PFLOW: OEM MEMS mass air flow sensors for medical and process control applications
With high resistance towards clogging and pressure shocks
The flow sensing die consists of two thermopiles symmetrically positioned up and downstream from a heater element which heats up the hot junctions. Each thermopile consists of 20 thermocouples in series to reveal the highest level of sensitivity. When a flow passes over the die, the thermopiles generates an output voltage proportional to the temperature gradient (asymmetric) between the hot and the cold junction due to the Seebeck effect. In case the medium is static (no flow), the temperature profile of both - up and downstream - is symmetric.
Five standard measurement ranges are offered from 0...10 SCCM to 0...2000 SCCM, as well as customer specific ranges between 10 and 2000 SCCM. Accuracy is better than +/-2.0% FS, and the temperature compensated analog output (0...50°C) is highly linear with flow. Analog voltage output is between 1 and 5 VDC, and the sensor is resistant to water condensation. Response time is very fast, and about 1-3 ms.
PEWATRON specialises in physical and geometrical sensors, power supplies and e-components. PEWATRON delivers a wide assortment of electronic components ranging from standard products, modified standard products or customised solutions. PEWATRON works closely with developers and can be involved right from the start of a product development process to ensure that together with the customer, the best fitting component for the application is found.
PEWATRON, an independent company that belongs to the Angst+Pfister Group, has been serving customers in Europe with prompt and individual service for over 30 years. Through the Angst+Pfister Group, PEWATRON belongs to a world-wide network of over 1000 employees and 60 000 satisfied customers.
Press releases you might also be interested in
Weitere Informationen zum Thema "Medizintechnik":
Diese Vorteile bietet Machine Learning mit Apache Kafka
Machine Learning sowie das zugehörige Deep Learning nehmen Fahrt auf, da Machine Learning es Computern ermöglicht, versteckte Erkenntnisse zu gewinnen, ohne dass diese explizit programmiert sein müssen, worauf sie schauen sollen. Diese Fähigkeit wird für die Analyse unstrukturierter Daten, Bilderkennung, Spracherkennung und intelligente Entscheidungsfindung benötigt. Das macht einen großen Unterschied zur traditionellen Programmierung mit Java, .NET oder Python.Weiterlesen