Photocatalytic-Fenton Process under Simulated Solar Radiation Promoted by a Suitable Catalyst Selection

Considering water scarcity, photo-based processes have been presented as a depollution technique, which should be optimized in order to be applied in the future. For that, the addition of an active photocatalyst and the usage of solar radiation are mandatory steps. Thus, Fe3O4–SiO2–TiO2 was synthesized, and its performance was evaluated using simulated solar radiation and methylene blue as a model pollutant. Under optimal conditions, 86% degradation was attained in 1 h. These results were compared to recent published data, and the better performance can be attributed to both the operational conditions selection and the higher photocatalyst activity. Indeed, Fe3O4–SiO2–TiO2 was physico-chemically characterized with techniques such as XRD, N2 isotherms, spectrophotometry, FTIR, electrochemical assays and TEM.

Computational methods for training set selection and error assessment applied to catalyst design: guidelines for deciding which reactions to run first and which to run next

Different subset selection methods are examined to guide catalyst selection in optimization campaigns. Error assessment methods are used to quantitatively inform selection of new catalyst candidates from in silico libraries of catalyst structures.

Computational Methods for Training Set Selection and Error Assessment Applied to Catalyst Design: Guidelines for Deciding Which Reactions to Run First and Which to Run Next

The application of machine learning (ML) to problems in homogeneous catalysis has emerged as a promising avenue for catalyst optimization. An important aspect of such optimization campaigns is determining which reactions to run at the outset of experimentation and which future predictions are the most reliable. Herein, we explore methods for these two tasks in the context of our previously developed chemoinformatics workflow. First, different methods for training set selection are compared, including algorithmic selection and selection informed by unsupervised learning methods. Next, an array of different metrics for assessment of prediction confidence are examined in multiple catalyst manifolds. These approaches will inform future computer-guided studies to accelerate catalyst selection and reaction optimization. Finally, this work demonstrates the generality of the Average Steric Occupancy (ASO) and Average Electronic Indicator Field (AEIF) descriptors in their application to transition metal catalysts for the first time. <br>

Computational Methods for Training Set Selection and Error Assessment Applied to Catalyst Design: Guidelines for Deciding Which Reactions to Run First and Which to Run Next

The application of machine learning (ML) to problems in homogeneous catalysis has emerged as a promising avenue for catalyst optimization. An important aspect of such optimization campaigns is determining which reactions to run at the outset of experimentation and which future predictions are the most reliable. Herein, we explore methods for these two tasks in the context of our previously developed chemoinformatics workflow. First, different methods for training set selection are compared, including algorithmic selection and selection informed by unsupervised learning methods. Next, an array of different metrics for assessment of prediction confidence are examined in multiple catalyst manifolds. These approaches will inform future computer-guided studies to accelerate catalyst selection and reaction optimization. Finally, this work demonstrates the generality of the Average Steric Occupancy (ASO) and Average Electronic Indicator Field (AEIF) descriptors in their application to transition metal catalysts for the first time. <br>

Identifying competitive tin- or metal-free catalyst combinations to tailor polyurethane prepolymer and network properties

Polyurethane-based mastics, industrially obtained via a prepolymerization/crosslinking process, benefit from catalyst selection at both stages.

Dynamic polyurethane thermosets: tuning associative/dissociative behavior by catalyst selection

Catalyst selection can tune the associative/dissociative dynamic behaviour of polyurethane themosets.