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1. Shape Matters: Gold Nanoparticle Shape Impacts the Biological Activity of siRNA Delivery

   https://doi.org/10.1021/acs.bioconjchem.9b00004  

   Morgan, Erin ; Wupperfeld, Dominik ; Morales, Demosthenes ; Reich, Norbert ( March 2019 , Bioconjugate Chemistry)
2. A hybrid shape memory polymer filled metallic foam composite: shape restoring, strain sensing, Joule heating, strengthening, and toughening

   https://doi.org/10.1088/1361-665X/ac7d7d  

   Abedin, Rubaiyet ; Konlan, John ; Feng, Xiaming ; Mensah, Patrick ; Li, Guoqiang ( July 2022 , Smart Materials and Structures)

   Abstract In this paper, an open-cell metallic foam was filled in by a tough shape memory polymer (SMP), to form a hybrid metal/polymer composite with multifunctionalities and enhanced mechanical properties. This work aims to study the positive composite actions between the metallic skeleton and the SMP filler. Mechanical, thermal, and conductive properties of the resulting hybrid composite were evaluated and compared to the individual components. Uniaxial compression tests and shape memory effect tests were conducted. Results demonstrated an improvement in the compressive strength and toughness. The hybrid composite also exhibited excellent shape recovery and high recovery stress of 1.76 MPa. Infrared thermography has been used to verify the free shape recovery by Joule heating. Sandwich structures with the hybrid composite as the core were studied through low velocity impact test and three-point bending test. The sandwich structures with the composite foam core showed significant performance improvement in both tests. Electrical resistivity study during the three-point bending test validates the possible application of this multifunctional polymer-aluminum open cell foam composite as strain sensor. This type of hybrid composites can be beneficial in many industrial sectors that search for an ideal combination of high strength, high toughness, low weight, damage sensing, and excellent energy absorption capabilities. 

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3. Correlation between cyclic topology and shape memory properties of an amine-based thermoset shape memory polymer: a coarse-grained molecular dynamics study

   https://doi.org/10.1088/1361-665X/ac8bb5  

   Nourian, Pouria ; Wick, Colin D ; Li, Guoqiang ; Peters, Andrew J ( September 2022 , Smart Materials and Structures)

   Abstract Defects in crosslinked networks have a negative effect on mechanical and functional properties. In this study, an epoxy resin diglycidyl ether of bisphenol A crosslinked by a hardener 4,4-diaminodiphenyl methane with various cyclic topologies was simulated to find correlations between the mechanical/shape memory properties (i.e. glassy/rubbery elastic modulus, shape recovery ratio, and recovery stress) and cyclic topologies (i.e. number of total loops, number of defective loops (DLs), etc). The effect of cyclic topology on shape memory properties was more significant than its effect on mechanical properties, altering recovery stress by more than 25% on average. After analyzing several topological fingerprints such as total number of loops, number of DLs, and number of higher order loops, we found that the effect of cyclic topology on the mechanical/shape memory properties of the systems can be best understood by the fraction of hardeners reacted with four distinct epoxy molecules (tetra-distinctly-reacted (TDR) hardeners). By increasing the number of TDR hardeners, the network is closer to ideal, resulting in an increase in the number of higher order loops and a reduction in the number of DLs, which in turn leads to an increase in rubbery elastic modulus and shape recovery ratio to a lesser degree, but a substantial increase in recovery stress. These results suggest that utilization of experimental techniques such as semibatch monomer addition, which leads to a more expanded and defect-free network, can result in a simultaneous increase in both shape recovery ratio and recovery stress in thermoset shape memory polymers (TSMPs). Moreover, topology alteration can be used to synthesize TSMPs with improved recovery stress without significantly increasing their stiffness. 

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4. Geo-FARM: Geodesic Factor Regression Model for Misaligned Pre-shape Responses in Statistical Shape Analysis

   https://doi.org/10.1109/CVPR46437.2021.01133  

   Huang, Chao ; Srivastava, Anuj ; Liu, Rongjie ( June 2021 , 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))

   The problem of using covariates to predict shapes of objects in a regression setting is important in many fields. A formal statistical approach, termed Geodesic regression model, is commonly used for modeling and analyzing relationships between Euclidean predictors and shape responses. Despite its popularity, this model faces several key challenges, including (i) misalignment of shapes due to pre-processing steps, (ii) difficulties in shape alignment due to imaging heterogeneity, and (iii) lack of spatial correlation in shape structures. This paper proposes a comprehensive geodesic factor regression model that addresses all these challenges. Instead of using shapes as extracted from pre-registered data, it takes a more fundamental approach, incorporating alignment step within the proposed regression model and learns them using both pre-shape and covariate data. Additionally, it specifies spatial correlation structures using low-dimensional representations, including latent factors on the tangent space and isotropic error terms. The proposed framework results in substantial improvements in regression performance, as demonstrated through simulation studies and a real data analysis on Corpus Callosum contour data obtained from the ADNI study. 

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5. Confounder Adjustment in Shape-on-Scalar Regression Model: Corpus Callosum Shape Alterations in Alzheimer’s Disease

   https://doi.org/10.3390/stats6040061  

   Dogra, Harshita ; Ding, Shengxian ; Yeon, Miyeon ; Liu, Rongjie ; Huang, Chao ( December 2023 , Stats)

   Large-scale imaging studies often face challenges stemming from heterogeneity arising from differences in geographic location, instrumental setups, image acquisition protocols, study design, and latent variables that remain undisclosed. While numerous regression models have been developed to elucidate the interplay between imaging responses and relevant covariates, limited attention has been devoted to cases where the imaging responses pertain to the domain of shape. This adds complexity to the problem of imaging heterogeneity, primarily due to the unique properties inherent to shape representations, including nonlinearity, high-dimensionality, and the intricacies of quotient space geometry. To tackle this intricate issue, we propose a novel approach: a shape-on-scalar regression model that incorporates confounder adjustment. In particular, we leverage the square root velocity function to extract elastic shape representations which are embedded within the linear Hilbert space of square integrable functions. Subsequently, we introduce a shape regression model aimed at characterizing the intricate relationship between elastic shapes and covariates of interest, all while effectively managing the challenges posed by imaging heterogeneity. We develop comprehensive procedures for estimating and making inferences about the unknown model parameters. Through real-data analysis, our method demonstrates its superiority in terms of estimation accuracy when compared to existing approaches.

    

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