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The results of a techno-economic model of distributed wind-hydrogen systems (WHS) located at each existing wind farm on the island of Ireland are presented in this paper. Hydrogen is produced by water electrolysis from wind energy and backed up by grid electricity, compressed before temporarily stored, then transported to the nearest injection location on the natural gas network. The model employs a novel correlation-based approach to select an optimum electrolyser capacity that generates a minimum levelised cost of hydrogen production (LCOH) for each WHS. Three scenarios of electrolyser operation are studied: (1) curtailed wind, (2) available wind, and (3) full capacity operations. Additionally, two sets of input parameters are used: (1) current and (2) future techno-economic parameters. Additionally, two electricity prices are considered: (1) low and (2) high prices. A closest facility algorithm in a geographic information system (GIS) package identifies the shortest routes from each WHS to its nearest injection point. By using current parameters, results show that small wind farms are not suitable to run electrolysers under available wind operation. They must be run at full capacity to achieve sufficiently low LCOH. At full capacity, the future average LCOH is 6-8 €/kg with total hydrogen production capacity of 49 kilotonnes per year, or equivalent to nearly 3% of Irish natural gas consumption. This potential will increase significantly due to the projected expansion of installed wind capacity in Ireland from 5 GW in 2020 to 10 GW in 2030.

The ability to reversibly and non-invasively modulate region-specific brain activity in vivo suggests Low Intensity Focused Ultrasound (LIFU) as potential therapeutics for neurological dysfunctions such as epilepsy and Parkinson's disease. While in vivo studies provide evidence of the bioeffects of LIFU on neuronal activity, they merely hint at potential mechanisms but do not fully explain how this technology achieves these effects. One potential hypothesis is that LIFU produces local membrane depolarization by mechanically perturbing the neuronal cell membrane, or activating channels or other proteins embedded in the membrane. Proteins that sense mechanical perturbations of the membrane, such as those gated by membrane tension, are prime candidates for activating in response to LIFU and thus leading to the neurological responses that have been measured. Here we use the bacterial mechanosensitive channel MscL, which has been purified and reconstituted in liposomes, to determine how LIFU may affect the activation of this membrane-tension gated channel. Two bacterial voltage-gated channels, KvAP and NaK2K F92A channels were also studied. Surprisingly, the results suggest that ultrasound modulation and membrane perturbation does not induce channel gating, but rather induces pore formation at the membrane protein-lipid interface. However, in vesicles with high MscL mechanosensitive channel concentrations, apparent decreases in pore formation are observed, suggesting that this membrane-tension-sensitive protein may serve to increase the elasticity of the membrane, presumably because of expansion of the channel in the plane of the membrane independent of channel gating.

The ability to reversibly and non-invasively modulate region-specific brain activity in vivo suggests Low Intensity Focused Ultrasound (LIFU) as potential therapeutics for neurological dysfunctions such as epilepsy and Parkinson's disease. While in vivo studies provide evidence of the bioeffects of LIFU on neuronal activity, they merely hint at potential mechanisms but do not fully explain how this technology achieves these effects. One potential hypothesis is that LIFU produces local membrane depolarization by mechanically perturbing the neuronal cell membrane, or activating channels or other proteins embedded in the membrane. Proteins that sense mechanical perturbations of the membrane, such as those gated by membrane tension, are prime candidates for activating in response to LIFU and thus leading to the neurological responses that have been measured. Here we use the bacterial mechanosensitive channel MscL, which has been purified and reconstituted in liposomes, to determine how LIFU may affect the activation of this membrane-tension gated channel. Two bacterial voltage-gated channels, KvAP and NaK2K F92A channels were also studied. Surprisingly, the results suggest that ultrasound modulation and membrane perturbation does not induce channel gating, but rather induces pore formation at the membrane protein-lipid interface. However, in vesicles with high MscL mechanosensitive channel concentrations, apparent decreases in pore formation are observed, suggesting that this membrane-tension-sensitive protein may serve to increase the elasticity of the membrane, presumably because of expansion of the channel in the plane of the membrane independent of channel gating.

Some biological sensors undergo structural changes, such as the opening of a large pore, upon matrix deformation. Other biological molecules have the ability to bind heavy metals, such as nickel, cadmium, copper, etc. We have been inspired by these findings to propose a fusion of these biological attributes into a single nanosensor. The long-range goal of this project is identify conditions in which we can utilize a biological mechano-sensor, MscL, to engineer a nano-scale mechanically-activated solid-state relay device. To do this, it would be preferable to design a metal, or nanoparticle, binding site within the pore of this biological sensor that allows electrical conduction only upon mechanical-stress of the matrix in which it is embedded. This would allow stability under conditions of little moisture. Using funding from the BioInspired Concepts (BIC) program of the AFOSR, we have utilized biochemical and electrophysiology assays to identify regions of the sensor that are exposed in the pore lumen. We have mutated these candidate sites to cysteine and histidine regions, amino acids that are known to bind heavy metals. Electrophysiological studies have now confirmed that some of these substituted residues within the sensor do indeed bind metals; however, many of these interactions deleteriously affect the probability or energetics of sensor activation. Hence, we needed to define those regions exposed in the open pore of the stimulated channel. To accomplish this, we have designed and used microbial screens to determine candidates for residues that are exposed only upon sensor activation.

The modelling and test of a single stage and a two-stage monolithic millimetre-wave (Ka-Band) pseudomorphic HEMT feedback amplifier is reported. The amplifiers used 0.25µm x 120µm devices and were processed on 200µm thick GaAs wafers. The PHEMT s-parameters were measured on-wafer to 40GHz in conjunction with on-wafer Line-Reflect-Line (LRL) calibration standards, which are described. The amplifier modelling approach combined a device equivalent circuit model with a fullwave analysis of the microstrip features and selected measurement-based foundry models. Measured and fitted data are compared for the PHEMT device and for the amplifiers. A single stage feedback amplifier gain of 7.8dB was measured at 33.8GHz and a two-stage feedback amplifier gain of 13.2dB ± 0.4dB from 28.6GHz to 37.9GHz. Both measurements were in good agreement with simulations. The on-wafer measured noise figure of the two stage amplifier was 3.4dB at 35GHz.

The modelling and test of a single stage and a two-stage monolithic millimetre-wave (Ka-Band) pseudomorphic HEMT feedback amplifier is reported. The amplifiers used 0.25µm x 120µm devices and were processed on 200µm thick GaAs wafers. The PHEMT s-parameters were measured on-wafer to 40GHz in conjunction with on-wafer Line-Reflect-Line (LRL) calibration standards, which are described. The amplifier modelling approach combined a device equivalent circuit model with a fullwave analysis of the microstrip features and selected measurement-based foundry models. Measured and fitted data are compared for the PHEMT device and for the amplifiers. A single stage feedback amplifier gain of 7.8dB was measured at 33.8GHz and a two-stage feedback amplifier gain of 13.2dB ± 0.4dB from 28.6GHz to 37.9GHz. Both measurements were in good agreement with simulations. The on-wafer measured noise figure of the two stage amplifier was 3.4dB at 35GHz.