A comprehensive approach to understand the mechanical behavior of materials involves costly and time-consuming experiments. Recent advances in machine learning and in the field of computational material science could significantly reduce the need for experiments by enabling the prediction of a material’s mechanical behavior. In this paper, a reliable data pipeline consisting of experimentally validated phase field simulations and finite element analysis was created to generate a dataset of dual-phase steel microstructures and mechanical behaviors under different heat treatment conditions. Afterwards, a deep learning-based method was presented, which was the hybridization of two well-known transfer-learning approaches, ResNet50 and VGG16. Hyper parameter optimization (HPO) and fine-tuning were also implemented to train and boost both methods for the hybrid network. By fusing the hybrid model and the feature extractor, the dual-phase steels’ yield stress, ultimate stress, and fracture strain under new treatment conditions were predicted with an error of less than 1%.

Tuning the electromechanical performance in piezoresistive composite strain sensors is primarily attained through appropriately employing the materials system and the fabrication process. High sensitivity along with flexibility in the strain sensing devices needs to be met according to the application (e.g. human motion detection, health and sports monitoring). In this paper, a highly stretchable and sensitive strain sensor with a low-cost fabrication is proposed which is acquired by embedding the chopped carbon fibers sandwiched in between silicone rubber layers. The electrical and mechanical features of the sensor were characterized through stretch/release loading tests where a considerably high sensitivity (the gauge factor about 100) was observed with very low hysteresis. This implies high strain reversibility (i.e. full recovery in each cycle) over 700 loading cycles. Moreover, the sensors exhibited ultra-high stretchability (up to ∼300% elongation) in addition to a low stiffness meaning minimal mechanical effects induced by the sensor for sensitive human motion monitoring applications including large and small deformations. The results suggest the promising capability of the present sensor in reflecting the human body motion detection.

Nanohydrogel wound healing refers to the use of nanotechnology-based hydrogel materials to promote the healing of wounds. Hydrogel dressings are made up of a three-dimensional network of hydrophilic polymers that can absorb and retain large amounts of water or other fluids. Nanohydrogels take this concept further by incorporating nanoscale particles or structures into the hydrogel matrix. These nanoparticles can be made of various materials, such as silver, zinc oxide, or nanoparticles derived from natural substances like chitosan. The inclusion of nanoparticles can provide additional properties and benefits to the hydrogel dressings. Nanohydrogels can be designed to release bioactive substances, such as growth factors or drugs, in a controlled manner. This allows for targeted delivery of therapeutics to the wound site, promoting healing and reducing inflammation. Nanoparticles can reinforce the structure of hydrogels, improving their mechanical strength and stability. Nanohydrogels often incorporate antimicrobial nanoparticles, such as silver or zinc oxide. These nanoparticles have shown effective antimicrobial activity against a wide range of bacteria, fungi, and other pathogens. By incorporating them into hydrogel dressings, nanohydrogels can help prevent or reduce the risk of infection in wounds. Nanohydrogels can be designed to encapsulate and release bioactive substances, such as growth factors, peptides, or drugs, in a controlled and sustained manner. This targeted delivery of therapeutic agents promotes wound healing by facilitating cell proliferation, reducing inflammation, and supporting tissue regeneration. The unique properties of nanohydrogels, including their ability to maintain a moist environment and deliver bioactive agents, can help accelerate the wound healing process. By creating an optimal environment for cell growth and tissue repair, nanohydrogels can promote faster and more efficient healing of wounds.

In this study, a micromechanical finite element model is proposed based on experimental data and the rule of the mixture (RoM) in order to predict the tensile behavior of mechanical properties of heat-treated dual-phase medical-grade titanium (Ti–6Al–4V). Tensile tests, micro-hardness measurements, and RoM were used to obtain flow curves for the α and β phases. Scanning electron microscopy (SEM) imaging was used to determine phase fractions and to create representative volume elements (RVEs). Furthermore, the Gurson-Tvergaard-Needleman (GTN) damage model was calibrated using the Taguchi design of experiment (DOE) method in order to predict damage in the microstructure and the results were compared to fracture surface obtained using fractography in order to investigate failure mechanisms. The final micromechanical model could accurately predict stress-strain curves and showed that void formation and coalescence is the primary mechanism of failure. Finally, analyses of the surfaces showed that a fully ductile fracture occurs at the failure point, which agrees with the results of the damage model. The results suggest that the proposed model can predict the failure of heat-treated Ti–6Al–4V bio-alloys.