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IR Frequency Region: Fingerprint Region01:03

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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Infrared (IR) Spectroscopy: Overview01:09

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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.
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IR Spectrometers01:25

IR Spectrometers

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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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Applications of IR Spectroscopy: Overview01:11

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The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...
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IR Spectrum01:19

IR Spectrum

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When infrared (IR) radiation passes through a molecule, the bonds stretch or bend by absorbing the radiation. This absorption creates the molecule's absorption spectrum, which is the plot of its percentage transmittance versus wavenumber.
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Total Internal Reflection Fluorescence Microscopy01:05

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Total internal reflection fluorescence microscopy or TIRF is an advanced microscopic technique used to visualize fluorophores in samples close to a solid surface with a higher refractive index, such as a glass coverslip. TIRF only allows fluorophores in proximity to the solid surface to be excited. When light from a medium with a lower refractive index (such as air) hits the glass coverslip at a critical angle, the light undergoes total internal reflection stead of passing through the glass.
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Identification of Textile Fibres Using a Near Infra-Red (NIR) Camera.

Fariborz Eghtedari1, Leszek Pecyna1, Rhys Evans1

  • 1The Manufacturing Technology Centre, Ansty Business Park, Pilot Way, Coventry CV7 9JU, UK.

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|April 25, 2025
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Summary

This study explores using Near Infra-Red (NIR) cameras for textile material identification, crucial for recycling. A new metric reliably distinguishes cotton and polyester, even with challenging dyes.

Keywords:
NIRcarbon black dyehyperspectralmaterials identificationrecyclingspectroscopytextiles

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Area of Science:

  • Materials Science
  • Textile Engineering
  • Spectroscopy

Background:

  • Accurate textile composition identification is vital for effective textile reuse and recycling processes.
  • Current methods face challenges in reliably distinguishing between different fabric types, particularly common materials like cotton and polyester.
  • The presence of dyes, such as carbon black, further complicates material analysis.

Purpose of the Study:

  • To investigate the feasibility of using a Near Infra-Red (NIR) camera for identifying textile materials.
  • To develop a transportable metric for distinguishing between cotton and polyester textiles.
  • To assess the system's performance in the presence of challenging dyes like carbon black.

Main Methods:

  • Utilized a Near Infra-Red (NIR) camera equipped with a bandpass filter to capture textile images.
  • Employed statistical methods to analyze the intensity data from the NIR camera's pixel array.
  • Defined and validated a repeatable, stable metric for material identification, independent of camera exposure and illumination.

Main Results:

  • A reliable metric was identified capable of distinguishing between cotton (average metric value 0.68, ±2% deviation) and polyester (average metric value 1.0, ±1% deviation).
  • The metric demonstrated independence from camera exposure settings and variations in physical illumination.
  • The system successfully detected carbon black dye, enabling analysis of non-dyed areas.

Conclusions:

  • Near Infra-Red (NIR) imaging with a defined metric offers a feasible solution for accurate textile material identification.
  • The developed technique provides a stable and repeatable method for distinguishing cotton from polyester, overcoming common industry challenges.
  • This approach shows promise for enhancing textile recycling by enabling precise material sorting, even with difficult-to-analyze dyed fabrics.