Exploration and Insights on Topology Adjustment of Giant Heterometallic Cages Featuring Inorganic Skeletons Assisted by Machine Learning

Electrospray Ionization Mass Spectrometry Insights into the Assembly of Lanthanide-Containing Clusters

ConspectusAtomically precise metal clusters with well-defined crystal structures have emerged as a rapidly growing field within coordination and materials chemistry. Among them, lanthanide-containing clusters (LCCs) are particularly notable for their aesthetically pleasing architectures and intriguing properties. Achieving precise synthesis and accurate structural characterization of these clusters is crucial for unlocking their potential applications. Mass spectrometry (MS), particularly electrospray ionization mass spectrometry (ESI-MS), has proven to be a powerful tool, providing exceptional sensitivity and clarity in revealing the formation mechanisms and structural details of metal clusters. In this Account, we explore the synthesis, characterization, and assembly mechanisms of LCCs utilizing ESI-MS. We begin by tracing the historical development of LCCs, emphasizing the critical role of single-crystal X-ray diffraction in structural confirmation and the challenges associated with it. We then discuss the application of ESI-MS in characterizing LCCs, highlighting how this technique can monitor the formation processes of LCCs and determine their molecular weights and charge states. We introduce the mass difference fingerprint of isomorphism (MDFI) method, which can facilitate rapid analysis of LCCs' mass spectrometry data. Furthermore, we discuss the state of LCCs in solution and the challenges in their characterization. By utilizing ESI-MS, we enhance the understanding of the assembly mechanisms of LCCs and propose new strategies for designing and synthesizing new LCCs with tailored structures and functions. Looking forward, the ESI-MS method will play increasingly significant roles in LCC research. The continued development of these technologies will deepen the understanding of the structure-property relationships. With the ongoing convergence of computational chemistry and information science, we anticipate more precise design and synthesis of LCCs, leading to broader applications in magnetism, optics, and catalysis.

Lanthanide-Incorporated PIII-SbIII-Heteroatom-Templated Tetrahedral Heteropolyoxometalate Cluster for Detecting Early Tumor Marker MicroRNA-155

Heteropolyoxometalates have garnered widespread attention owing to their diverse structures and intriguing physicochemical properties. Herein, we successfully synthesized a unique lanthanide-incorporated PIII-SbIII-heteroatom-templated tetrahedral polyoxometalate cluster [H2N(CH3)2]20Na7H7{[Er6(H2O)6][W4O16][HPSbW15O54]4}·132H2O (1). The polyoxoanion of 1 can be perceived as four trilacunary Dawson [HPSbW15O54]11- {PSbW15} building blocks encapsulating a deca-nuclear heterometallic [Er6(H2O)6W4O16]10+ cluster. This heterometallic cluster comprises an inner tetrahedral [W4O16]8- ({W4}) core surrounded by an octahedral [Er6(H2O)6]18+ ({Er6}) shell. Consequently, the polyoxoanion of 1 exhibits the distinctive ({W4} ⊂ {Er6} ⊂ [{PSbW15}]4) three-shell structure. Considering the electron transfer characteristics of 1, it was coelectropolymerized with N-methylpyrrole (NMPy) to fabricate the 1-PNMPy film (PNMPy = polyNMPy). Notably, the incorporation of 1 improves the electron distribution within the PNMPy backbone, thereby narrowing the band gap of PNMPy and enhancing the conductive performance of the 1-PNMPy film. Therefore, the 1-PNMPy film is utilized as the modified electrode material to construct an electrochemical biosensor for the sensitive detection of the tumor marker microRNA-155. This research not only provides a viable approach for the fabrication of multishell heteropolyoxometalates but also promotes the exploration and application of multicomposition polyoxometalates in electrochemical biosensing microRNA.

Porous 3 d-4 f Coordination Clusters for Selective Visible-Light Photocatalytic CO2 Reduction to CO

We report herein two families of porous coordination clusters (PCCs) with 216 nuclearity (M120RE96 or PCC-216MR) and 300 nuclearity (Co144Gd156 or PCC-300CG). For the first family M could be either nickel or cobalt, and RE = Pr, Nd, Sm, Eu, and Gd; while the latter features the highest nuclearity of transition-rare earth metal clusters. Characterized by their cube-like, hollow structures, these clusters exhibit the ability to absorb N2 and CO2. Besides, these clusters can be dissolved in both aqueous and acetonitrile/methanol solutions, and capable of acting as homogeneous catalysts for converting CO2 to CO under visible light. The gadolinium analogues of these clusters all show turnover numbers over 10000 and turnover frequencies over 1 s-1. In particular, the nickel based bimetallic cluster (PCC-216NG) demonstrates nearly 100 % selectivity for the reduction product, which may open a new direction for the design and development of PCCs based catalysts.

