Enhancing Sensitivity In Natural Product Nmr: Exploring Optimal Non-Uniform Sampling

Recent optimizations of NMR spectroscopy have focused their attention on innovations in new hardware, such as novel probes and higher field strengths. Only recently has the potential to enhance the sensitivity of NMR through data acquisition strategies been investigated. This thesis has focused on the practice of enhancing the signal-to-noise ratio (SNR) of NMR using non-uniform sampling (NUS). After first establishing the concept and exact theory of compounding sensitivity enhancements in multiple non-uniformly sampled indirect dimensions, a new result was derived that NUS enhances both SNR and resolution at any given signal evolution time. In contrast, uniform sampling alternately optimizes SNR (t < 1.26T2) or resolution (t~3T2), each at the expense of the other. Experiments were designed and conducted on a plant natural product to explore this behavior of NUS in which the SNR and resolution continue to improve as acquisition time increases. Possible absolute sensitivity improvements of 1.5 and 1.9 are possible in each indirect dimension for matched and 2x biased exponentially decaying sampling densities, respectively, at an acquisition time of ¿T2. Recommendations for breaking into the linear regime of maximum entropy (MaxEnt) are proposed. Furthermore, examination into a novel sinusoidal sampling density resulted in improved line shapes in MaxEnt reconstructions of NUS data and comparable enhancement to a matched exponential sampling density. The Absolute Sample Sensitivity derived and demonstrated here for NUS holds great promise in expanding the adoption of non-uniform sampling.

Performance Tuning Non-Uniform Sampling for Sensitivity Enhancement of Signal-Limited Biological NMR

Non-uniform sampling (NUS) has been established as a route to obtaining true sensitivity enhancements when recording indirect dimensions of decaying signals in the same total experimental time as traditional uniform incrementation of the indirect evolution period. Theory and experiments have shown that NUS can yield up to two-fold improvements in the intrinsic signal-to-noise ratio (SNR) of each dimension, while even conservative protocols can yield 20-40 % improvements in the intrinsic SNR of NMR data. Applications of biological NMR that can benefit from these improvements are emerging, and in this work we develop some practical aspects of applying NUS nD-NMR to studies that approach the traditional detection limit of nD-NMR spectroscopy. Conditions for obtaining high NUS sensitivity enhancements are considered here in the context of enabling H-1,N-15-HSQC experiments on natural abundance protein samples and H-1,C-13-HMBC experiments on a challenging natural product. Through systematic studies we arrive at more precise guidelines to contrast sensitivity enhancements with reduced line shape constraints, and report an alternative sampling density based on a quarter-wave sinusoidal distribution that returns the highest fidelity we have seen to date in line shapes obtained by maximum entropy processing of non-uniformly sampled data.

Enhanced Sensitivity by Nonuniform Sampling Enables Multidimensional MAS NMR Spectroscopy of Protein Assemblies

We report dramatic sensitivity enhancements in multidimensional MAS NMR spectra by the use of nonuniform sampling (NUS) and introduce maximum entropy interpolation (MINT) processing that assures the linearity between the time and frequency domains of the NUS acquired data sets. A systematic analysis of sensitivity and resolution in 2D and 3D NUS spectra reveals that with NUS, at least 1.5- to 2-fold sensitivity enhancement can be attained in each indirect dimension without compromising the spectral resolution. These enhancements are similar to or higher than those attained by the newest-generation commercial cryogenic probes. We explore the benefits of this NUS/MaxEnt approach in proteins and protein assemblies using 1-73-(U-C-13,N-15)/74-108-(U-N-15) Escherichia coil thioredoxin reassembly. We demonstrate that in thioredoxin reassembly, NUS permits acquisition of high-quality 3D-NCACX spectra, which are inaccessible with conventional sampling due to prohibitively long experiment times. Of critical importance, issues that hinder NUS-based SNR enhancement in 3D-NMR of liquids are mitigated in the study of solid samples in which theoretical enhancements on the order of 3-4 fold are accessible by compounding the NUS-based SNR enhancement of each indirect dimension. NUS/MINT is anticipated to be widely applicable and advantageous for multidimensional heteronuclear MAS NMR spectroscopy of proteins, protein assemblies, and other biological systems.

