Rapid Estimation of Repetition Time (Tr) for Quantitative Nuclear Magnetic Resonance

Abstract

Despite significant advances, many scientists within the Nuclear Magnetic Resonance (NMR) community continue to express reservations about the capability of NMR for quantitative analysis. A major source of this skepticism stems from the gap between standard NMR acquisition settings (e.g., default relaxation delay, D1 as 1 s) and those required for quantitative NMR (qNMR), with one critical requirement being the determination of longitudinal relaxation times (T₁). This illustrates that standard NMR experiments often cannot achieve complete spin relaxation. However, by taking advantage of this phenomenon, it may provide a simple and straightforward approach to estimate the appropriate D1 value for qNMR parameter setup. So, the present study explores an alternative approach that simplifies the process of D1 estimation, connects standard NMR setting with repetition delays (T_r) and avoids T₁ determination in qNMR applications. Specifically, this study applies a single-exponential decay regression model to predict optimal T_r, using only a minimal set of 1D standard NMR experiments. With just three data sets acquired at varying D1 values, the proposed approach accurately estimates T_r values corresponding to nearly maximal (≥99.99%) signal response intensity. Experimental validation across six chemical compounds and three solvents confirmed the robustness of the approach. The predicted T_r values consistently fell within a “sweet spot” range, approximately 7 - 10 times the measured T₁, which is sufficient for reliable quantitative analysis. Additionally, this study shows that such T_r prediction not only reduces the technical barrier for users but also helps them understand the importance of qNMR acquisition settings, thereby streamlining qNMR workflows.