combined effect of mech. and electrostatic-based filtration. Cotton, the most widely used material for cloth masks performs better at higher weave densities (i.e., thread count) and can make a significant difference in filtration efficiencies. Our studies also imply that gaps (as caused by an improper fit of the mask) can result in over a 60% decrease in the filtration efficiency, implying the need for future cloth mask design studies to take into account issues of "fit" and leakage, while allowing the exhaled air to vent efficiently. Overall, we find that combinations of various commonly available fabrics used in cloth masks can potentially provide significant protection against the transmission of aerosol particles.
- 11Shrestha, A.; Mahmood, A. Review of Deep Learning Algorithms and Architectures. IEEE Access 2019, 7, 53040– 53065, DOI: 10.1109/ACCESS.2019.2912200
- 12Lecun, Y.; Bengio, Y.; Hinton, G. Deep Learning. Nature 2015, 521 (7553), 436– 444, DOI: 10.1038/nature14539[Crossref], [PubMed], [CAS], Google Scholar12https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXht1WlurzP&md5=8bc52a4d89944b3bc753cad905aba9e1Deep learningLeCun, Yann; Bengio, Yoshua; Hinton, GeoffreyNature (London, United Kingdom) (2015), 521 (7553), 436-444CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
- 13Sommer, C.; Straehle, C.; Kothe, U.; Hamprecht, F. A. Ilastik: Interactive Learning and Segmentation Toolkit. Proc. - Int. Symp. Biomed. Imaging 2011, 2014, 230– 233, DOI: 10.1109/ISBI.2011.5872394
- 14Ronneberger, O.; Fischer, P.; Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015; MICCAI 2015. Lecture Notes in Computer Science, vol 9351; Navab, N., Hornegger, J., Wells, W., Frangi, A., Eds.; Springer: Switzerland, 2015; pp 234– 241 DOI: 10.1007/978-3-319-24574-4_28 .
- 15Falk, T.; Mai, D.; Bensch, R.; Cicek, O.; Abdulkadir, A.; Marrakchi, Y.; Bohm, A.; Deubner, J.; Jackel, Z.; Seiwald, K.; Dovzhenko, A.; Tietz, O.; Dal Bosco, C.; Walsh, S.; Saltukoglu, D.; Tay, T. L.; Prinz, M.; Palme, K.; Simons, M.; Diester, I.; Brox, T.; Ronneberger, O. U-Net: Deep Learning for Cell Counting, Detection, and Morphometry. Nat. Methods 2019, 16 (1), 67– 70, DOI: 10.1038/s41592-018-0261-2[Crossref], [PubMed], [CAS], Google Scholar15https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXisFCitL%252FF&md5=deaf6c538787729ef17d07cf1b97b528U-Net deep learning for cell counting, detection, and morphometryFalk, Thorsten; Mai, Dominic; Bensch, Robert; Cicek, Oezguen; Abdulkadir, Ahmed; Marrakchi, Yassine; Boehm, Anton; Deubner, Jan; Jaeckel, Zoe; Seiwald, Katharina; Dovzhenko, Alexander; Tietz, Olaf; Dal Bosco, Cristina; Walsh, Sean; Saltukoglu, Deniz; Tay, Tuan Leng; Prinz, Marco; Palme, Klaus; Simons, Matias; Diester, Ilka; Brox, Thomas; Ronneberger, OlafNature Methods (2019), 16 (1), 67-70CODEN: NMAEA3; ISSN:1548-7091. (Nature Research)U-Net is a generic deep-learning soln. for frequently occurring quantification tasks such as cell detection and shape measurements in biomedical image data. We present an ImageJ plugin that enables non-machine-learning experts to analyze their data with U-Net on either a local computer or a remote server/cloud service. The plugin comes with pretrained models for single-cell segmentation and allows for U-Net to be adapted to new tasks on the basis of a few annotated samples.
