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您的检索式：作者名="Christopher J.Butch"
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| 1 | Asymmetric Schottky Contacts in van der Waals Metal-Semiconductor-Metal Structures Based on Two-Dimensional Janus Materials显示文摘Physical and electronic asymmetry plays a crucial role in rectifiers and other devices with a directionally variant current-voltage(I-V)ratio.Several strategies for practically creating asymmetry in nanoscale components have been demonstrated,but complex fabrication procedures,high cost,and incomplete mechanistic understanding have significantly limited large-scale applications of these components.In this work,we present density functional theory calculations which demonstrate asymmetric electronic properties in a metal-semiconductor-metal(MSM)interface composed of stacked van der Waals(vdW)heterostructures.Janus MoSSe has an intrinsic dipole due to its asymmetric structure and,consequently,can act as either an n-type or p-type diode depending on the face at the interior of the stacked structure(SeMoS-SMoS vs.SMoSe-SMoS).In each configuration,vdW forces dominate the interfacial interactions,and thus,Fermi level pinning is largely suppressed.Our transport calculations show that not only does the intrinsic dipole cause asymmetric I-V characteristics in the MSM structure but also that different transmission mechanisms are involved across the S-S(direct tunneling)and S-Se interface(thermionic excitation).This work illustrates a simple and practical method to introduce asymmetric Schottky barriers into an MSM structure and provides a conceptual framework which can be extended to other 2D Janus semiconductors. | Jia Liu Ji-Chang Ren Tao Shen Xinyi Liu Christopher J.Butch Shuang Li Wei Liu | 2020 | Research2020，，1： | 0 |
| 2 | Predicting single-phase solid solutions in as-sputtered high entropy alloys:High-throughput screening with machine-learning model显示文摘Searching for single-phase solid solutions(SPSSs)in high-entropy alloys(HEAs)is a prerequisite for the intentional design and manipulation of microstructures of alloys in vast composition space.However,to date,reported SPSS HEAs are still rare due to the lack of reliable guiding principles for the synthesis of new SPSS HEAs.Here,we demonstrate an ensemble machine-learning method capable of discovering SPSS HEAs by directly predicting quinary phase diagrams based only on atomic composition.A total of 2198 experimental structure data are extracted from as-sputtered quinary HEAs in the literature and used to train a random forest classifier(termed AS-RF)utilizing bagging,achieving a prediction accuracy of 94.6%compared with experimental results.The AS-RF model is then utilized to predict 224 quinary phase diagrams including∼32,000 SPSS HEAs in Cr-Co-Fe-Ni-Mn-Cu-Al composition space.The extrapolation capability of the AS-RF model is then validated by performing first-principle calculations using density functional theory as a benchmark for the predicted phase transition of newly predicted HEAs.Finally,interpretation of the AS-RF model weighting of the input parameters also sheds light on the driving forces behind HEA formation in sputtered systems with the main contributors being:valance electron concentration,work function,atomic radius difference and elementary symmetries. | Ji-Chang Ren Junjun Zhou Christopher J.Butch Zhigang Ding Shuang Li Yonghao Zhao Wei Liu | 2023 | Journal of Materials Science & Technology2023，，7： | 0 |
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