Harmonic resonance and entrainment of propagating chemical waves by external mechanical stimulation in BZ self-oscillating hydrogels.

Smart polymer materials that are nonliving yet exhibit complex "life-like" or biomimetic behaviors have been the focus of intensive research over the past decades, in the quest to broaden our understanding of how living systems function under nonequilibrium conditions. Identification of how chemical and mechanical coupling can generate resonance and entrainment with other cells or external environment is an important research question. We prepared Belousov-Zhabotinsky (BZ) self-oscillating hydrogels which convert chemical energy to mechanical oscillation. By cyclically applying external mechanical stimulation to the BZ hydrogels, we found that when the oscillation of a gel sample entered into harmonic resonance with the applied oscillation during stimulation, the system kept a "memory" of the resonant oscillation period and maintained it post stimulation, demonstrating an entrainment effect. More surprisingly, by systematically varying the cycle length of the external stimulation, we revealed the discrete nature of the stimulation-induced resonance and entrainment behaviors in chemical oscillations of BZ hydrogels, i.e., the hydrogels slow down their oscillation periods to the harmonics of the cycle length of the external mechanical stimulation. Our theoretical model calculations suggest the important roles of the delayed mechanical response caused by reactant diffusion and solvent migration in affecting the chemomechanical coupling in active hydrogels and consequently synchronizing their chemical oscillations with external mechanical oscillations.

Ensemble dynamics and information flow deduction from whole-brain imaging data.

The recent advancements in large-scale activity imaging of neuronal ensembles offer valuable opportunities to comprehend the process involved in generating brain activity patterns and understanding how information is transmitted between neurons or neuronal ensembles. However, existing methodologies for extracting the underlying properties that generate overall dynamics are still limited. In this study, we applied previously unexplored methodologies to analyze time-lapse 3D imaging (4D imaging) data of head neurons of the nematode Caenorhabditis elegans. By combining time-delay embedding with the independent component analysis, we successfully decomposed whole-brain activities into a small number of component dynamics. Through the integration of results from multiple samples, we extracted common dynamics from neuronal activities that exhibit apparent divergence across different animals. Notably, while several components show common cooperativity across samples, some component pairs exhibited distinct relationships between individual samples. We further developed time series prediction models of synaptic communications. By combining dimension reduction using the general framework, gradient kernel dimension reduction, and probabilistic modeling, the overall relationships of neural activities were incorporated. By this approach, the stochastic but coordinated dynamics were reproduced in the simulated whole-brain neural network. We found that noise in the nervous system is crucial for generating realistic whole-brain dynamics. Furthermore, by evaluating synaptic interaction properties in the models, strong interactions within the core neural circuit, variable sensory transmission and importance of gap junctions were inferred. Virtual optogenetics can be also performed using the model. These analyses provide a solid foundation for understanding information flow in real neural networks.

Effects of producing high levels of hyperthermophile-specific C25,C25-archaeal membrane lipids in Escherichia coli.

A hyperthermophilic archaeon, Aeropyrum pernix, synthesizes C25,C25-archaeal membrane lipids, or extended archaeal membrane lipids, which contain two C25 isoprenoid chains that are linked to glycerol-1-phosphate via ether bonds and are longer than the usual C20,C20-archaeal membrane lipids. The C25,C25-archaeal membrane lipids are believed to allow the archaeon to survive under harsh conditions, because they are able to form lipid membranes that are impermeable at temperatures approaching the boiling point. The effect that C25,C25-archaeal membrane lipids exert on living cells, however, remains unproven along with an explanation for why the hyperthermophilic archaeon synthesizes these specific lipids instead of the more common C20,C20-archaeal lipids or double-headed tetraether lipids. To shed light on the effects that these hyperthermophile-specific membrane lipids exert on living cells, we have constructed an E. coli strain that produces C25,C25-archaeal membrane lipids. However, a resultant low level of productivity would not allow us to assess the effects of their production in E. coli cells. Herein, we report an enhancement of the productivity of C25,C25-archaeal membrane lipids in engineered E. coli strains via the introduction of metabolic pathways such as an artificial isoprenol utilization pathway where the precursors of isoprenoids are synthesized via a two-step phosphorylation of prenol and isoprenol supplemented to a growth medium. In the strain with the highest titer, a major component of C25,C25-archaeal membrane lipids reached ∼11 % of total lipids of E. coli. It is noteworthy that the high production of the extended archaeal lipids did not significantly affect the growth of the bacterial cells. The permeability of the cell membrane of the strain became slightly lower in the presence of the exogenous membrane lipids with longer hydrocarbon chains, which demonstrated the possibility to enhance bacterial cell membranes by the hyperthermophile-specific lipids, along with the surprising robustness of the E. coli cell membrane.

Anisotropically self-oscillating gels by spatially patterned interpenetrating polymer network.

Here we introduce sub-millimeter self-oscillating gels that undergo the Belousov-Zhabotinsky (BZ) reaction and can anisotropically oscillate like cardiomyocytes. The anisotropically self-oscillating gels in this study were realized by spatially patterning an acrylic acid-based interpenetrating network (AA-IPN). We found that the patterned AA-IPN regions, locally introduced at both ends of the gels through UV photolithography, can constrain the horizontal gel shape deformation during the BZ reaction. In other words, the two AA-IPN regions could act as a physical barrier to prevent isotropic deformation. Furthermore, we controlled the anisotropic deformation behavior during the BZ reaction by varying the concentration of acrylic acid used in the patterning process of the AA-IPN. As a result, a specific directional deformation behavior (66% horizontal/vertical amplitude ratio) was fulfilled, similar to that of cardiomyocytes. Our study can provide a promising insight to fabricating robust gel systems for cardiomyocyte modeling or designing novel autonomous microscale soft actuators.

