Bio-high entropy alloys: Progress, challenges, and opportunities

A Systematic Review of the Contribution of Additive Manufacturing toward Orthopedic Applications

Human bone holds an inherent capacity for repairing itself from trauma and damage, but concerning the severity of the defect, the choice of implant placement is a must. Additive manufacturing has become an elite option due to its various specifications such as patient-specific custom development of implants and its easy fabrication rather than the conventional methods used over the years. Additive manufacturing allows customization of the pore size, porosity, various mechanical properties, and complex structure design and formulation. Selective laser melting, powder bed fusion, electron beam melting, and fused deposition modeling are the various AM methods used extensively for implant fabrication. Metals, polymers, biocrystals, composites, and bio-HEA materials are used for implant fabrication for various applications. A wide variety of polymer implants are fabricated using additive manufacturing for nonload-bearing applications, and β-tricalcium phosphate, hydroxyapatite, bioactive glass, etc. are mainly used as ceramic materials in additive manufacturing due to the biological properties that could be imparted by the latter. For decades metals have played a major role in implant fabrication, and additive manufacturing of metals provides an easy approach to implant fabrication with augmented qualities. Various challenges and setbacks faced in the fabrication need postprocessing such as sintering, coating, surface polishing, etc. The emergence of bio-HEA materials, printing of shape memory implants, and five-dimensional printing are the trends of the era in additive manufacturing.

High-Entropy Materials for Prospective Biomedical Applications: Challenges and Opportunities

With their unique structural characteristics, customizable chemical composition, and adjustable functional characteristics, high-entropy materials (HEMs) have triggered a wide range of interdisciplinary research, especially in the biomedical field. In this paper, the basic concept, core properties, and preparation methods of HEMs are first summarized, and then the application and development of HEMs in the field of biomedical are briefly described. Subsequently, based on the diverse and comprehensive properties of HEMs and a few reported cases, the possible application scenarios of HEMs in biological fields such as biosensors, antibacterial materials, therapeutics, bioimaging, and tissue engineering are prospectively predicted and discussed. Finally, their potential advantages and major challenges is summarized, which may provide useful guidance and principles for researchers to develop and optimize novel HEMs.

Research progress of metal biomaterials with potential applications as cardiovascular stents and their surface treatment methods to improve biocompatibility

Facing the growing issue of cardiovascular diseases, metallic materials with higher tensile strength and fatigue resistance play an important role in treating diseases. This review lists the advantages and drawbacks of commonly used medical metallic materials for vascular stents. To avoid post-procedural threats such as thrombosis and in-stent restenosis, surface treatments, and coating methods have been used to further improve the biocompatibility of these materials. Surface treatments including laser, plasma treatment, polishing, oxidization, and fluorination can improve biocompatibility by modifying the surface charges, surface morphology, and surface properties of the material. Coating methods based on polymer coatings, carbon-based coatings, and drug-functional coatings can regulate the surface properties, and also serve as an effective barrier to the interaction of metallic biomaterial surfaces with biomolecules, which can be used to improve corrosion resistance and stability, as well as improve their biocompatibility. Biocompatibility serves as the most fundamental property of cardiovascular stents, and maintaining the excellent and stable biocompatibility of cardiovascular stent surfaces is a current research bottleneck. Few reviews have been published on metallic biomaterials as cardiovascular stents and their surface treatments. For the purpose of advancing research on cardiovascular stents, common metal biomaterials, surface treatment methods, and coating methods to improve biocompatibility and comprehensive properties of the materials are described in this review. Finally, we suggest future directions for stent development, including continuously improving the durability and stability of permanent stents, accelerating the development of biodegradable stents, and strengthening feedback to improve the safety and reliability of cardiovascular stents.

Machine Learning Paves the Way for High Entropy Compounds Exploration: Challenges, Progress, and Outlook

Machine learning (ML) has emerged as a powerful tool in the research field of high entropy compounds (HECs), which have gained worldwide attention due to their vast compositional space and abundant regulatability. However, the complex structure space of HEC poses challenges to traditional experimental and computational approaches, necessitating the adoption of machine learning. Microscopically, machine learning can model the Hamiltonian of the HEC system, enabling atomic-level property investigations, while macroscopically, it can analyze macroscopic material characteristics such as hardness, melting point, and ductility. Various machine learning algorithms, both traditional methods and deep neural networks, can be employed in HEC research. Comprehensive and accurate data collection, feature engineering, and model training and selection through cross-validation are crucial for establishing excellent ML models. ML also holds promise in analyzing phase structures and stability, constructing potentials in simulations, and facilitating the design of functional materials. Although some domains, such as magnetic and device materials, still require further exploration, machine learning's potential in HEC research is substantial. Consequently, machine learning has become an indispensable tool in understanding and exploiting the capabilities of HEC, serving as the foundation for the new paradigm of Artificial-intelligence-assisted material exploration.