Plasmonic heating assisted deposition of bare Au nanoparticles on titania nanoshells

2010 ◽  
Vol 351 (2) ◽  
pp. 576-579 ◽  
Author(s):  
Ivano Alessandri
Keyword(s):  
Au Nanoparticles ◽  
Plasmonic Heating

scholarly journals Multifunctional Hybrid Fe2O3-Au Nanoparticles for Efficient Plasmonic Heating

10.3791/53598 ◽  
2016 ◽  
Author(s):  
Simona E. Hunyadi Murph ◽  
George K. Larsen ◽  
Robert J. Lascola
Keyword(s):  
Au Nanoparticles ◽  
Plasmonic Heating

Plasmonic heating induced by Au nanoparticles for quasi-ballistic thermal transport in multi-walled carbon nanotubes

Nanoscale ◽  
2019 ◽  
Vol 11 (16) ◽  
pp. 7572-7581 ◽  
Author(s):  
Yanru Xu ◽  
Xiaoguang Zhao ◽  
Aobo Li ◽  
Yanan Yue ◽  
Jin Jiang ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Thermal Transport ◽  
Au Nanoparticles ◽  
Au Nanoparticle ◽  
Multi Walled Carbon Nanotubes ◽  
Plasmonic Heating ◽  
Walled Carbon Nanotubes

Nanoscale thermal probing based on Au nanoparticle enhanced Raman thermometry is achieved to characterize ballistic thermal transport of 1D materials.

scholarly journals Laser-driven structural transformations in dextran-graft-PNIPAM copolymer/Au nanoparticles hybrid nanosystem: the role of plasmon heating and attractive optical forces

RSC Advances ◽  
2018 ◽  
Vol 8 (67) ◽  
pp. 38400-38409 ◽  
Author(s):  
Oleg A. Yeshchenko ◽  
Antonina P. Naumenko ◽  
Nataliya V. Kutsevol ◽  
Iulia I. Harahuts
Keyword(s):  
Au Nanoparticles ◽  
Structural Transformations ◽  
Optical Forces ◽  
Plasmonic Heating ◽  
Hybrid Nanosystem ◽  
Synergetic Action

Structural transformations in D-g-PNIPAM/AuNPs hybrid nanosystem arise from the synergetic action of plasmonic heating and attractive optical plasmonic forces.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

Synergistic effects of SnO2 and Au nanoparticles decorated on WS2 nanosheets for flexible, room-temperature CO gas sensing

2021 ◽  
Vol 332 ◽  
pp. 129493
Author(s):  
Jae-Hun Kim ◽  
Jin-Young Kim ◽  
Ali Mirzaei ◽  
Hyoun Woo Kim ◽  
Sang Sub Kim
Keyword(s):  
Room Temperature ◽  
Au Nanoparticles ◽  
Gas Sensing ◽  
Synergistic Effects ◽  
Ws2 Nanosheets ◽  
Co Gas
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