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IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...

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Related Experiment Video

Updated: Jun 29, 2025

Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing

Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing

Published on: March 22, 2019

Highly sensitive mid-infrared methane remote sensor using a deep neural network filter.

Senyuan Wang, Shicheng Yang, Shouzheng Zhu

|April 4, 2024

Summary

This summary is machine-generated.

A new methane remote sensor uses a tunable laser and advanced filtering to accurately measure atmospheric gas concentrations. This innovative system offers highly sensitive and stable detection for environmental monitoring.

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Last Updated: Jun 29, 2025

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Area of Science:

Environmental Science
Optical Engineering
Spectroscopy

Background:

Methane is a potent greenhouse gas requiring accurate atmospheric monitoring.
Remote sensing offers a non-invasive method for measuring gas concentrations over large areas.
Conventional filtering techniques can limit the sensitivity of methane detection systems.

Purpose of the Study:

To develop a novel mid-infrared methane remote sensor with enhanced sensitivity and stability.
To utilize a movable platform for versatile atmospheric monitoring.
To improve signal denoising using deep learning for more accurate measurements.

Main Methods:

Integration of a 3.291-µm interband cascade laser (ICL) with wavelength modulation spectroscopy (WMS).
Implementation of a coaxial, visualized optical layout for minimal energy loss.
Employment of a deep neural network (DNN) filter for second harmonic (2f) signal denoising.
Utilizing a retro-reflector as a cooperative target for remote measurements up to 100 meters.

Main Results:

Achieved a limit of detection (LOD) of 86.62 ppb methane with a 1-second average time, improving to 12.03 ppb with a 229-second average time after DNN filtering.
Demonstrated high sensitivity and stability through 24-hour continuous outdoor monitoring.
The DNN filter significantly improved performance compared to conventional filtering methods.

Conclusions:

The proposed methane remote sensor provides a novel and effective solution for high-sensitivity atmospheric monitoring.
The integration of DNN filtering enhances detection limits and stability.
The system is suitable for long-term, real-world environmental monitoring applications.