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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. 

Abstract The intrinsic complexity of many mesoscale (10–100 nm) cellular machineries makes it challenging to elucidate their topological arrangement and transition dynamics. Here, we exploit DNA origami nanospring as a model system to demonstrate that tens of piconewton linear force can modulate higher-order conformation dynamics of mesoscale molecular assemblies. By switching between two chemical structures (i.e., duplex and tetraplex DNA) in the junctions of adjacent origami modules, the corresponding stretching or compressing chemo-mechanical stress reversibly flips the backbone orientations of the DNA nanosprings. Both coarse-grained molecular dynamics simulations and atomic force microscopy measurements reveal that such a backbone conformational switch does not alter the right-handed chirality of the nanospring helix. This result suggests that mesoscale helical handedness may be governed by the torque, rather than the achiral orientation, of nanospring backbones. It offers a topology-based caging/uncaging concept to present chemicals in response to environmental cues in solution. 

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.