- 16Smistad, E.; Falch, T. L.; Bozorgi, M.; Elster, A. C.; Lindseth, F. Medical Image Segmentation on GPUs - A Comprehensive Review. Med. Image Anal. 2015, 20 (1), 1– 18, DOI: 10.1016/j.media.2014.10.012[Crossref], [PubMed], [CAS], Google Scholar16https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2Mzps1WjtQ%253D%253D&md5=30a4d5a665de03cd3f438ba821e655f0Medical image segmentation on GPUs--a comprehensive reviewSmistad Erik; Falch Thomas L; Bozorgi Mohammadmehdi; Elster Anne C; Lindseth FrankMedical image analysis (2015), 20 (1), 1-18 ISSN:.Segmentation of anatomical structures, from modalities like computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound, is a key enabling technology for medical applications such as diagnostics, planning and guidance. More efficient implementations are necessary, as most segmentation methods are computationally expensive, and the amount of medical imaging data is growing. The increased programmability of graphic processing units (GPUs) in recent years have enabled their use in several areas. GPUs can solve large data parallel problems at a higher speed than the traditional CPU, while being more affordable and energy efficient than distributed systems. Furthermore, using a GPU enables concurrent visualization and interactive segmentation, where the user can help the algorithm to achieve a satisfactory result. This review investigates the use of GPUs to accelerate medical image segmentation methods. A set of criteria for efficient use of GPUs are defined and each segmentation method is rated accordingly. In addition, references to relevant GPU implementations and insight into GPU optimization are provided and discussed. The review concludes that most segmentation methods may benefit from GPU processing due to the methods' data parallel structure and high thread count. However, factors such as synchronization, branch divergence and memory usage can limit the speedup.
- 17Lee, Y.; Wadsworth, L. C. Structure and Filtration Properties of Melt Blown Polypropylene Webs. Polym. Eng. Sci. 1990, 30 (22), 1413– 1419, DOI: 10.1002/pen.760302202[Crossref], [CAS], Google Scholar17https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK3MXot1Si&md5=e42aa5d9b932b98919df54dfc96ebea5Structure and filtration properties of melt-brown polypropylene websLee, Youngchul; Wadsworth, Larry C.Polymer Engineering and Science (1990), 30 (22), 1413-19CODEN: PYESAZ; ISSN:0032-3888.Polypropylene melt-blown webs were studied in terms of the relationships among the processing conditions, structure, and filtration efficiency. The effects of the processing conditions on filtration efficiency to aerosolized latex particles, pore size, fiber diam., and air permeability were investigated. The melt-blowing process conditions investigated were die and air temps., die-to-collector distance, and attenuation air flow rate at the die. The filtration efficiency increased linearly as mean pore size decreased. The degree of fiber entanglement increased, therefore, the pore size and air permeability decreased with increasing processing temp., increasing air flow rate at the die, or decreasing die-to-collector distance. Av. fiber diam. appeared to change little with die-to-collector distance, but decreased with increasing die temp. or with increasing air flow rate.
- 18De Rovere, A.; Shambaugh, R. L.; O’Rear, E. Investigation of Gravity-Spun, Melt-Spun, and Melt-Blown Polypropylene Fibers Using Atomic Force Microscopy. J. Appl. Polym. Sci. 2000, 77, 1921– 1937, DOI: 10.1002/1097-4628(20000829)77:9<1921::AID-APP8>3.0.CO;2-1[Crossref], [CAS], Google Scholar18https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3cXks1Oiuro%253D&md5=48a8ed581ceab19e41da806d18fac9c8Investigation of gravity-spun, melt-spun, and melt-blown polypropylene fibers using atomic force microscopyDe Rovere, Anne; Shambaugh, Robert L.; O'Rear, Edgar A.Journal of Applied Polymer Science (2000), 77 (9), 1921-1937CODEN: JAPNAB; ISSN:0021-8995. (John Wiley & Sons, Inc.)The morphol. exhibited in a polymer depends on the particular process and processing conditions used to shape and modify the polymer. This morphol. has an important influence on the final polymer product (sheet, molded part, etc.). Ten years ago, at. force microscopy (AFM) was applied for the first time on polymer materials. Since then, AFM has been used extensively on polypropylene (PP) surfaces, but still very little has been reported on the use of AFM for analyzing PP fibers. The purpose of our work was to show the modifications of (a) the morphol. and (b) the microstiffness of PP fiber surfaces processed under different operating conditions. Three fiber prodn. processes were used: gravity spinning, melt spinning, and melt blowing.
