Tailored Oxide Materials via Thermolytic Molecular Precursor (TMP) Methods

2005 ◽  
pp. 69-115 ◽  
Author(s):  
Kyle L. Fujdala ◽  
Richard L. Brutchey ◽  
T. Don Tilley
Keyword(s):  
Oxide Materials ◽  
Molecular Precursor

Tungsten oxide by non-hydrolytic sol–gel: effect of molecular precursor on morphology, phase and photocatalytic performance

2016 ◽  
Vol 40 (1) ◽  
pp. 217-222 ◽  
Author(s):  
Emma Oakton ◽  
Georges Siddiqi ◽  
Alexey Fedorov ◽  
Christophe Copéret
Keyword(s):  
Tungsten Oxide ◽  
Water Splitting ◽  
Photocatalytic Performance ◽  
Sol Gel ◽  
Oxide Materials ◽  
Photocatalytic Water Splitting ◽  
Gel Effect ◽  
Molecular Precursor

The synthesis, characterisation and photocatalytic water splitting activity of blue tungsten oxide materials prepared from W(vi) and W(iv) precursors is reported.

Layered High-Entropy Oxide Structures for Reversible Energy Storage

2020 ◽  
Author(s):  
Junbo Wang ◽  
Yanyan Cui ◽  
Qingsong Wang ◽  
Kai Wang ◽  
Xiaohui Wang ◽  
...  
Keyword(s):  
Energy Storage ◽  
Cathode Material ◽  
Spray Pyrolysis ◽  
Lattice Structure ◽  
Oxide Materials ◽  
High Entropy ◽  
New Class ◽  
Nebulized Spray Pyrolysis

<p>Layered Li<i><sub>x</sub></i>MO<sub>2</sub> materials, a new class of high-entropy oxides, have been synthesized by nebulized spray pyrolysis. Specifically, the lattice structure of Li(Ni<sub>1/3</sub>Mn<sub>1/3</sub>Co<sub>1/3</sub>)O<sub>2</sub> (NCM111) cathode material has been replicated successfully while increasing the number of cations in equimolar proportions, thereby allowing transition to high-entropy oxide materials.</p>

Towards a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning

2019 ◽  
Author(s):  
Seoin Back ◽  
Kevin Tran ◽  
Zachary Ulissi
Keyword(s):  
Machine Learning ◽  
Oxygen Evolution ◽  
Active Sites ◽  
Density Functional ◽  
Chemical Space ◽  
Density Functional Theory Calculations ◽  
Catalyst Design ◽  
Transition Metal Catalysts ◽  
Oxide Materials ◽  
Design Strategies

<div> <div> <div> <div><p>Developing active and stable oxygen evolution catalysts is a key to enabling various future energy technologies and the state-of-the-art catalyst is Ir-containing oxide materials. Understanding oxygen chemistry on oxide materials is significantly more complicated than studying transition metal catalysts for two reasons: the most stable surface coverage under reaction conditions is extremely important but difficult to understand without many detailed calculations, and there are many possible active sites and configurations on O* or OH* covered surfaces. We have developed an automated and high-throughput approach to solve this problem and predict OER overpotentials for arbitrary oxide surfaces. We demonstrate this for a number of previously-unstudied IrO2 and IrO3 polymorphs and their facets. We discovered that low index surfaces of IrO2 other than rutile (110) are more active than the most stable rutile (110), and we identified promising active sites of IrO2 and IrO3 that outperform rutile (110) by 0.2 V in theoretical overpotential. Based on findings from DFT calculations, we pro- vide catalyst design strategies to improve catalytic activity of Ir based catalysts and demonstrate a machine learning model capable of predicting surface coverages and site activity. This work highlights the importance of investigating unexplored chemical space to design promising catalysts.<br></p></div></div></div></div><div><div><div> </div> </div> </div>

scholarly journals Thermochemical Energy Storage Performance Analysis of (Fe,Co,Mn)Ox Mixed Metal Oxides

Catalysts ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 362
Author(s):  
Yabibal Getahun Dessie ◽  
Qi Hong ◽  
Bachirou Guene Lougou ◽  
Juqi Zhang ◽  
Boshu Jiang ◽  
...  
Keyword(s):  
Energy Storage ◽  
Metal Oxide ◽  
Metal Oxides ◽  
Redox Reaction ◽  
Gas Flow ◽  
Oxygen Exchange ◽  
Engineering Industry ◽  
Oxide Material ◽  
Oxide Materials ◽  
Uptake Ratio

Metal oxide materials are known for their ability to store thermochemical energy through reversible redox reactions. Metal oxides provide a new category of materials with exceptional performance in terms of thermochemical energy storage, reaction stability and oxygen-exchange and uptake capabilities. However, these characteristics are predicated on the right combination of the metal oxide candidates. In this study, metal oxide materials consisting of pure oxides, like cobalt(II) oxide, manganese(II) oxide, and iron(II, III) oxide (Fe3O4), and mixed oxides, such as (100 wt.% CoO, 100 wt.% Fe3O4, 100 wt.% CoO, 25 wt.% MnO + 75 wt.% CoO, 75 wt.% MnO + 25 wt.% CoO) and 50 wt.% MnO + 50.wt.% CoO), which was subjected to a two-cycle redox reaction, was proposed. The various mixtures of metal oxide catalysts proposed were investigated through the thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), energy dispersive X-ray (EDS), and scanning electron microscopy (SEM) analyses. The effect of argon (Ar) and oxygen (O2) at different gas flow rates (20, 30, and 50 mL/min) and temperature at thermal charging step and thermal discharging step (30–1400 °C) during the redox reaction were investigated. It was revealed that on the overall, 50 wt.% MnO + 50 wt.% CoO oxide had the most stable thermal stability and oxygen exchange to uptake ratio (0.83 and 0.99 at first and second redox reaction cycles, respectively). In addition, 30 mL/min Ar–20 mL/min O2 gas flow rate further increased the proposed (Fe,Co,Mn)Ox mixed oxide catalyst’s cyclic stability and oxygen uptake ratio. SEM revealed that the proposed (Fe,Co,Mn)Ox material had a smooth surface and consisted of polygonal-shaped structures. Thus, the proposed metallic oxide material can effectively be utilized for high-density thermochemical energy storage purposes. This study is of relevance to the power engineering industry and academia.

Machine learning aided design of perovskite oxide materials for photocatalytic water splitting

2021 ◽  
Vol 60 ◽  
pp. 351-359
Author(s):  
Qiuling Tao ◽  
Tian Lu ◽  
Ye Sheng ◽  
Long Li ◽  
Wencong Lu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Splitting ◽  
Perovskite Oxide ◽  
Oxide Materials ◽  
Photocatalytic Water Splitting ◽  
Aided Design
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