Overview
It can detect combustible Carbon Monoxide, Coal Gas and Liquefied Gas. The sensitivity can be adjusted by the potentiometer.
Hardware Overview
This is an Analog output sensor. It needs to be connected to any one Analog socket in Grove Base Shield. It is possible to connect the Grove module to Arduino directly by using jumper wires. When doing so, please refer to the connection table below:
|
Arduino |
Gas Sensor |
|
5V |
VCC |
|
GND |
GND |
|
NC |
NC |
|
Analog A0 |
SIG |
The output voltage from the Gas sensor increases when the concentration of gas. Sensitivity can be adjusted by rotating the potentiometer.
Please note that the best preheat time for the sensor is 24 hours and above.
Note: Hot-swapping the grove may lead to IC burnout, please turn off the power of main board before swapping grove.
Tech specs
Specification
|
Item |
Parameter |
Min |
Typical |
Max |
Unit |
|
VCC |
Working Voltage |
4.9 |
5 |
5.1 |
V |
|
PH |
Heating consumption |
0.5 |
- |
340 |
mW |
|
RL |
Load resistance |
adjustable |
|||
|
RH |
Heater resistance |
- |
33Ω±5% |
- |
Ω |
|
Rs |
Sensing Resistance |
2 |
- |
20000 |
Ω |
|
CO/CH4/LPG Scope |
Detecting Concentration |
200 |
- |
1000/10000/10000 |
ppm |
Technical Details
|
Dimensions |
130mm x 90mm x 23mm |
|
Weight |
G.W 15g |
Get Inspired
Use an Arduino Nicla Sense ME to see if you need to freshen up after a workout
Having constant, reliable access to a working HVAC system is vital for our way of living, as they provide a steady supply of fresh, conditioned air. In an effort to decrease downtime and maintenance costs from failures, Yunior González and Danelis Guillan have developed a prototype device that aims to leverage edge machine learning to predict issues before they occur. The duo went with a Nicla Sense ME due to its onboard accelerometer, and after collecting many readings from each of the three axes at a 10Hz sampling rate, they imported the data into Edge Impulse to create the model. This time, rather than using a classifier, they utilized a K-means clustering algorithm — which is great at detecting anomalous readings, such as a motor spinning erratically, compared to a steady baseline. Once the Nicla Sense ME had detected an anomaly, it needed a way to send this data somewhere else and generate an alert. González and Guillan's setup accomplishes the goal by connecting a Microchip AVR-IoT Cellular Mini board to the Sense ME along with a screen, and upon receiving a digital signal from the Sense ME, the AVR-IoT Cellular Mini logs a failure in an Azure Cosmos DB instance where it can be viewed later on a web app. To read more about this preventative maintenance project, you can read the pair’s write-up here on Hackster.io.