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Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...
Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...
IR Spectrometers01:25

IR Spectrometers

There are two main infrared (IR) spectrophotometers: dispersive IR spectrometers and Fourier transform infrared (FTIR) spectrometers. In a dispersive IR spectrometer, a beam of infrared radiation produced by a hot wire is divided into two parallel equal-intensity beams using mirrors. One beam passes through the sample, while another is a reference beam. The beams then move through the monochromator, which separates the radiations into a continuous spectrum of different frequencies. The...

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Related Experiment Video

Updated: May 28, 2026

Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing
10:42

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

Published on: March 22, 2019

Data compressive paradigm for multispectral sensing using tunable DWELL mid-infrared detectors.

Woo-Yong Jang1, Majeed M Hayat, Sebastián E Godoy

  • 1Center for High Technology Materials and Electrical and Computer Engineering Department, University of New Mexico, Albuquerque, New Mexico 87106, USA.

Optics Express
|October 15, 2011
PubMed
Summary

This study introduces a new algorithm for quantum dots-in-a-well (DWELL) infrared photodetectors, significantly reducing the number of bias values needed for multispectral sensing. This advancement enables faster and more efficient spectral analysis without spectral filters.

Related Experiment Videos

Last Updated: May 28, 2026

Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing
10:42

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

Published on: March 22, 2019

Area of Science:

  • Optoelectronics
  • Materials Science
  • Signal Processing

Background:

  • Quantum dots-in-a-well (DWELL) infrared photodetectors offer tunable spectral responses via applied bias.
  • Current multispectral sensing methods using DWELLs require numerous bias points, leading to long acquisition times.
  • Existing algorithms approximate spectral filters using weighted superpositions of DWELL responses, necessitating extensive data collection.

Purpose of the Study:

  • To develop a novel multispectral sensing algorithm for DWELL infrared photodetectors.
  • To substantially reduce the number of required bias values for efficient spectral information acquisition.
  • To maintain performance levels in sensing applications while minimizing data collection.

Main Methods:

  • Developed a new algorithm to identify a minimal set of bias values for DWELL photodetectors.
  • Focused on sensing only relevant spectral information for specific remote-sensing applications.
  • Investigated the trade-off between bias compression and sensing performance.

Main Results:

  • Demonstrated a significant reduction in the number of required bias values by a factor of 7 (e.g., from 30 to 4).
  • Experimental results validated the algorithm's effectiveness in target spectrometry and classification.
  • The algorithm successfully compresses bias values while preserving essential spectral information.

Conclusions:

  • The new algorithm offers a more efficient approach to multispectral sensing with DWELL infrared photodetectors.
  • Substantial bias compression is achievable without compromising critical sensing performance.
  • This method accelerates data acquisition for remote-sensing applications.