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On-chip multiplexed single-cell patterning and controllable intracellular delivery (open access)

On-chip multiplexed single-cell patterning and controllable intracellular delivery

Article presents a simple 3D electroporation platform that enables massively parallel single-cell manipulation and the intracellular delivery of macromolecules and small molecules.
Date: February 24, 2020
Creator: Dong, Zaizai; Jiao, Yanli; Xie, Bingteng; Hao, Yongcun; Wang, Pan; Liu, Yuanyuan et al.
System: The UNT Digital Library
Additive friction stir deposition: a deformation processing route to metal additive manufacturing (open access)

Additive friction stir deposition: a deformation processing route to metal additive manufacturing

This article outlines key advantages of additive friction stir deposition, e.g. rendering fully-dense material in the as-printed state with fine, equiaxed microstructures, identifies its niche engineering uses, and points out future research needs in process physics and materials innovation.
Date: November 24, 2020
Creator: Yu, Hang Z. & Mishra, Rajiv
System: The UNT Digital Library
Fabrication and characterization of inkjet-printed 2D perovskite optoelectronic devices (open access)

Fabrication and characterization of inkjet-printed 2D perovskite optoelectronic devices

Article presents the large scale synthesis of solution-processed 2D (CH₃(CH₂)₃NH₃)₂(CH₃NH₃)ₙ − 1PbₙI₃ₙ + 1 (n = 2, 3, and 4) perovskites, a family of layered compounds with composition-tunable bandgap, where inkjet printing was used to fabricate heterostructure, flexible photodetector devices. The flexible, inkjet-printed perovskite 2D heterostructures have significant potential for optoelectronic devices, which can enable broad possibilities with compositional tunability and versatility of the organohalide perovskites.
Date: August 24, 2020
Creator: Min, Misook; Hossain, Ridwan F.; Ma, Liang-Chieh & Kaul, Anupama
System: The UNT Digital Library
HAR-Depth: A Novel Framework for Human Action Recognition Using Sequential Learning and Depth Estimated History Images (open access)

HAR-Depth: A Novel Framework for Human Action Recognition Using Sequential Learning and Depth Estimated History Images

This is the Accepted Manuscript version of an article that proposes HAR-Depth with sequential and shape learning along with the novel concept of depth history image (DHI) to address the challenges of Human action recognition (HAR). Results suggest that the proposed work of this paper performs better in terms of overall accuracy, kappa parameter and precision compared to the other state-of-the-art algorithms present in the earlier reported literature.
Date: August 24, 2020
Creator: Sahoo, Suraj Prakash; Ari, Samit; Mahapatra, Kamalakanta & Mohanty, Saraju P.
System: The UNT Digital Library