We demonstrate a facile fabrication technique for highly conductive and transparent thin graphene films. Sheet conductivity of Langmuir-Blodgett assembled multi-layer graphene films is enhanced through doping with nitric acid, leading to a fivefold improvement while retaining the same transparency as un-doped films. Sheet resistivity of such chemically improved films reaches 10 k  W , with optical transmittance 78% in the visible. When the films are encapsulated, the enhanced sheet conductivity effect is stable in time. In addition, stacking of multiple layers, as well as the dependence of the sheet resistivity upon axial strain have been investigated.

Based on the premise that oxidative stress plays an important role in severe acute respiratory syndrome coronavirus (SARS-CoV-2) infection, we speculated that variations in the antioxidant activities of different members of the glutathione S-transferase family of enzymes might modulate individual susceptibility towards development of clinical manifestations in COVID-19. The distribution of polymorphisms in cytosolic glutathione S-transferases GSTA1, GSTM1, GSTM3, GSTP1 (rs1695 and rs1138272), and GSTT1 were assessed in 207 COVID-19 patients and 252 matched healthy individuals, emphasizing their individual and cumulative effect in disease development and severity. GST polymorphisms were determined by appropriate PCR methods. Among six GST polymorphisms analyzed in this study, GSTP1 rs1695 and GSTM3 were found to be associated with COVID-19. Indeed, the data obtained showed that individuals carrying variant GSTP1-Val allele exhibit lower odds of COVID-19 development (p = 0.002), contrary to carriers of variant GSTM3-CC genotype which have higher odds for COVID-19 (p = 0.024). Moreover, combined GSTP1 (rs1138272 and rs1695) and GSTM3 genotype exhibited cumulative risk regarding both COVID-19 occurrence and COVID-19 severity (p = 0.001 and p = 0.025, respectively). Further studies are needed to clarify the exact roles of specific glutathione S-transferases once the SARS-CoV-2 infection is initiated in the host cell.

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The ionospheric D-region affects propagation of electromagnetic waves including ground-based signals and satellite signals during its intensive disturbances. Consequently, the modeling of electromagnetic propagation in the D-region is important in many technological domains. One of sources of uncertainty in the modeling of the disturbed D-region is the poor knowledge of its parameters in the quiet state at the considered location and time period. We present the Quiet Ionospheric D-Region (QIonDR) model based on data collected in the ionospheric D-region remote sensing by very low/low frequency (VLF/LF) signals and the Long-Wave Propagation Capability (LWPC) numerical model. The QIonDR model provides both Wait’s parameters and the electron density in the D-region area of interest at a given daytime interval. The proposed model consists of two steps. In the first step, Wait’s parameters are modeled during the quiet midday periods as a function of the daily sunspot number, related to the long-term variations during solar cycle, and the seasonal parameter, providing the seasonal variations. In the second step, the output of the first step is used to model Wait’s parameters during the whole daytime. The proposed model is applied to VLF data acquired in Serbia and related to the DHO and ICV signals emitted in Germany and Italy, respectively. As a result, the proposed methodology provides a numerical tool to model the daytime Wait’s parameters over the middle and low latitudes and an analytical expression valid over a part of Europe for midday parameters.