Tunable Structured 2D Nanobiocatalysts: Synthesis, Catalytic Properties and New Horizons in Biomedical Applications.

2D materials have offered essential contributions to boosting biocatalytic efficiency in diverse biomedical applications due to the intrinsic enzyme-mimetic activity and massive specific surface area for loading metal catalytic centers. Since the difficulty of high-quality synthesis, the varied structure, and the tough choice of efficient surface loading sites with catalytic properties, the artificial building of 2D nanobiocatalysts still faces great challenges. Here, in this review, a timely and comprehensive summarization of the latest progress and future trends in the design and biotherapeutic applications of 2D nanobiocatalysts is provided, which is essential for their development. First, an overview of the synthesis-structure-fundamentals and structure-property relationships of 2D nanobiocatalysts, both metal-free and metal-based is provided. After that, the effective design of the active sites of nanobiocatalysts is discussed. Then, the progress of their applied research in recent years, including biomedical analysis, biomedical therapeutics, pharmacokinetics, and toxicology is systematically highlighted. Finally, future research directions of 2D nanobiocatalysts are prospected. Overall, this review to provide cutting-edge and multidisciplinary guidance for accelerating future developments and biomedical applications of 2D nanobiocatalysts is expected.

Pyridine-Bridged Covalent Organic Frameworks with Adjustable Band Gaps as Intelligent Artificial Enzymes for Light-Augmented Biocatalytic Sensing.

One of the biggest challenges in biotechnology and medical diagnostics is finding extremely sensitive and adaptable biosensors. Since metal-based enzyme-mimetic biocatalysts may lead to biosafety concerns on accumulative toxicity, it is essential to synthesize metal-free enzyme-mimics with optimal biocatalytic activity and superior selectivity. Here, the pyridine-bridged covalent organic frameworks (COFs) with specific oxidase-like (OXD-like) activities as intelligent artificial enzymes for light-augmented biocatalytic sensing of biomarkers are disclosed. Because of the adjustable bandgaps of pyridine structures on the photocatalytic properties of the pristine COF structures, the pyridine-bridged COF exhibit efficient, selective, and light-responsive OXD-like biocatalytic activity. Moreover, the pyridine-bridged COF structures show tunable and light-augmented biocatalytic detection capabilities, which outperform the recently reported state-of-the-art OXD-mimics regarding biosensing efficiency. Notably, the pyridine-bridged COF exhibits efficient and multifaceted diagnostic activity, including the extremely low limit of detection (LOD), which enables visual assays for abundant reducibility biomarkers. It is believed that this design will offer unique metal-free biocatalysts for high-sensitive and low-cost colorimetric detection and also provide new insights to create highly efficient enzyme-like COF materials via linkage-modulation strategies for future biocatalytic applications.

Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi-Supervised Learning Strategy.

The research of metamaterials has achieved enormous success in the manipulation of light in a prescribed manner using delicately designed subwavelength structures, so-called meta-atoms. Even though modern numerical methods allow for the accurate calculation of the optical response of complex structures, the inverse design of metamaterials, which aims to retrieve the optimal structure according to given requirements, is still a challenging task owing to the nonintuitive and nonunique relationship between physical structures and optical responses. To better unveil this implicit relationship and thus facilitate metamaterial designs, it is proposed to represent metamaterials and model the inverse design problem in a probabilistically generative manner, enabling to elegantly investigate the complex structure-performance relationship in an interpretable way, and solve the one-to-many mapping issue that is intractable in a deterministic model. Moreover, to alleviate the burden of numerical calculations when collecting data, a semisupervised learning strategy is developed that allows the model to utilize unlabeled data in addition to labeled data in an end-to-end training. On a data-driven basis, the proposed deep generative model can serve as a comprehensive and efficient tool that accelerates the design, characterization, and even new discovery in the research domain of metamaterials, and photonics in general.