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Aditya Prasad
Researcher at Rensselaer Polytechnic Institute
Publications - 15
Citations - 329
Aditya Prasad is an academic researcher from Rensselaer Polytechnic Institute. The author has contributed to research in topics: Dielectric & Bayesian optimization. The author has an hindex of 7, co-authored 15 publications receiving 247 citations. Previous affiliations of Aditya Prasad include Banaras Hindu University & Technical University of Denmark.
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Enhanced mass diffusion phenomena in highly defective doped ceria
Vincenzo Esposito,De Wei Ni,Zeming He,Wei Zhang,Aditya Prasad,Julie Glasscock,Christodoulos Chatzichristodoulou,Severine Ramousse,Andreas Kaiser +8 more
TL;DR: In this paper, the densification and grain growth of the solid state ionic conductor material Ce 0.9 Gd 0.1 O 1.95− δ (i.e., GDC10, gadolinium-doped ceria, with Gd 10.
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Tunable Multiscale Nanoparticle Ordering by Polymer Crystallization.
Dan Zhao,Vianney Gimenez-Pinto,Andrew Jimenez,Longxi Zhao,Jacques Jestin,Sanat K. Kumar,Brooke Kuei,Enrique D. Gomez,Aditya Prasad,Linda S. Schadler,Mohammad M. Khani,Brian C. Benicewicz +11 more
TL;DR: This assembly strategy allows us to create high modulus materials that retain the attractive high toughness and low density of polymers, in particular by leveraging the kinetics of polymer crystallization.
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Toward the development of a quantitative tool for predicting dispersion of nanocomposites under non-equilibrium processing conditions
Irene Hassinger,Xiaolin Li,He Zhao,Hongyi Xu,Yanhui Huang,Aditya Prasad,Linda S. Schadler,Wei Chen,L. Catherine Brinson +8 more
TL;DR: In this article, the relationship between the filler-matrix interfacial energy, the processing conditions, and the nanoparticle dispersion in polymer nanocomposites was investigated using TEM images of polypropylene, polystyrene, and poly(methyl methacrylate) nanoparticles under controlled processing conditions.
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Densification of Highly Defective Ceria by High Temperature Controlled Re-Oxidation
De Wei Ni,Julie Glasscock,Aenor Pons,Wei Zhang,Aditya Prasad,Simone Sanna,Nini Pryds,Vincenzo Esposito +7 more
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Mining structure–property relationships in polymer nanocomposites using data driven finite element analysis and multi-task convolutional neural networks
Yixing Wang,Min Zhang,Anqi Lin,Akshay Iyer,Aditya Prasad,Xiaolin Li,Yichi Zhang,Linda S. Schadler,Wei Chen,L. Catherine Brinson +9 more
TL;DR: A data driven and deep learning approach that builds a portion of the structure–property relationship for polymer nanocomposites and combines convolutional neural networks with multi-task learning for building quantitative correlations between microstructures and property values is presented.