Meta-atoms and artificially engineered materials for antenna applications

2018 ◽  
pp. 351-405 ◽  
Author(s):  
Ravi Kumar Arya ◽  
Shiyu Zhang ◽  
Shaileshachandra Pandey ◽  
Ashwani Kumar ◽  
Yiannis Vardaxoglou ◽  
...  
Keyword(s):  
Engineered Materials

Nanoscale Devices and Novel Engineered Materials

2001 ◽  
Author(s):  
S. J. Pearton ◽  
P. H. Holloway ◽  
R. K. Singh ◽  
A. F. Hebard ◽  
S. Hershfield
Keyword(s):  
Nanoscale Devices ◽  
Engineered Materials

Label-Free Characterization of Collagen Crosslinking in Bone-Engineered Materials Using Nonlinear Optical Microscopy

2021 ◽  
pp. 1-11
Author(s):  
Chao-Wei Hung ◽  
Nirmal Mazumder ◽  
Dan-Jae Lin ◽  
Wei-Liang Chen ◽  
Shih-Ting Lin ◽  
...  
Keyword(s):  
Optical Microscopy ◽  
Nonlinear Optical ◽  
Label Free ◽  
Nonlinear Optical Microscopy ◽  
Collagen Crosslinking ◽  
Engineered Materials

Abstract

scholarly journals Modern Engineered Materials and Technologies for Metal Forming Applications

2017 ◽  
Vol 2017 ◽  
pp. 1-2
Author(s):  
Sutasn Thipprakmas ◽  
Man S. Joun ◽  
Lars-Erik Lindgren
Keyword(s):  
Metal Forming ◽  
Engineered Materials

scholarly journals Current Trends in Integration of Nondestructive Testing Methods for Engineered Materials Testing

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6175
Author(s):  
Ramesh Kumpati ◽  
Wojciech Skarka ◽  
Sunith Kumar Ontipuli
Keyword(s):  
Nondestructive Testing ◽  
Data Acquisition ◽  
Mechanical Testing ◽  
Impact Testing ◽  
Materials Testing ◽  
Testing Methods ◽  
Molecular Arrangement ◽  
Nondestructive Techniques ◽  
Nondestructive Testing Methods ◽  
Engineered Materials

Material failure may occur in a variety of situations dependent on stress conditions, temperature, and internal or external load conditions. Many of the latest engineered materials combine several material types i.e., metals, carbon, glass, resins, adhesives, heterogeneous and nanomaterials (organic/inorganic) to produce multilayered, multifaceted structures that may fail in ductile, brittle, or both cases. Mechanical testing is a standard and basic component of any design and fabricating process. Mechanical testing also plays a vital role in maintaining cost-effectiveness in innovative advancement and predominance. Destructive tests include tensile testing, chemical analysis, hardness testing, fatigue testing, creep testing, shear testing, impact testing, stress rapture testing, fastener testing, residual stress measurement, and XRD. These tests can damage the molecular arrangement and even the microstructure of engineered materials. Nondestructive testing methods evaluate component/material/object quality without damaging the sample integrity. This review outlines advanced nondestructive techniques and explains predominantly used nondestructive techniques with respect to their applications, limitations, and advantages. The literature was further analyzed regarding experimental developments, data acquisition systems, and technologically upgraded accessory components. Additionally, the various combinations of methods applied for several types of material defects are reported. The ultimate goal of this review paper is to explain advanced nondestructive testing (NDT) techniques/tests, which are comprised of notable research work reporting evolved affordable systems with fast, precise, and repeatable systems with high accuracy for both experimental and data acquisition techniques. Furthermore, these advanced NDT approaches were assessed for their potential implementation at the industrial level for faster, more accurate, and secure operations.

scholarly journals Establishing exhaustive metasurface robustness against fabrication uncertainties through deep learning

Nanophotonics ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ronald P. Jenkins ◽  
Sawyer D. Campbell ◽  
Douglas H. Werner
Keyword(s):  
Deep Learning ◽  
Design Process ◽  
Functional Materials ◽  
Computational Electromagnetics ◽  
Inverse Design ◽  
Design Tools ◽  
Structural Error ◽  
Significant Performance ◽  
Engineered Materials ◽  
Exhaustive Study

Abstract Photonic engineered materials have benefitted in recent years from exciting developments in computational electromagnetics and inverse-design tools. However, a commonly encountered issue is that highly performant and structurally complex functional materials found through inverse-design can lose significant performance upon being fabricated. This work introduces a method using deep learning (DL) to exhaustively analyze how structural issues affect the robustness of metasurface supercells, and we show how systems can be designed to guarantee significantly better performance. Moreover, we show that an exhaustive study of structural error is required to make strong guarantees about the performance of engineered materials. The introduction of DL into the inverse-design process makes this problem tractable, enabling optimization runtimes to be measurable in days rather than months and allowing designers to establish exhaustive metasurface robustness guarantees.

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