Steels for sharp edges or tools typically have martensitic microstructures, high carbide contents, and various coatings to exhibit high hardness and wear resistance. Yet they become practically unusable upon cutting much softer materials such as human hair, cheese, or potatoes. Despite this being an everyday observation, the underlying physical micromechanisms are poorly understood because of the structural complexity of the interacting materials and the complex boundary conditions of their co-deformation. To unravel this complexity, we carried out interrupted tests and in situ electron microscopy cutting experiments with two micromechanical testing setups. We investigated the findings analytically and numerically, revealing that the spatial variation of lath martensite structure plays the key role leading to a mixed-mode II-III cracking phenomenon before appreciable wear.

Surfaces with switchable adhesive properties are employed by robots to quickly grip and release objects and thereby to perform dexterous manipulation and locomotion tasks. Robotic grippers with switchable adhesion have been developed using structured polymers and electrostatic mechanisms. However, manipulating delicate items can be challenging as this requires strong, switchable gripping forces that do not damage the target object. Soft nanocomposite electroadhesives (SNEs) were recently introduced as an option for handling such objects. The technology integrates an electrostatic adhesion mechanism into a mechanically compliant surface formed from dielectric-coated carbon nanotubes (CNTs) to ensure soft contact with target objects. In this study we explore the scaling of the electrostatic adhesion of SNEs, toward their potential application in macroscale grasping and manipulation. We measure electroadhesive pressures on millimeter-scale areas of up to ∼20 kPa with an on/off adhesion ratio of ∼700. Based on the measured forces and simple modeling, we conclude that the maximum achievable SNE adhesion forces are determined by dielectric breakdown in the insulating coating and surrounding air. Consequently, the SNE surface behaves as a parallel capacitor plate placed at an effective distance of 2.9 μm from the target object, despite being in contact with the target and therefore having the contacting CNTs separated from the surface by ∼2 nm dielectric coating. This mechanistic understanding of soft nanocomposite electroadhesives outlines the capabilities of the technology and informs their design for advanced manufacturing applications.

Hydrogen, while being a potential energy solution, creates arguably the most important embrittlement problem in high-strength metals. However, the underlying hydrogen-defect interactions leading to embrittlement are challenging to unravel. Here, we investigate an intriguing hydrogen effect to shed more light on these interactions. By designing an in situ electron channeling contrast imaging experiment of samples under no external stresses, we show that dislocations (atomic-scale line defects) can move distances reaching 1.5 μm during hydrogen desorption. Combining molecular dynamics and grand canonical Monte Carlo simulations, we reveal that grain boundary hydrogen segregation can cause the required long-range resolved shear stresses, as well as short-range atomic stress fluctuations. Thus, such segregation effects should be considered widely in hydrogen research.

Absorption of interstitial alloying elements like H, O, C, and N in metals and their continuous relocation and interactions with various microstructural features such as vacancies, dislocations, and grain boundaries have crucial influences on metals’ properties. However, besides limitations in experimental tools in capturing these mechanisms, the inefficiency of numerical tools also inhibits modeling efforts. Here, we present an efficient framework to perform hybrid grand canonical Monte Carlo and molecular dynamics simulations that allow for parallel insertion/deletion of Monte Carlo moves. A new methodology for calculation of the energy difference at trial moves that can be applied to many-body potentials as well as pair ones is a primary feature of our implementation. We study H diffusion in Fe (ferrite phase) and Ni polycrystalline samples to demonstrate the efficiency and scalability of the algorithm and its application. The computational cost of using our framework for half a million atoms is a factor of 250 less than the cost of using existing libraries.