A Bayesian method for selecting data points for thermodynamic modeling of off-stoichiometric metal oxides

Wilson, Steven A.; Muhich, Christopher L.

doi:10.1039/d3ta06627d

J. Mater. Chem. A

2024

DOI: 10.1039/d3ta06627d

|View full text |Cite

A Bayesian method for selecting data points for thermodynamic modeling of off-stoichiometric metal oxides

Steven A. Wilson,

Christopher L. Muhich

Abstract: Thermodynamic characterization of metal oxide reduction/re-oxidation plays a vital role in material identification and optimization of many chemical processes. However, this characterization generally requires significant data collection (spanning several hundred...

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

An updated review and perspective on efficient hydrogen generation via solar thermal water splitting

Tran,

Warren,

Wilson

et al. 2024

WIREs Energy & Environment

View full text Add to dashboard Cite

Solar thermal water splitting (STWS) produces renewable (or green) hydrogen from water using concentrated sunlight. Because STWS utilizes energy from the entire solar spectrum to drive the reduction–oxidation (redox) reactions that split water, it can achieve high theoretical solar‐to‐hydrogen efficiencies. In a two‐step STWS process, a metal oxide that serves as a redox mediator is first heated with concentrated sunlight to high temperatures (T >1000°C) to reduce it and evolve oxygen. In the second step, the reduced material is exposed to steam to reoxidize it to its original oxidation state and produce hydrogen. Various aspects of this process are comprehensively reviewed in this work, including the reduction and oxidation chemistries of active materials considered to date, the solar reactors developed to facilitate the STWS reactions, and the effects of operating conditions—including the recent innovation of elevated oxidant pressure—on efficiency. To conclude the review, a perspective on the future optimization of STWS is provided.This article is categorized under: Sustainable Energy > Solar Energy Emerging Technologies > Hydrogen and Fuel Cells Emerging Technologies > New Fuels

show abstract

An updated review and perspective on efficient hydrogen generation via solar thermal water splitting

Tran,

Warren,

Wilson

et al. 2024

WIREs Energy & Environment

View full text Add to dashboard Cite

show abstract

Perovskite oxide redox materials for two-step solar thermochemical CO2 splitting

Tran,

Li,

Liu

2024

Chemical Engineering Journal

View full text Add to dashboard Cite

Extracting metal oxide redox thermodynamics from TGA measurements requires moving beyond the linearized van ‘t Hoff approach

Wilson,

Sarsam,

Stechel

et al. 2024

Front. Energy Res.

View full text Add to dashboard Cite

Thermodynamic modeling of metal oxide reduction is crucial for optimizing chemical processes and materials in systems dependent on off-stoichiometric reduction/re-oxidation cycling. Two prevalent methods for extracting reduction thermodynamics from thermogravimetric data are linearized van ‘t Hoff (VH) analysis and the compound energy formalism (CEF). This work evaluates the accuracy of these methods by constructing invertible ground truth thermodynamic models, generating hypothetical thermogravimetric data, and determining the reduction thermodynamic using both VH and CEF methods. Our findings reveal that the VH method produces absolute errors 3–5 times higher than the CEF in kJ/mol O or J/mol O K for enthalpy and entropy of reduction, respectively. In contrast, the CrossFit CEF (CF-CEF) method yields errors often less than 10 kJ/mol O or J/mol O K. Moreover, the CF-CEF method provides models based on mole fraction, temperature, and extent of reduction, while a typical VH analysis provides thermodynamics of only the specific compositions measured. Although simple to implement, the VH method suffers from significant, non-systematic errors due to entropy/enthalpy compensation and defect modeling. Consequently, we recommend the more complex but robust, CF-CEF method for extracting redox thermodynamics from thermogravimetric measurements.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A Bayesian method for selecting data points for thermodynamic modeling of off-stoichiometric metal oxides

Abstract: Thermodynamic characterization of metal oxide reduction/re-oxidation plays a vital role in material identification and optimization of many chemical processes. However, this characterization generally requires significant data collection (spanning several hundred...

Cited by 3 publications

References 70 publications

An updated review and perspective on efficient hydrogen generation via solar thermal water splitting

An updated review and perspective on efficient hydrogen generation via solar thermal water splitting

Perovskite oxide redox materials for two-step solar thermochemical CO2 splitting

Extracting metal oxide redox thermodynamics from TGA measurements requires moving beyond the linearized van ‘t Hoff approach

Contact Info

Product

Resources

About