Controlled Surface Fermi-level on the SeS2-passivated n-GaAs (100)

1998 ◽  
Vol 510 ◽  
Author(s):  
Jing xi Sun ◽  
F. J. Himpsel ◽  
T. F. Kuech
Keyword(s):  
Fermi Level ◽  
Photoemission Spectroscopy ◽  
Ambient Conditions ◽  
Surface Band ◽  
Band Bending ◽  
Term Stability ◽  
Long Term Stability ◽  
Synchrotron Radiation Photoemission ◽  
Surface Fermi Level

AbstractSelenium disulfide surface treatment can unpin the surface Fermi-level on n-GaAs (100) surfaces, resulting in a reduction in the surface band bending. The long-term stability of the surface Fermi-level unpinning has been studied using photoreflectance spectroscopy under room ambient conditions. Our results show that the SeS2-treated n-GaAs (100) surface is stable up to four months with negligible shift in the surface Fermi-level being noted. The mechanism of the long-term stability is attributed to the layered surface structure formed on the SeS2-treated n- GaAs (100) surface. The chemical structure of the passivated surface was determined by synchrotron radiation photoemission spectroscopy. The outermost layer of sulfur and arsenicbased sulfides and selenides may protect the electronic passivating layer, which consists of gallium-based selenides, from interaction with the atmosphere.

scholarly journals Long-term stability in γ-CsPbI3 perovskite via an ultraviolet-curable polymer network

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Nam-Kwang Cho ◽  
Hyun-Jae Na ◽  
Jeeyoung Yoo ◽  
Youn Sang Kim
Keyword(s):  
Polymer Network ◽  
Lone Pair ◽  
Ambient Conditions ◽  
Absorption Properties ◽  
Term Stability ◽  
Long Term Stability ◽  
Ultraviolet Curable ◽  
Oxygen Lone Pair ◽  
Lone Pair Electrons

AbstractBlack-colored (α, γ-phase) CsPbI3 perovskites have a small bandgap and excellent absorption properties in the visible light regime, making them attractive for solar cells. However, their long-term stability in ambient conditions is limited. Here, we demonstrate a strategy to improve structural and electrical long-term stability in γ-CsPbI3 by the use of an ultraviolet-curable polyethylene glycol dimethacrylate (PEGDMA) polymer network. Oxygen lone pair electrons from the PEGDMA are found to capture Cs+ and Pb2+ cations, improving crystal growth of γ-CsPbI3 around PEGDMA. In addition, the PEGDMA polymer network strongly contributes to maintaining the black phase of γ-CsPbI3 for more than 35 days in air, and an optimized perovskite film retained ~90% of its initial electrical properties under red, green, and blue light irradiation.

scholarly journals Chemical Robotics Enabled Exploration of Stability and Photoluminescent Behavior in Multicomponent Hybrid Perovskites via Machine Learning

2020 ◽  
Author(s):  
Kate Higgins ◽  
Sai Mani Valleti ◽  
Maxim Ziatdinov ◽  
Sergei Kalinin ◽  
Mahshid Ahmadi
Keyword(s):  
Phase Diagram ◽  
Gaussian Process Regression ◽  
Optimization Methods ◽  
Combinatorial Synthesis ◽  
Ambient Conditions ◽  
Term Stability ◽  
Long Term Stability ◽  
Sensing Applications ◽  
Multiple Materials

<p></p><p>Hybrid organic-inorganic perovskites have attracted immense interest as a promising material for a variety of optoelectronic and sensing applications. However, issues regarding long-term stability have emerged as the key bottleneck for applications and still require further study. Here, we develop automated experimental workflow based on combinatorial synthesis and rapid throughput characterization to explore long-term stability of these materials in ambient conditions, and apply it to four model perovskite systems: <a></a><a>MA<i><sub>x</sub></i>FA<i><sub>y</sub></i>Cs<sub>1-<i>x</i>-<i>y</i></sub>PbBr<sub>3</sub>, MA<i><sub>x</sub></i>FA<i><sub>y</sub></i>Cs<sub>1-<i>x</i>-<i>y</i></sub>PbI<sub>3</sub>, Cs<i><sub>x</sub></i>FA<i><sub>y</sub></i>MA<sub>1-<i>x</i>-<i>y</i></sub>Pb(Br<i><sub>x</sub></i><sub>+<i>y</i></sub>I<sub>1-<i>x</i>-<i>y</i></sub>)<sub>3</sub> and Cs<i><sub>x</sub></i>MA<i><sub>y</sub></i>FA<sub>1-<i>x</i>-<i>y</i></sub>Pb(I<i><sub>x</sub></i><sub>+<i>y</i></sub>Br<sub>1-<i>x</i>-<i>y</i></sub>)<sub>3</sub></a>. We have both established a new workflow and found out the main tendencies in the mixed cation and anion systems, which led to the discovery of non-trivial composition regions with high stability. The Non-negative Matrix Factorization and Gaussian Process regression are used <i>to</i> <i>interpolate the photoluminescent behavior of vast compositional space</i> and <i>to study the overall behavior of the phase diagram</i>. This interpolative regression analysis helps to distinguish mixtures that form solid solutions from those that segregate into multiple materials, pointing out the most stable regions of the phase diagram. We find the stability dependence on composition to be extremely non-uniform within the composition space, suggesting the presence of potential preferential compositional regions. <a>This proposed workflow is universal and can be applied to other perovskite systems and solution-processable materials. </a>Furthermore, incorporation of experimental optimization methods, e.g., those based on Gaussian Processes, will enable the transition from combinatorial synthesis to guide materials research and optimization.</p><p></p>

