Nuclear magnetic resonance (NMR) spectroscopy serves as a fundamental analytical technique finding valuable applications across chemistry, biology, and materials science, yet its widespread utility is frequently constrained by its inherently low sensitivity and concomitant noise, making noise reduction a critical processing step. Existing denoising methods typically impose a difficult trade-off, namely, traditional iterative algorithms are interpretable and widely adoptable but time-consuming, while deep learning approaches achieve fast denoising and better performance at the cost of generalizability and interpretability. To break this dilemma, we propose a denoising protocol, namely CCA-NMR, to unlock clean signals in noisy NMR spectra. This protocol exploits the idea of blind source separation to separate noise and real signals based on autocorrelation coefficients, delivering good noise reduction, fast computational time, and decent universality. Owing to these advantages, it enables reliable sensitivity benefits and recovers genuine peaks obscured by noise, thus reducing the necessary experimental acquisition times and facilitating subsequent quantitative analysis. Its performance is evidenced through experimental validation across a diverse range of NMR platforms, including relaxation and diffusion Laplace NMR, multidimensional protein NMR, and time-resolved NMR during real-time electrocatalytic reactions. As a consequence, these findings substantially expand the practical applicability of NMR spectroscopy, and indicate its considerable potential for broad chemical and biomedical applications.
Haolin Zhan, Yueyao Li, Yongqi Yuan et al.· Chemical Science· 0 citations
Diffusion Ordered NMR Spectroscopy (DOSY) is a powerful technique for studying mixtures by probing the diffusion behavior of mixed compounds and enabling their identification and separation in mixture samples. The performance of DOSY generally relies on the adopted reconstruction algorithm to determine diffusion coefficients from diffusion-dependent signal decays, thus producing a 2D spectrum that resolves components by chemical shift and molecular diffusion. Although deep-learning provides an effective approach to DOSY reconstruction, existing deep-learning-based reconstruction methods generally face the limitation of inadequate feature extraction, which may lead to reconstruction artifacts in some scenarios. Here, we propose STELT (Spatiotemporal Extraction Laplace Transform), a lightweight deep-learning framework based on spatiotemporal feature extraction. STELT uses a dual-branch architecture that combines a temporal convolution module to capture dynamic patterns in decay signals with a self-attention module to extract spatial features along the chemical-shift dimension. Experimental results demonstrate the proposed method achieves superior reconstruction accuracy and noise suppression with significantly reduced computational overhead, thereby offering a practical and efficient solution for high-quality DOSY analysis.
Jing-Min Lin, Bo Chen, Guolan Peng et al.· Journal of Chemical Physics· 0 citations
Multidimensional NMR spectroscopy provides rich molecular-level information on species and structures, with broad significance across chemistry, biology, and materials science. However, its widespread application is generally limited by prolonged acquisition times. Combining non-uniform sampling techniques with spectra reconstruction methods offers a promising solution to this acquisition bottleneck. Traditional reconstruction methods are robust but constrained by algorithmic assumptions and approximations, whereas deep learning approaches can potentially overcome these limitations and achieve higher reconstruction fidelity, though generalization to unseen data remains challenging. Here, we present an accelerated conditional diffusion model for multidimensional NMR spectra reconstruction, formulating the task as a probabilistic iterative denoising process that progressively refines undersampled spectra under physical constraints. Experiments demonstrate that this method outperforms both traditional and end-to-end deep learning algorithms in peak recovery, artifact suppression, and robustness across multiple sampling conditions and experimental datasets.
Bo Chen, Xun Guan, Zhuoran Rong et al.· National Science Review· 0 citations
This study presents a diffusion-based deep-learning framework for automatic phase-sensitive NMR spectrum reconstruction directly from common NMR experimental data, free of quadrature acquisition and phase correction operation.
Zhuoran Rong, Bo Chen, Jie Shao et al.· Analytical Chemistry· 0 citations
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