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   Hoffmann, Axel (December 2021, Journal of Magnetism and Magnetic Materials)
2. Helical spin structure in iron chains with hybridized boundaries

   https://doi.org/10.1063/5.0022926  

   Vargas, Nicolas M.; Torres, Felipe; Baker, Alexander A.; Lee, Jonathan R.; Kiwi, Miguel; Willey, Trevor M.; Monton, Carlos; Schuller, Ivan K. (November 2020, Applied Physics Letters)

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3. Graphene Oxide-Hybridized Waterborne Epoxy Coating for Simultaneous Anticorrosive and Antibiofilm Functions

   https://doi.org/10.3389/fmats.2022.910152  

   Zhou, Ying; Wang, Haoran; Zhang, Cheng; Zhou, Qixin; Rodrigues, Debora F. (July 2022, Frontiers in Materials)

   Multifunctional coatings with simultaneous antibacterial and anticorrosive properties are essential for marine environments, oil and gas industry, medical settings, and domestic/public appliances to preserve integrity and functionality of pipes, instruments, and surfaces. In this work, we developed a simple and effective method to prepare graphene oxide (GO)-hybridized waterborne epoxy (GOWE) coating to simultaneously improve anticorrosive and antibacterial properties . The effects of different GO filler ratios (0.05, 0.1, and 0.5, 1 wt%) on the electrochemical and antibacterial behaviors of the waterborne epoxy coating were investigated over short- and long-term periods. The electrochemical behavior was analyzed with salt solution for 64 days. The antibacterial effect of GOWE coating was evaluated with Shewanella oneidensis (MR-1), which is a microorganism that can be involved in corrosion. Our results revealed that concentrations as low as 0.1 wt% of the GO was effective performance than the waterborne epoxy coating without graphene oxide. This result is due to the high hydrophilicity of the graphene oxide fillers, which allowed great dispersion in the waterborne epoxy coating matrix. Furthermore, this study used a corrosion relevant bacterium as a model organism, that is, Shewanella oneidensis (MR-1), which is more relevant for real-word applications. This as-prepared GO-hybridized waterborne polymeric hybrid film provides new insight into the application of 2D nanomaterial polymer composites for simultaneous anticorrosive and antibacterial applications. 

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4. Quadrupolar excitons and hybridized interlayer Mott insulator in a trilayer moiré superlattice

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   Lian, Zhen; Chen, Dongxue; Ma, Lei; Meng, Yuze; Su, Ying; Yan, Li; Huang, Xiong; Wu, Qiran; Chen, Xinyue; Blei, Mark; et al (August 2023, Nature Communications)

   Abstract Transition metal dichalcogenide (TMDC) moiré superlattices, owing to the moiré flatbands and strong correlation, can host periodic electron crystals and fascinating correlated physics. The TMDC heterojunctions in the type-II alignment also enable long-lived interlayer excitons that are promising for correlated bosonic states, while the interaction is dictated by the asymmetry of the heterojunction. Here we demonstrate a new excitonic state, quadrupolar exciton, in a symmetric WSe₂-WS₂-WSe₂trilayer moiré superlattice. The quadrupolar excitons exhibit a quadratic dependence on the electric field, distinctively different from the linear Stark shift of the dipolar excitons in heterobilayers. This quadrupolar exciton stems from the hybridization of WSe₂valence moiré flatbands. The same mechanism also gives rise to an interlayer Mott insulator state, in which the two WSe₂layers share one hole laterally confined in one moiré unit cell. In contrast, the hole occupation probability in each layer can be continuously tuned via an out-of-plane electric field, reaching 100% in the top or bottom WSe₂under a large electric field, accompanying the transition from quadrupolar excitons to dipolar excitons. Our work demonstrates a trilayer moiré system as a new exciting playground for realizing novel correlated states and engineering quantum phase transitions. 

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5. Hybridized distance- and contact-based hierarchical structure modeling for folding soluble and membrane proteins

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   Roche, Rahmatullah; Bhattacharya, Sutanu; Bhattacharya, Debswapna (February 2021, PLOS Computational Biology)

   Kolodny, Rachel (Ed.)

   Crystallography and NMR system (CNS) is currently a widely used method for fragment-free ab initio protein folding from inter-residue distance or contact maps. Despite its widespread use in protein structure prediction, CNS is a decade-old macromolecular structure determination system that was originally developed for solving macromolecular geometry from experimental restraints as opposed to predictive modeling driven by interaction map data. As such, the adaptation of the CNS experimental structure determination protocol for ab initio protein folding is intrinsically anomalous that may undermine the folding accuracy of computational protein structure prediction. In this paper, we propose a new CNS-free hierarchical structure modeling method called DConStruct for folding both soluble and membrane proteins driven by distance and contact information. Rigorous experimental validation shows that DConStruct attains much better reconstruction accuracy than CNS when tested with the same input contact map at varying contact thresholds. The hierarchical modeling with iterative self-correction employed in DConStruct scales at a much higher degree of folding accuracy than CNS with the increase in contact thresholds, ultimately approaching near-optimal reconstruction accuracy at higher-thresholded contact maps. The folding accuracy of DConStruct can be further improved by exploiting distance-based hybrid interaction maps at tri-level thresholding, as demonstrated by the better performance of our method in folding free modeling targets from the 12th and 13th rounds of the Critical Assessment of techniques for protein Structure Prediction (CASP) experiments compared to popular CNS- and fragment-based approaches and energy-minimization protocols, some of which even using much finer-grained distance maps than ours. Additional large-scale benchmarking shows that DConStruct can significantly improve the folding accuracy of membrane proteins compared to a CNS-based approach. These results collectively demonstrate the feasibility of greatly improving the accuracy of ab initio protein folding by optimally exploiting the information encoded in inter-residue interaction maps beyond what is possible by CNS. 

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