Browsing School of Engineering and Materials Science by Author "Garberoglio, G"
Now showing items 1-11 of 11
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Designing graphene based nanofoams with nonlinear auxetic and anisotropic mechanical properties under tension or compression
Pedrielli, A; Taioli, S; Garberoglio, G; Pugno, NM (2017-01) -
Designing graphene based nanofoams with nonlinear auxetic and anisotropic mechanical properties under tension or compression (vol 111, pg 796, 2017)
Pedrielli, A; Taioli, S; Garberoglio, G; Pugno, NM (2017-05) -
Gas adsorption and dynamics in Pillared Graphene Frameworks
Pedrielli, A; Taioli, S; Garberoglio, G; Pugno, NM (2018-02) -
Mechanical and thermal properties of graphene random nanofoams via Molecular Dynamics simulations
Pedrielli, A; Taioli, S; Garberoglio, G; Pugno, NM (2018-06) -
Monte Carlo simulations of measured electron energy-loss spectra of diamond and graphite: Role of dielectric-response models
Azzolini, M; Morresi, T; Garberoglio, G; Calliari, L; Pugno, NM; Taioli, S; Dapor, M (2017-07) -
A novel combined experimental and multiscale theoretical approach to unravel the structure of SiC/SiOx core/shell nanowires for their optimal design
Morresi, T; Timpel, M; Pedrielli, A; Garberoglio, G; Tatti, R; Verucchi, R; Pasquali, L; Pugno, NM; Nardi, MV; Taioli, S (2018-07-28) -
Production and processing of graphene and related materials
Backes, C; Abdelkader, AM; Alonso, C; Andrieux-Ledier, A; Arenal, R; Azpeitia, J; Balakrishnan, N; Banszerus, L; Barjon, J; Bartali, R (2020-04) -
Spider silk reinforced by graphene or carbon nanotubes
Lepore, E; Bosia, F; Bonaccorso, F; Bruna, M; Taioli, S; Garberoglio, G; Ferrari, AC; Pugno, NM (2017-09) -
Spider silk reinforced by graphene or carbon nanotubes (vol 4, 031013, 2017)
Lepore, E; Bosia, F; Bonaccorso, F; Bruna, M; Taioli, S; Garberoglio, G; Ferrari, AC; Pugno, NM (2018-10) -
Synthesis of single layer graphene on Cu(111) by C-60 supersonic molecular beam epitaxy
Tatti, R; Aversa, L; Verucchi, R; Cavaliere, E; Garberoglio, G; Pugno, NM; Speranza, G; Taioli, S (2016) -
Understanding anharmonic effects on hydrogen desorption characteristics of MgnH2n nanoclusters by ab initio trained deep neural network
Pedrielli, A; Trevisanutto, PE; Monacelli, L; Garberoglio, G; Pugno, NM; Taioli, S (2022)