# Core–Shell Metal–Organic Framework Composites: A Review of Synthetic Strategies and Applications in Catalysis and Adsorption

## TL;DR

This review discusses how core-shell metal-organic framework composites are made and used in catalysis and gas adsorption, highlighting their potential for future advancements.

## Contribution

The paper systematically reviews synthetic strategies and applications of core-shell MOFs, emphasizing future directions for research and development.

## Key findings

- Core-shell MOFs improve physicochemical stability and functional performance through integration with other materials.
- Applications include catalysis and gas adsorption, such as CO2 capture and natural gas purification.
- Future research should focus on interfacial growth mechanisms and scalable synthesis methods.

## Abstract

Core–shell metal–organic framework (MOF) composites, owing to their unique structural advantages, have emerged as a prominent research focus in the field of chemistry, advanced materials and chemical engineering. By integrating MOFs with other functional components such as MOFs, covalent organic frameworks (COFs), metal oxides, carbon materials, ionic liquids or polymers into synergistic heterogeneous architectures, coreshell MOFs can markedly enhance physicochemical stability and enable diversified functional performances. This work provides a systematic overview of the major construction strategies for these materials, including in situ growth, self-templating, seed-mediated methods, one-pot synthesis and post-synthetic modification. It also summarizes recent applications in catalysis (thermal, electrocatalytic and photocatalytic processes) as well as gas adsorption and separation (such as CO2 capture from flue gas, natural gas purification and acetylene separation). The final section discusses future research directions, including a deeper understanding of interfacial growth mechanisms, the development of green and scalable synthesis routes, the validation of engineering-oriented applications, and the integration of machine learning with high-throughput computation for structural prediction and accelerated materials screening, thereby providing important guidance for the development of high-performance core–shell MOFs.