Understanding how junction resistances impact the conduction mechanism in nano-networks.

Networks of nanowires, nanotubes, and nanosheets are important for many applications in printed electronics. However, the network conductivity and mobility are usually limited by the resistance between the particles, often referred to as the junction resistance. Minimising the junction resistance has proven to be challenging, partly because it is difficult to measure. Here, we develop a simple model for electrical conduction in networks of 1D or 2D nanomaterials that allows us to extract junction and nanoparticle resistances from particle-size-dependent DC network resistivity data. We find junction resistances in porous networks to scale with nanoparticle resistivity and vary from 5 Ω for silver nanosheets to 24 GΩ for WS2 nanosheets. Moreover, our model allows junction and nanoparticle resistances to be obtained simultaneously from AC impedance spectra of semiconducting nanosheet networks. Through our model, we use the impedance data to directly link the high mobility of aligned networks of electrochemically exfoliated MoS2 nanosheets (≈ 7 cm2 V-1 s-1) to low junction resistances of ∼2.3 MΩ. Temperature-dependent impedance measurements also allow us to comprehensively investigate transport mechanisms within the network and quantitatively differentiate intra-nanosheet phonon-limited bandlike transport from inter-nanosheet hopping.

Highly Sensitive Composite Foam Bodily Sensors Based on the g-Putty Ink Soaking Procedure.

Electrically conductive composite materials are highlighted as a potential tech path toward future flexible devices for wearable health technologies. To be commercially viable, these materials must not only be mechanically soft, highly sensitive to deformation, and report a sustainable signal but also utilize manufacturing methods that facilitate large-scale production. An ideal candidate for these envisioned technologies is the viscous, electromechanically sensitive composite material g-putty. Inks based on g-putty here are shown to transform a commercial polymer foam into a sensitive strain sensing material through a simple, scalable soaking procedure. Foam composites reported here have sensitives as high as ∼20 in terms of compressive strain and ∼0.4 kPa-1 with respect to applied compressive stress; both values being comparable to the parent g-putty material. Through g-putty's self-adhering nature, the foams used acted as an elastic scaffolding that aided in overcoming many of the hysteresis effects associated with g-putty without the need for further encapsulation methods. From this, these composite foams were demonstrated to have a sustainable signal that allowed for effective impact and vital sign sensing.

Pristine graphene induces innate immune training.

Graphene-based materials are of increasing interest for their potential use in biomedical applications. However, there is a need to gain a deeper understanding of how graphene modulates biological responses before moving towards clinical application. Innate immune training is a recently described phenomenon whereby cells of the innate immune system are capable of being programmed to generate an increased non-specific response upon subsequent challenge. This has been well established in the case of certain microbes and microbial products. However, little is known about the capacity of particulate materials, such as pristine graphene (pGr), to promote innate immune training. Here we report for the first time that while stimulation with pGr alone does not directly induce cytokine secretion by bone-marrow derived macrophages (BMDMs), it programs them for enhanced secretion of proinflammatory cytokines (IL-6, TNF-α) and a concomitant decrease in production of the regulatory cytokine, IL-10 after Toll-like receptor (TLR) ligand stimulation. This capacity of pGr to program cells for enhanced inflammatory responses could be overcome if the nanomaterial is incorporated in a collagen matrix. Our findings thus demonstrate the potential of graphene to modulate innate immunity over long timescales and have implications for the design and biomedical use of pGr-based materials.

Liquid phase exfoliation of MoO2 nanosheets for lithium ion battery applications.

Molybdenum dioxide (MoO2) is a layered material which shows promise for a number of applications in the electrochemical energy storage arena. Mostly studied as a bulk layered material, MoO2 has not previously been exfoliated in large quantities. Here we demonstrate the liquid phase exfoliation of MoO2 in the solvent isopropanol, yielding reasonable amounts of good quality nanosheets. However, we found that, when dispersed in isopropanol under ambient conditions, MoO2 nanosheets are gradually oxidized to higher oxides such as MoO3 over a period of days. Conversely, if the nanosheets are processed into dried films immediately after exfoliation, and before oxidation has had a chance to progress, the nanosheets are relatively stable under ambient conditions, remaining unoxidised unless the films are heated. We also found that MoO2 nanosheets can be size selected by controlled centrifugation and show size-dependent optical properties. This allows us to propose spectroscopic metrics which allow concentration- and size-estimation from extinction spectra. Finally, we found that liquid-exfoliated MoO2 nanosheets could be used to produce lithium ion battery anodes with capacities of up to 1140 mA h g-1.

Non-resonant light scattering in dispersions of 2D nanosheets.

Extinction spectra of nanomaterial suspensions can be dominated by light scattering, hampering quantitative spectral analysis. No simple models exist for the wavelength-dependence of the scattering coefficients in suspensions of arbitrary-sized, high-aspect-ratio nanoparticles. Here, suspensions of BN, talc, GaS, Ni(OH)2, Mg(OH)2 and Cu(OH)2 nanosheets are used to explore non-resonant scattering in wide-bandgap 2D nanomaterials. Using an integrating sphere, scattering coefficient (σ) spectra were measured for a number of size-selected fractions for each nanosheet type. Generally, σ scales as a power-law with wavelength in the non-resonant regime: σ(λ)∝[λ/〈L〉]-m, where 〈L〉 is the mean nanosheet length. For all materials, the scattering exponent, m, forms a master-curve, transitioning from m = 4 to m = 2, as the characteristic nanosheet area increases, indicating a transition from Rayleigh to van der Hulst scattering. In addition, once material density and refractive index are factored out, the proportionality constant relating σ to [λ/〈L〉]-m, also forms a master-curve when plotted versus 〈L〉.

Graphene-coated polymer foams as tuneable impact sensors.

The use of graphene-based nanocomposites as electromechanical sensors has been broadly explored in recent times with a number of papers describing porous, foam-like composites. However, there are no reported foam-based materials that are capable of large dynamic compressive load measurements and very few studies on composite impact sensing. In this work, we describe a simple method of infusing commercially-available foams with pristine graphene to form conductive composites, which we refer to as G-foam. Displaying a strain-dependent electrical response, G-foam was found to be a reasonably effective pressure sensing material. More interestingly, G-foam is a sensitive impact-sensing material. Through the addition of various amounts of polymer filler, the mechanical properties of the composites can be tuned leading to the controllable variation of the impact sensing range. We have developed a simple model which quantitatively explains all our impact sensing data.