While cracks are usually considered detrimental, crack generation can be harnessed for various applications, for example in ceramic materials, via directing crack propagation and crack opening. Here, we find that electron beam irradiation prompts a crack suppression phenomenon in a copper (Cu) thin film on a polyimide substrate, allowing for the control of crack formation in terms of both location and shape. Under tensile strain, cracks form on the unirradiated region of the Cu film whereas cracks are prevented on the irradiated region. We attribute this to the enhancement of the adhesion at the Cu–polyimide interface by electrons transmitted through the Cu film. Finally, we selectively form conductive regions in a Cu film on a polyimide substrate under tension and fabricate a strain-responsive organic light-emitting device.

The mechanical properties of steel are intricately connected to their composition and service temperature. Predicting these properties across different work temperatures using traditional statistical methods, algorithms, and equations is highly challenging due to these complex interdependencies. To address this, we developed an artificial-neural-network (ANN) model to elucidate the relationships between composition, temperature, and mechanical properties of 5Cr-0.5Mo steels. Our model demonstrated high accuracy, with minimal percentage errors in predicting YS, UTS, and El (%)—3.5%, 0.97%, and 1.9%, respectively. The ANN predictions are realistic and closely match the experimental results. We propose an easy-to-use model’s GUI to predict steel composition to achieve desired properties at any temperature. The ANN model’s findings offer valuable insights for researchers and designers, aiding in developing steel components with optimized properties. This technique is expected to significantly enhance the planning of practical experiments and improve material performance overall.

The creep rupture life of 5Cr-0.5Mo steels used in high-temperature applications is significantly influenced by factors such as minor alloying elements, hardness, austenite grain size, non-metallic inclusions, service temperature, and applied stress. The relationship of these variables with the creep rupture life is quite complex. In this study, the creep rupture life of 5Cr-0.5Mo steel was predicted using various machine learning (ML) models. To achieve higher accuracy, various ML techniques, including random forest (RF), gradient boosting (GB), linear regression (LR), artificial neural network (ANN), AdaBoost (AB), and extreme gradient boosting (XGB), were applied with careful optimization of hidden parameters. Among these, the ANN-based model demonstrated superior performance, yielding high accuracy with minimal prediction errors for the test dataset (RMSE = 0.069, MAE = 0.053, MAPE = 0.014, and R² = 1). Additionally, we developed a user-friendly graphical user interface (GUI) for the ANN model, enabling users to predict and optimize creep rupture life. This tool helps materials scientists and industrialists prevent failures in high-temperature applications and design steel compositions with enhanced creep resistance.

This work presents a variant of compressed sensing that incorporates problem-specific information in order to achieve better image reconstructions for homogeneous materials in nanotomography.