- 19Nayak, R.; Kyratzis, I. L.; Truong, Y. B.; Padhye, R.; Arnold, L.; Peeters, G.; O’Shea, M.; Nichols, L. Fabrication and Characterisation of Polypropylene Nanofibres by Meltblowing Process Using Different Fluids. J. Mater. Sci. 2013, 48 (1), 273– 281, DOI: 10.1007/s10853-012-6742-2[Crossref], [CAS], Google Scholar19https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhtFSqsb%252FO&md5=604c1c0f707669db59120c606e00abceFabrication and characterisation of polypropylene nanofibres by meltblowing process using different fluidsNayak, Rajkishore; Kyratzis, Ilias Louis; Truong, Yen Bach; Padhye, Rajiv; Arnold, Lyndon; Peeters, Gary; O'Shea, Mike; Nichols, LanceJournal of Materials Science (2013), 48 (1), 273-281CODEN: JMTSAS; ISSN:0022-2461. (Springer)In nonwoven industry, meltblowing was widely used as an important technique for the prodn. of nonwoven webs consisting of microfibres, suitable for various applications. Recently, great attention is being paid to fabricate nonwoven webs consisting of nanofibres, commonly known as nanowebs. In this paper, polypropylene was successfully used for the fabrication of nanowebs by meltblowing process with the injection of different fluids (such as air and water) at the vent port of com. meltblowing equipment. The lowest av. fiber diams. achieved were 755 and 438 nm by the use of air and water, resp. Differential scanning calorimetry results showed the presence of single melting peaks in the first heating cycle and double melting peaks in the second, due to the re-crystn. and re-organization by heating during the expts. The results obtained from thermo gravimetric anal. and intrinsic viscosity studies showed thermal degrdn. of the nanofibres during meltblowing. X-ray diffraction studies showed that all the meltblown polypropylene fibers produced with the injection of the fluids contained low degrees of crystallinity and monoclinic α-form crystals. The crystallinity was increased with annealing. Similar Fourier transform IR spectra of the polymer and the fibers indicated no change to the chem. functionality of the nanofibres by the application of the fluids and high temp. during meltblowing.
- 20Chang, J. S.; Lawless, P. A.; Yamamoto, T. Corona Discharge Processes. IEEE Trans. Plasma Sci. 1991, 19 (6), 1152– 1166, DOI: 10.1109/27.125038[Crossref], [CAS], Google Scholar20https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK38Xlt1ajt7o%253D&md5=7e9277aca31b7367c5f9ff1c7657182cCorona discharge processesChang, Jen Shih; Lawless, Phil A.; Yamamoto, ToshiakiIEEE Transactions on Plasma Science (1991), 19 (6), 1152-66CODEN: ITPSBD; ISSN:0093-3813.A review with 53 refs. of the elec. corona. Applications of corona discharge induced plasmas and unipolar ions. Current state-of-the-knowledge of ionized environments and the function of corona discharge processes are also discussed in detail.
- 21Overney, R. M.; Lüthi, R.; Haefke, H.; Frommer, J.; Meyer, E.; Güntherodt, H. J.; Hild, S.; Fuhrmann, J. An Atomic Force Microscopy Study of Corona-Treated Polypropylene Films. Appl. Surf. Sci. 1993, 64 (3), 197– 203, DOI: 10.1016/0169-4332(93)90025-7[Crossref], [CAS], Google Scholar21https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK3sXisFGnt78%253D&md5=cc75de86f57f664e82cd2fab5348058cAn atomic force microscopy study of corona-treated polypropylene filmsOverney, R. M.; Luethi, R.; Haefke, H.; Frommer, J.; Meyer, E.; Guentherodt, H. J.; Hild, S.; Fuhrmann, J.Applied Surface Science (1993), 64 (3), 197-203CODEN: ASUSEE; ISSN:0169-4332.The surfaces of corona-treated isotactic polypropylene films are investigated by at. force microscopy. The occurrence of droplets on the film surfaces is related to the energy dose of the corona discharge. The sizes of these droplets correlate with the corona dose. The loss of adhesive strength of self-adhered polypropylene films can be explained on the basis of morphol. changes during corona treatment. A comparative study of uniaxial and biaxial polypropylene films is presented.
