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MKR2UNO Adapter

SKU TSX00005 Barcode 813747020213 Show more
Original price €0
Original price €15,85 - Original price €15,85
Original price
Current price €15,85
€15,85 - €15,85
Current price €15,85
VAT included
Simply mount your MKR1000 to this new adapter and plug in any of your UNO shields!

Overview

The MKR2UNO Adapter allows you to turn your Arduino UNO form factor based project into a MKR based one without too much effort! You can so upgrade your project with a powerful board with integrated LiPo battery charger.

Please note that currently the MKR2UNO adapter is compatible only with MKR1000 without headers

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Tech specs

Operating Voltage 3.3V
Input Voltage (recommended) 7-12V
Input Voltage (limit) 6-16V
Analog Input Pins 6
Analog Output Pins 1
Digital I/O Pins 14
DC Current for 3.3V Pin 700 mA
DC Current for 5V Pin 700 mA
Length 68.6 mm
Width 53.4 mm
Weight 24 g

Conformities

The following Declarations of Conformities have been granted for this board:
CE
FCC
RoHS
REACH
For any further information about our certifications please visit docs.arduino.cc/certifications

Resources for Safety and Products

Manufacturer Information

The production information includes the address and related details of the product manufacturer.

Arduino S.r.l.
Via Andrea Appiani, 25
Monza, MB, IT, 20900
https://www.arduino.cc/ 

Responsible Person in the EU

An EU-based economic operator who ensures the product's compliance with the required regulations.

Arduino S.r.l.
Via Andrea Appiani, 25
Monza, MB, IT, 20900
Phone: +39 0113157477
Email: support@arduino.cc

 

Documentation

OSH: Schematics

MKR2UNO is open-source hardware! You can build your own board using the following files: 

EAGLE FILES IN .ZIP SCHEMATICS IN .PDF

Please keep in mind that the MKR2UNO is just a form factor adapter and so is NOT present any voltage translation. Be careful before connecting a shield since shield without IOREF level shifting capability may not work properly.


Like in many others Arduino boards this adapter allows to power your MKR board using a barrel jack with center positive polarity or the VIN pin on the headers.

Unlike the Arduino UNO, the MKR2UNO Adapter do NOT have I2C signals on A4 and A5 and SPI on pins 11, 12 and 13.

All the pins on the MKR2UNO Adapter are wired one to one from the MKR to the UNO form factor with some exception:

Get Inspired

PROJECT HUB
Serrje: An autonomous greenhouse
Serrje: An autonomous greenhouse
Project Tutorial by blaiseecam

Just a simple and enjoyable autonomous greenhouse

read more
BLOG
Small, MKR WAN 1310-powered device monitors CO2 levels in classrooms
Small, MKR WAN 1310-powered device monitors CO2 levels in classrooms
February 17, 2023

Humans are animals and like all animals, we evolved in mostly outdoor conditions where the air is nice and fresh. But modern society keeps most of us indoors the vast majority of the time, which could have negative health effects. There are many potential hazards, including a lack of sunlight and psychological effects, but CO2 may pose a more tangible risk. To keep tabs on that risk within classrooms, a team from Polytech Sorbonne built this small CO2 monitor. This CO2 monitor performs two functions: it shows anyone nearby the CO2 levels in the area and it uploads that data over LoRaWAN to a central hub that can track the levels across many locations. A school could, for example, put one of these CO2 monitors in every classroom. An administrator could then see the CO2 levels in every room in real time, along with historical records. That would alert them to immediate dangers and to long term trends. At the heart of this CO2 monitor is an Arduino MKR WAN 1310 development board, which has built-in LoRa® connectivity. It uses a Seeed Studio Grove CO2, temperature, and humidity sensor to monitor local conditions. To keep power consumption to a minimum, the data displays on an e-ink screen and an Adafruit TPL5110 timer only wakes the device up every ten minutes for an update. Power comes from a lithium-ion battery pack, with a DFRobot solar charger topping up the juice. It uploads data through The Things Network to a PlatformIO web interface. An Edge Impulse machine learning model detects anomalies, so it can sound a warning even if nobody is watching. The enclosure is 3D-printable.

read more

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