Fig. 2: Integrative schematic overview of interconnected synergy between synthesis, post-etching processing, characterization, bioengineering application, and computation-based/predictive modeling of MXenes (Created in BioRender).

This figure provides a conceptual framework illustrating the interdependence of different aspects of MXene research from synthesis to biomedical application,s with computational approaches acting as integrative bridges across stages. The innermost concentric circle depicts the different forms of MXenes, including multi-layered nanosheets to single/few-layered nanosheets to quantum dots, representing the foundation for all subsequent processes and applications. Surrounding this, the outer circle represents the main elements covered in the review and their interconnected flow in an anticlockwise direction: material synthesis to post-etching processing to physiochemical characterization to bioengineering applications. The top rectangular box summarizes various synthesis methods, including conventional HF-based and fluoride-free etching, bottom-up routes, physicochemical and emerging methods such as electrochemical and molten-salt etching, and chemical vapor deposition. The left box outlines post-etching processing steps such as intercalation with ions or organic molecules, delamination via probe or bath sonication, and surface functionalization with chemical groups, peptides, or polymers to tailor properties for biomedical use. At the bottom, the physicochemical characterization box lists critical microscopy- and spectroscopy-based techniques essential for structural, compositional, and functional validation of MXenes. Encircling arcs emphasize how computational and analytical tools link these domains: machine learning-based parameter prediction connects synthesis with post-etching optimization; composition simulations bridge processing and characterization; and deep learning-based data analysis links characterization to bioengineering applications through structure–function correlations. Collectively, this framework highlights how computational integration deepens insights into MXene research, guiding optimization and emerging biomedical applications.