14 citations as recorded by crossref.

1. A Monte Carlo Method for Analyzing Systematic and Random Uncertainty in Quantitative Nuclear Magnetic Resonance Measurements G. Khirich https://doi.org/10.1021/acs.analchem.1c00407
2. Accurate measurements of self‐diffusion coefficients with benchtop NMR using a QM model‐based approach E. Steimers et al. https://doi.org/10.1002/mrc.5300
3. Advancing quantitative NMR for high-precision isotopic analysis with rnmrfit 2.0 K. Isaac et al. https://doi.org/10.1016/j.jmr.2025.107991
4. Calibration‐Free Chemical Process and Quality Control Units as Enablers for Modular Production  M. Bornemann‐Pfeiffer et al. https://doi.org/10.1002/cite.202000150
5. Deep set model for the automated NMR fingerprinting of unknown mixtures J. Wagner et al. https://doi.org/10.1039/D5DD00490J
6. Spectral and chromatographic analysis of food-related components produced by bacteria, plants, and fungi. A review F. Granados-Chinchilla https://doi.org/10.1016/j.microc.2023.109499
7. Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few in Vivo Data D. Chen et al. https://doi.org/10.1109/TCI.2023.3267623
8. NMR-Onion - a transparent multi-model based 1D NMR deconvolution algorithm M. Brinks Sørensen et al. https://doi.org/10.1016/j.heliyon.2024.e36998
9. Parameter Visualization of Benchtop Nuclear Magnetic Resonance Spectra toward Food Process Monitoring K. Hara et al. https://doi.org/10.3390/pr10071264
10. Solubility of lithium, nickel, and cobalt oxides in deep eutectic solvents R. Rahman et al. https://doi.org/10.1016/j.molliq.2025.128875
11. Rational method for defining and quantifying pseudo-components based on NMR spectroscopy T. Specht et al. https://doi.org/10.1039/D3CP00509G
12. Investigation of Reaction Kinetics of the Selective Hydrogenation of a Terminal Alkyne Under Industrially Relevant Conditions With Benchtop NMR Spectroscopy P. Sterner et al. https://doi.org/10.1002/mrc.70091
13. Automated nuclear magnetic resonance fingerprinting of mixtures T. Specht et al. https://doi.org/10.1002/mrc.5381
14. Application of Odd-Order Derivatives in Fourier Transform Nuclear Magnetic Resonance Spectroscopy toward Quantitative Deconvolution S. Chen et al. https://doi.org/10.1021/acsomega.4c04536