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可调节二维表面铋掺入的砷化铟纳米片。

Tuneable 2D surface Bismuth incorporation on InAs nanosheets.

机构信息

NanoLund & Department of Physics, Lund University, Box 118, 22100 Lund, Sweden.

Department of Physics and Astronomy, Materials Theory, Box 516, 751 20 Uppsala, Sweden.

出版信息

Nanoscale. 2023 Jun 1;15(21):9551-9559. doi: 10.1039/d3nr00454f.

Abstract

The chemical bonding at the interface between compound semiconductors and metals is central in determining electronic and optical properties. In this study, new opportunities for controlling this are presented for nanostructures. We investigate Bi adsorption on 2D wurtzite InAs (112̄0) nanosheets and find that temperature-controlled Bi incorporation in either anionic- or cationic-like bonding is possible in the easily accesible range between room temperature and 400 °C. This separation could not be achieved for ordinary zinc blende InAs(110) surfaces. As the crystal structures of the two surfaces have identical nearest neighbour configurations, this indicates that overall geometric differences can significantly alter the adsorption and incorporation. theoretical modelling confirms observed adsorption results, but indicate that both the formation energies as well as kinetic barriers contributes to the observed temperature dependent behaviour. Further, we find that the Bi adsorption rate can differ by at least 2.5 times between the two InAs surfaces while being negligible for standard Si substrates under similar deposition conditions. This, in combination with the observed interface control, provides an excellent opportunity for tuneable Bi integration on 2D InAs nanostructures on standard Si substrates.

摘要

化合物半导体和金属之间界面的化学键合对于决定电子和光学性质至关重要。在这项研究中,为纳米结构提出了控制这种键合的新机会。我们研究了 Bi 在二维纤锌矿 InAs(112̄0)纳米片上的吸附,发现室温至 400°C 之间容易达到的范围内,可以通过温度控制以阴离子或阳离子类似键合的方式实现 Bi 的掺入。对于普通闪锌矿 InAs(110)表面,这是无法实现的。由于这两个表面的晶体结构具有相同的最近邻构型,这表明整体几何差异可显著改变吸附和掺入。理论建模证实了观察到的吸附结果,但表明形成能以及动力学势垒都有助于观察到的温度相关行为。此外,我们发现 Bi 的吸附速率在两种 InAs 表面之间至少相差 2.5 倍,而在类似沉积条件下对于标准 Si 衬底则可以忽略不计。这与观察到的界面控制相结合,为在标准 Si 衬底上的二维 InAs 纳米结构上进行可调谐的 Bi 集成提供了极好的机会。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6af4/10234255/185989cab92c/d3nr00454f-f1.jpg

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