Crystal Chemistry and Electronic Structure of the Photovoltaic Buffer Layer, (In1-xAlx)2S3

2011 ◽  
Vol 23 (23) ◽  
pp. 5168-5176 ◽  
Author(s):  
A. Lafond ◽  
X. Rocquefelte ◽  
M. Paris ◽  
C. Guillot-Deudon ◽  
V. Jouenne
Keyword(s):  
Electronic Structure ◽  
Buffer Layer ◽  
Crystal Chemistry

ChemInform Abstract: Crystal Chemistry and Electronic Structure of the Photovoltaic Buffer Layer, (In1-xAlx)2S3.

ChemInform ◽  
2012 ◽  
Vol 43 (9) ◽  
pp. no-no
Author(s):  
A. Lafond ◽  
X. Rocquefelte ◽  
M. Paris ◽  
C. Guillot-Deudon ◽  
V. Jouenne
Keyword(s):  
Electronic Structure ◽  
Buffer Layer ◽  
Crystal Chemistry

scholarly journals Field Effect on Crystal Phase of Silicon in Si/CeO2/SiO2Structure

2008 ◽  
Vol 2008 ◽  
pp. 1-4
Author(s):  
Dmitry E. Milovzorov
Keyword(s):  
Electronic Structure ◽  
Spatial Distribution ◽  
Band Gap ◽  
Buffer Layer ◽  
Field Effect ◽  
Cerium Dioxide ◽  
Silicon Film ◽  
Crystal Phase ◽  
Great Role ◽  
Defect Levels

The structural, optical, and conductivity properties of silicon film deposited on cerium dioxide buffer layer were studied. The electronic structure of system consists of various defect levels inside band gap. The temperature spatial distribution plays a great role in silicon crystallization. The field destruction of crystal phase and its restoration, after annealing, were investigated.

Crystal chemistry and electronic structure of actinide compounds

1981 ◽  
Vol 100 (1-3) ◽  
pp. 167-177 ◽  
Author(s):  
D. Damien ◽  
C.H. de Novion
Keyword(s):  
Electronic Structure ◽  
Crystal Chemistry

Improving performance by Na doping of a buffer layer-chemical and electronic structure of the InxSy:Na/CuIn(S,Se)2thin-film solar cell interface

2018 ◽  
Vol 26 (5) ◽  
pp. 359-366 ◽  
Author(s):  
Dirk Hauschild ◽  
Frank Meyer ◽  
Andreas Benkert ◽  
Dagmar Kreikemeyer-Lorenzo ◽  
Thomas Dalibor ◽  
...  
Keyword(s):  
Electronic Structure ◽  
Solar Cell ◽  
Buffer Layer ◽  
Na Doping ◽  
Cell Interface

Crystal Chemistry and Electronic Structure of the Metallic Ternary Nitride, SrTiN2.

ChemInform ◽  
2003 ◽  
Vol 34 (51) ◽  
Author(s):  
Gael Farault ◽  
Regis Gautier ◽  
Charles F. Baker ◽  
Amy Bowman ◽  
Duncan H. Gregory
Keyword(s):  
Electronic Structure ◽  
Crystal Chemistry ◽  
Ternary Nitride

Crystal Chemistry, Optical–Electronic Properties, and Electronic Structure of Cd1–xIn2+2x/3S4 Compounds (0 ≤ x ≤ 1), Potential Buffer in CIGS-Based Thin-Film Solar Cells

2018 ◽  
Vol 57 (20) ◽  
pp. 12624-12631 ◽  
Author(s):  
Catherine Guillot-Deudon ◽  
Maria Teresa Caldes ◽  
Adrien Stoliaroff ◽  
Léo Choubrac ◽  
Michaël Paris ◽  
...  
Keyword(s):  
Thin Film ◽  
Electronic Structure ◽  
Solar Cells ◽  
Electronic Properties ◽  
Crystal Chemistry ◽  
Thin Film Solar Cells

scholarly journals Identifying the ‘inorganic gene’ for high-temperature piezoelectric perovskites through statistical learning

2011 ◽  
Vol 467 (2132) ◽  
pp. 2271-2290 ◽  
Author(s):  
Prasanna V. Balachandran ◽  
Scott R. Broderick ◽  
Krishna Rajan
Keyword(s):  
Electronic Structure ◽  
High Temperature ◽  
Statistical Learning ◽  
Crystal Chemistry ◽  
Recursive Partitioning ◽  
Linear Manifold ◽  
Structure Property ◽  
Starting Point ◽  
Relationship Model ◽  
Data Driven Approach

This paper develops a statistical learning approach to identify potentially new high-temperature ferroelectric piezoelectric perovskite compounds. Unlike most computational studies on crystal chemistry, where the starting point is some form of electronic structure calculation, we use a data-driven approach to initiate our search. This is accomplished by identifying patterns of behaviour between discrete scalar descriptors associated with crystal and electronic structure and the reported Curie temperature ( T C ) of known compounds; extracting design rules that govern critical structure–property relationships; and discovering in a quantitative fashion the exact role of these materials descriptors. Our approach applies linear manifold methods for data dimensionality reduction to discover the dominant descriptors governing structure–property correlations (the ‘genes’) and Shannon entropy metrics coupled to recursive partitioning methods to quantitatively assess the specific combination of descriptors that govern the link between crystal chemistry and T C (their ‘sequencing’). We use this information to develop predictive models that can suggest new structure/chemistries and/or properties. In this manner, BiTmO 3 –PbTiO 3 and BiLuO 3 –PbTiO 3 are predicted to have a T C of 730 ° C and 705 ° C, respectively. A quantitative structure–property relationship model similar to those used in biology and drug discovery not only predicts our new chemistries but also validates published reports.

Sign in / Sign up

Export Citation Format

Share Document