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DOE OSTI2026

Detecting impurity-specific effects on structure and radiolytic hydrogen production in aluminum hydroxide

While radiolytic hydrogen (H 2 ) generation is an intrinsic property of aqueous and mineral radiolysis in nuclear waste systems, detection of the sub-ns events leading to H 2 generation is challenging. Interfacial processes involving key mineral phases in the sludge, e.g., gibbsite (α-Al(OH) 3 ), have been implicated, with impurities affecting the amount of H 2 generated. To understand why gibbsite synthesized from nitrate precursors produces less H 2 than gibbsite from chloride precursors, we paired 27 Al multiple quantum magic angle spinning (MQMAS) NMR spectroscopy to determine structural heterogeneity with transverse-field muon spin rotation (TF-μSR) to probe electron availability. MQMAS revealed greater structural disorder in the gibbsite synthesized with nitrate (NO 3 -gibbsite). Correspondingly, TF-μSR showed a larger diamagnetic fraction for NO 3 -gibbsite, indicating reduced persistence of μ + -electron bound states (muonium or other radicals) and thus fewer electrons available for reaction on the sub-ns timescale. This establishes a correlation between impurity-induced disorder and electron loss. The diamagnetic fraction serves as a signature for these sub-ns events, as it provides a key constraint for predictive models without currently resolving whether the electron is lost to direct chemical scavenging or trapping at lattice defects.

Graham, Trent R. [Pacific Northwest National Labor↗

DOE OSTI2022

DESPERATE: A Python Library for Processing and Denoising NMR Spectra

NMR spectroscopy is an inherently insensitive technique with respect to the amount of observable signal. A common element in all NMR spectra is random thermal noise that is often characterized by a signal-to-noise ratio (SNR). SNR can be generically improved experimentally with repetitive signal averaging or during post-processing with apodization; the former of which often results in long experimental times and the latter results in the loss of spectral resolution. Denoising techniques can instead be used during post-processing to enhance SNR without compromising resolution. The most common approach relies on the singular-value decomposition (SVD) to discard noisy components of NMR data. SVD-based approaches work well, such as Cadzow and PCA, but are computationally expensive when used for large datasets that are often encountered in NMR (e.g., Carr-Purcell/Meiboom-Gill and nD datasets). Herein, we describe the implementation of a new wavelet transform (WT) routine for the fast and robust denoising of 1D and 2D NMR spectra. Several simulated and experimental datasets are denoised with both SVD-based Cadzow or PCA and WT’s, and the resulting SNR enhancements and spectral uniformity are compared. WT denoising offers similar and improved denoising compared with SVD and operates faster by several orders-of-magnitude in some cases. Further, all denoising and processing routines used in this work are included in a free and open-source Python library called DESPERATE.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