Enhancement of thermal rectification by asymmetry engineering of thermal conductivity and geometric structure for multi-segment thermal rectifier

Du, Fu-Ye; Zhang, Wang; Wang, Hui‐Qiong; Zheng, Jin‐Cheng

doi:10.1088/1674-1056/acc78c

Cited by 6 publications

(3 citation statements)

References 91 publications

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“…Local symmetry has been identified as a significant factor in various phenomena, including thermoelectric transport various phenomena, e.g. thermoelectric transport, 31 thermal rectification, 32 and interfacial electronic states. 33 Recent theoretical work has shown the presence of two interfaces driven by local symmetry in the (ZnO) n /(w-FeO) n superlattices.…”

Section: Discussionmentioning

confidence: 99%

Interfacial electronic state between hexagonal ZnO and cubic NiO

Chan,

Tey,

Wang

2024

RSC Adv.

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Section: Discussionmentioning

confidence: 99%

Interfacial electronic state between hexagonal ZnO and cubic NiO

Chan,

Tey,

Wang

2024

RSC Adv.

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“…These two features can be utilized to design multi-segment thermal rectifiers, as demonstrated in our recent simulations. [46] By applying pressure [47] including strain engineering [48][49][50][51][52] or applying "chemical pressure" with chemical impurities [53] or alloying, [54] it is possible to tune properties of functional materials in different ways. Laser-induced method provides an alternative approach for doping with the advantage of flexibility by post-treatment and enables selected-area doping.…”

Section: Prospective Of Laser-induced Doping Methodsmentioning

confidence: 99%

Electronic and thermal properties of Ag-doped single crystal zinc oxide via laser-induced technique

et al. 2023

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The doping of ZnO has attracted lots of attention because it is an important way to tune the properties of ZnO. Post-doping after growth is one of the efficient strategies. Here, we report a unique approach to successfully dope the single crystalline ZnO with Ag by the laser-induced method, which can effectively further post-treat grown samples. Magnetron sputtering was used to coat the Ag film with a thickness of about 50 nm on the single crystalline ZnO. Neodymium-doped yttrium aluminum garnet (Nd: YAG) laser was chosen to irradiate the Ag-capped ZnO samples, followed by annealing at 700℃ for two hours to form ZnO:Ag. The 3D information of the elemental distribution of Ag in ZnO was obtained through time-of-flight secondary ion mass spectrometry (TOF-SIMS). TOF-SIMS and core-level X-ray photoelectron spectroscopy (XPS) demonstrated that the Ag impurities could be effectively doped into single crystalline ZnO samples as deep as several hundred nanometers. Obvious broadening of core level XPS profiles of Ag from the surface to depths of hundred nms was observed, indicating the variance of chemical state changes in laser-induced Ag-doped ZnO. Interesting features of electronic mixing states were detected in the valence band XPS of ZnO: Ag, suggesting the strong coupling or interaction of Ag and ZnO in the sample rather than their simple mixture. The Ag-doped ZnO also showed a narrower bandgap and a decrease in thermal diffusion coefficient compared to the pure ZnO, which would be beneficial to thermoelectric performance.

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“…Discovery of new internal logics, patterns, or rules [10][11][12][13], and the study of complex systems, including nanostructures [14 -29], alloys [30][31][32][33][34][35], superlattices [22,[36][37][38], surfaces [39][40][41], and interfaces [40,[42][43][44][45][46], as well as from materials to devices [47][48], are typical research topics in materials science. These areas could be addressed according to user specifications [49][50] by leveraging ML and big data statistical methods [51], which have advanced to a stage where users can utilize them to achieve large and complicated objectives with complex models.…”

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confidence: 99%

Advances of machine learning in materials science: Ideas and techniques

Sin¹,

Ng²,

Wang³

et al. 2023

Front. Phys.

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In this big data era, the use of large dataset in conjunction with machine learning (ML) has been increasingly popular in both industry and academia. In recent times, the field of materials science is also undergoing a big data revolution, with large database and repositories appearing everywhere. Traditionally, materials science is a trial-and-error field, in both the computational and experimental departments. With the advent of machine learning-based techniques, there has been a paradigm shift: materials can now be screened quickly using ML models and even generated based on materials with similar properties; ML has also quietly infiltrated many sub-disciplinary under materials science. However, ML remains relatively new to the field and is expanding its wing quickly. There are a plethora of readily-available big data architectures and abundance of ML models and software; The call to integrate all these elements in a comprehensive research procedure is becoming an important direction of material science research. In this review, we attempt to provide an introduction and reference of ML to materials scientists, covering as much as possible the commonly used methods and applications, and discussing the future possibilities.

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Enhancement of thermal rectification by asymmetry engineering of thermal conductivity and geometric structure for multi-segment thermal rectifier

Cited by 6 publications

References 91 publications

Interfacial electronic state between hexagonal ZnO and cubic NiO

Interfacial electronic state between hexagonal ZnO and cubic NiO

Electronic and thermal properties of Ag-doped single crystal zinc oxide via laser-induced technique

Advances of machine learning in materials science: Ideas and techniques

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