Selective Growth of Graphene-Confined Inkjet-Printed Sn Nanoparticles on Plastic Using Intense Pulsed Light Annealing.

Printing graphene-based nanomaterials on flexible substrates has become a burgeoning platform for next-generation technologies. Combining graphene and nanoparticles to create hybrid nanomaterials has been proven to boost device performance, thanks to their complementary physical and chemical properties. However, high growth temperatures and long processing times are often required to produce high-quality graphene-based nanocomposites. For the first time, we report a novel scalable approach for additive manufacturing of Sn patterns on polymer foil and their selective conversion into nanocomposite films under atmospheric conditions. A combination of inkjet printing and intense flashlight irradiation techniques is studied. Light pulses that are selectively absorbed by the printed Sn patterns cause a temperature of over 1000 °C to be reached locally in a split second without damaging the underlying polymer foil. The top surface of the polymer foil at the interface with printed Sn becomes locally graphitized and acts as a carbon source, transforming printed Sn into Sn@graphene (Sn@G) core-shell patterns. Our results revealed a decrease in electrical sheet resistance, with an optimal value (Rs = 72 ± 2 Ω/sq) reached when light pulses with an energy density of 12.8 J/cm2 were applied. These graphene-protected Sn nanoparticle patterns exhibit excellent resistance against air oxidation for months. Finally, we demonstrate the implementation of Sn@G patterns as electrodes for Li-ion microbatteries (LIBs) and triboelectric nanogenerators (TENGs), showing remarkable performance. This work offers new insight into the development of a versatile, eco-friendly, and cost-effective technique for producing well-defined patterns of graphene-based nanomaterials directly on a flexible substrate using different light-absorbing nanoparticles and carbon sources.

On the mechanism ruling the morphology of silicon nanowires obtained by one-pot metal-assisted chemical etching.

One-pot Ag-assisted chemical etching (SACE) of silicon provides an effective, simple way to obtain Si nanowires (NWs) of potential interest for technological applications ranging from photovoltaics to thermoelectricity. The detailed mechanism ruling the process has not been yet fully elucidated, however. In this paper we report the results of an extended analysis of the interplay among doping level and type of silicon, nanowire nanomorphology and the parameters controlling the chemistry of the etching process. We provide evidence that the SACE mechanism entirely occurs at the interface between the etching solution and the Si substrate as a result of Si extrusion by sinking self-propelled Ag particles. Also, a rationale is advanced to explain the reported formation of (partially) porous NWs at high doping levels in both p- and n-type Si. A model not relying on the asserted formation of potential barriers enables to recover full consistency between SACE electrochemistry and the mechanism of formation of porous silicon in electrochemical cells.

Heart rhythm characterization through induced physiological variables.

Atrial fibrillation remains a major cause of morbi-mortality, making mass screening desirable and leading industry to actively develop devices devoted to automatic AF detection. Because there is a tendency toward mobile devices, there is a need for an accurate, rapid method for studying short inter-beat interval time series for real-time automatic medical monitoring. We report a new methodology to efficiently select highly discriminative variables between physiological states, here a normal sinus rhythm or atrial fibrillation. We generate induced variables using the first ten time derivatives of an RR interval time series and formally express a new multivariate metric quantifying their discriminative power to drive state variable selection. When combined with a simple classifier, this new methodology results in 99.9% classification accuracy for 1-min RR interval time series (n = 7,400), with heart rate accelerations and jerks being the most discriminant variables. We show that the RR interval time series can be drastically reduced from 60 s to 3 s, with a classification accuracy of 95.0%. We show that heart rhythm characterization is facilitated by induced variables using time derivatives, which is a generic methodology that is particularly suitable to real-time medical monitoring.

Piezoelectric tantalum pentoxide studied for optical tunable applications.

Piezoelectric transparent thin films are of great interest for use in tunable filters. We present experimental results on Ta2O5 single layers coated on fused-silica substrates with an electron-beam deposition process. Above 450 degrees C, coatings change from an amorphous to a polycrystallized structure. When this structure shows a preferred orientation matching the piezoelectric tensor of the Ta2O5 crystal and the external electric field, variation in the piezoelectric layer thickness is expected. We detail experimental results in terms of optical (spectrophotometric and scattering measurements) and nonoptical characterizations (x-ray diffraction and scanning electron microscopy). Then the resultant thickness variation under oscillating applied voltage is measured with an extrinsic Fabry-Perot interferometer setup.