KEYWORDS: Gas sensors, Nose, Light emitting diodes, Deep learning, Sensors, Pattern recognition, Internet of things, Ultraviolet radiation, Safety, Power consumption
The demand for gas sensors is increasing as interests in air quality monitoring related to environmental pollution and industrial safety grow. The semiconductor metal oxide (SMO) type sensor is preferred for its low cost, high sensitivity, mass production, and small size, but it suffers from poor selectivity. To solve this issue, an ultra-low-power electronic nose (e-nose) system was developed using ultraviolet (UV) micro-LED (μLED) gas sensors and a convolutional neural network (CNN). This e-nose system was highly selective, with a gas classification accuracy of 99.32%, and had a gas concentration regression error of 13.82% for five different gases. The μLED-based e-nose system is battery-driven, has a total power consumption of 0.38 mW, and is expected to be widely used in environmental internet of things (IoT) applications.
For environment-friendly renewable energy, blue energy, which is ocean-related energy, has gained an increasing interest due to its huge potential and vast wasted energy. This study suggests a promising buoy-type ocean energy harvesting structure based on a thermoplastic polymer, poly(methyl methacrylate) (PMMA) as a dielectric film for ocean monitoring system. The PMMA dielectric thin film can be easily fabricated with nanopatterned morphological characteristics by thermal nanoimprinting lithography. The buoy-type suggested energy harvesting structure can convert ocean wave energy into electrical energy to power a commercially available Li-ion battery using a regulator-based circuit.
We introduced tunable Fabry-Pérot resonator using metal-insulator-metal multilayer, in which the insulator is hydrogel foam of chitosan [1]. The chitosan, one of polysaccharide, is responsive to external humidity, so the thickness and refractive index of chitosan change in response to relative humidity (RH); this trait can be utilized to tune resonance wavelegths of the resonator. This tunable color filter can function as humidity sensor when incorporated with photovoltaic (PV) cell. The PV cell transmit input optical spectrum to output current, which enables to determine the relationship between RH h and Response S defined the change in current before and after injection of humidity: S = -0.00002*h^2+0.0046*h-0.0238. Therefore, the response may correctly indicate RH of ambient in real-time. The proposed sensor would be simple to fabricate and potentially have zero-power consumption due to combination with PV cell, which makes the sensor useful for monitoring RH in enclosed spaces, workplaces and storage areas.
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