Initial Aggregation and Ordering Mechanism of Diphenylalanine from Microsecond All-Atom Molecular Dynamics Simulations

Advanced Materials for Energy Harvesting and Soft Robotics: Emerging Frontiers to Enhance Piezoelectric Performance and Functionality

Piezoelectric energy harvesting captures mechanical energy from a number of sources, such as vibrations, the movement of objects and bodies, impact events, and fluid flow to generate electric power. Such power can be employed to support wireless communication, electronic components, ocean monitoring, tissue engineering, and biomedical devices. A variety of self-powered piezoelectric sensors, transducers, and actuators have been produced for these applications, however approaches to enhance the piezoelectric properties of materials to increase device performance remain a challenging frontier of materials research. In this regard, the intrinsic polarization and properties of materials can be designed or deliberately engineered to enhance the piezo-generated power. This review provides insights into the mechanisms of piezoelectricity in advanced materials, including perovskites, active polymers, and natural biomaterials, with a focus on the chemical and physical strategies employed to enhance the piezo-response and facilitate their integration into complex electronic systems. Applications in energy harvesting and soft robotics are overviewed by highlighting the primary performance figures of merits, the actuation mechanisms, and relevant applications. Key breakthroughs and valuable strategies to further improve both materials and device performance are discussed, together with a critical assessment of the requirements of next-generation piezoelectric systems, and future scientific and technological solutions.

Machine Learning-Guided Adaptive Parametrization for Coupling Terms in a Mixed United-Atom/Coarse-Grained Model for Diphenylalanine Self-Assembly in Aqueous Ionic Liquids

Precise regulation of the peptide self-assembly into ordered nanostructures with intriguing properties has attracted intense attention. However, predicting peptide assembly at atomic resolution is a challenge due to both the structural flexibility of peptides and the associated huge computational costs. A machine learning-guided adaptive parametrization method was proposed for developing a mixed atomic and coarse-grained (CG) model through a multiobjective optimization strategy. Our model incorporates the united-atom (UA) model for diphenylalanine (P) and the polarizable electrostatic-variable coarse-grained (VaCG) model for aqueous ionic liquid [BMIM]+[BF4]- solution. In this mixed model, the coupling van der Waals (vdW) interaction is addressed by introducing virtual sites (VS) in the UA model to interact with solvent CG beads. The coupling parameters, including the electrostatic parameter and vdW parameters, are automatically optimized through ML-guided adaptive parametrization. The performance of this model was tested by some microstructural properties, e.g., the average number of P-P intermolecular hydrogen bonds (HBs) and radius distribution functions (RDFs) between P and different fragments of IL, in comparison with all-atom (AA) simulations. The computational cost is significantly reduced using such a parametrization scheme, which could search tens of thousands of force-field parameter sets, while needing only a small fraction of them to be assessed with molecular dynamics (MD) simulations. We used such a mixed resolution model to investigate the self-assembly in IL-water mixtures with variants of IL concentration (X). The long-range-ordered fibril structure is formed in a pure water system (X = 0). With an increase of IL concentrations, the formation of an ordered self-assembly nanostructure is prohibited, instead forming branched fibril at X = 2 mol % or amorphous aggregates when X > 10 mol %, resulting from the interplay between π-stacking and HB interactions between P and IL. The qualitative agreement between the simulated structures and the observed morphologies in experiments indicates the applicability of ML-guided parametrization strategy in the study of complex systems, such as polymers, lipid bilayers, and polysaccharides.

Nanotubes and water-channels from self-assembling dipeptides

Dipeptides are attractive building blocks for biomaterials in light of their inherent biocompatibility, biodegradability, and simplicity of preparation. Since the discovery of diphenylalanine (Phe-Phe) self-assembling ability into nanotubes, research efforts have been devoted towards the identification of other dipeptide sequences capable of forming these interesting nanomorphologies, although design rules towards nanotube formation are still elusive. In this review, we analyze the dipeptide sequences reported thus far for their ability to form nanotubes, which often feature water-filled supramolecular channels as revealed by single-crystal X-ray diffraction, as well as their properties, and their potential biological applications, which span from drug delivery and regenerative medicine, to bioelectronics and bioimaging.

