Humidity, Temperature 0 ~ 100% RH I2C ±1% RH 4 s Surface Mount #CommonPartsLibrary #sensor #SHT4x #Transducer #Moisture Sensors

Magnetic Sensors MMC5603NJ FLIPCHIP-4_L0.8-W0.8-R2-C2-P0.40-TL LCSC Part Number: C404328 JLCPCB Part Class: undefined Manufactured by MEMSIC(美新)

802.15.4, Bluetooth, WiFi Bluetooth v5.0, Zigbee Transceiver Module 2.4GHz PCB Trace Surface Mount The ESP32-C6-WROOM-1-N8 integrates a 32-bit RISC-V single-core processor running up to 160 MHz, along with 8 MB of embedded SPI flash memory. It supports multiple wireless communication protocols, including: Wi-Fi 6 (802.11ax) in the 2.4 GHz band for improved efficiency and lower latency Bluetooth Low Energy (BLE 5) for short-range communication IEEE 802.15.4 for protocols such as Thread and Zigbee The module features a built-in PCB antenna, RF circuitry, crystal oscillator, and passive components, making it a complete solution that simplifies hardware design. It provides a wide range of GPIO pins and peripheral interfaces such as UART, SPI, I2C, PWM, ADC, and more, enabling flexible integration with sensors and external devices. Designed for low power consumption, it includes multiple power-saving modes, making it suitable for battery-powered applications. Typical Applications Smart home and IoT devices Wireless sensors and control systems Industrial automation Smart lighting and energy management Matter-compatible devices (via Thread support)

XIAO MG24 Sense is an ultra-low-power wireless development board based on Silicon Labs' EFR32MG24 SoC, featuring a high-performance 78MHz ARM Cortex®-M33 core. It‘s Matter® native over Thread® and Bluetooth® Low Energy 5.3, all supported by the Arduino® Core. With 4MB Flash onboard, 19 GPIOs, LED, and charging circuit onboard, it boasts extremely low operating current and ultra-low-power modes, making it an ideal choice for IoT applications, especially battery-powered projects utilizing Matter® protocols. Additionally, the onboard analog microphone and six-axis IMU sensors make XIAO MG24 Sense a great fit for TinyML applications such as posture detection