Chemical Robotics Enabled Exploration of Stability and Photoluminescent Behavior in Multicomponent Hybrid Perovskites via Machine Learning

2020 ◽  
Author(s):  
Kate Higgins ◽  
Sai Mani Valleti ◽  
Maxim Ziatdinov ◽  
Sergei Kalinin ◽  
Mahshid Ahmadi
Keyword(s):  
Machine Learning ◽  
Optimization Methods ◽  
Combinatorial Synthesis ◽  
Ambient Conditions ◽  
Term Stability ◽  
Long Term Stability ◽  
Composition Space ◽  
Materials Research ◽  
The Stability

<p>Hybrid organic-inorganic perovskites have attracted immense interest as a promising material for the next-generation solar cells; however, issues regarding long-term stability still require further study. Here, we develop automated experimental workflow based on combinatorial synthesis and rapid throughput characterization to explore long-term stability of these materials in ambient conditions, and apply it to four model perovskite systems: MA<sub>x</sub>FA<sub>y</sub>Cs<sub>1-x-y</sub>PbBr<sub>3</sub>, MA<sub>x</sub>FA<sub>y</sub>Cs<sub>1-x-y</sub>PbI<sub>3</sub>, (Cs<sub>x</sub>FA<sub>y</sub>MA<sub>1-x-y</sub>Pb(Br<sub>x+y</sub>I<sub>1-x-y</sub>)<sub>3</sub>) and (Cs<sub>x</sub>MA<sub>y</sub>FA<sub>1-x-y</sub>Pb(I<sub>x+y</sub>Br<sub>1-x-y</sub>)<sub>3</sub>). We also develop a machine learning-based workflow to quantify the evolution of each system as a function of composition based on overall changes in photoluminescence spectra, as well as specific peak positions and intensities. We find the stability dependence on composition to be extremely non-uniform within the composition space, suggesting the presence of potential preferential compositional regions. This proposed workflow is universal and can be applied to other perovskite systems and solution-processable materials. Furthermore, incorporation of experimental optimization methods, e.g., those based on Gaussian Processes, will enable the transition from combinatorial synthesis to guide materials research and optimization.</p>

scholarly journals Chemical Robotics Enabled Exploration of Stability and Photoluminescent Behavior in Multicomponent Hybrid Perovskites via Machine Learning

2020 ◽  
Author(s):  
Kate Higgins ◽  
Sai Mani Valleti ◽  
Maxim Ziatdinov ◽  
Sergei Kalinin ◽  
Mahshid Ahmadi
Keyword(s):  
Phase Diagram ◽  
Gaussian Process Regression ◽  
Optimization Methods ◽  
Combinatorial Synthesis ◽  
Ambient Conditions ◽  
Term Stability ◽  
Long Term Stability ◽  
Sensing Applications ◽  
Multiple Materials

<p></p><p>Hybrid organic-inorganic perovskites have attracted immense interest as a promising material for a variety of optoelectronic and sensing applications. However, issues regarding long-term stability have emerged as the key bottleneck for applications and still require further study. Here, we develop automated experimental workflow based on combinatorial synthesis and rapid throughput characterization to explore long-term stability of these materials in ambient conditions, and apply it to four model perovskite systems: <a></a><a>MA<i><sub>x</sub></i>FA<i><sub>y</sub></i>Cs<sub>1-<i>x</i>-<i>y</i></sub>PbBr<sub>3</sub>, MA<i><sub>x</sub></i>FA<i><sub>y</sub></i>Cs<sub>1-<i>x</i>-<i>y</i></sub>PbI<sub>3</sub>, Cs<i><sub>x</sub></i>FA<i><sub>y</sub></i>MA<sub>1-<i>x</i>-<i>y</i></sub>Pb(Br<i><sub>x</sub></i><sub>+<i>y</i></sub>I<sub>1-<i>x</i>-<i>y</i></sub>)<sub>3</sub> and Cs<i><sub>x</sub></i>MA<i><sub>y</sub></i>FA<sub>1-<i>x</i>-<i>y</i></sub>Pb(I<i><sub>x</sub></i><sub>+<i>y</i></sub>Br<sub>1-<i>x</i>-<i>y</i></sub>)<sub>3</sub></a>. We have both established a new workflow and found out the main tendencies in the mixed cation and anion systems, which led to the discovery of non-trivial composition regions with high stability. The Non-negative Matrix Factorization and Gaussian Process regression are used <i>to</i> <i>interpolate the photoluminescent behavior of vast compositional space</i> and <i>to study the overall behavior of the phase diagram</i>. This interpolative regression analysis helps to distinguish mixtures that form solid solutions from those that segregate into multiple materials, pointing out the most stable regions of the phase diagram. We find the stability dependence on composition to be extremely non-uniform within the composition space, suggesting the presence of potential preferential compositional regions. <a>This proposed workflow is universal and can be applied to other perovskite systems and solution-processable materials. </a>Furthermore, incorporation of experimental optimization methods, e.g., those based on Gaussian Processes, will enable the transition from combinatorial synthesis to guide materials research and optimization.</p><p></p>

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