Temperature-Adaptative Self-Oscillating Gels: Toward Autonomous Biomimetic Soft Actuators with Broad Operating Temperature Region.

Self-oscillating gel systems exhibiting an expanded operating temperature and accompanying functional adaptability are showcased. The developed system contains nonthermoresponsive main-monomers, such as N,N-dimethylacrylamide (DMAAm) or 2-acrylamido-2-methylpropane sulfonic acid (AMPS) or acrylamide (AAm) or 3-(methacryloylamino)propyl trimethylammonium chloride (MAPTAC). The gels volumetrically self-oscillate within the range of the conventional (20.0 °C) and extended (27.0 and 36.5 °C) temperatures. Moreover, the gels successfully adapt to the environmental changes; they beat faster and smaller as the temperature increases. The period and amplitude are also controlled by tuning the amount of main-monomers and N-(3-aminopropyl) acrylamide. Furthermore, the record amplitude in the bulk gel system consisting of polymer strand and cross-linker at 36.5 °C is achieved (≈10.8%). The study shows new self-oscillation systems composed of unprecedented combinations of materials, giving the community a robust material-based insight for developing more life-like autonomous biomimetic soft robots with various operating temperatures and beyond.

Fabrication of submillimeter-sized spherical self-oscillating gels and control of their isotropic volumetric oscillatory behaviors.

In this study, we established a fabrication method and analyzed the volumetric self-oscillatory behaviors of submillimeter-sized spherical self-oscillating gels. We validated that the manufactured submillimeter-sized spherical self-oscillating gels exhibited isotropic volumetric oscillations during the Belousov-Zhabotinsky (BZ) reaction. In addition, we experimentally elucidated that the volumetric self-oscillatory behaviors (i.e., period and amplitude) and the oscillatory profiles depended on the following parameters: (1) the molar composition of N-(3-aminopropyl)methacrylamide hydrochloride (NAPMAm) in the gels and (2) the concentration of Ru(bpy)3-NHS solution containing an active ester group on conjugation. These clarified relationships imply that controlling the amount of Ru(bpy)3 in the gel network could influence the gel volumetric oscillation during the BZ reaction. These results of submillimeter-sized and spherical self-oscillating gels bridge knowledge gaps in the current field because the gels with corresponding sizes and shapes have not been systematically explored yet. Therefore, our study could be a cornerstone for diverse applications of (self-powered) gels in various scales and shapes, including soft actuators exhibiting life-like functions.

A surface-grafted hydrogel demonstrating thermoresponsive adhesive strength change.

Here, we designed a surface-grafted hydrogel (SG gel) that exhibits thermoresponsive changes in surface properties. Quantitative measurements using a self-made device showed that the adhesive strength between the SG gel surface and a Bakelite plate due to hydrophobic interaction changed significantly with temperature.

SMiPoly: Generation of a Synthesizable Polymer Virtual Library Using Rule-Based Polymerization Reactions.

Recent advances in machine learning have led to the rapid adoption of various computational methods for de novo molecular design in polymer research, including high-throughput virtual screening and inverse molecular design. In such workflows, molecular generators play an essential role in creation or sequential modification of candidate polymer structures. Machine learning-assisted molecular design has made great technical progress over the past few years. However, the difficulty of identifying synthetic routes to such designed polymers remains unresolved. To address this technical limitation, we present Small Molecules into Polymers (SMiPoly), a Python library for virtual polymer generation that implements 22 chemical rules for commonly applied polymerization reactions. For given small organic molecules to form a candidate monomer set, the SMiPoly generator conducts possible polymerization reactions to generate an exhaustive list of potentially synthesizable polymers. In this study, using 1083 readily available monomers, we generated 169,347 unique polymers forming seven different molecular types: polyolefin, polyester, polyether, polyamide, polyimide, polyurethane, and polyoxazolidone. By comparing the distribution of the virtually created polymers with approximately 16,000 real polymers synthesized so far, it was found that the coverage and novelty of the SMiPoly-generated polymers can reach 48 and 53%, respectively. Incorporating the SMiPoly library into a molecular design workflow will accelerate the process of de novo polymer synthesis by shortening the step to select synthesizable candidate polymers.

Construction of a nano-phase-separated structure on a hydrogel surface.

A hydrogel surface with a nano-phase-separated structure was successfully fabricated by grafting a fluorine-containing polymer using activators regenerated by electron transfer atom transfer radical polymerisation (ARGET ATRP). The modified hydrogel surface exhibits water repellency and high elasticity with maintaining transparency.

Functional Output Regression for Machine Learning in Materials Science.

In recent years, there has been a rapid growth in the use of machine learning in material science. Conventionally, a trained predictive model describes a scalar output variable, such as thermodynamic, electronic, or mechanical properties, as a function of input descriptors that vectorize the compositional or structural features of any given material, such as molecules, chemical compositions, or crystalline systems. In machine learning of material data, on the other hand, the output variable is often given as a function. For example, when predicting the optical absorption spectrum of a molecule, the output variable is a spectral function defined in the wavelength domain. Alternatively, in predicting the microstructure of a polymer nanocomposite, the output variable is given as an image from an electron microscope, which can be represented as a two- or three-dimensional function in the image coordinate system. In this study, we consider two unified frameworks to handle such multidimensional or functional output regressions, which are applicable to a wide range of predictive analyses in material science. The first approach employs generative adversarial networks, which are known to exhibit outstanding performance in various computer vision tasks such as image generation, style transfer, and video generation. We also present another type of statistical modeling inspired by a statistical methodology referred to as functional data analysis. This is an extension of kernel regression to deal with functional outputs, and its simple mathematical structure makes it effective in modeling even with small amounts of data. We demonstrate the proposed methods through several case studies in materials science.