| dc.description.abstract | Networks of two-dimensional nanosheets have demonstrated significant promise across a host of applications that span the breadth of materials science. While this has driven research into nanosheet-based devices at a remarkable pace, a prevailing observation has been that the superlative physical properties of nanosheets do not naturally translate to their networks. To begin to address this dichotomy and realise their full potential, the electrical, mechanical and morphological properties of nanosheet networks are investigated in this work.
Composites of 2D nanosheets mixed with 1D single-walled carbon nanotubes (SWNTs) represent an exciting class of materials for electrochemical applications. Active material is supplied by the nanosheets while SWNTs provide mechanical reinforcement and enhanced electrical conductivity. Although charge transport in these systems has been studied, their mechanical properties have not yet been quantitatively examined. Here, both the mechanical and morphological character of SWNT / MoS2 1D:2D nanocomposites are investigated as a function of SWNT volume fraction, phi. Microscopic analysis reveals the reinforcing SWNT network to evolve from a loosely connected structure for phi < 1 vol % to an entangled and continuous architecture for phi > 1 vol %. This transition has a considerable effect on the composite mechanical properties. Below 1 vol %, the composite modulus and failure-strain exhibit short-fibre composite behaviour. However, above 1 vol % both increase with phi in a manner consistent with fibrous networks. The composite tensile strength similarly evolves from a regime limited by the matrix-fibre interface at low-phi, to one limited by the strength of the nanotube ropes for phi > 1 vol %. Crucially, while the composite tensile toughness is constant at low-phi, it increases rapidly for phi > 1 vol % consistent with percolation theory. Quantitative models are presented to describe this mechanical evolution, which renders the composites robust at additive levels as low as 5 vol % SWNTs.
Owing to the diverse electronic properties of their constituent nanosheets, 2D networks are well-placed to feature prominently in the growing field of printed electronics. While it is known that electrical performance in printed 2D networks is impeded by inter-nanosheet junctions, work to characterise this effect has been limited. To address this, the electrical conductivity of printed nanosheet networks is investigated as a function of constituent nanosheet length, lNS. A family of size-selected WSe2, graphene and silver 2D inks, each spanning an order of magnitude in lNS, are synthesised and printed. The in-plane conductivity of spray-coated WSe2 networks is observed to decrease by an order of magnitude as lNS is reduced from 462 nm to 62 nm. Significantly, the conductivity in both graphene and silver nanoplatelet networks exhibits the opposite response, scaling with 1/lNS. A model to describe this length-dependent conductivity scaling in networks of solution-processed nanosheets is developed.
To conclude, focused ion beam and scanning electron microscope nanotomography (FIB:SEM NT) is presented as a novel technique to assess the morphology of nanostructured systems. This is demonstrated through a length-dependent investigation of printed graphene networks for lNS = 947, 630 and 215 nm. The network porosity is observed to steadily decrease from 49 % to 40 % as the constituent nanosheet length is reduced from 947 nm to 215 nm. Interestingly, the pore volume in each network is found to be highly contiguous with > 99 % of the total pore volume contained in a single open pore. A reduction in nanosheet size is seen to increase the specific surface area of the printed networks from 14 m2/g to 23 m2/g as lNS is decreased from 947 to 215 nm. Notably, both pore size and shape are found to be a function of nanosheet length. Finally, the alignment of each printed network is evaluated using Fourier transforms, where networks comprising larger nanosheets demonstrate enhanced alignment. | en |
