Gas Sensors

Image Part Number Description / PDF Quantity Rfq
IR22EJ

IR22EJ

Amphenol

SERIES 2, 19MM, 0-5%/100% HC IR

0

IR603/2

IR603/2

Amphenol

IR SENSOR HEAD FOR 0-5%/100% BRO

0

ESCO2D2AM

ESCO2D2AM

Carlo Gavazzi

SEN CO2 2000PPM 4-20MA OUT W/COM

0

CCS801B-COPD500

CCS801B-COPD500

ams

CCS801B-COPD500 DFN4 LF T&R

0

MNS2-9-W2-GS-C1

MNS2-9-W2-GS-C1

Monnit

ALTA (CO) GAS SENSOR AA BATTERY

0

FIT0227

FIT0227

DFRobot

CARBON MONOXIDE SENSOR (MQ7)

0

ESCO2W5A

ESCO2W5A

Carlo Gavazzi

SEN CO2 5000PPM 4-20MA OUT

0

CGESC02W2S

CGESC02W2S

Carlo Gavazzi

SEN CO2 SWITCHING OUT 2000PPM

0

STC31-R5

STC31-R5

Sensirion

CO2 THERMAL CONDUCTIVITY SENSOR

0

SGX-4CO

SGX-4CO

Amphenol

4 SERIES CO SENSOR 2,000PPM

96

ESCOD3AM

ESCOD3AM

Carlo Gavazzi

SEN CO 300PPM 4-20A OUT W/COMM

0

SEN-17052

SEN-17052

SparkFun

HYDROGEN SULFIDE GAS SENSOR - MQ

30

IR22GJ

IR22GJ

Amphenol

SERIES 2 , 19MM, 0-5%/100% HC IR

0

ESCO2W2V

ESCO2W2V

Carlo Gavazzi

SEN CO2 2000PPM 0-10V OUT

0

INIR-ME5%

INIR-ME5%

Amphenol

METHANE GAS SENSOR 0-5%

0

ESCO2D5AM

ESCO2D5AM

Carlo Gavazzi

SEN CO2 5000PPM 4-20MA OUT W/COM

0

IR602/3

IR602/3

Amphenol

IR SENSOR HEAD FOR 0-5%/100% HC

0

SEN-17050

SEN-17050

SparkFun

DUAL GAS CO AND CH4 DETECTION SE

28

IR21BD

IR21BD

Amphenol

EX-IA CO2 INFRARED GAS SENSOR

24

ESCO2D2V

ESCO2D2V

Carlo Gavazzi

SEN CO2 2000PPM 0-10V OUT

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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