1. Introduction

Nuclear Magnetic Resonance (NMR) spectroscopy is a cornerstone analytical technique for determining the structure, dynamics, and interactions of molecules. NMR spectroscopy utilizes the properties of nuclei to identify atoms within molecules. The number of protons and neutrons, both of which possess an intrinsic property called spin (with values of plus or minus half), determine whether a particular atom is detectable using NMR spectroscopic methods[1]. Depending on the number of protons and neutrons in a nucleus, the net spin can be zero, half-integer, or integer. Nuclei with a non-zero net spin act like magnets, and it is this magnetic property that NMR spectroscopy exploits. NMR methods take advantage of the fact that the molecular environment subtly but measurably alters the spin of a nucleus. Therefore the protons within a -CH₂ group can be differentiated depending on the group(s) in their vicinity[2]. A good example of this shows as follows: Figure 1 shows the chemical structures of glutamine, glutamate, and alpha-ketoglutarate. NMR spectroscopy is able to differentiate their difference based on ¹³C and ¹H signals originating from C(3) and C(4).

Two-dimensional (2D) Nuclear Magnetic Resonance (NMR) spectroscopy is an advanced technique that enhances the capabilities of one dimensional (1D) NMR. While 1D NMR provides data about individual nuclei and their environments, 2D NMR correlates information between pairs of nuclei, offering deeper insights into molecular structure, dynamics, and interactions[3]. Cross-peaks in 2D NMR appear at the intersection of the chemical shifts of two correlated nuclei, indicating connectivity. 2D NMR is extensively utilized in various fields, and its potential for studying complex biochemical or chemical mixtures been widely demonstrated. 2D NMR enables the resolution overlapping peaks in 1D spectra, while providing both structural and quantitative information[4]. It aids in assigning peaks to specific atoms in molecules, identifying functional groups and connectivity. Additionally, it reveals three-dimensional molecular structures and dynamic, monitors ligand binding protein folding and complex formation in biomolecules, and characterizes polymers, composites, and nanomaterials.

2. 2D NMR

2D NMR techniques address the limitations of 1D NMR by spreading spectral information across two axes, enhancing resolution and simplifying complex spectra. The two dimensions typically represent:

1. F1 Axis: A heteronuclear (^13C, ^15N, etc.) or homonuclear (^1H) dimension.
2. F2 Axis: A ^1H dimension.

Key 2D techniques include:

1. COSY (Correlation Spectroscopy): Reveals proton-proton interactions.
2. HSQC (Heteronuclear Single Quantum Coherence): Detects direct ^1H-X (e.g., ^13C or ^15N) couplings.
3. HMBC (Heteronuclear Multiple Bond Correlation): Maps long-range ^1H-X correlations, providing structural insights.

2D NMR Experiments include homonuclear, heteronuclear experiments and NOESY(Nuclear Overhauser Effect Spectroscopy) etc. Homonuclear experiments examine interactions between the same type of nuclei, often ^1H-^1H[5]. Like COSY(Correlation Spectroscopy) and TOCSY(Total Correlation Spectroscopy) are this kind of experiments, COSY reveals direct couplings between protons in spin systems, while TOCSY maps all protons within a spin system[6]. Heteronuclear experiments examine interactions between different nuclei, such as ^1H-^13C or ^1H-^15N. It includes HSQC (Heteronuclear Single Quantum Coherence) and HMBC (Heteronuclear Multiple Bond Correlation). HSQC shows direct correlations between protons and heteronuclei, HMBC detects long-range correlations (two or more bonds away). And NOESY measures spatial proximity between nuclei, providing information about three-dimensional molecular structure[7]. Among the array of NMR methods, Heteronuclear Single Quantum Coherence (HSQC) stands out as a robust 2D technique that correlates protons (^1H) with heteronuclei such as ^13C or ^15N through direct one-bond scalar (J) couplings. This review explores the principles, applications, and recent research on HSQC NMR, emphasizing its transformative impact on organic, polymer, and biomolecular chemistry.

You can watch this video to get more information about those three 2D NMR techniques:

HSQC is particularly powerful because it links protons directly to heteronuclei, offering clear and interpretable data about molecular fragments.

