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In the advancing field of 5G technologies, particularly at the 60 GHz band, dielectric resonator antennas (DRAs) stand out for their low conduction loss and high radiation efficiency. However, the traditional design process for DRAs, predominantly reliant on intuitive reasoning and trial‐and‐error methods, is notably inefficient and resource‐intensive. Addressing this critical challenge, our research introduces a pioneering approach: a generative adversarial network (GAN)‐based model specifically tailored for automating DRA structure design. This novel model represents the first of its kind in the domain, marking a significant departure from conventional methods. Our GAN model uniquely integrates a simulator for DRA modeling and a generator for DRA structure design, streamlining the design process. To effectively train this model, we created a simulated data set comprising pattern–annotation pairs of geometric shapes andS₁₁parameters. This data set enabled the GAN to capture the intrinsic principles underlying DRA design. The practical impact of our model is profound; it significantly expedites the DRA design process, aligning it more closely with specific user requirements while conserving valuable time and resources. This breakthrough approach not only enhances the efficiency of DRA design but also sets a new standard in antenna technology development for future wireless communications.

 

A plasmon-enhanced pyroelectric membrane was applied to control the current flow in a graphene transistor for light detection. The graphene transistor was built on a free-standing, 15-μm-thick PVDF membrane, which was doped using gold nanorods to facilitate its optical absorption. Under the resonant condition, the device exhibited a responsivity of 0.79 μA/mW.

 

Herein, a wavelength‐selective pyroelectric sensor based on a graphene field‐effect transistor (gFET) with a plasmon‐enhanced pyroelectric gate (PG) is reported. The PG gFET (PG‐gFET) uses a poly(vinylidene fluoride‐co‐trifluoroethylene) or PVDF‐TrFE membrane doped with plasmonic nanoparticles as the gate. Gold nanorods (AuNRs) or silver nanoparticles (AgNPs) are incorporated into the PVDF‐TrFE membrane to enhance the photothermal conversion efficiency of PVDF‐TrFE in a specific narrowband wavelength range. The wavelength‐selective photothermal effect can optically modulate the gate potential of the PG‐gFET; this, in turn, leads to a change in current through the graphene film of the transistor. The PG‐gFET with AuNRs exhibits a maximum responsivity of 0.79 μA mW⁻¹at the wavelength of 660 nm. Replacing AuNRs with AgNPs in the PVDF‐TrFE membrane results in tuning the plasmonic response of the transistor to 488 nm with a maximum responsivity of 0.68 μA mW⁻¹. When plasmonic nanoparticles are absent from the PVDF‐TrFE membrane, the maximum response wavelength of the transistor is shifted to a midinfrared regime at 3125 nm, which is associated with the CC absorption of PVDF‐TrFE. The ability of the PG‐gFET to selectively respond to different light wavelengths will benefit many fields, including pyroelectric sensors, spectroscopy, and imaging.

 

Elevation is a major driver of plant ecology and sediment dynamics in tidal wetlands, so accurate and precise spatial data are essential for assessing wetland vulnerability to sea-level rise and making forecasts. We performed survey-grade elevation and vegetation surveys of the Global Change Research Wetland, a brackish microtidal wetland in the Chesapeake Bay estuary, Maryland (USA), to both intercompare unbiased digital elevation model (DEM) creation techniques and to describe niche partitioning of several common tidal wetland plant species. We identified a tradeoff between scalability and performance in creating unbiased DEMs, with more data intensive methods such as kriging performing better than 3 more scalable methods involving postprocessing of light detection and ranging (LiDAR)-based DEMs. The LiDAR Elevation Correction with Normalized Difference Vegetation Index (LEAN) method provided a compromise between scalability and performance, although it underpredicted variability in elevation. In areas where native plants dominated, the sedge Schoenoplectus americanus occupied more frequently flooded areas (median: 0.22, 95% range: 0.09 to 0.31 m relative to North America Vertical Datum of 1988 [NAVD88]) and the grass Spartina patens, less frequently flooded (0.27, 0.1 to 0.35 m NAVD88). Non-native Phragmites australis dominated at lower elevations more than the native graminoids, but had a wide flooding tolerance, encompassing both their ranges (0.19, −0.05 to 0.36 m NAVD88). The native shrub Iva frutescens also dominated at lower elevations (0.20, 0.04 to 0.30 m NAVD88), despite being previously described as a high marsh species. These analyses not only provide valuable context for the temporally rich but spatially restricted data collected at a single well-studied site, but also provide broad insight into mapping techniques and species zonation. 

One of the challenges of exploiting extracellular vesicles (EVs) as a disease biomarker is to differentiate EVs released by similar cell types or phenotypes. This paper reports a high-throughput and label-free EV microarray technology to differentiate EVs by simultaneous characterization of a panel of EV membrane proteins. The EsupplV microarray platform, which consists of an array of antibodies printed on a photonic crystal biosensor and a microscopic hyperspectral imaging technique, can rapidly assess the binding of the EV membrane proteins with their corresponding antibodies. The EV microarray assay requires only a 2 μL sample volume and a detection time of less than 2 h. The EV microarray assay was validated by not only quantifying seven membrane proteins carried by macrophage-derived EVs but also distinguishing the EVs secreted by three macrophage phenotypes. In particular, the EV microarray technology can generate a molecular fingerprint of target EVs that can be used to identify the EVs' parental cells, and thus has utility for basic science research as well as for point-of-care disease diagnostics and therapeutics.