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2D NMR: Overview of Homonuclear Correlation Techniques

2D NMR: Overview of Homonuclear Correlation Techniques

Homonuclear correlation spectroscopy (COSY) is a powerful technique used in Nuclear Magnetic Resonance (NMR) spectroscopy to study the correlations between nuclei of the same type within a molecule. It provides information about scalar couplings between adjacent nuclei, which helps determine connectivity and structural information. There are several COSY variants, each with its unique strengths and experimental parameters.
COSY90 is the standard two-dimensional (2D) COSY experiment that...

¹H NMR: Interpreting Distorted and Overlapping Signals

¹H NMR: Interpreting Distorted and Overlapping Signals

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...

Two-Dimensional (2D) NMR: Overview

Two-Dimensional (2D) NMR: Overview

The 1D NMR spectrum of large and complex molecules like natural products has complicated splitting patterns and overlapping signals, which can be easily interpreted using 2-dimensional (2D) NMR. Unlike 1D NMR, 2D NMR has two frequency axes that provide the coupling information between the nucleus A and nucleus B in a molecule. The process from which 2D spectra are obtained has four steps.
The first step is the preparation period, during which nucleus A is excited with a radiofrequency pulse....

2D NMR: Overview of Heteronuclear Correlation Techniques

2D NMR: Overview of Heteronuclear Correlation Techniques

Heteronuclear correlation spectroscopy is an analytical technique that investigates the coupling between different types of nuclei, often a proton and an X-nucleus, such as carbon-13 or nitrogen-15. This method is commonly used in nuclear magnetic resonance (NMR) spectroscopy to gain insights into complex chemical compounds' structural and compositional aspects. A typical heteronuclear correlation spectrum displays X-nucleus chemical shifts on one axis and a proton spectrum on the other...

¹H NMR Signal Multiplicity: Splitting Patterns

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When protons A and X are coupled, their nuclear spin energy levels are slightly modified. This is because the energy required to excite proton A to a spin state parallel to proton X is slightly different from the energy required for it to become anti-parallel to spin X. Consequently, there are two possible excitation frequencies for A (A1 and A2), depending on the spin state of X, and vice versa. The mutual nature of coupling implies that the difference between frequencies A1 and A2, indicated...

¹H NMR Signal Integration: Overview

¹H NMR Signal Integration: Overview

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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Measuring Interactions of Globular and Filamentous Proteins by Nuclear Magnetic Resonance Spectroscopy NMR and Microscale Thermophoresis MST

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Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component Materials.

Shunji Yamada^1,2, Eisuke Chikayama^2,3, Jun Kikuchi^1,2,4

¹Graduate School of Bioagricultural Sciences, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan.

International Journal of Molecular Sciences

|January 27, 2021

Summary

This summary is machine-generated.

Signal deconvolution and prediction methods for solid-state nuclear magnetic resonance (ssNMR) spectra improve the analysis of complex solid materials. These techniques enhance macromolecular characterization and material design by resolving overlapping spectral data.

Keywords:

Euglena gracilis T2 relaxation anisotropy cellulose degradation macromolecules plastics prediction short-time Fourier transform signal deconvolution solid-state NMR

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Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

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Published on: December 9, 2010

Area of Science:

Materials Science
Spectroscopy
Data Analysis

Background:

Solid-state nuclear magnetic resonance (ssNMR) spectroscopy is crucial for understanding native structures and dynamics in solid materials.
Analyzing multi-component solid materials using ssNMR is challenging due to broad and overlapping spectral signals.
Effective signal deconvolution and prediction are essential for accurate ssNMR analysis.

Purpose of the Study:

To investigate signal deconvolution methods (STFT, NTF, NMF) for complex ssNMR spectra.
To explore generative topographic mapping regression (GTMR) for predicting NMR signals and material properties.
To demonstrate the application of these methods in analyzing macromolecular samples.

Main Methods:

Short-time Fourier transform (STFT) for signal deconvolution.
Non-negative tensor/matrix factorization (NTF, NMF) for spectral separation.
Generative topographic mapping regression (GTMR) for signal and property prediction.

Main Results:

STFT and NTF successfully separated ssNMR spectra of cellulose degradation samples into distinct components (cellulose, proteins, lipids).
GTMR accurately predicted cellulose degradation products (acetate, CO2) and computed NMR signals from physical properties of polylactic acid.
ssNMR spectra of poly-ε-caprolactone were resolved into crystalline and amorphous signals using these methods.

Conclusions:

The developed signal deconvolution and prediction methods effectively address the challenge of overlapping spectra in ssNMR.
These techniques enhance the characterization of macromolecules and support the design of novel solid materials.
The study showcases the utility of STFT, NTF, and GTMR in diverse applications including biopolymers and plastics.