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Full-Text Articles in Engineering
Strategies For Controlling The Spatial Orientation Of Single Molecules Tethered On Dna Origami Templates Physisorbed On Glass Substrates: Intercalation And Stretching, Keitel Cervantes-Salguero, Austin Biaggne, John M. Youngsman, Brett M. Ward, Young C. Kim, Lan Li, John A. Hall, William B. Knowlton, Elton Graugnard, Wan Kuang
Strategies For Controlling The Spatial Orientation Of Single Molecules Tethered On Dna Origami Templates Physisorbed On Glass Substrates: Intercalation And Stretching, Keitel Cervantes-Salguero, Austin Biaggne, John M. Youngsman, Brett M. Ward, Young C. Kim, Lan Li, John A. Hall, William B. Knowlton, Elton Graugnard, Wan Kuang
Electrical and Computer Engineering Faculty Publications and Presentations
Nanoarchitectural control of matter is crucial for next-generation technologies. DNA origami templates are harnessed to accurately position single molecules; however, direct single molecule evidence is lacking regarding how well DNA origami can control the orientation of such molecules in three-dimensional space, as well as the factors affecting control. Here, we present two strategies for controlling the polar (θ) and in-plane azimuthal (ϕ) angular orientations of cyanine Cy5 single molecules tethered on rationally-designed DNA origami templates that are physically adsorbed (physisorbed) on glass substrates. By using dipolar imaging to evaluate Cy5′s orientation and super-resolution microscopy, the absolute …
Processing Time, Temperature, And Initial Chemical Composition Prediction From Materials Microstructure By Deep Network For Multiple Inputs And Fused Data, Amir Abbas Kazemzadeh Farizhandi, Mahmood Mamivand
Processing Time, Temperature, And Initial Chemical Composition Prediction From Materials Microstructure By Deep Network For Multiple Inputs And Fused Data, Amir Abbas Kazemzadeh Farizhandi, Mahmood Mamivand
Mechanical and Biomedical Engineering Faculty Publications and Presentations
Prediction of the chemical composition and processing history from microstructure morphology can help in material inverse design. In this work, we propose a fused-data deep learning framework that can predict the processing history of a microstructure. We used the Fe-Cr-Co alloys as a model material. The developed framework is able to predict the heat treatment time, temperature, and initial chemical compositions by reading the morphology of Fe distribution and its concentration. The results show that the trained deep neural network has the highest accuracy for chemistry and then time and temperature. We identified two scenarios for inaccurate predictions; 1) There …
Novel Damage Index-Based Rapid Evaluation Of Civil Infrastructure Subsurface Defects Using Thermography Analytics, Tianjie Zhang, Md Asif Rahman, Alex Peterson, Yang Lu
Novel Damage Index-Based Rapid Evaluation Of Civil Infrastructure Subsurface Defects Using Thermography Analytics, Tianjie Zhang, Md Asif Rahman, Alex Peterson, Yang Lu
Civil Engineering Faculty Publications and Presentations
The qualitative measurement is a common practice in infrastructure condition inspection when using Infrared Thermography (IRT), as it can effectively locate the defected area non-destructively and non-contact. However, a quantitative evaluation becomes more significant because it can help decision makers figure out specific compensation plans to deal with defects. In this work, an IRT-based novel damage index, damage density, was proposed to quantify the significance of subsurface defects. This index is extracted from IR images using our thermography analytics framework. The proposed framework includes thermal image processing, defect edge detection, and thermal gradient map calculations. A modified root mean square …
Deep Learning Approach For Chemistry And Processing History Prediction From Materials Microstructure, Amir Abbas Kazemzadeh Farizhandi, Omar Betancourt, Mahmood Mamivand
Deep Learning Approach For Chemistry And Processing History Prediction From Materials Microstructure, Amir Abbas Kazemzadeh Farizhandi, Omar Betancourt, Mahmood Mamivand
Mechanical and Biomedical Engineering Faculty Publications and Presentations
Finding the chemical composition and processing history from a microstructure morphology for heterogeneous materials is desired in many applications. While the simulation methods based on physical concepts such as the phase-field method can predict the spatio-temporal evolution of the materials’ microstructure, they are not efficient techniques for predicting processing and chemistry if a specific morphology is desired. In this study, we propose a framework based on a deep learning approach that enables us to predict the chemistry and processing history just by reading the morphological distribution of one element. As a case study, we used a dataset from spinodal decomposition …