Results 191 to 200 of about 100,490 (349)

Thermoelectric properties of the multi-dot quantum transport model. [PDF]

Sci Rep
Bhandari TR, Timsina H, Dhakal C, Adhikari NP.   +3 more
europepmc   +1 more source

Corrigendum to “Effects of replacing Cu with Ni and Ni-Zn on the structural, magnetic, and thermoelectric properties of the solution-processed Cu12Sb4S13 tetrahedrites” [Mater. Sci. Semicond. Proc. 198 (2025), 109803]

Oleksandr Anatoliiovych Dobrozhan, Roman Pshenychnyi, Maksym Yermakov, Bohdan Boiko, V. Tkáč, Serhii Vorobiov, Anatoliy Opanasyuk   +6 more
openalex   +1 more source

Computational prediction of nanostructured alloys with enhanced thermoelectric properties

, 2019
Jeff W. Doak, Shiqiang Hao, Scott Kirklin, Christopher Wolverton   +3 more
openalex   +2 more sources

Thermoelectric Properties of Ice

Thermoelectric Properties of Ice
International Conference on Low Temperature Science. I. Conference on Physics of Snow and Ice, II. Conference on Cryobiology. (August, 14-19, 1966, Sapporo, Japan)
openaire  

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Defect-induced optical and thermoelectric properties of cobalt doped ZnO nanostructures prepared through hydrothermal route. [PDF]

Sci Rep
Arif D, Kiani SS, Khan R, Abid AY, Safeen K, Alotaibi KM, Shah WH, Ali A, Shah S, Girma WM, Shah AU, Safeen A.   +11 more
europepmc   +1 more source

Effects of Al doping on phase transition and thermoelectric properties of β–Zn4Sb3

, 2012
Qingqing Wang, Xiaoying Qin
openalex   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Unveiling Thermoelectric Properties of SURMOF Nanofilms: A New Frontier in Molecular Thermoelectrics. [PDF]

Adv Sci (Weinh)
Park J, Cho SJ, Lee DS, Park S.
europepmc   +1 more source

Thermodynamic, Structural and Thermoelectric Properties of AgSbTe2 Thick Films Developed by Melt Spinning [PDF]

, 2018
Baoli Du, Ming Liu, Jian Xu, Baofu Hu, Bingguo Liu, Taichao Su, Jian Wang   +6 more
openalex   +1 more source