scholarly journals A neural network-based algorithm for high-throughput characterisation of viscoelastic properties of flowing microcapsules

Soft Matter ◽  
2021 ◽  
Author(s):  
Tao Lin ◽  
Zhen Wang ◽  
Wen Wang ◽  
Yi Sui
Keyword(s):  
Neural Network ◽  
High Throughput ◽  
Viscoelastic Properties ◽  
Dynamic Deformation ◽  
Membrane Elasticity ◽  
Hybrid Neural Network ◽  
High Throughput Method

We have developed a high-throughput method, by combining a hybrid neural network with a mechanistic capsule model, to predict membrane elasticity and viscosity of microcapsules from their dynamic deformation in a branched microchannel.

scholarly journals Correction: A neural network-based algorithm for high-throughput characterisation of viscoelastic properties of flowing microcapsules

Soft Matter ◽  
2021 ◽  
Author(s):  
Tao Lin ◽  
Zhen Wang ◽  
Wen Wang ◽  
Yi Sui
Keyword(s):  
Neural Network ◽  
High Throughput ◽  
Viscoelastic Properties ◽  
Soft Matter

Correction for ‘A neural network-based algorithm for high-throughput characterisation of viscoelastic properties of flowing microcapsules’ by Tao Lin et al., Soft Matter, 2021, DOI: 10.1039/d0sm02121k.

A Fluorescent High Throughput Method To Identify Potential Skin Sensitizers

Planta Medica ◽  
2016 ◽  
Vol 82 (05) ◽  
Author(s):  
C Avonto ◽  
AG Chittiboyina ◽  
D Rua ◽  
IA Khan
Keyword(s):  
High Throughput ◽  
High Throughput Method

Convolutional Neural Network of Atomic Surface Structures to Predict Binding Energies for High-Throughput Screening of Catalysts

2019 ◽  
Author(s):  
Seoin Back ◽  
Junwoong Yoon ◽  
Nianhan Tian ◽  
Wen Zhong ◽  
Kevin Tran ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
High Throughput Screening ◽  
Binding Energies ◽  
Surface Structures ◽  
Voronoi Polyhedra ◽  
Atomic Surface

We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis.

A hybrid neural network goal attain optimization for failed sensor(s) radiation pattern in linear array

2019 ◽  
Vol 4 (4) ◽  
pp. 101-109
Author(s):  
Navaamsini Boopalan ◽  
Agileswari K. Ramasamy ◽  
Farrukh Hafiz Nagi
Keyword(s):  
Neural Network ◽  
Radiation Pattern ◽  
Array Signal Processing ◽  
Linear Array ◽  
Signal To Noise Ratio ◽  
Beam Pattern ◽  
Sensor Failure ◽  
Signal To Noise ◽  
Hybrid Neural Network ◽  
Noise Ratio

Array sensors are widely used in various fields such as radar, wireless communications, autonomous vehicle applications, medical imaging, and astronomical observations fault diagnosis. Array signal processing is accomplished with a beam pattern which is produced by the signal's amplitude and phase at each element of array. The beam pattern can get rigorously distorted in case of failure of array element and effect its Signal to Noise Ratio (SNR) badly. This paper proposes on a Hybrid Neural Network layer weight Goal Attain Optimization (HNNGAO) method to generate a recovery beam pattern which closely resembles the original beam pattern with remaining elements in the array. The proposed HNNGAO method is compared with classic synthesize beam pattern goal attain method and failed beam pattern generated in MATLAB environment. The results obtained proves that the proposed HNNGAO method gives better SNR ratio with remaining working element in linear array compared to classic goal attain method alone. Keywords: Backpropagation; Feed-forward neural network; Goal attain; Neural networks; Radiation pattern; Sensor arrays; Sensor failure; Signal-to-Noise Ratio (SNR)

scholarly journals High Throughput Centrifugal Electrospinning of Polyacrylonitrile Nanofibers for Carbon Fiber Nonwovens

Polymers ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1313
Author(s):  
Andreas Hoffmann ◽  
Alexander J. C. Kuehne
Keyword(s):  
High Throughput ◽  
Carbon Nanofiber ◽  
Fiber Diameter ◽  
Carbon Precursor ◽  
Production Methods ◽  
High Throughput Method ◽  
Radial Forces ◽  
Fiber Deposition ◽  
The Impact ◽  
Pan Fibers

Carbon nanofiber nonwovens are promising materials for electrode or filtration applications; however, their utilization is obviated by a lack of high throughput production methods. In this study, we utilize a highly effective high-throughput method for the fabrication of polyacrylonitrile (PAN) nanofibers as a nonwoven on a dedicated substrate. The method employs rotational-, air pressure- and electrostatic forces to produce fibers from the inner edge of a rotating bell towards a flat collector. We investigate the impact of all above-mentioned forces on the fiber diameter, morphology, and bundling of the carbon-precursor PAN fibers. The interplay of radial forces with collector-facing forces has an influence on the uniformity of fiber deposition. Finally, the obtained PAN nanofibers are converted to carbon nonwovens by thermal treatment.

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