eLichens Cranberry, eLichens Mulberry & eLichens Bilberry Gas Sensors are ultra-low power NDIR sensors designed to detect and monitor Methane & Hydrocarbons (CH4), Carbon Dioxide (CO2), Refrigerant R32. eLichens Mulberry is available in a 4-pin or 5-pin design.

eLichens Avolta is our battery operated wireless Natural Gas Detector including eLichens NDIR gas sensor. It detects methane at 10% LEL level anywhere and has a 10+ years lifespan. Exist in a connected (NB-IOT/LTE-M, LoRa+BLE, Milli-5, Wi-SUN, ) and non-connected version.

Comes with our advanced online dashboard offers a comprehensive solution for managing your eLichens Avolta detectors. With multiple features integrated into a single platform, users can easily monitor and control their fleet of detectors. You can track real-time data and access detailed analytics to ensure an optimal management.

eLichens P2M50 EmiVolta is a battery-powered, remote methane emission monitor engineered for precise detection and continuous monitoring of low-level methane emissions (LOD 50ppm).

Ideal for LDAR (Leak Detection and Repair) programs in industrial environments, it delivers reliable performance and real-time data to help ensure safety, regulations compliance, and environmental responsibility.

eMETS is our patented end-to-end solution for the detection, localization, quantification and reporting of methane emissions. It ensures reliable performance from the smallest emission to the largest event, turning raw sensor data into certified metrics.

Aura-CO2 is the latest version of the Indoor Air Quality monitoring station which measures and provides a highly accurate real-time indoor air quality data to its users. Powered by eLichens’ gas sensor.

Comes with an iOS/Android App, a screen and a LED status indicator, end user gets real time visual and audible alerts alarm indicators in case of unhealthy environment.

eLichens Air Quality Map provides both real-time and forecast localized information about the air quality including pollution, pollen, weather.

Empowered by:

• An in-house pollution forecast model, data fusion and machine learning

• Our measurement coming from eLos Outdoor Air Quality Stations