scholarly journals Label-Free SARS-CoV-2 Detection on Flexible Substrates

2021 ◽  
Author(s):  
Debadrita Paria ◽  
Kam Sang Kwok ◽  
Piyush Raj ◽  
Peng Zheng ◽  
David Gracias ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nanoimprint Lithography ◽  
Detection Accuracy ◽  
Flexible Substrates ◽  
Test Results ◽  
Label Free ◽  
Transfer Printing ◽  
Large Area ◽  
Sensor Platforms ◽  
Metal Insulator Metal

One of the most important strategies for mitigation and managing pandemics is widespread, rapid and inexpensive testing and isolation of infected patients. In this study, we demonstrate large area, label-free, and rapid testing sensor platforms fabricated on both rigid and flexible substrates for fast and accurate detection of SARS-CoV-2. SERS enhancing metal insulator metal (MIM) nanostructures are modeled using finite element simulations and then fabricated using nanoimprint lithography (NIL) and transfer printing. The SERS signal of various viral samples, including spiked saliva, was analyzed using machine learning classifiers. We observe that our approach can obtain the test results typically within 25 minutes with a detection accuracy of at least 83% for the viral samples. We envision that this approach which features large area nanopatterning, fabrication in both rigid and flexible formats for wearables, SERS spectroscopy and machine learning can enable new types of rapid, label-free biosensors for screening pathogens and managing current and future pandemics.

scholarly journals Thermally assisted nanotransfer printing with sub–20-nm resolution and 8-inch wafer scalability

2020 ◽  
Vol 6 (31) ◽  
pp. eabb6462
Author(s):  
Tae Wan Park ◽  
Myunghwan Byun ◽  
Hyunsung Jung ◽  
Gyu Rac Lee ◽  
Jae Hong Park ◽  
...  
Keyword(s):  
Self Assembly ◽  
Transfer Printing ◽  
Combined Method ◽  
Large Area ◽  
Metal Insulator ◽  
Metal Insulator Metal ◽  
Good Pattern ◽  
Ordered Nanostructures ◽  
20 Nm ◽  
Pattern Resolution

Nanotransfer printing (nTP) has attracted considerable attention due to its good pattern resolution, process simplicity, and cost-effectiveness. However, the development of a large-area nTP process has been hampered by critical reliability issues related to the uniform replication and regular transfer printing of functional nanomaterials. Here, we present a very practical thermally assisted nanotransfer printing (T-nTP) process that can easily produce well-ordered nanostructures on an 8-inch wafer via the use of a heat-rolling press system that provides both uniform pressure and heat. We also demonstrate various complex pattern geometries, such as wave, square, nut, zigzag, and elliptical nanostructures, on diverse substrates via T-nTP. Furthermore, we demonstrate how to obtain a high-density crossbar metal-insulator-metal memristive array using a combined method of T-nTP and directed self-assembly. We expect that the state-of-the-art T-nTP process presented here combined with other emerging patterning techniques will be especially useful for the large-area nanofabrication of various devices.

Rolling-based direct-transfer printing: A process for large-area transfer of micro- and nanostructures onto flexible substrates

2016 ◽  
Vol 120 (9) ◽  
pp. 093103 ◽  
Author(s):  
D. S. Grierson ◽  
F. S. Flack ◽  
M. G. Lagally ◽  
K. T. Turner
Keyword(s):  
Flexible Substrates ◽  
Direct Transfer ◽  
Transfer Printing ◽  
Large Area

Hologram Images Patterned in Shrink BOPP Film by Large-Area Roller Nanoimprint Lithography

2013 ◽  
Vol 873 ◽  
pp. 503-506 ◽  
Author(s):  
Meng Lin Jiang ◽  
Shi Wei Lin ◽  
Wen Kai Jiang
Keyword(s):  
Formation Mechanism ◽  
High Throughput ◽  
Nanoimprint Lithography ◽  
Low Cost ◽  
Nanometer Scale ◽  
Flexible Substrates ◽  
Large Area ◽  
Biaxially Oriented Polypropylene ◽  
Oriented Polypropylene ◽  
Lithography Technique

Thermal roller nanoimprint lithography with the ability of larger area micro-to nanometer-scale patterning on flexible substrates possesses the advantages of low cost and high throughput, and is widely being practiced in industry. Hologram images have been successfully embossed in shrink biaxially oriented polypropylene films by the large-area roller nanoimprint lithography technique. The defects which occur during embossing processes have been studied in order to identify the underlying formation mechanism.

scholarly journals Ferroelectric polymer nanopillar arrays on flexible substrates by reverse nanoimprint lithography

2016 ◽  
Vol 4 (25) ◽  
pp. 5914-5921 ◽  
Author(s):  
Jingfeng Song ◽  
Haidong Lu ◽  
Keith Foreman ◽  
Shumin Li ◽  
Li Tan ◽  
...  
Keyword(s):  
Nanoimprint Lithography ◽  
Low Pressure ◽  
Flexible Substrates ◽  
Large Area ◽  
Ferroelectric Polymer ◽  
Soft Mold ◽  
Nanopillar Arrays

Large area ferroelectric polymer nanopillar arrays were prepared directly on flexible substrates using soft-mold reverse nanoimprint lithography at low pressure.

scholarly journals High Performance Printed Electronics on Large Area Flexible Substrates