How to accelerate the inorganic materials synthesis: from computational guidelines to data-driven method?

The development of novel functional materials has attracted widespread attention to meet the constantly growing demand for addressing the major global challenges facing humanity, among which experimental synthesis emerges as one of the crucial challenges. Understanding the synthesis processes and predicting the outcomes of synthesis experiments are essential for increasing the success rate of experiments. With the advancements in computational power and the emergence of machine learning (ML) techniques, computational guidelines and data-driven methods have significantly contributed to accelerating and optimizing material synthesis. Herein, a review of the latest progress on the computation-guided and ML-assisted inorganic material synthesis is presented. First, common synthesis methods for inorganic materials are introduced, followed by a discussion of physical models based on thermodynamics and kinetics, which are relevant to the synthesis feasibility of inorganic materials. Second, data acquisition, commonly utilized material descriptors, and ML techniques in ML-assisted inorganic material synthesis are discussed. Third, applications of ML techniques in inorganic material synthesis are presented, which are classified according to different material data sources. Finally, we highlight the crucial challenges and promising opportunities for ML-assisted inorganic materials synthesis. This review aims to provide critical scientific guidance for future advancements in ML-assisted inorganic materials synthesis.

Anion-Guided Hierarchical Assembly of Heterometallic Clusters

Anions are important as templates for the construction of many structurally sophisticated and functional supramolecular architectures. Deciphering the specific role of such templates remains a great challenge due to the unique characteristics of anions. Herein, three heterometallic Ce-Ni clusters, namely, [Ce24Ni36L36(μ4-OAc)6(OH)52(Cl)8]Cl6·110H2O (Ce24Ni36, OAc = acetate ion, L = N-methyliminodiacetate), [Ce17Ni27L27(μ6-C2O4)6(OH)15(Cl)16(H2O)19](ClO4)2Cl6·45H2O (Ce17Ni27), and [Ce21Ni30L30(μ6-C2O4)2(μ4-OAc)(OH)32(Cl)15(H2O)14](ClO4)4Cl7·55H2O (Ce21Ni30), were obtained and structurally characterized; the formation of these cage-like clusters was assisted, respectively, by acetate, oxalate, and a combination of the two as templates. Structural analyses generate key information on the anion-directed hierarchical assembly, starting from a primary building block of {CeNi3} to two anion-specific secondary building blocks (SBUs)─the tetragonal {Ce4Ni8(OAc)} and the pentagonal {Ce5Ni9(C2O4)}. Further assembly steps are guided by these curved SBUs, affording a truncated octahedral Ce24Ni36 and a lantern-like Ce17Ni27, with acetate and oxalate being the sole template, respectively. The combined use of acetate and oxalate produced Ce21Ni30, a cluster with an unprecedented structure viewable as a hybrid of a truncated octahedron and a dodecahedron.

Application of Hard and Soft Acid-base Theory to Construct Heterometallic Materials with Metal-oxo Clusters

Heterometallic cluster-based materials offer the potential to incorporate multiple functionalities, leveraging the aggregation effects of clusters and translating this heterogeneity and complexity into unexpected properties that are more than just the sum of their components. However, the rational construction of heterometallic cluster-based materials remains challenging due to the complexity of metal cation coordination and structural unpredictability. This minireview provides insights into a general synthetic strategy based on Hard and Soft Acids and Bases (HSAB) theory, summarizing its advantages in the designed synthesis of discrete heterometallic clusters (intracluster assembly) and infinite heterometallic cluster-based materials (intercluster assembly). Furthermore, it emphasizes the potential to exploit the intrinsic properties of mixed components to achieve breakthroughs across a broad range of applications. The insights from this review are expected to drive the progress of heterometallic cluster-based materials in a controllable and predictable manner.

