Miniature robots are untethered actuators, which have great prospects to transform targeted drug delivery because they can potentially deliver high concentrations of medicine to the disease site(s) with minimal complications. However, existing miniature robots cannot perform advanced targeted combination therapy; majority of them can at most transport one type of drug, while those that can carry multiple drugs are unable to change their drug-dispensing sequence and dosage. Furthermore, the latter robots cannot transport more than three types of drugs, selectively dispense their drugs, maintain their mobility, or release their drugs at multiple sites. Here, a millimeter-scale soft robot is proposed, which can be actuated by alternating magnetic fields to dispense four types of drugs with reprogrammable drug-dispensing sequence and dosage (dispensing rates: 0.0992-0.231 µL h-1). This robot has six degrees-of-freedom motions, and it can deliver its drugs to multiple desired sites by rolling and two-anchor crawling across unstructured environments with negligible drug leakage. Such dexterity is highly desirable and unprecedented for miniature robots with drug-dispensing capabilities. The soft robot therefore has great potential to enable advanced targeted combination therapy, where four types of drugs must be delivered to various disease sites, each with a specific sequence and dosage of drugs.

Current mesh-based simulation approaches face significant challenges in continuously modeling the mechanical behaviors of foods through processing, storage, deconstruction, and digestion. This is primarily due to the limitations of continuum mechanics in dealing with systems characterized by free boundaries, substantial deformations, mechanical failures, and non-homogenized mechanical properties. The dynamic nature of food microstructure and the transformation of the food bolus, in relation to its composition, present formidable obstacles in computer-aided food design. In response, the Pizza3 project adopts an innovative methodology, utilizing an explicit microstructural representation to construct and subsequently deconstruct food products in a modular, Lego-like fashion. Central to this simulation approach are \"food atoms\", conceptualized from the principles of smoothed particle hydrodynamics. These units are significantly larger than actual atoms but are finely scaled to represent both solid and liquid states of food faithfully. In solid phases, food atoms interact via pairwise forces akin to bond-peridynamic methods, thus extending the capabilities of continuum mechanics to encompass large deformations and fracturing phenomena. For liquids, the model employs artificial conservative and dissipative forces, enabling the simulation of a variety of phenomena within the framework of partial compressibility. The interaction dynamics between rigid and soft objects and fluids are accurately captured through Hertzian contact mechanics, offering a versatile parameterization applicable to impermeable (but possibly penetrable) surfaces and enforcing no-slip conditions. The efficacy of this framework is showcased through the successful modeling of three time-dependent 3D scenarios, each rigorously validated against established analytical and experimental models. Advancing beyond these initial applications, the framework is further extended to more intricate cases inadequately addressed in current literature. This extension sheds light on the underlying mechanisms of in-mouth texture perception, offering new insights and tools for food engineering and design.

UNASSIGNED: Atomic force microscopy (AFM) is one of the main techniques used to characterize the mechanical properties of soft biological samples and biomaterials at the nanoscale. Despite efforts made by the AFM community to promote open-source data analysis tools, standardization continues to be a significant concern in a field that requires common analysis procedures. AFM-based mechanical measurements involve applying a controlled force to the sample and measure the resulting deformation in the so-called force-distance curves. These may include simple approach and retract or oscillatory cycles at various frequencies (microrheology). To extract quantitative parameters, such as the elastic modulus, from these measurements, AFM measurements are processed using data analysis software. Although open tools exist and allow obtaining the mechanical properties of the sample, most of them only include standard elastic models and do not allow the processing of microrheology data. In this work, we have developed an open-source software package (called PyFMLab, as of python force microscopy laboratory) capable of determining the viscoelastic properties of samples from both conventional force-distance curves and microrheology measurements.
UNASSIGNED: PyFMLab has been written in Python, which provides an accessible syntax and sufficient computational efficiency. The software features were divided into separate, self-contained libraries to enhance code organization and modularity and to improve readability, maintainability, testability, and reusability. To validate PyFMLab, two AFM datasets, one composed of simple force curves and another including oscillatory measurements, were collected on HeLa cells.
UNASSIGNED: The viscoelastic parameters obtained on the two datasets analysed using PyFMLab were validated against data processing proprietary software and against validated MATLAB routines developed before obtaining equivalent results.
UNASSIGNED: Its open-source nature and versatility makes PyFMLab an open-source solution that paves the way for standardized viscoelastic characterization of biological samples from both force-distance curves and microrheology measurements.

Memristors show promising features for neuromorphic computing. Here we report a soft memristor based on the liquid-vapor surface of a microbubble. The thickness of the liquid film was modulated by electrostatic and interfacial forces, enabling resistance switches. We found a pinched current hysteresis at scanning periods between 1.6 and 51.2 s, while representing a resistor below 1.6 s and a diode-like behavior above 51.2 s. We approximate the thickening/thinning dynamics of liquid film by pressure-driven flow at the interface and derived the impacts of salt concentration and voltage amplitude on the memory effects. Our work opens a new approach to building nanofluidic memristors by a soft interface, which may be useful for new types of neuromorphic computing in the future.

