Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

IR and UV–Vis Spectroscopy of Aldehydes and Ketones01:29

IR and UV–Vis Spectroscopy of Aldehydes and Ketones

Infrared spectroscopy, also known as vibrational spectroscopy, is mainly used to determine the types of bonds and functional groups in molecules. In aldehydes and ketones, the carbonyl (C=O) bond shows an absorption around 1710 cm-1. The C=O bond vibration of an aldehyde occurs at lower frequencies than that of a ketone. In addition to the C=O absorption in an aldehyde, the aldehydic C–H bond also gives two peaks in the 2700–2800 cm-1 range. This absorption, coupled with the C=O stretching, is...
IR Frequency Region: Alkene and Carbonyl Stretching01:29

IR Frequency Region: Alkene and Carbonyl Stretching

Double bonds in alkenes and carbonyl compounds exhibit stretching frequencies in the diagnostic region of the IR spectrum. In addition, alkenes exhibit vinylic C–H stretching and C–H out-of-plane bending absorptions that are useful for identifying substitution patterns.
Stretching frequencies are affected by several factors, such as resonance, inductive effects, ring strain, dipole moment, and hydrogen bonding. Consequently, the stretching frequency of the carbonyl double bond varies in...
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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 C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...
Spectroscopy of Carboxylic Acid Derivatives01:26

Spectroscopy of Carboxylic Acid Derivatives

Infrared spectroscopy is primarily used to determine the types of bonds and functional groups. In carboxylic acid derivatives, a typical carbonyl bond absorption is observed around 1650–1850 cm−1. For esters, the absorption is recorded at around 1740 cm−1, while acid halides show the absorption at about 1800 cm−1. Another acid derivative, the acid anhydrides, exhibit two carbonyl absorption around 1760 cm−1 and 1820 cm−1, arising from the symmetrical and unsymmetrical carbonyl vibration.
In the...
IR and UV–Vis Spectroscopy of Carboxylic Acids01:28

IR and UV–Vis Spectroscopy of Carboxylic Acids

In IR spectroscopy of carboxylic acids, the C=O bond shows a characteristic band between 1710 and 1760 cm⁻¹, and the O–H bond exhibits a broad band between 2500 and 3300 cm⁻¹.
However, the stretching absorptions for the C=O bond vary depending on the structure of carboxylic acids. The C=O bond of the free carboxylic acids shows a higher stretching frequency, 1760 cm−1, while H-bonded carboxylic acids (dimers) exhibit stretching absorptions at a lower frequency, 1710 cm−1. The C=O bond of the...
IR Spectrum01:19

IR Spectrum

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.
Transmittance is defined as the ratio of the radiant power passing through a sample to that from the radiation's source. Multiplying the transmittance by 100 gives the percent transmittance (%T), which varies between 100% (no absorption) and 0% (complete...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

RETRACTED ARTICLE: Transfer-learning guided design of high-performance conjugated polymers for low-voltage electrochemical transistors.

Nature communications·2026
Same author

Cycle-MS: A Closed-Loop End-to-End Framework for Mass Spectrometry Structure Elucidation.

Journal of chemical information and modeling·2026
Same author

Unsupervised Hierarchical Symbolic Regression for Interpretable Property Modeling in Complex Multi-Variable Systems.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Automation and AI-Powered Prediction in Chromatographic Separation.

Accounts of chemical research·2025
Same author

Computed ECD spectral data for over 10,000 chiral organic small molecules.

Scientific data·2025
Same author

One-Pot Decarbonylative Borylation of Aliphatic Aldehydes.

The Journal of organic chemistry·2025
Same journal

Precise Synthesis of Star-Shaped Redox-Responsive Segmented Polyurethanes with Controlled Arm Sequences for Drug Delivery.

Precision chemistry·2026
Same journal

Paradoxical Suppression of Exciton Diffusion by Long-Range Interactions: A Large-Scale Nonadiabatic Dynamics Study.

Precision chemistry·2026
Same journal

Chemically Recyclable Thermoplastic Elastomers: Preparation, Properties, and On-Demand Depolymerization.

Precision chemistry·2026
Same journal

Potent and Receptor-Selective Germination Inhibitor for <i>Striga hermonthica</i>.

Precision chemistry·2026
Same journal

Flavor-Enhancing Pentapeptide AGPNY from Tomato Proteins: Potential Dual-Targeting of Umami Receptors T1R1/T1R3 and GRM1 through Computational-Experimental Synergy.

Precision chemistry·2026
Same journal

Multivariate Synergy and Two-Dimensional Confinement: Research Progress and Opportunities of Two-Dimensional High-Entropy Alloys.

Precision chemistry·2026
See all related articles

Related Experiment Video

Updated: Jun 27, 2026

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

Infrared Spectra Prediction for a Carbonyl Group Utilizing a Graph Network Approach.

Jie Shi1,2, Chengchun Liu1,3, Fanyang Mo1,2,3,4

  • 1School of AI for Science, Peking University Shenzhen Graduate School Shenzhen 518055, China.

Precision Chemistry
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces NE-GNN, a novel graph neural network method for analyzing infrared spectra. It accurately predicts carbonyl group peaks, accelerating chemical analysis and data-driven discovery.

Keywords:
GNNcarbonyl groupcharacteristic peakinfrared spectrainterpretabilitymachine learning

More Related Videos

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation
20:12

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

Published on: October 8, 2011

Characterizing Lewis Pairs Using Titration Coupled with In Situ Infrared Spectroscopy
07:49

Characterizing Lewis Pairs Using Titration Coupled with In Situ Infrared Spectroscopy

Published on: February 20, 2020

Related Experiment Videos

Last Updated: Jun 27, 2026

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation
20:12

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

Published on: October 8, 2011

Characterizing Lewis Pairs Using Titration Coupled with In Situ Infrared Spectroscopy
07:49

Characterizing Lewis Pairs Using Titration Coupled with In Situ Infrared Spectroscopy

Published on: February 20, 2020

Area of Science:

  • Analytical Chemistry
  • Computational Chemistry
  • Spectroscopy

Background:

  • Infrared spectroscopy is vital in materials science and biomedicine.
  • Manual spectral interpretation is time-consuming.
  • Carbonyl groups offer significant predictive and diagnostic potential in spectral analysis.

Purpose of the Study:

  • To develop an efficient method for predicting carbonyl group peaks in infrared spectra.
  • To improve the interpretability and accuracy of spectral analysis using machine learning.
  • To accelerate data-driven discovery in chemistry.

Main Methods:

  • Constructed a dataset from experimental infrared spectroscopic data in chemical literature.
  • Developed NE-GNN, a graph neural network approach for feature extraction.
  • Integrated proximal structural analysis with graph neural networks for chemically intuitive framework.

Main Results:

  • NE-GNN achieved high precision in predicting carbonyl group peaks in infrared spectra.
  • The method demonstrates a balance between predictive accuracy and interpretability.
  • Successfully leveraged experimental data for robust model development.

Conclusions:

  • NE-GNN significantly enhances the efficiency of machine learning applications in spectroscopic analysis.
  • This approach supports and accelerates data-driven discovery in chemical research.
  • NE-GNN offers a powerful tool for interpreting infrared spectra, particularly for carbonyl-containing compounds.