- 22Kravtsov, A.; Brünig, H.; Zhandarov, S.; Beyreuther, R. Electret Effect in Polypropylene Fibers Treated in a Corona Discharge. Adv. Polym. Technol. 2000, 19 (4), 312– 316, DOI: 10.1002/1098-2329(200024)19:4<312::AID-ADV7>3.0.CO;2-X[Crossref], [CAS], Google Scholar22https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3cXnslKgsb4%253D&md5=7dec859941dc550477509c11828b56d7The electret effect in polypropylene fibers treated in a corona dischargeKravtsov, A.; Brunig, H.; Zhandarov, S.; Beyreuther, R.Advances in Polymer Technology (2000), 19 (4), 312-316CODEN: APTYD5; ISSN:0730-6679. (John Wiley & Sons, Inc.)Melt-spun polypropylene (PP) fibers were treated in an elec. field of a corona discharge, then characterized using thermally stimulated current (TSC) spectroscopy. The electret state of corona-treated PP fibers is a result of the combination of Maxwell-Wagner polarization and charge trapping. Activation energies and relaxation times for these processes were detd., and characteristics of trapping sites were calcd. The electret state induced in PP fibers by the corona discharge treatment is stable for several months. The effect of processing temp. and elec. field intensity on the characteristics of the electret state in melt-spun PP fibers were used allows to specify optimum technol. regimes for industrial prodn. of PP-based electret filter materials.
- 23Yovcheva, T. A.; Avramova, I. A.; Mekishev, G. A.; Marinova, T. S. Corona-Charged Polypropylene Electrets Analyzed by XPS. J. Electrost. 2007, 65 (10–11), 667– 671, DOI: 10.1016/j.elstat.2007.05.002[Crossref], [CAS], Google Scholar23https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXotVKksL0%253D&md5=879e62b118b7c46f1e681274c17f7538Corona-charged polypropylene electrets analyzed by XPSYovcheva, T. A.; Avramova, I. A.; Mekishev, G. A.; Marinova, T. S.Journal of Electrostatics (2007), 65 (10+11), 667-671CODEN: JOELDH; ISSN:0304-3886. (Elsevier B.V.)In the present study, we use XPS anal. to clarify how different polarities of corona initiate various changes in the surfaces of polypropylene (PP) electrets subject to corona discharge. The samples were charged in three-electrode corona discharge system using pos. and neg. corona polarities at both -20 and 75 °C temps. The tests were divided into four groups. The surface potentials of the electret samples were measured using the vibrating electrode method with compensation. XPS studies were carried out by means of a VG ESCALAB Mk II electronic spectrometer using an Al Kα excitation source (hν = 1486.6 eV). The spectra of C1s, O1s and N1s lines for all groups and for untreated samples were recorded and analyzed. The investigations that we carried out show that for neg.-corona-charged samples, the oxygen content is approx. 2.4 times higher than that in pos.-corona-charged samples. Based on the results we have obtained, we may assume that the changes in oxygen content in samples charged by different polarity coronas lead to the formation of different surface local levels. This assumption agrees well with the exptl. measurement made on the electrets.