OBJECTIVE: Soft materials, particularly elastomers, are extensively studied, but investigations into purely soft gel contact systems are limited due to their complex dual phases consisting of polymer and free liquids. While Dual Wavelength-Reflection Interference Confocal Microscopy (DW-RICM) is effective for noninvasively visualizing interfaces from a bottom view, it faces challenges in gel studies due to close refractive indices of polymeric networks and free liquids. We hypothesize that modulating the refractive index of soft gels using nanoparticles (NPs) enhances the visualization of contact zone beneath the free surface, providing insights into the configuration of phase-separated free oil within gel-on-gel contact systems.
METHODS: Gel-on-gel contact systems were fabricated using immiscible organogels and hydrogels. Titanium dioxide (TiO2) NPs were introduced into the organogel to modulate refractive indices. Given the lack of prior studies on the hidden contact zone between gels, various techniques, including DW-RICM, side-view imaging, and inverted optical microscopy, were employed to observe and validate our findings. Comparative analyses were conducted with elastomer-on-rigid, elastomer-on-gel, and gel-on-rigid contact systems.
RESULTS: Our investigation demonstrated that a minimal amount of TiO2 NPs effectively delineates the direct contact radius between organogel polymeric networks and hydrogel surfaces. Comparative experiments showed that TiO2 addition did not alter the gels\' mechanical and surface properties but significantly enhanced information on gel contact deformation. This enhanced visualization technique has the potential to advance our understanding of adhesive contacts in gels, providing valuable insights into interface phenomena involving biological soft tissues and cells.

Magnetically responsive soft smart materials have garnered significant academic attention due to their flexibility, remote controllability, and reconfigurability. However, traditional soft materials used in the construction of these magnetically responsive systems typically exhibit low density and poor thermal and electrical conductivities. These limitations result in suboptimal performance in applications such as medical radiography, high-performance electronic devices, and thermal management. To address these challenges, magnetically responsive gallium-based liquid metals have emerged as promising alternatives. In this review, we summarize the methodologies for achieving magnetically responsive liquid metals, including the integration of magnetic agents into the liquid metal matrix and the utilization of induced Lorentz forces. We then provide a comprehensive discussion of the key physicochemical properties of these materials and the factors influencing them. Additionally, we explore the advanced and potential applications of magnetically responsive liquid metals. Finally, we discuss the current challenges in this field and present an outlook on future developments and research directions.

During embryonic morphogenesis, tissues undergo dramatic deformations in order to form functional organs. Similarly, in adult animals, living cells and tissues are continually subjected to forces and deformations. Therefore, the success of embryonic development and the proper maintenance of physiological functions rely on the ability of cells to withstand mechanical stresses as well as their ability to flow in a collective manner. During these events, mechanical perturbations can originate from active processes at the single-cell level, competing with external stresses exerted by surrounding tissues and organs. However, the study of tissue mechanics has been somewhat limited to either the response to external forces or to intrinsic ones. In this work, we use an active vertex model of a 2D confluent tissue to study the interplay of external deformations that are applied globally to a tissue with internal active stresses that arise locally at the cellular level due to cell motility. We elucidate, in particular, the way in which this interplay between globally external and locally internal active driving determines the emergent mechanical properties of the tissue as a whole. For a tissue in the vicinity of a solid-fluid jamming or unjamming transition, we uncover a host of fascinating rheological phenomena, including yielding, shear thinning, continuous shear thickening, and discontinuous shear thickening. These model predictions provide a framework for understanding the recently observed nonlinear rheological behaviors in vivo.

Standard deep learning algorithms require differentiating large nonlinear networks, a process that is slow and power-hungry. Electronic contrastive local learning networks (CLLNs) offer potentially fast, efficient, and fault-tolerant hardware for analog machine learning, but existing implementations are linear, severely limiting their capabilities. These systems differ significantly from artificial neural networks as well as the brain, so the feasibility and utility of incorporating nonlinear elements have not been explored. Here, we introduce a nonlinear CLLN-an analog electronic network made of self-adjusting nonlinear resistive elements based on transistors. We demonstrate that the system learns tasks unachievable in linear systems, including XOR (exclusive or) and nonlinear regression, without a computer. We find our decentralized system reduces modes of training error in order (mean, slope, curvature), similar to spectral bias in artificial neural networks. The circuitry is robust to damage, retrainable in seconds, and performs learned tasks in microseconds while dissipating only picojoules of energy across each transistor. This suggests enormous potential for fast, low-power computing in edge systems like sensors, robotic controllers, and medical devices, as well as manufacturability at scale for performing and studying emergent learning.

We report the use of wet-spinning to 3D-print gels from low-molecular-weight gelators (LMWGs) based on the 1,3 : 2,4-dibenzylidenesorbitol (DBS) scaffold. Gel stripes assembled from DBS-CONHNH2 and DBS-COOH are printed, and their conductivities assessed. Printed gels based on DBS-CONHNH2 can be loaded with Au(III), which is reduced in situ to form embedded gold nanoparticles (AuNPs). The conductivity of these gels increases because of electron transport mediated by the AuNPs, whereas the conductivity of DBS-COOH, which does not promote AuNP formation, remains lower. We then fabricate multi-component gel patterns comprised of spatially well-defined domains of printed DBS-CONHNH2/AuNP (higher conductivity) and DBS-COOH (lower conductivity) resulting in soft multi-domain materials with differential conductivity. Such materials have future prospects in applications such as soft nanoelectronics or tissue engineering.

A self-healing gel with self-healing kinetics that can be regulated by heat is developed. The gel is composed of a polymer having benzophenone (BP) substituents, which are cross-linked with a main alkyl chain via ester bonds, titanium chloride, and zinc. This gel material shows a self-healing property at room temperature. Also, its self-healing behavior can be accelerated by heating the gel. This gel having self-healing kinetics that can be regulated by heat is favorable for practical use. When we want to use a self-healing property as a stop-gap measure, a rapid self-healing property is demanded. On the other hand, when we want materials repaired beautifully or decomposed surfaces need to be attached beautifully, a slow self-healing property is favorable. These opposite demands can be answered by the gel with self-healing kinetics that can be regulated by heat.