3. Principles and Analysis of HSQC NMR

3.1 Key Concepts and Principles

Heteronuclear single quantum coherence (HSQC) is a 2D NMR method that integrates the sensitivity of ¹H and the resolution of ¹³C by extending 1D ¹H NMR signals to 2D ¹H-¹³C HSQC cross peaks along the ¹³C dimension, offering an excellent solution to the peak overlaps and allowing the dispersed peak intensities to be determined accurately for quantitative analysis[8]. It transfers magnetization between ^¹H and a heteronucleus (^¹³C or ¹⁵^N) through scalar coupling, enabling detection of between these nuclei. The spectrum contains about the relative incorporation ^¹³C labeled nuclei into metabolites. This information is in multiplet of each resonance, with the pattern of the multiplet being due to presence or absence of ^¹³C nuclei in positions to the detected nuclei. The extent of peak splitting due to isotope incorporation (known as J-coupling) depends on the nuclei involved and the local chemical structure. Each multiplet virtually unique for a specific resonance of each metabol. As many different of patterns may be in, the overall pattern can be complex but always be derived as a linear superposition of the individual multiplet components. Signal annotation and multiplet analysis are the biggest challenges in the analysis of 2D HSQC NMR spectra, as precise resonance positions of its resonances can change due to pH or sample composition changes. This can provide significant challenges in crowded areas of the NMR spectrum, especially for similar multiplet signals, albeit distinct ^¹³C/^¹³C constants. In addition, the overall of a specific multiplet differs significantly depending the choice of tracer and the specific activity of metabolic pathways[9].

The 2D HSQC NMR spectrum has two axis, one is Horizontal axis, which is Proton chemical shifts, other one is Vertical axis, which is Heteronuclear chemical shifts. Each peak represents a ^1H-X bond, providing direct connectivity information. It has high sensitivity compared to techniques like HMQC. It simplifies spectra by focusing on directly bonded nuclei and is also compatible with isotopically enriched samples (e.g., ^¹³C-labeled biomolecules)[10]. Although compared to 1D NMR experiments, HSQC usually takes a longer running time, but by coupling it with multiple techniques, such as non-uniform sampling (NUS), acceleration by sharing adjacent polarization (ASAP) and the excitation of selective bands, the measure time can be reduced to a few minutes. Up to now, HSQC has already been successfully used in the differentiation and quantification of a variety of components that share similar chemical structures in different complex samples.

3.2 Analysis of HSQC NMR

The analysis of HSQC spectra requires a detailed understanding of cross-peaks, spectral patterns, and the structural information they reveal. Here, we delve deeper into the key aspects of interpreting HSQC spectra, focusing on practical methods and strategies for resolving complex data. Each cross-peak in an HSQC spectrum represents a direct correlation between a proton (^¹H) and a heteronucleus (^¹³C or ^¹⁵N) connected via one bond. The location and intensity of these peaks provide information on molecular structure and bonding[11]. The first step is to match peaks to functional groups, use known chemical shift ranges to assign peaks to functional groups. Also, use chemical shift trends to analyse the spectrum, electron-withdrawing groups shift ^¹³C peaks downfield (higher ppm), while electron-donating groups shift them upfield (lower ppm). For instance, a carbon adjacent to a hydroxyl group will appear further downfield than a purely aliphatic carbon. We can also cross reference 1D Spectra, Correlate ^¹H and ^¹³C peaks from 1D NMR spectra with the 2D HSQC cross-peaks to ensure accurate assignments[12].

Overlapping signals are a common challenge in HSQC spectra, especially for complex molecules. Those are some techniques to address this problem, phase-sensitive HSQC experiments can be used to distinguish between -CH, -CH₂, and -CH₃ groups by modulating signal intensity. This editing simplifies spectral interpretation in crowded regions. Other 2D NMR method have better performance on this issue, like COSY can identify proton-proton correlations, while HMBC reveals long-range proton-carbon couplings. These techniques help differentiate overlapping peaks by providing additional connectivity data. Also, the chemical shift variations need to be handled with, minor changes in pH or solvent composition can shift resonance positions. Standardize conditions to minimize variability and improve spectral clarity[13]. Evaluating J-coupling patterns to resolve closely spaced peaks is another solution, For example, a doublet of doublets might indicate coupling with two distinct nuclei. Peak intensity correlates with the number of protons on the carbon. For example, a -CH₃ group will have a stronger signal than -CH₂. Isotope effects and relaxation times also influence intensity. Broadening can result from conformational flexibility, exchange processes, or sample impurities. Identifying the cause helps refine peak assignments.

