Steric indices are parameters used in chemistry to describe the spatial arrangement of atoms or groups of atoms in molecules. They are important in determining the reactivity, stability, and physical properties of chemical compounds. One commonly used steric index is the steric hindrance, which refers to the obstruction or hindrance of movement in a molecule caused by bulky substituents or functional groups. Steric hindrance can affect the reactivity of a molecule by altering the accessibility of its reactive sites and influencing the geometry of its transition states. Notably, the Tolman cone angle and % are prominent among these indices. Actually, steric effects can also be described using the concept of steric bulk, which refers to the space occupied by a molecule or functional group. Steric bulk can affect the solubility, melting point, boiling point, and viscosity of a substance. Even though electronic indices are more widely used, they have certain drawbacks that might shift preferences towards others. They present a higher computational cost, and often, the weight of electronics in correlation with chemical properties, binding energies, falls short in comparison to %. However, it is worth noting that this may be because the steric index inherently captures part of the electronic content. Overall, steric indices play an important role in understanding the behaviour of chemical compounds and can be used to predict their reactivity, stability, and physical properties. Predictive chemistry is an approach to chemical research that uses computational methods to anticipate the properties and behaviour of these compounds and reactions, facilitating the design of new compounds and reactivities. Within this domain, predictive catalysis specifically targets the prediction of the performance and behaviour of catalysts. Ultimately, the goal is to identify new catalysts with optimal properties, leading to chemical processes that are both more efficient and sustainable. In this framework, % can be a key metric for deepening our understanding of catalysis, emphasizing predictive catalysis and sustainability. Those latter concepts are needed to direct our efforts toward identifying the optimal catalyst for any reaction, minimizing waste, and reducing experimental efforts while maximizing the efficacy of the computational methods.
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Molecules
January 2025
GSK Carbon Neutral Laboratories for Sustainable Chemistry, Jubilee Campus, University of Nottingham, Triumph Road, Nottingham NG7 2TU, UK.
The range of chemical databases available has dramatically increased in recent years, but the reliability and quality of their data are often negatively affected by human-error fidelity. The size of chemical databases can make manual data curation/checking of such sets time consuming; thus, automated tools to help this process are highly desirable. Herein, we propose the use of Graph Neural Networks (GNNs) to identifying potential stereochemical misassignments in the primary asymmetric catalysis literature.
View Article and Find Full Text PDFMolecules
January 2025
Department of Chemistry, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Fudan University, Shanghai 200438, China.
The technologies used for the characterization and quantitative analysis of dairy products based on Raman spectroscopy have developed rapidly in recent years. At the level of spectral data, there are not only traditional Raman spectra but also two-dimensional correlation spectra, which can provide rich compositional and characteristic information about the samples. In terms of spectral preprocessing, there are various methods, such as normalization, wavelet denoising, and feature extraction.
View Article and Find Full Text PDFNat Chem
January 2025
Institute of Bioengineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
Protein catalysis and allostery require the atomic-level orchestration and motion of residues and ligand, solvent and protein effector molecules. However, the ability to design protein activity through precise protein-solvent cooperative interactions has not yet been demonstrated. Here we report the design of 14 membrane receptors that catalyse G protein nucleotide exchange through diverse engineered allosteric pathways mediated by cooperative networks of intraprotein, protein-ligand and -solvent molecule interactions.
View Article and Find Full Text PDFLangmuir
January 2025
Chemistry and Structure of novel Materials, University of Siegen, Paul-Bonatz Strasse 9-11, 57068 Siegen, Germany.
The surface charge of metal oxides is an important property that significantly contributes to a wide range of phenomena, including adsorption, catalysis, and material science. The surface charge can be predicted by determining the isoelectric point (IEP) of a material and the pH of a solution. Although there have been several studies of the IEP of metal oxide (nano)particles, only a few have reported the IEP of metal oxide films.
View Article and Find Full Text PDFAdv Mater
January 2025
Hefei National Research Center for Physical Sciences at the Microscale, Department of Materials Science and Engineering, CAS Key Laboratory of Materials for Energy Conversion, University of Science and Technology of China, Hefei, 230026, China.
Electrocatalytic biomass conversion offers a sustainable route for producing organic chemicals, with electrode design being critical to determining reaction rate and selectivity. Herein, a prediction-synthesis-validation approach is developed to obtain electrodes for precise biomass conversion, where the coexistence of multiple metal valence states leads to excellent electrocatalytic performance due to the activated redox cycle. This promising integrated foam electrode is developed via acid-induced surface reconstruction to in situ generate highly active metal (oxy)hydroxide or oxide (MOH or MO) species on inert foam electrodes, facilitating the electrooxidation of 5-hydroxymethylfurfural (5-HMF) to 2,5-furandicarboxylic acid (FDCA).
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