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Register a new userStructure-property relationships of elastin-like polypeptides -- a review of experimental and computational studies
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Added by
Diego L\\'opez Barreiro
Publication date
2021
fields
Physics
and research's language is
English
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Elastin is a structural protein with outstanding mechanical properties (e.g., elasticity and resilience) and biologically relevant functions (e.g., triggering responses like cell adhesion or chemotaxis). It is formed from its precursor tropoelastin, a 60-72 kDa water-soluble and temperature-responsive protein that coacervates at physiological temperature, undergoing a phenomenon termed lower critical solution temperature (LCST). Inspired by this behaviour, many scientists and engineers are developing recombinantly produced elastin-inspired biopolymers, usually termed elastin-like polypeptides (ELPs). These ELPs are generally comprised of repetitive motifs with the sequence VPGXG, which corresponds to repeats of a small part of the tropoelastin sequence, X being any amino acid except proline. ELPs display LCST and mechanical properties similar to tropoelastin, which renders them promising candidates for the development of elastic and stimuli-responsive protein-based materials. Unveiling the structure-property relationships of ELPs can aid in the development of these materials by establishing the connections between the ELP amino acid sequence and the macroscopic properties of the materials. Here we present a review of the structure-property relationships of ELPs and ELP-based materials, with a focus on LCST and mechanical properties and how experimental and computational studies have aided in their understanding.
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Emergent functionalities of structural and topological defects in ferroelectric materials underpin an extremely broad spectrum of applications ranging from domain wall electronics to high dielectric and electromechanical responses. Many of these have been discovered and quantified via local scanning probe microscopy methods. However, the search for these functionalities has until now been based by either trial and error or using auxiliary information such as topography or domain wall structure to identify potential objects of interest based on the intuition of operator or preexisting hypotheses, with subsequent manual exploration. Here, we report the development and implementation of a machine learning framework that actively discovers relationships between local domain structure and polarization switching characteristics in ferroelectric materials encoded in the hysteresis loop. The latter and descriptors such as nucleation bias, coercive bias, hysteresis loop area, or more complex functionals of hysteresis loop shape and corresponding uncertainties are used to guide the discovery via automated piezoresponse force microscopy (PFM) and spectroscopy experiments. As such, this approach combines the power of machine learning methods to learn the correlative relationships between high dimensional data, and human-based physics insights encoded in the acquisition function. For ferroelectric, this automated workflow demonstrates that the discovery path and sampling points of on-field and off-field hysteresis loops are largely different, indicating the on-field and off-field hysteresis loops are dominated by different mechanisms. The proposed approach is universal and can be applied to a broad range of modern imaging and spectroscopy methods ranging from other scanning probe microscopy modalities to electron microscopy and chemical imaging.
ABX3 perovskites have attracted intensive research interest in recent years due to their versatile composition and superior optoelectronic properties. Their counterparts, antiperovskites (X3BA), can be viewed as electronically inverted perovskite derivatives, but they have not been extensively studied for solar applications. Therefore, understanding their composition-property relationships is crucial for future photovoltaic application. Here, taking six antiperovskite nitrides X3NA (X2+ = Mg, Ca, Sr; A3- = P, As, Sb, Bi) as an example, we investigate the effect of X- and A-sites on the electronic, dielectric, and mechanical properties from the viewpoint of the first-principles calculations. Our calculation results show that the X-site dominates the conduction band, and the A-site has a non-negligible contribution to the band edge. These findings are completely different from traditional halide perovskites. Interestingly, when changing X- or A-site elements, a linear relationship between the tolerance factor and physical quantities, such as electronic parameters, dielectric constants, and Youngs modulus, is observed. By designing the Mg3NAs1-xBix alloys, we further verify this power of the linear relationship, which provides a predictive guidance for experimental preparation of antiperovskite alloys. Finally, we make a comprehensive comparison between the antiperovskite nitrides and conventional halide perovskites for pointing out the future device applications.
The existence of the exclusion zone (EZ), a layer of water in which plastic microspheres are repelled from hydrophilic surfaces, has now been independently demonstrated by several groups. A better understanding of the mechanisms which generate EZs would help with understanding the possible importance of EZs in biology and in engineering applications such as filtration and microfluidics. Here we review the experimental evidence for EZ phenomena in water and the major theories that have been proposed. We review experimental results from birefringence, neutron radiography, nuclear magnetic resonance, and other studies. Pollack and others have theorized that water in the EZ exists has a different structure than bulk water, and that this accounts for the EZ. We present several alternative explanations for EZs and argue that Schurrs theory based on diffusiophoresis presents a compelling alternative explanation for the core EZ phenomenon. Among other things, Schurrs theory makes predictions about the growth of the EZ with time which have been confirmed by Florea et al. and others. We also touch on several possible confounding factors that make experimentation on EZs difficult, such as charged surface groups, dissolved solutes, and adsorbed nanobubbles.
Delafossites are promising candidates for photocatalysis applications because of their chemical stability and absorption in the solar region of the electromagnetic spectrum. For example, CuAlO2 has good chemical stability but has a large indirect bandgap (~3 eV), so that efforts to improve its absorption in the solar region through alloying are investigated. The effect of dilute alloying on the optical absorption of powdered CuAl1-xFexO2 (x = 0.0-1.0) is measured and compared to electronic band structures calculations using a generalized gradient approximation with Hubbard exchange-correlation parameter and spin. A new absorption feature is observed at 1.8 eV for x = 0.01, which redshifts to 1.4 eV for x = 0.10. This feature is associated with transitions from the L-point valence band maximum to the Fe-3d state that appears below the conduction band of the spin-down band structure. The feature increases the optical absorption below the bandgap of pure CuAlO2, making dilute CuAl1-xFexO2 alloys better suited for solar photocatalysis.
The finely tuned structures of membrane channels allow selective passage of ions through the available aqueous pores. In order to understand channel function, it is crucial to locate the pore and study its physical and chemical properties. Recently obtained X-ray crystal structures of bacterial chloride channel homologues reveal a complicated topology with curvilinear pores. The commonly used HOLE program encounters difficulties in studying such pores. Here we propose a new pore-searching algorithm (TransPath) which uses the Configurational Bias Monte Carlo (CBMC) method to generate transmembrane trajectories driven by both geometric and electrostatic features. The trajectories are binned into groups determined by a vector distance criterion. From each group, a representative trajectory is selected based on the Rosenbluth weight, and the geometrically optimal path is obtained by simulated annealing. Candidate ion pathways can then be determined by analysis of the radius and potential profiles. The proposed method and its implementation are illustrated using the bacterial KcsA potassium channel as an example. The procedure is then applied to the more complex structures of the bacterial E. coli ClC channel homologues.
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