ABSTRACT
"Signal-off" nanozyme sensing platforms are usually employed to detect analytes (e.g., ascorbic acid (AA) and alkaline phosphatase (ALP)), which are mostly based on oxidase (OXD) nanozymes. However, their drawbacks, like dissolved oxygen-dependent catalysis capability, relatively low enzyme activity, limited amount, and kind, may not favor sensing platforms' optimization. Meanwhile, with the need for sustainable development, a reusable "signal-off" sensing platform is essential for cutting down the cost of the assay, but it is rarely developed in previous studies. Magnetic peroxidase (POD) nanozymes potentially make up the deficiencies and become reusable and better "signal-off" sensing platforms. As a proof of concept, we first construct Fe3O4@polydopamine-supported Pt/Ru alloy nanoparticles (IOP@Pt/Ru) without stabilizers. IOP@Pt/Ru shows high POD activity with Vmax of 83.24 × 10-8 M·s-1 for 3,3',5,5'-Tetramethylbenzidine (TMB) oxidation. Meanwhile, its oxidation rate for TMB is slower than the reduction of oxidized TMB by reducers, favorable for a more significant detection signal. On the other hand, IOP@Pt/Ru possesses great magnet-responsive capability, making itself be recycled and reused for at least 15-round catalysis. When applying IOP@Pt/Ru for AA (ALP) detection, it performs better detectable adaptability, with a linear range of 0.01-0.2 mM (0.1-100 U/L) and a limit of detection of 0.01 mM (0.05 U/L), superior to most of OXD nanozyme-based ALP sensing platform. Finally, IOP@Pt/Ru's reusable assay was demonstrated in real blood samples for ALP assay, which has never been explored in previous studies. Overall, this study develops a reusable "signal-off" nanozyme sensing platform with superior assay capabilities than traditional OXD nanozymes, paves a new way to optimize nanozyme-based "signal-off" sensing platforms, and provides an idea for constructing inexpensive and sustainable sensing platforms.
ABSTRACT
Image clustering is a research hotspot in machine learning and computer vision. Existing graph-based semi-supervised deep clustering methods suffer from three problems: 1) because clustering uses only high-level features, the detailed information contained in shallow-level features is ignored; 2) most feature extraction networks employ the step odd convolutional kernel, which results in an uneven distribution of receptive field intensity; and 3) because the adjacency matrix is precomputed and fixed, it cannot adapt to changes in the relationship between samples. To solve the above problems, we propose a novel graph-based semi-supervised deep clustering method for image clustering. First, the parity cross-convolutional feature extraction and fusion module is used to extract high-quality image features. Then, the clustering constraint layer is designed to improve the clustering efficiency. And, the output layer is customized to achieve unsupervised regularization training. Finally, the adjacency matrix is inferred by actual network prediction. A graph-based regularization method is adopted for unsupervised training networks. Experimental results show that our method significantly outperforms state-of-the-art methods on USPS, MNIST, street view house numbers (SVHN), and fashion MNIST (FMNIST) datasets in terms of ACC, normalized mutual information (NMI), and ARI.
ABSTRACT
Peroxidase (POD) Nanozyme-based hydrogen peroxide (H2 O2 ) detection is popular, but hardly adapt to high concentration of H2 O2 owing to narrow linear range (LR) and low LR maximum. Here, a solution of combining POD and catalase (CAT) is raised to expand the LR of H2 O2 assay via decomposing part of H2 O2 . As a proof of concept, a cascade enzyme system (rGRC) is constructed by integrating ruthenium nanoparticles (RuNPs), CAT and graphene together. The rGRC-based sensor does perform an expanded LR and higher LR maximum for H2 O2 detection. Meanwhile, it is confirmed that LR expansion is closely associated with apparent Km of rGRC, which is determined by the relative enzyme activity between CAT and POD both in theory and in experiment. At last, rGRC is successfully used to detect high concentration of H2 O2 (up to 10 mm) in contact lens care solution, which performs higher assay accuracy (close to 100% recovery at 10 mm of H2 O2 ) than traditional POD nanozymes. This study brings up a kind of POD/CAT cascade enzyme system and provides a new concept for accurate and facile H2 O2 detection. Additionally, it replenishes a new enzyme-substrate model of achieving the same pattern with competitive inhibition in enzyme reactions.
