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Abstract Specificity and activity are often at odds for natural enzymes. In this work, specificity and activity in coronazymes made of an Au nanoparticle (AuNP) and coated with DNA aptamer for glucose substrates are decoupled. By single‐molecule fluorescent MT‐HILO (magnetic tweezers coupled with highly inclined and laminated optical sheet) microscopy, it is found that this coronazyme has ≈30 times higher activity on thed‐glucose compared to bare AuNP nanozymes. Significantly, the new coronazyme demonstrates long‐range modulations by circularly polarized light (CPL) according to the matching chirality between the CPL and DNA corona, which follows the rule of chiral induced spin selectivity (CISS). Although the aptamer in the coronazyme is evolved againstd‐glucose, surprisingly, this coronazyme catalyzesl‐glucose better thand‐glucose, likely due to the faster rates for the aptamer to interact with thel‐ overd‐glucose. These results demonstrate, for the first time, an artificial enzyme with its catalytic activity controlled by short‐range intermolecular forces, whereas its chiral specificity is modulated by long‐range CPLs. This decoupled arrangement is pivotal to forge premier catalysts with activity and specificity superior to natural enzymes by separately optimizing these two properties. 

Catalytic DNA has gained significant attention in recent decades as a highly efficient and tunable catalyst, thanks to its flexible structures, exceptional specificity, and ease of optimization. Despite being composed of just four monomers, DNA’s complex conformational intricacies enable a wide range of nuanced functions, including scaffolding, electrocatalysis, enantioselectivity, and mechano-electro spin coupling. DNA catalysts, ranging from traditional DNAzymes to innovative DNAzyme hybrids, highlight the remarkable potential of DNA in catalysis. Recent advancements in spectroscopic techniques have deepened our mechanistic understanding of catalytic DNA, paving the way for rational structural optimization. This review will summarize the latest studies on the performance and optimization of traditional DNAzymes and provide an in-depth analysis of DNAzyme hybrid catalysts and their unique and promising properties. 

Titanium dioxide nanoparticles (TiO2 NPs) have traditionally been utilized as industrial catalysts, finding widespread application in various chemical processes due to their exceptional stability and minimal toxicity. However, quantitatively assessing the reactive sites on TiO2 NPs remains a challenge. In this study, we employed a fluorogenic reaction to probe the apparent reactivity of TiO2 NPs. By manipulating the number of defect sites through control of hydrolysis speed and annealing temperature, we determined that the Ti(Ⅲ) content is positively correlated with the reactivity of TiO2 NPs. Additionally, these Ti(Ⅲ) sites could be introduced by reducing commercial TiO2 NPs using NaBH4. Our findings suggest that fluorogenic oxidation of Amplex Red is an effective method for probing defect site densities on TiO2 NPs. Utilizing single-molecule fluorescence imaging, we demonstrated the ability to map defect site density within TiO2 nanowires. Achieving sub-nanoparticle spatial resolution, we observed significant intraparticle and interparticle variations in the defect site distribution, leading to substantial reactivity heterogeneity. 

Catalytic processes are used in about 1/3 of US manufacturing, from the field of chemical engineering to renewable energy. Assessing the activity of single-molecules, or individual molecules, is necessary to the development of efficient catalysts. Their heterogeneity structure leads to particle-specific catalytic activity. Experimentation with single-molecules can be time consuming and difficult. We purpose a Machine learning (ML) model that allows chemical researchers to run shorter single-molecule experiments to obtain the same level of results. We use common and widely understood ML methods to reduce complexity and enable accessibility to the chemical engineering community. We reduce the experiment time by up to 83%. Our evaluation shows that a small data set is sufficient to train an acceptable model. 300 experiments are needed, including the validation set. We use a well understood multilayer perceptron (MLP) model. We show that more complex models are not necessary and simpler methods are not sufficient.