| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
 |
Adafruit |
MOD ADXL335 5V READY 3AXIS +-3G |
284 |
|
 |
Adafruit |
AT42QT1010 BREAKOUT BOARD |
266 |
|
 |
Adafruit |
CCS811 AIR QUALITY SENSOR BREAKO |
49 |
|
 |
Adafruit |
DGTL UV INDEX/IR/VSBL LGHT SENSR |
81 |
|
 |
Adafruit |
SGP30 AIR QUALITY SENSOR BREAKOU |
849 |
|
 |
Adafruit |
HTU21DF TEMP & HUMIDITY SENSOR B |
26 |
|
 |
Adafruit |
BREADBOARD-FRIENDLY MINI PIR MOT |
90 |
|
 |
Adafruit |
ADAFRUIT LTR390 UV LIGHT SENSOR |
232 |
|
 |
Adafruit |
ADAFRUIT MICS5524 CO, ALCOHOL AN |
11 |
|
 |
Adafruit |
AS7262 6-CHANNEL VISIBLE LIGHT |
124 |
|
 |
Adafruit |
ADXL345 3AXIS ACCEL BREAKOUT BRD |
63 |
|
 |
Adafruit |
ADAFRUIT MPRLS PORTED PRESSURE S |
328 |
|
 |
Adafruit |
SENSOR HUMID/TEMP 5V I2C 2% MOD |
119 |
|
 |
Adafruit |
BOARD HTU21D-F TEMP/HUM SENSOR |
207 |
|
 |
Adafruit |
BREAKOUT BD TEMP/HUMIDITY SENSOR |
0 |
|
 |
Adafruit |
TSL2591 DIG LIGHT SENSE BREAKOUT |
327 |
|
 |
Adafruit |
AT42QT1012 TOUCH SENSOR BREAKOUT |
157 |
|
 |
Adafruit |
BREAKOUT ACCEL/MAG/GYR/TEMP 9DOF |
0 |
|
 |
Adafruit |
EVAL BOARD FXAS21002 FXOS8700 |
172 |
|
 |
Adafruit |
VL53L0X TIME FLIGHT DIST STEMMA |
824 |
|
Evaluation boards for sensors are specialized hardware platforms designed to test, validate, and develop sensor-based applications. These boards integrate sensor elements with processing units, communication interfaces, and power management modules. They play a critical role in accelerating product development cycles in industries such as IoT, industrial automation, healthcare, and consumer electronics by enabling rapid prototyping and performance characterization.
| Type | Functional Features | Application Examples |
|---|---|---|
| Temperature Sensor Boards | High-precision thermal sensing with digital/analog outputs | Climate control systems, medical devices |
| Accelerometer Boards | 3-axis motion detection with programmable sensitivity | Vibration monitoring, fitness trackers |
| Pressure Sensor Boards | Atmospheric/differential pressure measurement | Weather stations, automotive systems |
| Environmental Sensor Boards | Multi-parameter detection (humidity, gas, light) | Smart agriculture, air quality monitors |
| Image Sensor Boards | High-resolution optical sensing with ISP integration | Surveillance cameras, machine vision |
Typical evaluation boards consist of: - Sensor element (MEMS, CMOS, or discrete transducers) - Microcontroller/SoC with ADC/DAC interfaces - Communication modules (I2C, SPI, UART, BLE/Wi-Fi) - Power management ICs and voltage regulators - Debugging interfaces (JTAG, SWD) - Auxiliary components (LED indicators, potentiometers) The PCB layout optimizes signal integrity while minimizing electromagnetic interference.
| Parameter | Description | Importance |
|---|---|---|
| Measurement Range | Minimum/maximum detectable values | Determines application suitability |
| Accuracy | Error margin vs. reference values | Impacts system reliability |
| Sampling Rate | Data acquisition frequency | Defines dynamic response capability |
| Power Consumption | Operating current/voltage requirements | Affects battery life and thermal design |
| Interface Type | Communication protocol compatibility | Dictates system integration complexity |
| Manufacturer | Representative Product | Key Features |
|---|---|---|
| STMicroelectronics | STEVAL-MKI187V1 | 6-axis IMU with advanced calibration |
| Texas Instruments | BOOSTXL-ULTRASONIC | Ultrasonic sensing for distance measurement |
| Analog Devices | EVAL-ADICUP3029 | Low-power Cortex-M4F based platform |
| NXP Semiconductors | FRDM-FXS-MULTI-B | Multi-sensor fusion for IoT applications |
Key considerations include: - Match sensor specifications to target application requirements - Verify compatibility with existing development ecosystems - Evaluate power budget and form factor constraints - Consider available software support (drivers, SDKs) - Assess calibration and certification requirements Example: For a wearable health monitor, prioritize low-power accelerometers with medical-grade accuracy.
Emerging trends include: - Integration of AI accelerators for edge computing - Development of wireless sensor nodes with energy harvesting - Advancements in MEMS fabrication for higher sensitivity - Standardization of sensor fusion algorithms - Growth of open-source hardware ecosystems Market projections indicate a 12% CAGR through 2027 driven by IoT and Industry 4.0 adoption.