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N.E.T. high-volume IRNET sensors for Methane (CH4) and Carbon dioxide (CO2) are the ideal solution for Landfill gas (LFG) and Biogas applications.
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Landfill gas (LFG)/Biogas; Waste water/Water treatment; Gas...
- Applications
Reliability and safety integrity level of industrial gas...
- Gas Selection
Landfill gas (LFG)/Biogas; Waste water/Water treatment; Gas...
- Products
N.E.T. manufactures and sells a complete range of gas...
- Certifications
All NET Infrared sensors have reached a safety integrity...
- Technologies
Non Dispersive Infrared (NDIR) gas sensing; Dual wavelength,...
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Today, N.E.T. manufactures and distributes a wide range of...
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The NT-CO-PL200 is a new, high sensitivity Premium Line...
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N.E.T. NDIR - the most complete range of Infrared (IR) gas sensor on the market for CO2, SF6, hydrocarbon and refrigerant gases. Ideal for any gas detector.
IRNET-P 20mm detects CO2 or hydrocarbon gases such as propane and methane in LEL range. The 0-100%Vol range version features N.E.T. DYNAMIC technology for the highest sensing accuracy ever seen in a compact sensor.
Today, N.E.T. manufactures and distributes a wide range of gas detection devices on an OEM basis for industrial and commercial applications, used by instrument manufacturers worldwide. Our...
The multi-gas sensor uses light of a specific wavelength in the infrared range to illuminate the sample depending on the type of gas to be analyzed. It measures the absorption of light when crossing the sample chamber and indicates the concentration of the gas in the sample chamber.
1 Ιαν 2021 · A major cause of these environmental threats is pollutants in the atmosphere. Here, we holistically investigated the environmental gas sensors based on nanostructures and discussed their enhanced sensing performance using class of composites by combination of nanoparticles/carbon materials.
20 Απρ 2021 · We summarize different chemresistive and FET gas sensors and highlight their shortcomings, and then propose the potential of machine learning as a possible and feasible option. The review concludes that machine learning can be very promising in terms of building the future generation of smart, sensitive, and selective sensors.
