[This corrects the article DOI: 10.1039/D0RA03264F.].
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9055159 | PMC |
| http://dx.doi.org/10.1039/d0ra90072a | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Implementing the Blowers-Masel Approximation to Scale Activation Energy Based on Reaction Enthalpy in Mean-Field Microkinetic Modeling for Catalytic Methane Partial Oxidation.
ACS Catal
May 2024
Department of Chemical Engineering, Northeastern University, Boston, Massachusetts 02115, United States.
Mean-field microkinetic modeling is a powerful tool for catalyst design and the simulation of catalytic processes. The reaction enthalpies in a microkinetic model often need to be adjusted when changing species' binding energies to model different catalysts, when performing thermodynamic sensitivity analyses, and when fitting experimental data. When altering reaction enthalpies, the activation energies should also be reasonably altered to ensure realistic reaction rates.
View Article and Find Full Text PDFVisualization and Mechanistic Understandings on Facet-Dependent Atomic Redispersion of Platinum on CeO.
Nano Lett
December 2023
Key Laboratory of Syngas Conversion of Shaanxi Province, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an 710119, China.
Redispersion is an effective method for regeneration of sintered metal-supported catalysts. However, the ambiguous mechanistic understanding hinders the delicate controlling of active metals at the atomic level. Herein, the redispersion mechanism of atomically dispersed Pt on CeO is revealed and manipulated by techniques combining well-designed model catalysts.
View Article and Find Full Text PDFA comparative analysis and multi-objective optimization of replacing syngas from a downdraft gasifier in an ordinary 300 MW power plant to lessen the environmental effect.
Chemosphere
September 2023
Department of Mechanical Engineering, University of Tabriz, Tabriz, Iran.
Due to environmental issues, disposing of household garbage is a significant obstacle for life on Earth. Due to this, several sorts of research on biomass conversion into useable fuel technologies are carried out. Among the most popular and effective technologies is the gasification process, which transforms trash into a synthetic gas that can be used in industry.
View Article and Find Full Text PDFPhysics-informed machine learning methods for biomass gasification modeling by considering monotonic relationships.
Bioresour Technol
February 2023
Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, PR China.
Machine learning methods have recently shown a broad application prospect in biomass gasification modeling. However, a significant drawback of the machine learning approaches is their poor physical interpretability when relying on limited experimental data. In the present work, a physics-informed neural network method (PINN) is developed to predict biomass gasification products (N, H, CO, CO, and CH).
View Article and Find Full Text PDFAccurate rate constants for elementary reactions of molecular hydrogen and carbon monoxide mixtures and the role of the H rich environment.
J Mol Model
July 2022
Departamento de Química e Física Molecular, Instituto de Química de São Carlos, Universidade de São Paulo, São Carlos, 13.560-970, São Paulo, Brazil.
This investigation provides accurate rate constant values for a set of elementary reactions relevant to mixtures between molecular hydrogen (H) and carbon monoxide (CO) such as syngas. We considered intermediates and products including formaldehyde (HCO), hydroxymethylene (c-HCOH and t-HCOH) and methanol (CHOH). The calculations were performed employing the improved canonical variational transition state theory with small-curvature tunneling corrections based on high-level electronic structure results.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!