Gas Sensors

Image Part Number Description / PDF Quantity Rfq
INIR-ME100%

INIR-ME100%

Amphenol

INTEGRATED INFRARED CH4 GAS SENS

9

VQ625/3

VQ625/3

Amphenol

VQ25 PELLISTOR, VQ600 HEAD, 0.75

0

EC4-1000-H2

EC4-1000-H2

Amphenol

4 SERIES H2 GAS SENSOR 1000PPM

7

VQ23TB

VQ23TB

Amphenol

PR PELLISTOR PAIR 2.5V/335MA OC

0

SGX-4NH3

SGX-4NH3

Amphenol

4 SERIES AMMONIA SENSOR - 100PPM

28

VQ635M/3

VQ635M/3

Amphenol

VQ35 PELLISTOR, VQ600 HEAD, 0.75

0

VQ606M/1

VQ606M/1

Amphenol

VQ6 PELLISTOR, VQ600 HEAD, METRI

0

IR12GM_1

IR12GM_1

Amphenol

16MM, HYDROCARBON INFRARED GAS S

19

INIR-RF-R32

INIR-RF-R32

Amphenol

INTEGRATED GAS SENSOR -R32

18

INIR-CD5%

INIR-CD5%

Amphenol

INTEGRATED INFRARED CO2 GAS SENS

14

NGM-1

NGM-1

Amphenol

LOW POWER NATURAL GAS MODULE

87

VQ21TSB

VQ21TSB

Amphenol

POISON RESISTANT 2V FLAMMABLE GA

44

MICS-VZ-89TE

MICS-VZ-89TE

Amphenol

VOC SENSOR IAQ MODULE W/ADDITION

96

IR12EM

IR12EM

Amphenol

16MM, HYDROCARBON INFRARED GAS S

24

VQ631M/1

VQ631M/1

Amphenol

VQ31 PELLISTOR VQ600 HEAD, METRI

21

VQ6MB

VQ6MB

Amphenol

PELLISTOR 2V, TC, CLOSED CAN COM

0

IR11EJ

IR11EJ

Amphenol

19MM, 0-5% VOLUME CO2 INFRARED G

27

VQ625/2

VQ625/2

Amphenol

VQ25 PELLISTOR, VQ600 HEAD, 0.5"

0

IR11BR

IR11BR

Amphenol

19MM, 100% VOLUME CO2 INFRARED G

21

VQ625/1

VQ625/1

Amphenol

VQ25 PELLISTOR VQ600 HEAD, METRI

0

Gas Sensors

1. Overview

Gas sensors are detection devices that identify and measure gas concentrations in the environment. They convert chemical interactions with gas molecules into electrical signals for quantitative analysis. These sensors play a critical role in industrial safety, environmental monitoring, healthcare, and smart home systems by preventing gas leaks, ensuring air quality, and enabling process control.

2. Major Types and Functional Classification

TypeFunctional FeaturesApplication Examples
ElectrochemicalHigh accuracy, stable baseline, requires oxygenCO detectors, O2 monitors
SemiconductorLow cost, broad detection range, temperature-dependentIndoor air quality sensors
Catalytic CombustionExplosive gas detection, requires periodic calibrationIndustrial methane detectors
Infrared (IR)Non-contact measurement, high selectivityCO2 HVAC monitoring
Photoionization (PID)VOC detection at ppm levels, UV lamp requiredEnvironmental pollution monitoring

3. Structure and Components

A typical gas sensor consists of: - Sensing element (metal oxide/electrolyte membrane) - Signal conditioning circuit (amplifier, ADC) - Housing with gas inlet ports - Temperature/humidity compensation module - Communication interface (UART/I2C)

4. Key Technical Specifications

ParameterDescription
Detection RangeMeasurable gas concentration span (ppm to %LEL)
SensitivitySignal change per gas concentration unit (mV/ppm)
Response TimeT90 response speed (3-300 seconds)
AccuracyMeasurement error margin ( 2-10%)
Operating TemperatureFunctional range (-20 C to +50 C typical)
Long-term StabilityDrift specification (5-15% per year)

5. Application Fields

  • Industrial safety: Fixed gas detection systems
  • Environmental monitoring: Urban air quality stations
  • Healthcare: Medical breath analyzers
  • Smart homes: Combustible gas alarms
  • Automotive: Cabin air quality management

6. Leading Manufacturers and Products

ManufacturerProduct SeriesKey Features
HoneywellXNX Universal TransmitterDual-sensor redundancy
Figaro EngineeringTGS2600Low-power VOC detection
MembraporToxic Gas SensorsEletrochemical cells for Cl2
SenseairK-30 CO2 ModuleNDIR technology, 30ppm accuracy
AMS (Austria)ENS160 MOX SensorAI-based gas discrimination

7. Selection Guidelines

Key consideration factors:

  1. Target gas chemical properties
  2. Environmental conditions (temperature/humidity range)
  3. Required detection threshold and repeatability
  4. Power consumption budget
  5. Maintenance accessibility for calibration
  6. Cost vs. lifetime trade-offs

Industry Trends Analysis

Emerging development trends include: - Miniaturization through MEMS technology - Multi-gas detection using AI pattern recognition - Wireless self-powered IoT sensor nodes - Enhanced selectivity via nanomaterial coatings - Reduced cross-sensitivity through hybrid sensing methods

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