Natural Piezoelectric Biomaterials: A Biocompatible and Sustainable Building Block for Biomedical Devices

The piezoelectric effect has been widely observed in biological systems, and its applications in biomedical field are emerging. Recent advances of wearable and implantable biomedical devices bring promise as well as requirements for the piezoelectric materials building blocks. Owing to their biocompatibility, biosafety, and environmental sustainability, natural piezoelectric biomaterials are known as a promising candidate in this emerging field, with a potential to replace conventional piezoelectric ceramics and synthetic polymers. Herein, we provide a thorough review of recent progresses of research on five major types of piezoelectric biomaterials including amino acids, peptides, proteins, viruses, and polysaccharides. Our discussion focuses on their structure- and phase-related piezoelectric properties and fabrication strategies to achieve desired piezoelectric phases. We compare and analyze their piezoelectric performance and further introduce and comment on the approaches to improve their piezoelectric property. Representative biomedical applications of this group of functional biomaterials including energy harvesting, sensing, and tissue engineering are also discussed. We envision that molecular-level understanding of the piezoelectric effect, piezoelectric response improvement, and large-scale manufacturing are three main challenges as well as research and development opportunities in this promising interdisciplinary field.

Implicit Solvation Using the Superposition Approximation (IS-SPA): Extension to Peptides in a Polar Solvent

Efficient, accurate, and adaptable implicit solvent models remain a significant challenge in the field of molecular simulation. A recent implicit solvent model, IS-SPA, based on approximating the mean solvent force using the superposition approximation, provides a platform to achieve these goals. IS-SPA was originally developed to handle nonpolar solutes in a polar solvent and did not accurately capture polar solvation. Here, we demonstrate that IS-SPA can accurately capture polar solvation by incorporating solvent orientation and accounting for the contributions from long ranged electrostatics. Solvent orientation is approximated as that of an ideal dipole aligned in a mean electrostatic field and an analytic form of the long ranged electrostatics is derived. Parameters for the model are calculated from explicit solvent simulations of an isolated atom or molecule and include atom-based solvent densities, mean electric field functions, radially symmetric averaged Lennard-Jones forces, and multipoles of the explicit solvent model. Using these parameters, IS-SPA accounts for asymmetry of charge solvation and reproduces the explicit solvent potential of mean force of dimerization of two oppositely charged Lennard-Jones spheres in chloroform with high fidelity. Additionally, the model more accurately captures the effect of explicit solvent on the monomer and dimer configurations of alanine dipeptide in chloroform than a generalized Born or constant density dielectric model. The current version of the algorithm is expected to outperform explicit solvent simulations for aggregation of small peptides at concentrations below 150 mM, well above the typical experimental concentrations for these materials.

Self-assembly of diphenylalanine peptides on graphene via detailed atomistic simulations

The self-assembly of diphenylalanine peptides (FF) on a graphene layer, in aqueous solution, is investigated, through all atom molecular dynamics simulations. Two interfacial systems are studied, with different concentrations of dipeptides and the results are compared with an aqueous solution of FF at room temperature. Corresponding length and time scales of the formed structures are quantified providing important insight into the adsorption mechanism of FF onto the graphene surface. A hierarchical formation of FF structures is observed involving two sequential processes: first, a stabilized interfacial layer of dipeptides onto the graphene surface is formulated, which next is followed by the development of a structure of self-aggregated dipeptides on top of this layer. The whole procedure is completed in almost 200 ns, whereas self-assembly in the system without graphene is accomplished much faster; in less than 50 ns cylindrical structures, the microscopic signal of the macroscopic fibrillar ones, are formed. Strong π-π* interactions between FF and the graphene lead to a parallel orientation to the graphene layer of the phenyl rings within a characteristic time of 80 ns, similar to the one indicated by the time evolution of the number of adsorbed FF atoms at the surface. Reduction in the number of hydrogen bonds between FF peptides is observed because of the graphene layer, since it disturbs their self-assembly propensity. The self-assembly of dipeptides and their adsorption onto the graphene surface destruct the hydrogen bond network of water, in the vicinity of FF, however, the total number of hydrogen bonds in all systems increases, promoting the formed structures.

Degradable Piezoelectric Biomaterials for Wearable and Implantable Bioelectronics

Current bioelectronics are facing a paradigm shift from old-fashioned unrecyclable materials to green and degradable functional materials with desired biocompatibility. As an essential electromechanical coupling component in many bioelectronics, new piezoelectric materials are being developed with biodegradability, as well as desired mechanical and electromechanical properties for the next generation implantable and wearable bioelectronics. In this review, we provide an overview of the major advancements in biodegradable piezoelectric materials. Different natural (such as peptide, amino acids, proteins, cellulose, chitin, silk, collagen, and M13 phage) and synthetic piezoelectric materials (such as polylactic acid) are discussed to reveal the underlying electromechanical coupling mechanism at the molecular level, together with typical approaches to the alignment of orientation and polarization to boost their electromechanical performance. Meanwhile, in vivo and in vitro degradation manners of those piezoelectric materials are summarized and compared. Representative developments of typical electronic prototypes leveraging these materials are also discussed. At last, challenges toward practical applications are pointed out together with potential research opportunities that might be critical in this new materials research area.