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Related Concept Videos

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

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When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
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¹H NMR Signal Integration: Overview00:58

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The intensity of a signal, which can be represented by the area under the peak, depends on the number of protons contributing to that signal. The area under each peak is shown as a vertical line called an integral, with the integral value listed under it, as seen in the proton NMR spectrum of benzyl acetate. Each integral value is divided by the smallest integral value to obtain the ratio of the number of protons producing each signal. The ratio reveals the relative number of protons and not...
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¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
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NMR Spectroscopy of Aromatic Compounds01:14

NMR Spectroscopy of Aromatic Compounds

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Aromatic compounds can be identified or analyzed using proton NMR and carbon‐13 NMR. Typically, aromatic hydrogens or hydrogens directly bonded to the aromatic rings are strongly deshielded by the aromatic ring current. Therefore, they absorb in the range of 6.5–8.0 ppm in proton NMR spectra. For instance, aromatic hydrogens directly bonded to the benzene ring absorb at 7.3 ppm. However, aromatic hydrogens of larger rings absorb farther upfield or downfield than the ideal range.
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Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule01:10

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In the AX proton spin system, proton A can sense the two spin states of a coupled proton X, resulting in a doublet NMR signal with two peaks of equal (1:1) intensity. When proton A is coupled to two equivalent protons (AX2 spin system), the spin states of each X can be aligned with or against the external field, creating three possible scenarios. This results in a 1:2:1  triplet signal, where the central peak corresponds to the chemical shift of A and is twice as large or intense as the...
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¹H NMR: Complex Splitting01:13

¹H NMR: Complex Splitting

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A proton M that is coupled to a proton X results in doublet signals for M. However, NMR-active nuclei can be simultaneously coupled to more than one nonequivalent nucleus. When M is coupled to a second proton A, such as in styrene oxide, each peak in the doublet is split into another doublet.
Splitting diagrams or splitting tree diagrams are routinely used to depict such complex couplings. While drawing splitting diagrams, the splitting with the larger coupling constant is usually applied...
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Updated: Jun 28, 2025

Quantification of Polybutylene Adipate Terephthalate-based Micro- and Nano-plastics from Soil Using Proton Nuclear Magnetic Resonance Spectroscopy
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Bayesian NMR petrophysical characterization.

S Pitawala1, P D Teal1

  • 1Victoria University of Wellington, Wellington, New Zealand.

Journal of Magnetic Resonance (San Diego, Calif. : 1997)
|April 10, 2024
PubMed
Summary
This summary is machine-generated.

A new Bayesian method improves nuclear magnetic resonance (NMR) data analysis for reservoir rocks. This technique enhances the estimation of pore size distributions and petrophysical properties, crucial for oil and gas exploration.

Keywords:
BayesianNMRT(2) distributions

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Area of Science:

  • Geoscience and Petrophysics
  • Data Analysis and Estimation Methods

Background:

  • Reservoir rock type identification is vital for oil and gas exploration and recovery.
  • Petrophysical properties like porosity and permeability are key to reservoir modeling and production planning.
  • Nuclear magnetic resonance (NMR) technology provides fast and accurate petrophysical characterization.

Purpose of the Study:

  • To introduce and evaluate a Bayesian estimation method for analyzing NMR T2 distributions.
  • To leverage prior knowledge of T2 distributions for improved petrophysical property estimation.
  • To compare the Bayesian method against existing techniques for reservoir rock characterization.

Main Methods:

  • Developed a Bayesian estimation approach incorporating prior information (mean and covariance) of T2 distributions.
  • Evaluated the method using simulated NMR decay data from synthesized T2 distributions.
  • Compared performance against T2LM and RMSE using porosity, bound fluid volume (BFV), and T2 distribution accuracy.

Main Results:

  • The Bayesian estimator demonstrated superior performance in estimating T2 distributions compared to existing methods.
  • Bayesian methods outperformed the ILT method for derived petrophysical properties, except under specific low-noise, short-relaxation time conditions.
  • Validation using experimental T2 distributions confirmed the effectiveness of the Bayesian approach.

Conclusions:

  • The proposed Bayesian method offers a robust and accurate approach for analyzing NMR data in reservoir characterization.
  • Incorporating prior knowledge significantly enhances the estimation of petrophysical properties from NMR T2 distributions.
  • This method is valuable for improving reservoir modeling and optimizing oil and gas recovery strategies.