2020 ◽  
Author(s):  
Mahesh Soni ◽  
Dhayalan Shakthivel ◽  
Adamos Christou ◽  
Ayoub Zumeit ◽  
Nivasan Yogeswaran ◽  
...  
Keyword(s):  
High Performance ◽  
Printed Electronics ◽  
Flexible Substrates ◽  
Large Area

scholarly journals OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4805
Author(s):  
Saad Abbasi ◽  
Mahmoud Famouri ◽  
Mohammad Javad Shafiee ◽  
Alexander Wong
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Network Architectures ◽  
Design Exploration ◽  
Convolutional Autoencoder ◽  
Acoustic Anomaly ◽  
Human Operators ◽  
Computational Resources

Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model’s latency show that the OutlierNet architectures can achieve as much as 30x lower latency than published networks.

scholarly journals Mastering of NIL Stamps with Undercut T-Shaped Features from Single Layer to Multilayer Stamps

Nanomaterials ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 956
Author(s):  
Philipp Taus ◽  
Adrian Prinz ◽  
Heinz D. Wanzenboeck ◽  
Patrick Schuller ◽  
Anton Tsenov ◽  
...  
Keyword(s):  
Electron Beam Lithography ◽  
Nanoimprint Lithography ◽  
Silicon Oxide ◽  
Three Dimensional ◽  
Single Layer ◽  
Fabrication Technology ◽  
Large Area ◽  
Structural Colors ◽  
Biomimetic Structures ◽  
Morpho Butterfly

Biomimetic structures such as structural colors demand a fabrication technology of complex three-dimensional nanostructures on large areas. Nanoimprint lithography (NIL) is capable of large area replication of three-dimensional structures, but the master stamp fabrication is often a bottleneck. We have demonstrated different approaches allowing for the generation of sophisticated undercut T-shaped masters for NIL replication. With a layer-stack of phase transition material (PTM) on poly-Si, we have demonstrated the successful fabrication of a single layer undercut T-shaped structure. With a multilayer-stack of silicon oxide on silicon, we have shown the successful fabrication of a multilayer undercut T-shaped structures. For patterning optical lithography, electron beam lithography and nanoimprint lithography have been compared and have yielded structures from 10 µm down to 300 nm. The multilayer undercut T-shaped structures closely resemble the geometry of the surface of a Morpho butterfly, and may be used in future to replicate structural colors on artificial surfaces.

scholarly journals Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4736
Author(s):  
Sk. Tanzir Mehedi ◽  
Adnan Anwar ◽  
Ziaur Rahman ◽  
Kawsar Ahmed
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Real Time ◽  
Transfer Learning ◽  
Security Requirements ◽  
Detection Accuracy ◽  
Area Network ◽  
Complex Data ◽  
Network Intrusion

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

scholarly journals Utilising Flow Aggregation to Classify Benign Imitating Attacks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1761
Author(s):  
Hanan Hindy ◽  
Robert Atkinson ◽  
Christos Tachtatzis ◽  
Ethan Bayne ◽  
Miroslav Bures ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Network Traffic ◽  
Cyber Attacks ◽  
Detection Accuracy ◽  
Computational Power ◽  
Human Understanding ◽  
Flow Aggregation ◽  
Additional Level ◽  
Attack Surfaces

Cyber-attacks continue to grow, both in terms of volume and sophistication. This is aided by an increase in available computational power, expanding attack surfaces, and advancements in the human understanding of how to make attacks undetectable. Unsurprisingly, machine learning is utilised to defend against these attacks. In many applications, the choice of features is more important than the choice of model. A range of studies have, with varying degrees of success, attempted to discriminate between benign traffic and well-known cyber-attacks. The features used in these studies are broadly similar and have demonstrated their effectiveness in situations where cyber-attacks do not imitate benign behaviour. To overcome this barrier, in this manuscript, we introduce new features based on a higher level of abstraction of network traffic. Specifically, we perform flow aggregation by grouping flows with similarities. This additional level of feature abstraction benefits from cumulative information, thus qualifying the models to classify cyber-attacks that mimic benign traffic. The performance of the new features is evaluated using the benchmark CICIDS2017 dataset, and the results demonstrate their validity and effectiveness. This novel proposal will improve the detection accuracy of cyber-attacks and also build towards a new direction of feature extraction for complex ones.

scholarly journals mSphere of Influence: the Rise of Artificial Intelligence in Infection Biology

mSphere ◽  
2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Artur Yakimovich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Label Free ◽  
Edge Label ◽  
Bacterial Colony ◽  
Link Type ◽  
Colony Counting ◽  
Anthrax Spores ◽  
Infection Biology

ABSTRACT Artur Yakimovich works in the field of computational virology and applies machine learning algorithms to study host-pathogen interactions. In this mSphere of Influence article, he reflects on two papers “Holographic Deep Learning for Rapid Optical Screening of Anthrax Spores” by Jo et al. (Y. Jo, S. Park, J. Jung, J. Yoon, et al., Sci Adv 3:e1700606, 2017, https://doi.org/10.1126/sciadv.1700606) and “Bacterial Colony Counting with Convolutional Neural Networks in Digital Microbiology Imaging” by Ferrari and colleagues (A. Ferrari, S. Lombardi, and A. Signoroni, Pattern Recognition 61:629–640, 2017, https://doi.org/10.1016/j.patcog.2016.07.016). Here he discusses how these papers made an impact on him by showcasing that artificial intelligence algorithms can be equally applicable to both classical infection biology techniques and cutting-edge label-free imaging of pathogens.

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