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Related Concept Videos

Nomenclature of Alkanes02:22

Nomenclature of Alkanes

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In the late 19th-century, the number of new chemical compounds discovered increased tremendously. Hence, the necessity arose to develop a naming system for the systematic nomenclature of these newly discovered compounds. IUPAC (International Union for Pure and Applied Chemistry), established in 1919, sets rules for the nomenclature.
The alkane nomenclature considers the length of the carbon chain, the number, and the location of the substituent to arrive at its systematic name. The IUPAC...
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Structure of Alkanes02:23

Structure of Alkanes

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The formation of carbon-carbon bonds leading to the creation of the carbon chain is the basis of organic chemistry. August Kekulé and Archibald Scott Couper independently developed this idea of carbon chain formation.
Hydrocarbons are the simplest organic compounds composed of carbons and hydrogens. Based on the bond order between carbons, the hydrocarbons are further classified into alkanes, alkenes, and alkynes. 
Alkanes are the simplest hydrocarbons with sp3 hybrid carbon atoms....
28.5K
Combustion Energy: A Measure of Stability in Alkanes and Cycloalkanes02:14

Combustion Energy: A Measure of Stability in Alkanes and Cycloalkanes

6.5K
The low reactivity in alkanes can be attributed to the non-polar nature of C–C and C–H σ bonds. Alkanes, therefore, were  initially termed as “paraffins,” derived from the Latin words: parum, meaning “too little,” and affinis, meaning “affinity.”
Alkanes undergo combustion in the presence of excess oxygen and high-temperature conditions to give carbon dioxide and water. A combustion reaction is the energy source in natural gas, liquified...
6.5K
Constitutional Isomers of Alkanes02:18

Constitutional Isomers of Alkanes

18.5K
Organic compounds of the same molecular formula can have different structural formulas called constitutional isomers, and the phenomenon is known as constitutional isomerism. Alkanes with four or more carbons showing multiple structures with the same molecular formula thereby exhibit constitutional isomerism.
The linear isomer of an alkane is prefixed by the term “n”; hence a linear isomer of pentane is known as n-pentane. Based on the type of branching, some of the...
18.5K
Physical Properties of Alkanes02:33

Physical Properties of Alkanes

11.6K
Alkanes are nonpolar molecules due to the presence of only carbon and hydrogen atoms. The electronegativity difference between carbon and hydrogen is minimal, and hence alkanes have a zero dipole moment. This leads to the presence of only dispersion forces between the molecules. The strength of dispersion forces is dependent on the surface area of the molecules on which they act. Since the surface area increases with the molecular length for straight-chain alkanes, the dispersion forces also...
11.6K
Mass Spectrometry: Branched Alkane Fragmentation01:29

Mass Spectrometry: Branched Alkane Fragmentation

1.1K
This lesson delves into the mass spectrometry of branched alkane fragmentation. Branched alkanes possess secondary or tertiary carbon atoms, which generate relatively stable carbocations if the cleavage occurs at the branching point. The high stability of carbocations drives the instant fragmentation of branched alkanes. Accordingly, the branched alkane's molecular ion peak is very weak or invisible in the mass spectra, especially in comparison to a linear alkane.
1.1K

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Fast Prediction of the Equivalent Alkane Carbon Number Using Graph Machines and Neural Networks.

Lucie Delforce1, François Duprat2, Jean-Luc Ploix2

  • 1University of Lille, CNRS, Centrale Lille, Université d'Artois, UMR 8181-UCCS-Unité de Catalyse et Chimie du Solide, F-59000Lille, France.

ACS Omega
|November 7, 2022
PubMed
Summary

Two artificial intelligence models, neural networks (NN) and graph machines (GM), rapidly predict oil hydrophobicity using equivalent alkane carbon number (EACN). The graph machine model demonstrated superior accuracy for homologous compounds compared to NN predictions.

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Area of Science:

  • Physical Chemistry
  • Computational Chemistry
  • Materials Science

Background:

  • Oil hydrophobicity is crucial for designing surfactant/oil/water (SOW) systems.
  • Experimental determination of equivalent alkane carbon number (EACN) is time-consuming.
  • Rapid prediction methods for EACN are needed.

Purpose of the Study:

  • To develop rapid mathematical models for predicting the EACN of oils.
  • To utilize artificial intelligence (machine learning) methods for EACN prediction.
  • To compare the performance of neural network (NN) and graph machine (GM) models.

Main Methods:

  • Developed two AI models: NN and GM.
  • GM model trained on SMILES codes of 111 molecules with known EACN.
  • NN model trained on σ-moment descriptors computed using COSMOtherm.
  • Used leave-one-out algorithm for model complexity selection.

Main Results:

  • Both NN and GM models showed comparable accuracy for complex cosmetic and perfumery molecules.
  • GM model results showed better agreement with experimental EACN trends for homologous compounds.
  • The developed models offer a faster alternative to experimental EACN determination.

Conclusions:

  • AI-driven models, particularly graph machines, can accurately predict oil EACN.
  • These models provide a significant advancement for designing SOW dispersed systems.
  • The study offers a downloadable tool for replicating results.