High-Throughput Exploration of Metal Vanadate Thin-Film Systems (M–V–O, M = Cu, Ag, W, Cr, Co, Fe) for Solar Water Splitting: Composition, Structure, Stability, and Photoelectrochemical Properties
IF 4.3 3区 材料科学Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Swati Kumari, João R. C. Junqueira, Wolfgang Schuhmann, Alfred Ludwig*
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引用次数: 8
Abstract
Combinatorial synthesis and high-throughput characterization of thin-film materials libraries enable to efficiently identify both photoelectrochemically active and inactive, as well as stable and instable systems for solar water splitting. This is shown on six ternary metal vanadate (M–V–O, M = Cu, Ag, W, Cr, Co, Fe) thin-film materials libraries, fabricated using combinatorial reactive magnetron cosputtering with subsequent annealing in air. By means of high-throughput characterization of these libraries correlations between composition, crystal structure, photocurrent density, and stability of the M–V–O systems in different electrolytes such as acidic, neutral and alkaline media were identified. The systems Cu–V–O and Ag–V–O are stable in alkaline electrolyte and exhibited photocurrents of 170 and 554 μA/cm2, respectively, whereas the systems W–V–O, Cr–V–O, and Co–V–O are not stable in alkaline electrolyte. However, the Cr–V–O and Co–V–O systems showed an enlarged photoactive region in acidic electrolyte, albeit with very low photocurrents (<10 μA/cm2). Complete data sets obtained from these different screening sets, including information on nonpromising systems, lays groundwork for their use to predict new systems for solar water splitting, for example, by machine learning.
高通量探索金属钒酸盐薄膜系统(M - v - o, M = Cu, Ag, W, Cr, Co, Fe)用于太阳能水分解:组成,结构,稳定性和光电化学性质
薄膜材料库的组合合成和高通量表征能够有效地识别光电化学活性和非活性,以及稳定和不稳定的太阳能水分解系统。这表现在六个三元金属钒酸盐(M - v - o, M = Cu, Ag, W, Cr, Co, Fe)薄膜材料库上,这些薄膜材料是用组合反应磁控溅射和随后在空气中退火制备的。通过对这些文库的高通量表征,确定了M-V-O体系在不同电解质(酸性、中性和碱性介质)中的组成、晶体结构、光电流密度和稳定性之间的相关性。Cu-V-O和Ag-V-O体系在碱性电解液中稳定,光电流分别为170和554 μA/cm2,而W-V-O、Cr-V-O和Co-V-O体系在碱性电解液中不稳定。然而,Cr-V-O和Co-V-O体系在酸性电解液中显示出较大的光活性区,尽管光电流很低(<10 μA/cm2)。从这些不同的筛选集中获得的完整数据集,包括关于没有前景的系统的信息,为它们用于预测新的太阳能水分解系统奠定了基础,例如,通过机器学习。