- 24Ragoubi, M.; George, B.; Molina, S.; Bienaimé, D.; Merlin, A.; Hiver, J. M.; Dahoun, A. Effect of Corona Discharge Treatment on Mechanical and Thermal Properties of Composites Based on Miscanthus Fibres and Polylactic Acid or Polypropylene Matrix. Composites, Part A 2012, 43 (4), 675– 685, DOI: 10.1016/j.compositesa.2011.12.025[Crossref], [CAS], Google Scholar24https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XivVyqurY%253D&md5=7458853efaaf8e6be1963f1fa6160b1bEffect of corona discharge treatment on mechanical and thermal properties of composites based on miscanthus fibres and polylactic acid or polypropylene matrixRagoubi, M.; George, B.; Molina, S.; Bienaime, D.; Merlin, A.; Hiver, J.-M.; Dahoun, A.Composites, Part A: Applied Science and Manufacturing (2012), 43 (4), 675-685CODEN: CASMFJ; ISSN:1359-835X. (Elsevier Ltd.)In this study, we investigated the mech. and thermal properties of composites based on miscanthus fibers and poly lactic acid or polypropylene matrixes. The treatment of fibers by corona discharge which results in a surface oxidn. and an etching effect as shown by XPS and SEM, leads to an improvement of the interfacial compatibility between matrix and fillers. Hence the homogeneity of materials (checked by X-ray tomog. and fractog. evaluation) is better, the mech. properties measured by classical tensile tests are improved (Young moduli increase around 10-20%). Dynamic mech. anal. performed on samples leads to similar conclusions (higher modules and slight increase of glass transition temp. hence restricted mol. movement). The thermal stability of composites was investigated by thermogravimetric anal. While the incorporation of raw fibers leads to a slight decrease of decompn. temp., it is systematically increased as soon as fillers have been treated.
- 25Schneider, C. A.; Rasband, W. S.; Eliceiri, K. W. NIH Image to ImageJ: 25 Years of Image Analysis. Nat. Methods 2012, 9 (7), 671– 675, DOI: 10.1038/nmeth.2089[Crossref], [PubMed], [CAS], Google Scholar25https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhtVKntb7P&md5=85ab928cd79f1e2f2351c834c0c600f0NIH Image to ImageJ: 25 years of image analysisSchneider, Caroline A.; Rasband, Wayne S.; Eliceiri, Kevin W.Nature Methods (2012), 9 (7_part1), 671-675CODEN: NMAEA3; ISSN:1548-7091. (Nature Publishing Group)For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the anal. of scientific images. We discuss the origins, challenges and solns. of these two programs, and how their history can serve to advise and inform other software projects.
- 26Makovetsky, R.; Piche, N.; Marsh, M. Dragonfly as a Platform for Easy Image-Based Deep Learning Applications. Microsc. Microanal. 2018, 24 (S1), 532– 533, DOI: 10.1017/S143192761800315X
- 27Badran, A.; Marshall, D.; Legault, Z.; Makovetsky, R.; Provencher, B.; Piché, N.; Marsh, M. Automated Segmentation of Computed Tomography Images of Fiber-Reinforced Composites by Deep Learning. J. Mater. Sci. 2020, 55 (34), 16273– 16289, DOI: 10.1007/s10853-020-05148-7[Crossref], [CAS], Google Scholar27https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhslyntrnN&md5=47e44357e7fd40fe9a894fffef8dd187Automated segmentation of computed tomography images of fiber-reinforced composites by deep learningBadran, Aly; Marshall, David; Legault, Zacharie; Makovetsky, Ruslana; Provencher, Benjamin; Piche, Nicolas; Marsh, MikeJournal of Materials Science (2020), 55 (34), 16273-16289CODEN: JMTSAS; ISSN:0022-2461. (Springer)Abstr.: A deep learning procedure has been examd. for automatic segmentation of 3D tomog. images from fiber-reinforced ceramic composites consisting of fibers and matrix of the same material (SiC), and thus identical image intensities. The anal. uses a neural network to distinguish phases from shape and edge information rather than intensity differences. It was used successfully to segment phases in a unidirectional composite that also had a coating with similar image intensity. It was also used to segment matrix cracks generated during in situ tensile loading of the composite and thereby demonstrate the influence of nonuniform fiber distribution on the nature of matrix cracking. By avoiding the need for manual segmentation of thousands of image slices, the procedure overcomes a major impediment to the extn. of quant. information from such images. The anal. was performed using recently developed software that provides a general framework for executing both training and inference. Graphic abstr.: [graphic not available: see fulltext].
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