scholarly journals Cation disorder and phase transitions in the structurally complex solar cell material Cu2ZnSnS4

2017 ◽  
Vol 5 (32) ◽  
pp. 16672-16680 ◽  
Author(s):  
C. J. Bosson ◽  
M. T. Birch ◽  
D. P. Halliday ◽  
K. S. Knight ◽  
A. S. Gibbs ◽  
...  
Keyword(s):  
Phase Transitions ◽  
Solar Cell ◽  
Transition Temperature ◽  
Neutron Scattering ◽  
Elemental Composition ◽  
Disorder Transition ◽  
Cell Material ◽  
Cation Disorder ◽  
Solar Cell Material ◽  
Disorder Transition Temperature

The highest-resolution neutron scattering yet reported is used to examine disorder, and the order–disorder transition temperature is found to depend on elemental composition.

scholarly journals Alloying effect on the order–disorder transformation in tetragonal FeNi

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Li-Yun Tian ◽  
Oliver Gutfleisch ◽  
Olle Eriksson ◽  
Levente Vitos
Keyword(s):  
Transition Temperature ◽  
High Performance ◽  
Density Functional ◽  
Permanent Magnets ◽  
Positive Impact ◽  
Design Strategies ◽  
Alloying Effect ◽  
Functional Theory ◽  
Disorder Transition ◽  
Disorder Transition Temperature

AbstractTetragonal ($${\hbox{L1}}_{0}$$ L1 0 ) FeNi is a promising material for high-performance rare-earth-free permanent magnets. Pure tetragonal FeNi is very difficult to synthesize due to its low chemical order–disorder transition temperature ($$\approx {593}$$ ≈ 593  K), and thus one must consider alternative non-equilibrium processing routes and alloy design strategies that make the formation of tetragonal FeNi feasible. In this paper, we investigate by density functional theory as implemented in the exact muffin-tin orbitals method whether alloying FeNi with a suitable element can have a positive impact on the phase formation and ordering properties while largely maintaining its attractive intrinsic magnetic properties. We find that small amount of non-magnetic (Al and Ti) or magnetic (Cr and Co) elements increase the order–disorder transition temperature. Adding Mo to the Co-doped system further enhances the ordering temperature while the Curie temperature is decreased only by a few degrees. Our results show that alloying is a viable route to stabilizing the ordered tetragonal phase of FeNi.

Increased Order–Disorder Transition Temperature for a Rod–Coil Block Copolymer in the Presence of a Magnetic Field

2011 ◽  
Vol 44 (19) ◽  
pp. 7503-7507 ◽  
Author(s):  
Bryan McCulloch ◽  
Giuseppe Portale ◽  
Wim Bras ◽  
Rachel A. Segalman
Keyword(s):  
Magnetic Field ◽  
Transition Temperature ◽  
Block Copolymer ◽  
Disorder Transition ◽  
Disorder Transition Temperature

Prediction of Solar Cell Materials via Unsupervised Literature Learning

2021 ◽  
Author(s):  
Lei Zhang ◽  
Mu He
Keyword(s):  
Solar Cells ◽  
Solar Cell ◽  
Language Processing ◽  
Perovskite Solar Cells ◽  
Dye Sensitized Solar Cells ◽  
Cell Types ◽  
Data Driven ◽  
Cell Material ◽  
Textual Data ◽  
Solar Cell Material

Abstract Despite the significant advancement of the data-driven studies for physical science, the textual data that are numerous in the literature are not fully embraced by the physics and materials community. In this manuscript, we successfully employ the natural language processing (NLP) technique to unsupervisedly predict the existence of solar cell types including the dye-sensitized solar cells and the perovskite solar cells based on literatures published prior to their first discovery without human annotation. Enlightened by this, we further identify possible solar cell material candidates via NLP starting with a comprehensive training database of 3.2 million paper abstracts published before 2021. The NLP model effectively predicts the existing solar cell materials, while an uncommon solar cell material namely PtSe2 is suggested as an appropriate candidate for the future solar cells. Its optoelectronic properties are comprehensive investigated via first-principles calculations to reveal the decent stability and optoelectronic performance of the NLP-predicted candidate. This study demonstrates the viability of the textual data for the data-driven materials prediction and highlights the NLP method as a powerful tool to reliably predict the solar cell materials.

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