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Recently published in MRS Communications, Volume 11, issue 4


Harnessing autocatalytic reactions in polymerization and depolymerization

Rajeev Kumar, Zening Liu, Brad Lokitz, Jihua Chen, Jan-michael Carrillo, Jacek Jakowski, C. Patrick Collier, Scott Retterer, Rigoberto Advincula, Oak Ridge National Laboratory and The University of Tennessee, Knoxville, USA

The authors discuss autocatalysis and its relevance to various polymeric systems by taking inspiration from biology. A number of research directions related to synthesis, characterization, and multiscale modeling are discussed in order to harness autocatalytic reactions in a useful manner for different applications ranging from chemical upcycling of polymers (depolymerization and reconstruction after depolymerization), self-generating micelles and vesicles, and polymer membranes. Overall, the authors propose a concerted effort involving in situ experiments, multiscale modeling, and machine learning algorithms to understand the mechanisms of physical and chemical autocatalysis. They argue that a control of the autocatalytic behavior in polymeric systems can revolutionize areas such as kinetic control of the self-assembly of polymeric materials, synthesis of self-healing and self-immolative polymers, as next generation of materials for a sustainable circular economy.

The potential of additively manufactured membranes for selective separation and capture of CO2

Dianne B. Gutierrez, Richard D. Espiritu, Department of Mining, Metallurgical, and Materials Engineering, University of the Philippines Diliman, Philippines; Eugene B. Caldona, Rigoberto C. Advincula, Department of Chemical and Biomolecular Engineering and Joint Institute for Advanced Materials, The University of Tennessee, Knoxville, USA

Additive manufacturing (or 3D printing) is an evolving technology that shows great potential as a sustainable method for fabricating gas separation membranes for carbon capture applications. Compared to other gas separation techniques or membranes fabricated by conventional formative methods, the use of 3D-printed membranes is more advantageous because of their simplicity, higher energy efficiency, practicality, flexible and tailorable designs, and high separation efficiency. Although polymeric, cementitious, and gel-based materials have been exploited for the development and fabrication of robust and highly efficient CO2-capturing membranes, these materials require further innovation to become fit and suitable as feedstock for 3D printers. In this work, the authors review several and potential membrane materials used for capturing CO2 and discuss their corresponding separation mechanisms and fabrication via 3D printing. They summarize the challenges and limitations in using 3D-printed membranes and provide perspectives toward high-performance membrane fabrication and future industrial applications.

Research Letters

Coupling high-throughput experiment and machine learning to optimize elemental composition in nickel-based superalloys

Zi Wang, AECC Commercial Aircraft Engine Co., Ltd., China; Baobin Xie, Qihong Fang, Jia Li, State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, China; Feng Liu, Liming Tan, Zaiwang Huang, State Key Laboratory of Powder Metallurgy, Central South University, China; Lei Zhao, Beijing Key Laboratory of Metal Material Characterization, China Iron & Steel Research Institute, China; Liang Jiang, Institute for Advanced Studies in Precision Materials, Yantai University, China

Establishing the relationship of elemental composition and mechanical property is a tremendous amount of work in superalloys. Here, machine learning coupled with high-throughput experiment is adopted to construct a “composition-hardness” model in nickel-based superalloys. The hardness estimated from experiment agrees well with the predicted value. Furthermore, optimal composition of high-hardness superalloys is accurately predicted by simulated annealing algorithm. Subsequently, optimal composition is validated by Thermo-Calc software, further demonstrating the effectiveness of the current approach. Here, a design strategy combined with high-throughput experiment and machine learning is proposed, which may be believed for accelerating the design of advanced materials with excellent performance.

Ab-initio prediction of temperature-dependent dielectric constants and Curie temperatures of cubic phase perovskite materials

Woon Ih Choi, Won-Joon Son, Munbo Shim, Inkook Jang, Dae Sin Kim, Computational Science and Engineering (CSE) Team, DIT Center, Samsung Electronics, Republic of Korea; Dae Jin Yang, Doh Won Jung, Samsung Advanced Institute of Technology, Samsung Electronics, Republic of Korea

Lattice anharmonicity is the essential ingredient for the description of the temperature-dependent dielectric response. Herein, using self-consistent phonon theory calculations that consider the lattice anharmonicity, the authors examined computational workflow to calculate dielectric permittivity. For this purpose, they selected the high symmetry cubic phase of SrTiO3, BaTiO3, PbTiO3, and KNbO3. It turns out that it is necessary to choose an appropriate set of a displacement–force data set and the cutoff distance for quartic interatomic force constants. The authors were also able to predict Curie temperatures out of temperature-dependent dielectric constants of ferroelectric materials.

On the discrepancies between the experimental realization and the thermodynamic predictions of stability of rhombohedral boron nitride

Philip M. Jean-Remy, Department of Materials Science and Engineering; Robert F. Davis, Department of Electrical and Computer Engineering, Carnegie Mellon University, USA

The authors performed equilibrium thermodynamic calculations to generate diagrams indicating the phase fields wherein either hexagonal or rhombohedral films of boron nitride can be deposited via chemical vapor deposition as a function of temperature, choice of B-source, and N/B ratio derived from NH3 and the B-source. Similar diagrams calculated using experimental conditions employed by groups who have synthesized r-BN films revealed that both in experiment and equilibrium, the choice of B-source strongly affects the size of the single-phase field for r-BN and, in general, deposition of r-BN can be realized at temperatures more than 100°C below that predicted by equilibria.

Bio-inspired and computer-supported design of modulated shape changes in polymer materials

Johan Bäckemo, Yue Liu, Andreas Lendlein, Institute of Active Polymers, Helmholtz-Zentrum Hereon, Germany

The Venus flytrap is a fascinating plant with a finely tuned mechanical bi-stable system, which can switch between mono- and bi-stability. The authors combine geometrical design of compliant mechanics and the function of shape-memory polymers to enable switching between bi- and mono-stable states. Digital design and modeling using the Chained Beam Constraint Model forecasted two geometries, which were experimentally realized as structured films of cross-linked poly[ethylene-co-(vinyl acetate)] supported by digital manufacturing. Mechanical evaluation confirmed predicted features. They demonstrate that a shape-memory effect could switch between bi- and mono-stability for the same construct, effectively imitating the Venus flytrap.

Correlation between complexity and mechanical recovery of metallic nanoarchitecture structures

H. Ke, I. Mastorakos, Department of Mechanical and Aeronautical Engineering, Clarkson University, USA; J. Ma, Department of Aerospace and Mechanical Engineering, Saint Louis University, USA

The authors investigate the effect of complexity on the mechanical behavior and recovery of metallic nanoarchitecture structures, studying four different suggested geometries with various levels of complexity using molecular dynamics simulations. The structures exhibited multiple degrees of self-recovery under compressive loading conditions at three temperatures: 300 K, 400 K, and 500 K. The authors used a methodology to qualitatively measure the geometric complexity. The results revealed correlations between the complexity of the structures and their recovery ability and strength, and the geometric cell size and temperature. These findings can guide the design of novel nanoarchitecture geometries for specific applications with tailored properties.


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Recently published in MRS Communications, Volume 11, issue 4. MRS Bulletin (2021).

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