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Catalysis

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Design and performance analysis of multi-enzyme activity-doped nanozymes assisted by machine learning.

Colloids and surfaces. B, Biointerfaces
Traditional design approaches for nanozymes typically rely on empirical methods and trial-and-error, which hampers systematic optimization of their structure and performance, thus limiting the efficiency of developing innovative nanozymes. This study...

MGT: Machine Learning Accelerates Performance Prediction of Alloy Catalytic Materials.

Journal of chemical information and modeling
The application of deep learning technology in the field of materials science provides a new method for predicting the adsorption energy of high-performance alloy catalysts in hydrogen evolution reactions and material discovery. The activity and sele...

Deep Learning-Assisted Fluorescence Single-Particle Detection of Fumonisin B Powered by Entropy-Driven Catalysis and Argonaute.

Analytical chemistry
Timely and accurate detection of trace mycotoxins in agricultural products and food is significant for ensuring food safety and public health. Herein, a deep learning-assisted and entropy-driven catalysis (EDC)-Argonaute powered fluorescence single-p...

Kinetics, central composite design and artificial neural network modelling of ciprofloxacin antibiotic photodegradation using fabricated cobalt-doped zinc oxide nanoparticles.

Scientific reports
Cobalt-doped zinc oxide nanoparticles were fabricated and examined in this study as a potential photocatalyst for the antibiotic ciprofloxacin (CIPF) degradation when exposed to visible LED light. The Co-precipitation technique created Cobalt-doped z...

Machine Learning for Reaction Performance Prediction in Allylic Substitution Enhanced by Automatic Extraction of a Substrate-Aware Descriptor.

Journal of chemical information and modeling
Despite remarkable advancements in the organic synthesis field facilitated by the use of machine learning (ML) techniques, the prediction of reaction outcomes, including yield estimation, catalyst optimization, and mechanism identification, continues...

An efficient catalyst screening strategy combining machine learning and causal inference.

Journal of environmental management
Due to the diversity of catalyst synthesis methods, the optimization of catalysts by traditional experimental methods have brought greater challenges. This study presents a new strategy for determining catalyst performance by substituting causal infe...

Deep learning-driven semi-rational design in phenylalanine ammonia-lyase for enhanced catalytic efficiency.

International journal of biological macromolecules
Phenylalanine ammonia-lyase (PAL) possesses significant potential in agriculture, industry, and the treatment of various diseases, including cancer. In particular, PAL derived from Anabaena variabilis (AvPAL) has been successfully utilized in clinica...

Machine Learning Accelerated Discovery of Covalent Organic Frameworks for Environmental and Energy Applications.

Environmental science & technology
Covalent organic frameworks (COFs) are porous crystalline materials obtained by linking organic ligands covalently. Their high surface area and adjustable pore sizes make them ideal for a range of applications, including CO capture, CH storage, gas s...

Artificial intelligence driven platform for rapid catalytic performance assessment of nanozymes.

Scientific reports
Traditional methods for synthesizing nanozymes are often time-consuming and complex, hindering efficiency. Artificial intelligence (AI) has the potential to simplify these processes, but there are very few dedicated nanozyme databases available, limi...

Tackling a textbook example of multistep enzyme catalysis with deep learning-driven design.

Molecular cell
Enzyme design has struggled to emulate the complexity and catalytic proficiency of natural enzymes. Lauko et al. show that with the help of deep learning, the design of serine hydrolases that rival nature's ingenuity is possible.