Nanozymes are one of the ideal alternatives to natural enzymes for various applications. The rational design of nanozymes with improved catalytic activity stimulates increasing attention to address the low activity of current nanozymes. Here, we reported a general strategy to fabricate the Co-based homobimetallic hollow nanocages (HNCs) (C−CoM−HNC, M = Ni, Mn, Cu, and Zn) by ion-assistant solvothermal reaction and subsequent low-temperature calcination from metal−organic frameworks. The C−CoM−HNCs are featured with HNCs composed of interlaced nanosheets with homogeneous bimetallic oxide dispersion. The hierarchical structure and secondary metallic doping endow the C−CoM−HNC highly active sites. In particular, the Cu-doped C−CoCu−HNCs nanostructures exhibit superior performances over the other C−CoM−HNC as both the oxidase mimicking and peroxymonosulfate (PMS) activator. A sensitive bioassay for acetylcholinesterase (AChE) was established based on the excellent oxidase-like activity of C−CoCu−HNC, offering a linear detection range from 0.0001 to 1 mU/mL with an ultralow detection limit of 0.1 mU/L. As the PMS activator, the C−CoCu−HNC was applied for targeted organic pollutant (rhodamine B, RhB) degradation. A highly efficient RhB degradation was realized, along with good adaptability in a wide pH range and good reusability during the eight-cycle run. The results suggest that C−CoCu−HNC holds a practical potential for clinical diagnostics and pollution removal. Further density functional theory calculation reveals that Cu doping leads to a tighter connection and more negative adsorption energy for O 2 / PMS, as well as an upshifted d-band center in the C−CoCu−HNCs nanostructures. These changes facilitated the adsorption of O 2 /PMS on the C−CoCu−HNC surface for dissociation. This work not only offers a promising multifunctional nanozyme catalyst for clinical diagnostics and pollution removal but also gives some clues for the further development of novel nanozymes with high catalytic activities.
Classification of galaxy morphology is a challenging but meaningful task for the enormous amount of data produced by the next-generation telescope. By introducing the adaptive polar-coordinate transformation, we develop a rotationally-invariant supervised machine-learning (SML) method that ensures consistent classifications when rotating galaxy images, which is always required to be satisfied physically, but difficult to achieve algorithmically. The adaptive polar-coordinate transformation, compared with the conventional method of data augmentation by including additional rotated images in the training set, is proved to be an effective and efficient method in improving the robustness of the SML methods. In the previous work, we generated a catalog of galaxies with well-classified morphologies via our developed unsupervised machine-learning (UML) method. By using this UML data set as the training set, we apply the new method to classify galaxies into five categories (unclassifiable, irregulars, late-type disks, early-type disks, and spheroids). In general, the result of our morphological classifications following the sequence from irregulars to spheroids agrees well with the expected trends of other galaxy properties, including Sérsic indices, effective radii, nonparametric statistics, and colors. Thus, we demonstrate that the rotationally-invariant SML method, together with the previously developed UML method, completes the entire task of automatic classification of galaxy morphology.
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