Nowadays, more and more software are used to analyse spectrum also, such as TopSpin, MestreNova, and SpinWorks, offer automated peak-picking and assignment capabilities. their key features include automated integration of cross-peaks, interactive editing for phase-sensitive HSQC data and advanced visualization for distinguishing overlapping signals. HSQC spectra can also be used for quantitative studies by integrating peak intensities. It has been used to estimate relative concentrations of functional groups and monitor reaction progress by tracking changes in cross-peak intensities over time[14].

4. Applications of HSQC NMR

• Organic Chemistry

In organic chemistry, 2D NMR is crucial for structural elucidation of complex molecules. For example, COSY (Correlation Spectroscopy) identifies proton-proton couplings, which helps assign spin systems in molecules like alkaloids or steroids. HSQC (Heteronuclear Single Quantum Coherence) maps direct one-bond correlations between ^1H and ^13C, allowing precise functional group identification in natural products such as flavonoids. Similarly, HMBC (Heteronuclear Multiple Bond Correlation) reveals long-range couplings, aiding in establishing connectivity between distant carbons and protons, such as in the elucidation of polyketide chains in antibiotics.

• Biochemistry

In biochemistry, 2D NMR serves as a cornerstone for studying proteins, nucleic acids, and metabolites. HSQC is indispensable in protein NMR for backbone and side-chain resonance assignments, particularly in isotope-labeled samples. For instance, ^1H-^15N HSQC spectra of a kinase protein can pinpoint amino acid residues involved in ligand binding through chemical shift perturbations. NOESY (Nuclear Overhauser Effect Spectroscopy) provides spatial proximity information critical for resolving tertiary structures of biomolecules, as demonstrated in the folding studies of small proteins or RNA hairpins. In metabolomics, 2D NMR, such as TOCSY (Total Correlation Spectroscopy), identifies interconnected spin systems of metabolites in biological fluids like blood or urine, offering a comprehensive profile of metabolic pathways.

• Materials science

In materials science, 2D NMR is applied to characterize synthetic and natural polymers, composites, and nanomaterials. For example, HSQC can determine the repeating units and branching in biopolymers like cellulose or chitosan, while HMBC elucidates cross-linking patterns in polyethylene derivatives. In composite materials, such as hemp-based hybrids, HSQC identifies the chemical interactions between fiber surfaces and polymer matrices, ensuring compatibility and performance. Nanomaterials like functionalized graphene have been studied using NOESY to assess the spatial arrangement of functional groups on their surfaces. Environmental chemistry also benefits from 2D NMR in identifying and characterizing organic pollutants. For instance, HSQC and COSY have been employed to study the decomposition products of lignin in soil, revealing critical insights into carbon cycling. Similarly, wastewater analysis uses 2D NMR to detect trace pollutants, helping to develop remediation strategies. In waste management, HSQC is used to analyze biodegradable polymers and track their degradation pathways under various conditions[15].

• Other applications

In food science, 2D NMR ensures quality control by profiling compounds responsible for flavor, nutrition, and safety. For example, HSQC has been employed to analyze the polyphenolic content in red wine, correlating specific compounds with antioxidant properties. Similarly, in coffee and tea, COSY helps identify alkaloids like caffeine alongside flavor-related metabolites. The technique is also critical for detecting adulterants in spices such as saffron or honey, ensuring product authenticity.

Industrial applications of 2D NMR are diverse, from petrochemical analysis to textile and cosmetic evaluations. In petrochemicals, HSQC identifies molecular structures in complex hydrocarbon mixtures, guiding refining processes. In textiles, 2D NMR characterizes functionalized fibers, such as cellulose treated with dyes or hydrophobic agents, to ensure durability and performance. In the cosmetics industry, the technique is used to study the interactions between surfactants and active ingredients, optimizing formulations for stability and efficacy.