When Metal Nanoclusters Meet Smart Synthesis

Atomically precise metal nanoclusters (MNCs) represent a fascinating class of ultrasmall nanoparticles with molecule-like properties, bridging conventional metal-ligand complexes and nanocrystals. Despite their potential for various applications, synthesis challenges such as a precise understanding of varied synthetic parameters and property-driven synthesis persist, hindering their full exploitation and wider application. Incorporating smart synthesis methodologies, including a closed-loop framework of automation, data interpretation, and feedback from AI, offers promising solutions to address these challenges. In this perspective, we summarize the closed-loop smart synthesis that has been demonstrated in various nanomaterials and explore the research frontiers of smart synthesis for MNCs. Moreover, the perspectives on the inherent challenges and opportunities of smart synthesis for MNCs are discussed, aiming to provide insights and directions for future advancements in this emerging field of AI for Science, while the integration of deep learning algorithms stands to substantially enrich research in smart synthesis by offering enhanced predictive capabilities, optimization strategies, and control mechanisms, thereby extending the potential of MNC synthesis.

Unraveling the Intertwining Factors Underlying the Assembly of High-Nuclearity Heterometallic Clusters

Two closely related yet distinctly different cationic clusters, [Dy52Ni44(HEIDA)36(OH)138(OAc)24(H2O)30]10+ (1) and [Dy112Ni76(HEIDA)44(EIDA)24(IDA)4(OH)268(OAc)48(H2O)44]4+ (2) (HEIDA=N-(2-hydroxyethyl)iminodiacetate), each featuring a multi-shell core of Platonic and Archimedean polyhedra, were obtained. Depending on the specific conditions used for the co-hydrolysis of Dy3+ and Ni2+, the product can be crystallized out as one particular type of cluster or as a mixture of 1 and 2. How the reaction process was affected by the amount of hydrolysis-facilitating base and/or by the reaction temperature and duration was investigated. It has been found that a reaction at a high temperature and/or for an extended period favors the formation of the compact and thermodynamically more stable 1, while a brief reaction with a large amount of the base is good for the kinetic product 2. By tuning these intertwining conditions, the reaction can be regulated toward a particular product.

Machine Learning Optimizing Enzyme/ZIF Biocomposites for Enhanced Encapsulation Efficiency and Bioactivity

In this study, we present the first example of using a machine learning (ML)-assisted design strategy to optimize the synthesis formulation of enzyme/ZIFs (zeolitic imidazolate framework) for enhanced performance. Glucose oxidase (GOx) and horseradish peroxidase (HRP) were chosen as model enzymes, while Zn(eIM)2 (eIM = 2-ethylimidazolate) was selected as the model ZIF to test our ML-assisted workflow paradigm. Through an iterative ML-driven training-design-synthesis-measurement workflow, we efficiently discovered GOx/ZIF (G151) and HRP/ZIF (H150) with their overall performance index (OPI) values (OPI represents the product of encapsulation efficiency (E in %), retained enzymatic activity (A in %), and thermal stability (T in %)) at least 1.3 times higher than those in systematic seed data studies. Furthermore, advanced statistical methods derived from the trained random forest model qualitatively and quantitatively reveal the relationship among synthesis, structure, and performance in the enzyme/ZIF system, offering valuable guidance for future studies on enzyme/ZIFs. Overall, our proposed ML-assisted design strategy holds promise for accelerating the development of enzyme/ZIFs and other enzyme immobilization systems for biocatalysis applications and beyond, including drug delivery and sensing, among others.

A Cubic Tinkertoy-like Heterometallic Cluster with a Record Magnetocaloric Effect

Clusters showing a giant magnetocaloric effect (MCE) are of interest as molecular coolants for magnetic refrigeration. Herein, we report two heterometallic clusters, denoted as Gd152Ni14@Cl24 and Sm152Ni8, just to highlight their inorganic core motifs, obtained by ligand-controlled co-hydrolysis of Ni2+ and Ln3+ (Ln = Gd, Sm) in the presence of N-(2-hydroxyethyl)iminodiacetic acid (H2HEIDA). Both clusters display fascinating cubic Tinkertoy-like structures, with the core motifs being built of multiple metallic shells of Platonic and Archimedean polyhedra. The isothermal magnetic entropy change─a direct measurement of MCE─was determined to be 52.65 J·kg-1·K-1 at 2.5 K and 7.0 T for the Gd-containing cluster; this value is the highest known for any molecular clusters so far reported.