Very recently, we have reported preparation of several types of Prussian Blue (PB) particles with varying particle sizes by systematically tuning the synthetic conditions (Angew. Chem., Int. Ed., 2012, 51, 984–988). Here, the obtained PB particles are used for removal of Cs ions from aqueous solutions, which will be useful for remediation of nuclear waste. To evaluate the uptake ability of Cs ions into the PB particles, we utilize quartz crystal microbalance (QCM) for real-time monitoring of uptake behavior of Cs ions into the PB particles. The frequency of the QCM is promptly decreased after injection of Cs ions solution into the QCM cell. Hollow PB nanoparticles of 190 nm in diameter have very high surface area (338 m² g⁻¹), in comparison with other PB particles, leading to efficient Cs adsorption capability eight times larger than that of the commercial PB particles. The diffusion in terms of dissociation constant (K_d), maximum amount of adsorbed Cs in PB particles (m_max), and the adsorption kinetics (k) of Cs ions into the PB particles are also discussed. Due to the selective uptake for Cs ions based on K_d and k values, the PB particles can be proposed as good candidates in waste management consideration.

Machine learning is emerging as a powerful tool for the discovery of novel high-performance functional materials. However, experimental datasets in the polymer-science field are typically limited and they are expensive to build. Their size (< 100 samples) limits the development of chemical intuition from experimentalists, as it constrains the use of machine-learning algorithms for extracting relevant information. We tackle this issue to predict and optimize adhesive materials by combining laboratory experimental design, an active learning pipeline and Bayesian optimization. We start from an initial dataset of 32 adhesive samples that were prepared from various molecular-weight bisphenol A-based epoxy resins and polyetheramine curing agents, mixing ratios and curing temperatures, and our data-driven method allows us to propose an optimal preparation of an adhesive material with a very high adhesive joint strength measured at 35.8 ± 1.1 MPa after three active learning cycles (five proposed preparations per cycle). A Gradient boosting machine learning model was used for the successive prediction of the adhesive joint strength in the active learning pipeline, and the model achieved a respectable accuracy with a coefficient of determination, root mean square error and mean absolute error of 0.85, 4.0 MPa and 3.0 MPa, respectively. This study demonstrates the important impact of active learning to accelerate the design and development of tailored highly functional materials from very small datasets. Graphical abstract

Architected cellular materials are a class of artificial materials with cellular architecture-dependent properties. Typically, designing cellular architectures paves the way to generate architected cellular materials with specific properties. However, most previous studies have primarily focused on a forward design strategy, wherein a geometry is generated using computer-aided design modeling, and its properties are investigated experimentally or via simulations. In this study, we developed an inverse design framework for a disordered architected cellular material (Voronoi lattices) using deep learning. This inverse design framework is a three-dimensional conditional generative adversarial network (3D-CGAN) trained based on supervised learning using a dataset consisting of voxelized Voronoi lattices and their corresponding relative densities and Young’s moduli. A well-trained 3D-CGAN adopts variational sampling to generate multiple distinct Voronoi lattices with the target relative density and Young’s modulus. Consequently, the mechanical properties of the 3D-CGAN generated Voronoi lattices are validated through uniaxial compression tests and finite element simulations. The inverse design framework demonstrates potential for use in bone implants, where scaffold implants can be automatically generated with the target relative density and Young’s modulus. Graphical Abstract

A planar-chiral pillar[5]arene derivative containing one π-conjugated unit was prepared. Chiroptical changes were observed upon addition of the achiral guest 1,4-dicyanobutane.