Cucurbit[n]uril (CB[n]) macrocycles (especially CB[5] to CB[8]) have shown exceptional attributes since their discovery in 2000. Their stability, water solubility, responsiveness to several stimuli, and remarkable binding properties have enabled a growing number of biological applications. Yet, soon after their discovery, the challenge of their functionalization was set. Nevertheless, after more than two decades, a myriad of CB[n] derivatives has been described, many of them used in cells or in vivo for advanced applications. This perspective summarizes key advances of this burgeoning field and points to the next opportunities and remaining challenges to fully express the potential of these fascinating macrocycles in biology and biomedical sciences.

In the past decades, machine learning (ML) has impacted the field of electrocatalysis. Modern researchers have begun to take advantage of ML‐based data‐driven techniques to overcome the computational and experimental limitations to accelerate rational catalyst design. Hence, significant efforts have been made to perform ML to accelerate calculation and aid electrocatalyst design for CO₂ reduction. This review discusses recent applications of ML to discover, design, and optimize novel electrocatalysts. First, insights into ML aided in accelerating calculation are presented. Then, ML aided in the rational design of the electrocatalyst is introduced, including establishing a data set/data source selection and validation of descriptor selection of ML algorithms validation and predictions of the model. Finally, the opportunities and future challenges are summarized for the future design of electrocatalyst for CO₂ reduction with the assistance of ML.

Periodic nanostructures, a group of nanomaterials consisting of single or multiple nano units/components periodically arranged into ordered patterns (e.g., vertical and lateral superlattices), have attracted tremendous attention in recent years due to their extraordinary physical and chemical properties that offer a huge potential for a multitude of applications in energy conversion, electronic and optoelectronic applications. Recent advances in the preparation strategies of periodic nanostructures, including self-assembly, epitaxy, and exfoliation, have paved the way to rationally modulate their ferroelectricity, superconductivity, band gap and many other physical and chemical properties. For example, the recent discovery of superconductivity observed in “magic-angle” graphene superlattices has sparked intensive studies in new ways, creating superlattices in twisted 2D materials. Recent development in the various state-of-the-art preparations of periodic nanostructures has created many new ideas and findings, warranting a timely review. In this review, we discuss the current advances of periodic nanostructures, including their preparation strategies, property modulations and various applications.