Assembly of different metal-organic framework (MOF) building blocks into hybrid MOF-on-MOF heterostructures is promising in chemistry and materials science, however the development of ternary MOF-on-MOF heterostructures with controllable architectural and compositional complexity is challenging. Here we report the synthesis of three types of ternary MOF-on-MOF heterostructures via a multiple selective assembly strategy. This strategy relies on the choice of one host MOF with more than one facet that can arrange the growth of a guest MOF, where the arrangement is site-selective without homogenous growth of guest MOF or homogenous coating of guest on host MOF. The growth of guest MOF on a selected site of host MOF in each step provides the opportunity to further vary the combinations of arrangements in multiple steps, leading to ternary MOF-on-MOF heterostructures with tunable complexity. The developed strategy paves the way towards the rational design of intricate and unprecedented MOF-based superstructures for various applications.

A site-specific epitaxial-growth strategy is developed to synthesize two types of MOF-on-MOF heterostructures with elaborately designed architectures, which are comprised of two MOFs with totally different compositions.

In vitro diagnosis (IVD) is one vital component of medical tests that detects biological samples of tissues or bio‐fluids. Recently, mass spectrometry and spectroscopy have been increasingly employed in the field of IVD, due to their high accuracy, facile sample preparation, and rapid detection. Notably, the large datasets generated by these two technology methods provide a wealth of information but subsequently involve complex and time‐consuming processing works. Machine learning (ML), an important branch of artificial intelligence (AI), has emerged as a promising solution for the decoding of big data. ML imitates the human brain to process data, significantly improving accuracy and efficiency compared with traditional processing methods. In this review, we first introduce the commonly used ML algorithms and advanced mass spectrometry and spectroscopy techniques in the field of IVD, respectively. The ML algorithms are summarized as four aspects according to different learning tasks. Then, the combinations of ML with mass spectrometry, spectroscopy, and multi‐modal analysis for IVD are presented, and the roles of ML in these combinations are elucidated by some representative examples. This review aims to provide a systematic and comprehensive summary of the literature on ML‐assisted mass spectrometry or spectroscopy. We believe that it will facilitate researchers to select suitable ML algorithms for supplementing existing detection techniques or to develop the potential of coupling more detection techniques with ML, thus promoting the development of mass spectrometry and spectroscopy in IVD.