Ring Opening Polymerization of Six- and Eight-Membered Racemic Cyclic Esters for Biodegradable Materials

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(3), P. 1647 - 1647

Published: Jan. 29, 2024

The low percentage of recyclability the polymeric materials obtained by olefin transition metal (TM) polymerization catalysis has increased interest in their substitution with more eco-friendly reliable physical and mechanical properties. Among variety known biodegradable polymers, linear aliphatic polyesters produced ring-opening (ROP) cyclic esters occupy a prominent position. polymer properties are highly dependent on macromolecule microstructure, control stereoselectivity is necessary for providing precise finely tuned In this review, we aim to outline main synthetic routes, also applications three commercially available materials: Polylactic acid (PLA), Poly(Lactic-co-Glycolic Acid) (PLGA), Poly(3-hydroxybutyrate) (P3HB), all easily accessible via ROP. framework, understanding origin enantioselectivity factors that determine it then crucial development suitable thermal

Language: Английский

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Catalysis in the digital age: Unlocking the power of data with machine learning

Wiley Interdisciplinary Reviews Computational Molecular Science, Journal Year: 2024, Volume and Issue: 14(5)

Published: Sept. 1, 2024

Abstract The design and discovery of new improved catalysts are driving forces for accelerating scientific technological innovations in the fields energy conversion, environmental remediation, chemical industry. Recently, use machine learning (ML) combination with experimental and/or theoretical data has emerged as a powerful tool identifying optimal various applications. This review focuses on how ML algorithms can be used computational catalysis materials science to gain deeper understanding relationships between properties their stability, activity, selectivity. development repositories, mining techniques, tools that navigate structural optimization problems highlighted, leading highly efficient sustainable future. Several data‐driven models commonly research diverse applications reaction prediction discussed. key challenges limitations using presented, which arise from catalyst's intrinsic complex nature. Finally, we conclude by summarizing potential future directions area ML‐guided catalyst development. article is categorized under: Structure Mechanism > Reaction Mechanisms Catalysis Data Science Artificial Intelligence/Machine Learning Electronic Theory Density Functional

Language: Английский

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Ligand-Based Principal Component Analysis Followed by Ridge Regression: Application to an Asymmetric Negishi Reaction

ACS Catalysis, Journal Year: 2024, Volume and Issue: 14(7), P. 5027 - 5038

Published: March 20, 2024

In this study, we introduce an approach for predicting the enantioselectivity of P-chiral monophosphorus ligands from ligand-based descriptors that can be applied to catalytic systems with small experimental datasets without reliance on mechanistic knowledge. Principal component analysis (PCA) is used map out chemical space described by steric and electronic computed dihydrobenzooxaphosphole (BOP) dihydrobenzoazaphosphole (BAP) ligands. The PCA captures trends in experimentally measured four C–C bond-forming reactions identifies "hotspots" selective provide insight into optimal balance sterics electronics each reaction. Furthermore, are train a ridge regression model quantitatively predicts Pd-catalyzed Negishi cross-coupling coefficients fundamental understanding reveal π-stacking interaction one results unexpected selectivity inversion. Overall, integrated combines qualitative quantitative (ridge regression) predictions.

Language: Английский

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Revisiting Stereoselective Propene Polymerization Mechanisms: Insights through the Activation Strain Model

Chemistry - An Asian Journal, Journal Year: 2024, Volume and Issue: 19(9)

Published: March 18, 2024

The stereoelectronic factors responsible for stereoselectivity in propene polymerization with several metallocene and post-metallocene transition metal catalysts have been revisited using a combined approach of DFT calculations, the Activation Strain Model, Natural Energy Decomposition Analysis molecular descriptor (%V

Language: Английский

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Ligand coordination controlled by monomer binding: a hint from DFT for stereoselective lactide polymerization

Catalysis Science & Technology, Journal Year: 2024, Volume and Issue: 14(19), P. 5624 - 5633

Published: Jan. 1, 2024

Switching the preference in stereocontrolled rac -LA ROP.

Language: Английский

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Pd nanoparticles supported on modified magnetic kaolin as a novel hydrogenation catalyst

Surfaces and Interfaces, Journal Year: 2025, Volume and Issue: unknown, P. 106037 - 106037

Published: Feb. 1, 2025

Language: Английский

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Late-stage functionalization of pharmaceuticals by C–C cross-coupling enabled by wingtip-flexible N-heterocyclic carbenes

Chem Catalysis, Journal Year: 2025, Volume and Issue: unknown, P. 101297 - 101297

Published: Feb. 1, 2025

Language: Английский

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Computational Tools for the Prediction of Site- and Regioselectivity of Organic Reactions

Chemical Science, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

This article reviews computational tools for the prediction of regio- and site-selectivity organic reactions. It spans from quantum chemical procedures to deep learning models showcases application presented tools.

Language: Английский

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Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions

International Journal of Quantum Chemistry, Journal Year: 2025, Volume and Issue: 125(7)

Published: March 19, 2025

ABSTRACT Machine learning has revolutionized computational chemistry by improving the accuracy of predicting thermodynamic and kinetic properties like activation energies Gibbs free energies, accelerating materials discovery optimizing reaction conditions in both academic industrial applications. This review investigates recent strides applying advanced machine techniques, including transfer learning, for accurately within complex chemical reactions. It thoroughly provides an extensive overview pivotal methods utilized this domain, sophisticated neural networks, Gaussian processes, symbolic regression. Furthermore, prominently highlights commonly adopted frameworks, such as Chemprop, SchNet, DeepMD, which have consistently demonstrated remarkable exceptional efficiency properties. Moreover, it carefully explores numerous influential studies that notably reported substantial successes, particularly focusing on predictive performance, diverse datasets, innovative model architectures profoundly contributed to enhancing methodologies. Ultimately, clearly underscores transformative potential significantly power intricate systems, bearing considerable implications cutting‐edge theoretical research practical

Language: Английский

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Towards the activity of twisted acyclic amides

RSC Advances, Journal Year: 2025, Volume and Issue: 15(11), P. 8207 - 8212

Published: Jan. 1, 2025

N , -Boc 2 amides have emerged as the most common class of acyclic twisted that been engaged in a range C–N activation and cross-coupling processes ubiquitous amide bonds.

Language: Английский

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