Boosting the resolution of multidimensional NMR spectra by complete removal of proton spin multiplicities

Over decades multidimensional NMR spectroscopy has become an indispensable tool for structure elucidation of natural products, peptides and medium sized to large proteins. Heteronuclear single quantum coherence (HSQC) spectroscopy is one of the work horses in that field often used to map structural connectivity between protons and carbons or other hetero nuclei. In overcrowded HSQC spectra, proton multiplet structures of cross peaks set a limit to the power of resolution and make a straightforward assignment difficult. In this work, we provide a solution to improve these penalties by completely removing the proton spin multiplet structure of HSQC cross peaks. Previously reported sideband artefacts are diminished leading to HSQC spectra with singlet responses for all types of proton multiplicities. For sideband suppression, the idea of restricted random delay (RRD) in chunk interrupted data acquisition is introduced and exemplified. The problem of irreducible residual doublet splitting of diastereotopic CH2 groups is simply solved by using a phase sensitive JRES approach in conjunction with echo processing and real time broadband homodecoupling (BBHD) HSQC, applied as a 3D experiment. Advantages and limitations of the method is presented and discussed.

Realtime optimization of multidimensional NMR spectroscopy on embedded sensing devices

The increasingly ubiquitous use of embedded devices calls for autonomous optimizations of sensor performance with meager computing resources. Due to the heavy computing needs, such optimization is rarely performed, and almost never carried out on-the-fly, resulting in a vast underutilization of deployed assets. Aiming at improving the measurement efficiency, we show an OED (Optimal Experimental Design) routine where quantities of interest of probable samples are partitioned into distinctive classes, with the corresponding sensor signals learned by supervised learning models. The trained models, digesting the compressed live data, are subsequently executed at the constrained device for continuous classification and optimization of measurements. We demonstrate the closed-loop method with multidimensional NMR (Nuclear Magnetic Resonance) relaxometry, an analytical technique seeing a substantial growth of field applications in recent years, on a wide range of complex fluids. The realtime portion of the procedure demands minimal computing load, and is ideally suited for instruments that are widely used in remote sensing and IoT networks.