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Gravity: I2C Oxygen Sensor

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SKU TPX00050 Barcode 6959420917020 Show more
Original price €0
Original price €62,85 - Original price €62,85
Original price
Current price €62,85
€62,85 - €62,85
Current price €62,85
VAT included

Measure the ambient O2 concentration accurately with the I2C Oxygen Sensor!

Overview

The Gravity: I2C Oxygen Sensor is based on electrochemical principles and it can measure the ambient O2 concentration accurately and conveniently. With high anti-interference ability, high stablility and high sensitivity, this arduino-compatible oxygen sensor can be widely applied to fields like portable device, air quality monitoring device, and industries, mines, warehouses and other spaces where air is not easy to circulate.

This compact dfrobot oxygen sensor supports I2C output, it can be calibrated in the air, can accurately measure the oxygen concentration in the environmentit. It is compatible with many mainboards like Arduino Uno, esp32, Raspberry Pi and so on. Its effective range is 0~25%Vol, and resolution can reach to 0.15%Vol. It supports wide range input voltage: 3.3V to 5.5V.

Moreover, the lifetime is as long as 2 years. With simple Gravity interface and practical sample code, you can build your own oxygen concentration monitor easily and conveniently.


Get Inspired

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Automate 35mm film scanning with Arduino and Python
Automate 35mm film scanning with Arduino and Python
September 24, 2020

While taking photos today is normally a digital affair, there is a wealth of visual information stored on film negatives. Digitization is possible, but it tends to be rather time-intensive, so photographer/hacker Seckin Sinan Isik decided to automate the process. His setup uses a film carrier augmented with a stepper motor and belt drive to advance the 35mm film under a tripod-mounted digital camera. This is controlled by an Arduino Nano, with the camera’s view shown via a video capture device on a nearby computer. In one mode, the user can adjust the film position semi-manually using pushbuttons, then scan the negative. The whole process can also be automated, with a Python computer vision routine. More details on the project can ben found in Isik's PetaPixel article here.

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