Rational design of MoS2-supported Cu single-atom catalysts by machine learning potential for enhanced peroxidase-like activity.

Two-dimensional molybdenum disulfide (2D-MoS2)-supported single atom nanomaterials with enhanced enzyme-like activities are potential substitutes for natural enzymes due to their huge specific surface areas, ease of decoration, high catalytic activity and high catalytic stability. However, their catalytic mechanism remains unclear, making the rational design of nanozymes difficult to achieve. Herein, the mechanisms have been explored to enhance the peroxidase-like activity of MoS2 for H2O2 decomposition. Global neutral network (G-NN) potentials were constructed to accurately and quickly illustrate the mechanisms of MoS2 catalysts and their surface modifications. The high peroxidase-like activity of the MoS2-supported Cu single atom catalyst with sulfur vacancy (Cu@MoS2-Vs) in acidic conditions was systematically evaluated using the trained G-NN potential and density functional theory (DFT), as well as experimental validation. Further analysis of the geometric and electronic properties of pivotal stationary structures revealed the enhanced electron transfer process for high catalytic performance with the modulation of the Cu single atom loading, sulfur vacancy engineering and the surrounding acidic and alkaline environment regulation on the MoS2 basal plane. The results also showed that Cu@MoS2-Vs in an acidic environment exhibited the highest peroxidase-like activity. This work is expected to provide broad implications for the rational design of substrate-supported single-atom catalysts with superior performance and lower cost by surface modification and acidic and alkaline environment regulation.

Fast extraction of three-dimensional nanofiber orientation from WAXD patterns using machine learning.

Structural disclosure of biological materials can help our understanding of design disciplines in nature and inspire research for artificial materials. Synchrotron microfocus X-ray diffraction is one of the main techniques for characterizing hierarchically structured biological materials, especially the 3D orientation distribution of their interpenetrating nanofiber networks. However, extraction of 3D fiber orientation from X-ray patterns is still carried out by iterative parametric fitting, with disadvantages of time consumption and demand for expertise and initial parameter estimates. When faced with high-throughput experiments, existing analysis methods cannot meet the real time analysis challenges. In this work, using the assumption that the X-ray illuminated volume is dominated by two groups of nanofibers in a gradient biological composite, a machine-learning based method is proposed for fast and automatic fiber orientation metrics prediction from synchrotron X-ray micro-focused diffraction data. The simulated data were corrupted in the training procedure to guarantee the prediction ability of the trained machine-learning algorithm in real-world experimental data predictions. Label transformation was used to resolve the jump discontinuity problem when predicting angle parameters. The proposed method shows promise for application in the automatic data-processing pipeline for fast analysis of the vast data generated from multiscale diffraction-based tomography characterization of textured biomaterials.

Coordination-Driven Self-Assembly Strategy-Activated Cu Single-Atom Nanozymes for Catalytic Tumor-Specific Therapy.

How to optimize the enzyme-like catalytic activity of nanozymes to improve their applicability has become a great challenge. Herein, we present an l-cysteine (l-Cys) coordination-driven self-assembly strategy to activate polyvinylpyrrolidone (PVP)-modified Cu single-atom nanozymes MoOx-Cu-Cys (denoted as MCCP SAzymes) aiming at catalytic tumor-specific therapy. The Cu single atom content of MCCP can be rationally modulated to 10.10 wt %, which activates the catalase (CAT)-like activity of MoOx nanoparticles to catalyze the decomposition of H2O2 in acidic microenvironments to increase O2 production. Excitingly, the maximized CAT-like catalytic efficiency of MCCP is 138-fold higher than that of typical MnO2 nanozymes and exhibits 14.3-fold higher affinity than natural catalase, as demonstrated by steady-state kinetics. We verify that the well-defined l-Cys-Cu···O active sites optimize CAT-like activity to match the active sites of natural catalase through an l-Cys bridge-accelerated electron transfer from Cys-Cu to MoOx disclosed by density functional theory calculations. Simultaneously, the high loading Cu single atoms in MCCP also enable generation of •OH via a Fenton-like reaction. Moreover, under X-ray irradiation, MCCP converts O2 to 1O2 for cascading radiodynamic therapy, thereby facilitating the multiple reactive oxygen species (ROS) for radiosensitization to achieve substantial antitumor.

Catalase-Like Nanozymes: Classification, Catalytic Mechanisms, and Their Applications.

The field of nanozymes has developed rapidly over the past decade. Among various oxidoreductases mimics, catalase (CAT)-like nanozyme, acting as an essential part of the regulation of reactive oxygen species (ROS), has attracted extensive research interest in recent years. However, CAT-like nanozymes are not as well discussed as other nanozymes such as peroxidase (POD)-like nanozymes, etc. Compared with natural catalase or artificial CAT enzymes, CAT-like nanozymes have unique properties of low cost, size-dependent properties, high catalytic activity and stability, and easy surface modification, etc., which make them widely used in various fields, especially in tumor therapy and disease treatment. Consequently, there is a great requirement to make a systematic discussion on CAT-like nanozymes. In this review, some key aspects of CAT-like nanozymes are deeply summarized as: 1) Typical CAT-like nanozymes classified by different nanomaterials; 2) The catalytic mechanisms proposed by experimental and theoretical studies; 3) Extensive applications in regard to tumor therapy, cytoprotection and sensing. Therefore, it is prospected that this review will contribute to the further design of CAT-like nanozymes and optimize their applications with much higher efficiency than before.

Understanding the Unique Antioxidation Property of Boron-Based Catalysts during Oxidative Dehydrogenation of Alkanes.

Boron-based catalysts show excellent performance in oxidative dehydrogenation (ODH) of light alkanes to alkenes with high selectivity and extremely good antioxidation properties. However, the anti-deep-oxidation mechanism remains unclear. Herein, we chose h-BN and B2O3 as representative boron-based catalysts to investigate their reactions with two important intermediates in the light alkane ODH, Et· (evolving to ethene) and EtO· (evolving to ethene or COx), to elucidate the origin of the antioxidation of alkanes. The density functional theory calculations reveal that surface boron sites could eliminate alkoxy in their vicinity, resulting in exceptional inhibition of alkane deep-oxidation. The analysis of the electronic and geometric structures of key stationary points showed that the oxophilicity of B determined the low deep-oxidation of alkanes, and the homoleptic coordination of B with all three ligating atoms being O moderately enhanced its oxophilicity. This work represents a novel conceptual advance in the mechanistic understanding of alkane ODH.