Coordination of {Mo142} Ring to La3+ Provides Elliptical {Mo134La10} Ring with a Variety of Coordination Modes

Lanthanides Singing the Blues: Their Fascinating Role in the Assembly of Gigantic Molybdenum Blue Wheels

Molybdenum blues (MBs) are a distinct class of polyoxometalates, exhibiting versatile/impressive architectures and high structural flexibility. In acidified and reduced aqueous environments, isopolymolybdates generate precisely organizable building blocks, which enable unique nanoscopic molecular systems (MBs) to be constructed and further fine-tuned by hetero elements such as lanthanide (Ln) ions. This Review discusses wheel-shaped MB-based structure types with strong emphasis on the ∼30 Ln-containing MBs as of August 2021, which include both organically hybridized and nonhybridized structures synthesized to date. The spotlight is thereby put on the lanthanide ions and ligand types, which are crucial for the resulting Ln-patterns and alterations in the gigantic structures. Several critical steps and reaction conditions in their synthesis are highlighted, as well as appropriate methods to investigate them both in solid state and in solution. The final section addresses the homogeneous/heterogeneous catalytic, molecular recognition and separation properties of wheel-shaped Ln-MBs, emphasizing their inimitable behavior and encouraging their application in these areas.

Human versus Robots in the Discovery and Crystallization of Gigantic Polyoxometalates

The discovery of new gigantic molecules formed by self-assembly and crystal growth is challenging as it combines two contingent events; first is the formation of a new molecule, and second its crystallization. Herein, we construct a workflow that can be followed manually or by a robot to probe the envelope of both events and employ it for a new polyoxometalate cluster, Na₆ [Mo₁₂₀ Ce₆ O₃₆₆ H₁₂ (H₂ O)₇₈ ]⋅200 H₂ O (1) which has a trigonal-ring type architecture (yield 4.3 % based on Mo). Its synthesis and crystallization was probed using an active machine-learning algorithm developed by us to explore the crystallization space, the algorithm results were compared with those obtained by human experimenters. The algorithm-based search is able to cover ca. 9 times more crystallization space than a random search and ca. 6 times more than humans and increases the crystallization prediction accuracy to 82.4±0.7 % over 77.1±0.9 % from human experimenters.