cond-mat.soft

arXiv:2501.11947v1 Announce Type: new Abstract: We propose a modeling framework for finite viscoelasticity, inspired by the kinematic assumption made by Green and Naghdi in plasticity. This approach fundamentally differs from the widely used multiplicative decomposition of the deformation gradient, as the intermediate configuration, a concept that remains debated, becomes unnecessary. The advent of the concept of generalized strains allows the Green-Naghdi assumption to be employed with different strains, offering a flexible mechanism to separate inelastic deformation from total deformation. This leads to a constitutive theory in which the kinematic separation is adjustable and can be calibrated. For quadratic configurational free energy, the framework yields a suite of finite linear viscoelasticity models governed by linear evolution equations. Notably, these models recover established models, including those by Green and Tobolsky (1946) and Simo (1987), when the Seth-Hill strain is chosen with the strain parameter being -2 and 2, respectively. It is also related to the model of Miehe and Keck (2000) when the strain is of the Hencky type. We further extend the approach by adopting coercive strains, which allows us to define an elastic deformation tensor locally. This facilitates modeling the viscous branch using general forms of the configurational free energy, and we construct a micromechanical viscoelastic model as a representative instantiation. The constitutive integration algorithms of the proposed models are detailed. We employ the experimental data of VHB 4910 to examine the proposed models, which demonstrate their effectiveness and potential advantages in the quality of fitting and prediction. Three-dimensional finite element analysis is also conducted to assess the influence of different strains on the viscoelastic behavior.

arXiv:2501.12025v1 Announce Type: cross Abstract: Ionic polymer actuators, in essence, consist of ion exchange polymers sandwiched between layers of electrodes. They have recently gained recognition as promising candidates for soft actuators due to their lightweight nature, noise-free operation, and low-driving voltages. However, the materials traditionally utilized to develop them are often not human/environmentally friendly. Thus, to address this issue, researchers have been focusing on developing biocompatible versions of this actuator. Despite this, such actuators still face challenges in achieving high performance, in payload capacity, bending capabilities, and response time. In this paper, we present a biocompatible ionic polymer actuator whose membrane is fully 3D printed utilizing a direct ink writing method. The structure of the printed membranes consists of biodegradable ionic fluid encapsulated within layers of activated carbon polymers. From the microscopic observations of its structure, we confirmed that the ionic polymer is well encapsulated. The actuators can achieve a bending performance of up to 124$^\circ$ (curvature of 0.82 $\text{cm}^{-1}$), which, to our knowledge, is the highest curvature attained by any bending ionic polymer actuator to date. It can operate comfortably up to a 2 Hz driving frequency and can achieve blocked forces of up to 0.76 mN. Our results showcase a promising, high-performing biocompatible ionic polymer actuator, whose membrane can be easily manufactured in a single step using a standard FDM 3D printer. This approach paves the way for creating customized designs for functional soft robotic applications, including human-interactive devices, in the near future.

arXiv:2501.06232v1 Announce Type: new Abstract: Predicting the lateral pile response is challenging due to the complexity of pile-soil interactions. Machine learning (ML) techniques have gained considerable attention for their effectiveness in non-linear analysis and prediction. This study develops an interpretable ML-based model for predicting p-y curves of monopile foundations. An XGBoost model was trained using a database compiled from existing research. The results demonstrate that the model achieves superior predictive accuracy. Shapley Additive Explanations (SHAP) was employed to enhance interpretability. The SHAP value distributions for each variable demonstrate strong alignment with established theoretical knowledge on factors affecting the lateral response of pile foundations.

arXiv:2501.03235v1 Announce Type: cross Abstract: Neural networks based on soft and biological matter constitute an interesting potential alternative to traditional implementations based on electric circuits. DNA is a particularly promising system in this context due its natural ability to store information. In recent years, researchers have started to construct neural networks that are based on DNA. In this chapter, I provide a very basic introduction to the concept of DNA neural networks, aiming at an audience that is not familiar with biochemistry.