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Related Concept Videos

Mass Spectrometry: Cycloalkane Fragmentation01:05

Mass Spectrometry: Cycloalkane Fragmentation

In mass spectrometry, cycloalkanes exhibit distinct fragmentation patterns due to the inherent stability of their molecular ions compared to linear or branched alkanes. The ring structure of cycloalkanes provides additional stability to the molecular ions, often resulting in prominent ion peaks in the mass spectrum.
For example, cyclohexane molecular ions have a mass-to-charge ratio (m/z) of 84, which tends to produce a stronger signal than linear alkanes like hexane. This stability comes from...
Conformations of Cycloalkanes02:29

Conformations of Cycloalkanes

Adolf von Baeyer attempted to explain the instabilities of small and large cycloalkane rings using the concept of angle strain — the strain caused by the deviation of bond angles from the ideal 109.5° tetrahedral value for sp3  hybridized carbons. However, while cyclopropane and cyclobutane are strained, as expected from their highly compressed bond angles, cyclopentane is more strained than predicted, and cyclohexane is virtually strain-free. Hence, Baeyer’s theory that was based on the...
Stability of Substituted Cyclohexanes02:30

Stability of Substituted Cyclohexanes

This lesson discusses the stability of substituted cyclohexanes with a focus on energies of various conformers and the effect of 1,3-diaxial interactions.
The two chair conformations of cyclohexanes undergo rapid interconversion at room temperature. Both forms have identical energies and stabilities, each comprising equal amounts of the equilibrium mixture. Replacing a hydrogen atom with a functional group makes the two conformations energetically non-equivalent.
For example, in...
Mass Spectrometry: Cycloalkene Fragmentation00:54

Mass Spectrometry: Cycloalkene Fragmentation

The molecular ions of cycloalkenes undergo fragmentation via a retro-Diels–Alder reaction.
Chair Conformation of Cyclohexane02:02

Chair Conformation of Cyclohexane

The chair conformation is the most stable form of cyclohexane due to the absence of angle and torsional strain. The absence of angle strain is a result of cyclohexane’s bond angle being very close to the ideal tetrahedral bond angle of 109.5° in its chair conformer. Similarly, the torsional strain is also absent owing to the perfectly staggered arrangement of bonds.
The hydrogen atoms linked to carbons are arranged in two different axial and equatorial orientations to achieve this staggered...
¹H NMR of Conformationally Flexible Molecules: Variable-Temperature NMR01:15

¹H NMR of Conformationally Flexible Molecules: Variable-Temperature NMR

The axial and equatorial protons in cyclohexane can be distinguished by performing a variable-temperature NMR experiment. In this process, except for one proton, the remaining eleven protons are replaced by deuterium. The deuterium substitution avoids the possible peak splitting caused by the spin-spin coupling between the adjacent protons. The remaining proton flips between the axial and equatorial positions.

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Related Experiment Video

Updated: Jun 18, 2026

Preparation of a Corannulene-functionalized Hexahelicene by Copper(I)-catalyzed Alkyne-azide Cycloaddition of Nonplanar Polyaromatic Units
09:35

Preparation of a Corannulene-functionalized Hexahelicene by Copper(I)-catalyzed Alkyne-azide Cycloaddition of Nonplanar Polyaromatic Units

Published on: September 18, 2016

Machine learning-based regression analysis of cyclopentane and cyclohexane molecular graphs.

Muhammad Irfan1, Nabeela Bashir1, M Umair Shahzad1,2

  • 1Department of Mathematics, University of Okara, Okara, Pakistan.

Scientific Reports
|June 16, 2026
PubMed
Summary
This summary is machine-generated.

This study analyzes topological indices for cyclopentane and cyclohexane polymers, developing machine learning models to predict chemical properties and enhance polymer design.

Keywords:
Cyclopentane and Cyclohexane.Molecular GraphsRegression Models

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On-line Analysis of Nitrogen Containing Compounds in Complex Hydrocarbon Matrixes
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On-line Analysis of Nitrogen Containing Compounds in Complex Hydrocarbon Matrixes

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Accessing Valuable Ligand Supports for Transition Metals: A Modified, Intermediate Scale Preparation of 1,2,3,4,5-Pentamethylcyclopentadiene
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Accessing Valuable Ligand Supports for Transition Metals: A Modified, Intermediate Scale Preparation of 1,2,3,4,5-Pentamethylcyclopentadiene

Published on: March 20, 2017

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Last Updated: Jun 18, 2026

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On-line Analysis of Nitrogen Containing Compounds in Complex Hydrocarbon Matrixes
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09:45

Accessing Valuable Ligand Supports for Transition Metals: A Modified, Intermediate Scale Preparation of 1,2,3,4,5-Pentamethylcyclopentadiene

Published on: March 20, 2017

Area of Science:

  • Polymer Science and Engineering
  • Computational Chemistry
  • Materials Science

Background:

  • Cyclopentane and cyclohexane are industrially relevant polymers with specialized applications.
  • Existing literature lacks systematic comparative analysis of topological indices for predicting their chemical properties.

Purpose of the Study:

  • To compute and analyze various molecular descriptors (topological indices) for cyclopentane and cyclohexane.
  • To establish quantitative structure-property relationships (QSPR) for these polymers.
  • To develop machine learning models for predicting polymer properties.

Main Methods:

  • Calculation of Zagreb indices, Randić index, atom bond connectivity index, geometric arithmetic index, sum connectivity index, and augmented Zagreb index.
  • Application of machine learning regression models to correlate topological indices with experimental property data.

Main Results:

  • Successful computation and analysis of fundamental molecular descriptors for cyclopentane and cyclohexane.
  • Development of QSPR models demonstrating the correlation between molecular structure and physicochemical properties.
  • Validation of machine learning models for accurate property estimation.

Conclusions:

  • Topological indices provide a valuable tool for correlating polymer structure with properties.
  • Machine learning models offer an efficient framework for predicting polymer properties.
  • This study lays the groundwork for applying advanced learning models to complex polymer networks.