In summary, the applications of 2D NMR across disciplines demonstrate its unparalleled ability to unravel molecular complexities, making it indispensable for research, industry, and environmental sustainability. Its versatility in resolving structural, spatial, and interaction-based queries highlights its importance as a foundational tool in modern science.

5. Related research

Article title: New Ester-Type Chemical Bonding Wood Adhesion with a Dicarboxylic Acid Compound

Link: https://doi-org.prox.lib.ncsu.edu/10.1007/s00226-024-01621-7

This study explores the development of a water-resistant wood adhesive using dicarboxylic acids, specifically comparing glutaric acid (GA) and citric acid (CA). The research focuses on the chemical bonding mechanisms and physical properties of wood moldings prepared with these adhesives. Using 2D HSQC-NMR spectroscopy, the study investigates the interphase between the adhesives and wood surfaces, aiming to clarify the reactivity and effectiveness of GA and CA as adhesives. And it founds out that GA forms clean and effective ester bonds with wood surfaces, showing higher reactivity compared to CA. Unlike CA, GA exhibits minimal side reactions and does not degrade polysaccharides during the adhesion process. GA-based wood moldings demonstrate better water resistance, with reversible thickness changes after immersion and drying. This indicates a stable chemical bonding mechanism. GA-based moldings are tougher (higher modulus of rupture) than CA-based moldings, although the modulus of elasticity was slightly lower. This toughness is attributed to GA’s linear molecular structure, which reduces steric hindrance. GA provides a promising foundation for sustainable wood adhesives, demonstrating superior bonding strength and environmental compatibility compared to traditional adhesives. Main figures as the typical HSQC NMR spectrum are as follows:

Figure 2 presents the heteronuclear single quantum coherence (HSQC) NMR spectra of GA- and CA-type wood-based moldings, focusing on the aliphatic region. The purpose is to identify the esterification reactions between the dicarboxylic acids and wood components, particularly polysaccharides and lignin. Signals indicate that GA reacts with hydroxyl groups on polysaccharides and lignin more effectively than CA. GA showed cleaner esterification without degradation of polysaccharides, likely due to reduced steric hindrance compared to CA. The enhanced reactivity of GA suggests its potential for stronger, more uniform adhesive bonds in wood molding applications.

Figure 3 explores reactions between GA, CA, and lignin aromatic structures. It investigates whether the adhesive compounds interact with lignin’s functional groups, contributing to bonding strength. After the investigation of HSQC NMR experiments, it founds out that GA effectively esterifies hydroxyl groups in lignin’s β-O-4 and β-5 substructures, as evidenced by unique signals in the aromatic region. CA showed additional signals related to furan and aldehyde compounds, indicative of side reactions not observed with GA. The absence of such by-products in GA suggests it forms cleaner and more stable chemical bonds with lignin, enhancing overall adhesion.

Figure 4 illustrates the potential chemical interactions at the adhesive interphase for GA- and CA-type moldings, summarizing the structural differences observed in HSQC NMR analyses. The results shows that, For GA, the adhesive interphase primarily relies on ester-type chemical bonds, ensuring uniform bonding and minimal interference with wood’s structural integrity. CA interactions involve additional hydrogen bonding and physical interactions with furan structures, which could introduce variability and compromise water resistance. GA’s linear structure and higher reactivity provide superior bonding characteristics compared to CA, making it a more effective and environmentally friendly adhesive for wood-based materials.

The study used 2D HSQC-NMR spectroscopy to investigate the interphase between adhesives and wood surfaces, aiming to clarify the reactivity and effectiveness of GA and CA as adhesives. It identifies GA as a viable dicarboxylic acid for water-resistant wood adhesion, outperforming CA in reactivity, water resistance, and mechanical properties. This research highlights the potential of dicarboxylic acids in creating eco-friendly adhesives, paving the way for future exploration of other bio-based adhesive compounds tailored to specific material properties. This work is significant for developing sustainable adhesive technologies that reduce reliance on petroleum-based products, addressing environmental concerns while maintaining high performance in wood-based materials.

6. the interactive elements:

7. Graphical abstract

8. References

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