A low-profile consolidated metastructure for multispectral signature management

This work pertains to the design, numerical investigation, and experimental demonstration of an optically transparent, lightweight, and conformable metastructure that exhibits multispectral signature management capabilities despite its extremely low-profile configuration. In comparison to the existing hierarchical approaches of designing multispectral stealth solutions, attention has been paid to accommodate the conflicting requirements of radar and infrared stealth using a single metasurface layer configuration, which required a few constraints to be incorporated during the design stage to ensure compatibility. This methodlogy promulgates the desired multispectral response with minimal manufacturing footprint and facilitates an efficient integration with the other existing countermeasure platforms. The resulting design exhibits a polarization-insensitive and incident angle stable broadband microwave absorption with at least 90% absorption ranging from 8.2 to 18.4 GHz. Concomitantly it also exhibits an averaged infrared emissivity of 0.46 in the 8-14µm long-wave infrared regime, along with high optical transparency (71% transmission at 632.8nm). Notably, the total thickness of the metastructure stands at 0.10λ_L (λ_L corresponds to the wavelength at lowest frequency). The metastructure has been fabricated with ITO coated PET sheets, on which the frequency selective pattern is machined using Excimer laser micromachining, and the performances are verified experimentally. Furthermore, a hybrid theoretical model has been developed that not only provides crucial insights into the operation of metastructure but also presents a methodical semi-analytical approach to design.

Selective neural stimulation methods improve cycling exercise performance after spinal cord injury: a case series

Background Exercise after paralysis can help prevent secondary health complications, but achieving adequate exercise volumes and intensities is difficult with loss of motor control. Existing electrical stimulation-driven cycling systems involve the paralyzed musculature but result in rapid force decline and muscle fatigue, limiting their effectiveness. This study explores the effects of selective stimulation patterns delivered through multi-contact nerve cuff electrodes on functional exercise output, with the goal of increasing work performed and power maintained within each bout of exercise. Methods Three people with spinal cord injury and implanted stimulation systems performed cycling trials using conventional (S-Max), low overlap (S-Low), low duty cycle (C-Max), and/or combined low overlap and low duty cycle (C-Low) stimulation patterns. Outcome measures include total work (W), end power (Pend), power fluctuation indices (PFI), charge accumulation (Q), and efficiency (η). Mann–Whitney tests were used for statistical comparisons of W and Pend between a selective pattern and S-Max. Welch’s ANOVAs were used to evaluate differences in PFIs among all patterns tested within a participant (n ≥ 90 per stimulation condition). Results At least one selective pattern significantly (p < 0.05) increased W and Pend over S-Max in each participant. All selective patterns also reduced Q and increased η compared with S-Max for all participants. C-Max significantly (p < 0.01) increased PFI, indicating a decrease in ride smoothness with low duty cycle patterns. Conclusions Selective stimulation patterns can increase work performed and power sustained by paralyzed muscles prior to fatigue with increased stimulation efficiency. While still effective, low duty cycle patterns can cause inconsistent power outputs each pedal stroke, but this can be managed by utilizing optimized stimulation levels. Increasing work and sustained power each exercise session has the potential to ultimately improve the physiological benefits of stimulation-driven exercise.

Selective Detection of Nitrogen-Containing Compound Gases

N-containing gaseous compounds, such as trimethylamine (TMA), triethylamine (TEA), ammonia (NH3), nitrogen monoxide (NO), and nitrogen dioxide (NO2) exude irritating odors and are harmful to the human respiratory system at high concentrations. In this study, we investigated the sensing responses of five sensor materials—Al-doped ZnO (AZO) nanoparticles (NPs), Pt-loaded AZO NPs, a Pt-loaded WO3 (Pt-WO3) thin film, an Au-loaded WO3 (Au-WO3) thin film, and N-doped graphene—to the five aforementioned gases at a concentration of 10 parts per million (ppm). The ZnO- and WO3-based materials exhibited n-type semiconducting behavior, and their responses to tertiary amines were significantly higher than those of nitric oxides. The N-doped graphene exhibited p-type semiconducting behavior and responded only to nitric oxides. The Au- and Pt-WO3 thin films exhibited extremely high responses of approximately 100,000 for 10 ppm of triethylamine (TEA) and approximately −2700 for 10 ppm of NO2, respectively. These sensing responses are superior to those of previously reported sensors based on semiconducting metal oxides. On the basis of the sensing response results, we drew radar plots, which indicated that selective pattern recognition could be achieved by using the five sensing materials together. Thus, we demonstrated the possibility to distinguish each type of gas by applying the patterns to recognition techniques.