Frequency-domain energy transport state-resolved Raman for measuring the thermal conductivity of suspended nm-thick MoSe2

Zobeiri, Hamidreza; Wang, Ridong; Wang, Tianyu; Lin, Huan; Deng, Chunming; Wang, Xinwei

doi:10.1016/j.ijheatmasstransfer.2019.01.012

Cited by 59 publications

(37 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More details of this process could be found in our previous works. 45,46 The Si substrate with the WS2 film on top of it is mounted on a stage inside a glass container. This container is filled with DI water.…”

Section: Sample Preparationmentioning

confidence: 99%

See 1 more Smart Citation

Thermal conductance between water and nm-thick WS₂: extremely localized probing using nanosecond energy transport state-resolved Raman

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Sample Preparationmentioning

confidence: 99%

“…More information about the lasers and Raman system could be found in our previous works. 46,55,56 Also, similar consideration should be involved in choosing the optimum CW laser power to prevent sample damage. Table 2 includes the laser power range for each sample under both heating states.…”

Section: Water-ws 2 Interface Thermal Conductancementioning

confidence: 99%

Thermal conductance between water and nm-thick WS₂: extremely localized probing using nanosecond energy transport state-resolved Raman

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…For FR-Raman, the data fitting is for the Raman shift against the modulation frequency, and it takes quite tremendous measurements. An alternative, named FET-Raman, is to fix the frequency, but vary the laser power and study the Raman shift change against laser power [ 27 , 28 ]. Here, we take the work on MoSe 2 to introduce this technique.…”

Section: Time-domain Differential and Frequency-resolved Ramanmentioning

confidence: 99%

“…( c ) The blue shift of Raman peak with the increase of sample thickness. Reprinted from [ 27 ], with permission from Elsevier, 2019.…”

Section: Figurementioning

confidence: 99%

Energy and Charge Transport in 2D Atomic Layer Materials: Raman-Based Characterization

Wang

Zobeiri

et al. 2020

Nanomaterials

Self Cite

View full text Add to dashboard Cite

As they hold extraordinary mechanical and physical properties, two-dimensional (2D) atomic layer materials, including graphene, transition metal dichalcogenides, and MXenes, have attracted a great deal of attention. The characterization of energy and charge transport in these materials is particularly crucial for their applications. As noncontact methods, Raman-based techniques are widely used in exploring the energy and charge transport in 2D materials. In this review, we explain the principle of Raman-based thermometry in detail. We critically review different Raman-based techniques, which include steady state Raman, time-domain differential Raman, frequency-resolved Raman, and energy transport state-resolved Raman techniques constructed in the frequency domain, space domain, and time domain. Detailed outlooks are provided about Raman-based energy and charge transport in 2D materials and issues that need special attention.

show abstract

“…From the perspective of engineering applications, low‐dimensional materials with extremely low or high thermal conductivities have the potential to be used in thermal management devices . It is worth noting that ML approaches also have broad applications in macroscopic building thermal management systems .…”

Section: Property Predictions Using Supervised Algorithmsmentioning

confidence: 99%

Machine learning and artificial neural network accelerated computational discoveries in materials science

Yang

Hou

Jiang

et al. 2019

WIREs Comput Mol Sci

View full text Add to dashboard Cite

Artificial intelligence (AI) has been referred to as the "fourth paradigm of science," and as part of a coherent toolbox of data-driven approaches, machine learning (ML) dramatically accelerates the computational discoveries. As the machinery for ML algorithms matures, significant advances have been made not only by the mainstream AI researchers, but also those work in computational materials science. The number of ML and artificial neural network (ANN) applications in the computational materials science is growing at an astounding rate. This perspective briefly reviews the state-of-the-art progress in some supervised and unsupervised methods with their respective applications. The characteristics of primary ML and ANN algorithms are first described. Then, the most critical applications of AI in computational materials science such as empirical interatomic potential development, ML-based potential, property predictions, and molecular discoveries using generative adversarial networks (GAN) are comprehensively reviewed. The central ideas underlying these ML applications are discussed, and future directions for integrating ML with computational materials science are given. Finally, a discussion on the applicability and limitations of current ML techniques and the remaining challenges are summarized.

show abstract

Frequency-domain energy transport state-resolved Raman for measuring the thermal conductivity of suspended nm-thick MoSe2

Cited by 59 publications

References 59 publications

Thermal conductance between water and nm-thick WS₂: extremely localized probing using nanosecond energy transport state-resolved Raman

Thermal conductance between water and nm-thick WS₂: extremely localized probing using nanosecond energy transport state-resolved Raman

Energy and Charge Transport in 2D Atomic Layer Materials: Raman-Based Characterization

Machine learning and artificial neural network accelerated computational discoveries in materials science

Contact Info

Product

Resources

About

Frequency-domain energy transport state-resolved Raman for measuring the thermal conductivity of suspended nm-thick MoSe2

Cited by 59 publications

References 59 publications

Thermal conductance between water and nm-thick WS2: extremely localized probing using nanosecond energy transport state-resolved Raman

Thermal conductance between water and nm-thick WS2: extremely localized probing using nanosecond energy transport state-resolved Raman

Energy and Charge Transport in 2D Atomic Layer Materials: Raman-Based Characterization

Machine learning and artificial neural network accelerated computational discoveries in materials science

Contact Info

Product

Resources

About

Thermal conductance between water and nm-thick WS₂: extremely localized probing using nanosecond energy transport state-resolved Raman

Thermal conductance between water and nm-thick WS₂: extremely localized probing using nanosecond energy transport state-resolved Raman