Sensitivity Gains, Linearity, and Spectral Reproducibility in Nonuniformly Sampled Multidimensional MAS NMR Spectra of High Dynamic Range

Recently, we have demonstrated that considerable inherent sensitivity gains are attained in MAS NMR spectra acquired by nonuniform sampling (NUS) and introduced maximum entropy interpolation (MINT) processing that assures the linearity of transformation between the time and frequency domains. In this report, we examine the utility of the NUS/MINT approach in multidimensional datasets possessing high dynamic range, such as homonuclear C-13-C-13 correlation spectra. We demonstrate on model compounds and on 1-73-(U-C-13,N-15)/74-108-(U-N-15) E. coli thioredoxin reassembly, that with appropriately constructed 50 % NUS schedules inherent sensitivity gains of 1.7-2.1-fold are readily reached in such datasets. We show that both linearity and line width are retained under these experimental conditions throughout the entire dynamic range of the signals. Furthermore, we demonstrate that the reproducibility of the peak intensities is excellent in the NUS/MINT approach when experiments are repeated multiple times and identical experimental and processing conditions are employed. Finally, we discuss the principles for design and implementation of random exponentially biased NUS sampling schedules for homonuclear C-13-C-13 MAS correlation experiments that yield high-quality artifact-free datasets.

New Results Concerning Probability Distributions with Increasing Generalized Failure Rates

The generalized failure rate of a continuous random variable has demonstrable importance in operations management. If the valuation distribution of a product has an increasing generalized failure rate (that is, the distribution is IGFR), then the associated revenue function is unimodal, and when the generalized failure rate is strictly increasing, the global maximum is uniquely specified. The assumption that the distribution is IGFR is thus useful and frequently held in recent pricing, revenue, and supply chain management literature. This note contributes to the IGFR literature in several ways. First, it investigates the prevalence of the IGFR property for the left and right truncations of valuation distributions. Second, we extend the IGFR notion to discrete distributions and contrast it with the continuous distribution case. The note also addresses two errors in the previous IGFR literature. Finally, for future reference, we analyze all common (continuous and discrete) distributions for the prevalence of the IGFR property, and derive and tabulate their generalized failure rates.

The Effect of Various Electrode Microstructure Parameters on the Effective Conductivity and Three-Phase Boundary Density of Infiltrated SOFC Electrodes

Solid oxide fuel cell (SOFC) technology has the potential to be a significant player in our future energy technology repertoire based on its ability to convert chemical energy into electrical energy. Infiltrated SOFCs, in particular, have demonstrated improved performance and at lower cost than traditional SOFCs. An infiltrated electrode comprises porous ceramic scaffolding (typically constructed from the oxygen ion conducting material) that is infiltrated with electron conducting and catalytic particles. Two important SOFC electrode properties are effective conductivity and three phase boundary density (TPB). Researchers study these electrode properties separately, and fail to recognize them as competing properties. This thesis aims to (1) develop a method to model the TPB density and use it to determine the effect of porosity, scaffolding particle size, and pore former size on TPB density as well as to (2) compare the effect of porosity, scaffolding particle size, and pore former size on TPB density and effective conductivity to determine a desired set of parameters for infiltrated SOFC electrode performance. A computational model was used to study the effect of microstructure parameters on the effective conductivity and TPB density of the infiltrated SOFC electrode. From this study, effective conductivity and TPB density are determined to be competing properties of SOFC electrodes. Increased porosity, scaffolding particle size, and pore former particle size increase the effective conductivity for a given infiltrate loading above percolation threshold. Increased scaffolding particle size and pore former size ratio, however, decreases the TPB density. The maximum TPB density is achievable between porosities of 45% and 60%. The effect of microstructure parameters are more prominent at low loading with scaffolding particle size being the most significant factor and pore former size ratio being the least significant factor.