Chelsea M. Goetzman

Remote monitoring of dissolver activities in H-Canyon can help operators avoid delays associated with excessive levels of fuel fragments remaining after a run. SRNL has proposed that effective monitoring can be achieved by using a Raman spectrometer to measure NO2 concentrations in the offgas stream sampled from the facility stack. Prior work (SRNL-STI-2021-00451) measuring the offgas from one dissolution batch of High Flux Isotope Reactor (HFIR) fuel suggested a relationship between %NO2 levels and fragment height. Herein, we report the results of monitoring and analysis of the dissolution of four batches of Material Test Reactor (MTR) fuel. A rigorous quantitative relationship between %NO2 measurements and fragment heights could not be established, due to high uncertainties associated with both measurements. Uncertainties with gas measurements are associated with the %NO2 levels in the offgas being close to the detection limit for the analyzer. Alternative gas measurement strategies are discussed which could improve sensitivity and reduce uncertainty. Limitations to the precision of the probe measurements are also discussed. It is also noted that the offgas is an average of the products from simultaneous dissolution of elements in multiple wells. Detection of a high fragment height level in an individual well may be hindered by low levels in other wells.

Fibromyalgia (FM) is a chronic central sensitivity syndrome characterized by augmented pain processing at diffuse body sites and presents as a multimorbid clinical condition. Long COVID (LC) is a heterogenous clinical syndrome that affects 10–20% of individuals following COVID-19 infection. FM and LC share similarities with regard to the pain and other clinical symptoms experienced, thereby posing a challenge for accurate diagnosis. This research explores the feasibility of using surface-enhanced Raman spectroscopy (SERS) combined with soft independent modelling of class analogies (SIMCAs) to develop classification models differentiating LC and FM. Venous blood samples were collected using two supports, dried bloodspot cards (DBS, n = 48 FM and n = 46 LC) and volumetric absorptive micro-sampling tips (VAMS, n = 39 FM and n = 39 LC). A semi-permeable membrane (10 kDa) was used to extract low molecular fraction (LMF) from the blood samples, and Raman spectra were acquired using SERS with gold nanoparticles (AuNPs). Soft independent modelling of class analogy (SIMCA) models developed with spectral data of blood samples collected in VAMS tips showed superior performance with a validation performance of 100% accuracy, sensitivity, and specificity, achieving an excellent classification accuracy of 0.86 area under the curve (AUC). Amide groups, aromatic and acidic amino acids were responsible for the discrimination patterns among FM and LC syndromes, emphasizing the findings from our previous studies. Overall, our results demonstrate the ability of AuNP SERS to identify unique metabolites that can be potentially used as spectral biomarkers to differentiate FM and LC.

Fibromyalgia (FM) is a chronic central sensitivity syndrome characterized by augmented pain processing at diffuse body sites and presents as a multimorbid clinical condition. Long COVID (LC) is a heterogenous clinical syndrome that affects 10-20% of individuals following COVID-19 infection. FM and LC shares similarities with regards to the pain and other clinical symptoms experienced, thereby posing a challenge for accurate diagnosis. This research explores the feasibility of using surface enhanced Raman spectroscopy (SERS) combined with soft independent modelling of class analogies (SIMCA) to develop classification models differentiating LC and FM. Venous blood samples were collected using two supports, dried bloodspot cards (DBS, n= 48 FM and n= 46 LC) and volumetric absorptive micro sampling tips (VAMS, n= 39 FM and n= 39 LC). A semi-permeable membrane (10 kDa) was used to extract low molecular fraction (LMF) from the blood samples, and Raman spectra was acquired using SERS with gold nanoparticles (AuNPs). Soft Independent Modelling of Class Analogy (SIMCA) models developed with spectral data of blood samples collected in VAMS tips showed superior performance with a validation performance of 100% accuracy, sensitivity, and specificity achieving an excellent classification accuracy of 0.86 area under the curve (AUC). Amide groups, aromatic and acidic amino acids were responsible for the discrimination patterns among FM and LC syndromes, emphasizing the findings from our previous studies. Overall, our results demonstrate the ability of AuNPs SERS to identify unique metabolites that can be potentially used as spectral biomarkers to differentiate FM and LC.

Fibromyalgia (FM) is a chronic muscle pain disorder that shares several clinical features with other related rheumatologic disorders. This study investigates the feasibility of using surface-enhanced Raman spectroscopy (SERS) with gold nanoparticles (AuNPs) as a fingerprinting approach to diagnose FM and other rheumatic diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), osteoarthritis (OA), and chronic low back pain (CLBP). Blood samples were obtained on protein saver cards from FM (n = 83), non-FM (n = 54), and healthy (NC, n = 9) subjects. A semi-permeable membrane filtration method was used to obtain low-molecular-weight fraction (LMF) serum of the blood samples. SERS measurement conditions were standardized to enhance the LMF signal. An OPLS-DA algorithm created using the spectral region 750 to 1720 cm⁻¹ enabled the classification of the spectra into their corresponding FM and non-FM classes (Rcv > 0.99) with 100% accuracy, sensitivity, and specificity. The OPLS-DA regression plot indicated that spectral regions associated with amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases. This exploratory work suggests that the AuNP SERS method in combination with OPLS-DA analysis has great potential for the label-free diagnosis of FM.