nuclear magnetic
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2022 ◽  
Vol 138 ◽  
pp. 41-48
Author(s):  
S. Picard ◽  
M. Cambert ◽  
J.-M. Roger ◽  
A. Davenel ◽  
R. Girault ◽  
...  

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 518
Author(s):  
Reza Rezaee

A nuclear magnetic resonance (NMR) logging tool can provide important rock and fluid properties that are necessary for a reliable reservoir evaluation. Pore size distribution based on T2 relaxation time and resulting permeability are among those parameters that cannot be provided by conventional logging tools. For wells drilled before the 1990s and for many recent wells there is no NMR data available due to the tool availability and the logging cost, respectively. This study used a large database of combinable magnetic resonance (CMR) to assess the performance of several well-known machine learning (ML) methods to generate some of the NMR tool’s outputs for clastic rocks using typical well-logs as inputs. NMR tool’s outputs, such as clay bound water (CBW), irreducible pore fluid (known as bulk volume irreducible, BVI), producible fluid (known as the free fluid index, FFI), logarithmic mean of T2 relaxation time (T2LM), irreducible water saturation (Swirr), and permeability from Coates and SDR models were generated in this study. The well logs were collected from 14 wells of Western Australia (WA) within 3 offshore basins. About 80% of the data points were used for training and validation purposes and 20% of the whole data was kept as a blind set with no involvement in the training process to check the validity of the ML methods. The comparison of results shows that the Adaptive Boosting, known as AdaBoost model, has given the most impressive performance to predict CBW, FFI, permeability, T2LM, and SWirr for the blind set with R2 more than 0.9. The accuracy of the ML model for the blind dataset suggests that the approach can be used to generate NMR tool outputs with high accuracy.


2022 ◽  
Author(s):  
Ao Li ◽  
Wei Xu ◽  
Xiao Chen ◽  
Bing-Nan Yao ◽  
Jun-Tao Huo ◽  
...  

Abstract High-temperature nuclear magnetic resonance (NMR) has proven to be very useful for detecting the temperature-induced structural evolution and dynamics in melts. However, the sensitivity and precision of high-temperature NMR probes are limited. Here we report a sensitive and stable high-temperature NMR probe based on laser-heating, suitable for in situ studies of metallic melts, which can work stably at the temperature of up to 2000 K. In our design, a well-designed optical path and the use of a water-cooled copper radio-frequency (RF) coil significantly optimize the signal-to-noise ratio (S/NR) at high temperatures. Additionally, a precise temperature controlling system with an error of less than ±1 K has been designed. After temperature calibration, the temperature measurement error is controlled within ±2 K. As a performance testing, 27Al NMR spectra are measured in Zr-based metallic glass-forming liquid in situ. Results show that the S/NR reaches 45 within 90 s even when the sample's temperature is up to 1500 K and that the isothermal signal drift is better than 0.001 ppm per hour. This high-temperature NMR probe can be used to clarify some highly debated issues about metallic liquids, such as glass transition and liquid-liquid transition.


Diagnostics ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 157
Author(s):  
Ljubica Tasic ◽  
Nataša Avramović ◽  
Melissa Quintero ◽  
Danijela Stanisic ◽  
Lucas G. Martins ◽  
...  

Pediatric cancer NMR-metabonomics might be a powerful tool to discover modified biochemical pathways in tumor development, improve cancer diagnosis, and, consequently, treatment. Wilms tumor (WT) is the most common kidney tumor in young children whose genetic and epigenetic abnormalities lead to cell metabolism alterations, but, so far, investigation of metabolic pathways in WT is scarce. We aimed to explore the high-resolution magic-angle spinning nuclear magnetic resonance (HR-MAS NMR) metabonomics of WT and normal kidney (NK) samples. For this study, 14 WT and 7 NK tissue samples were obtained from the same patients and analyzed. One-dimensional and two-dimensional HR-MAS NMR spectra were processed, and the one-dimensional NMR data were analyzed using chemometrics. Chemometrics enabled us to elucidate the most significant differences between the tumor and normal tissues and to discover intrinsic metabolite alterations in WT. The metabolic differences in WT tissues were revealed by a validated PLS-DA applied on HR-MAS T2-edited 1H-NMR and were assigned to 16 metabolites, such as lipids, glucose, and branched-chain amino acids (BCAAs), among others. The WT compared to NK samples showed 13 metabolites with increased concentrations and 3 metabolites with decreased concentrations. The relative BCAA concentrations were decreased in the WT while lipids, lactate, and glutamine/glutamate showed increased levels. Sixteen tissue metabolites distinguish the analyzed WT samples and point to altered glycolysis, glutaminolysis, TCA cycle, and lipid and BCAA metabolism in WT. Significant variation in the concentrations of metabolites, such as glutamine/glutamate, lipids, lactate, and BCAAs, was observed in WT and opened up a perspective for their further study and clinical validation.


2022 ◽  
Author(s):  
Fanxin Liu ◽  
Wujun Zeng ◽  
Yinglei Wei

Abstract Nuclear Magnetic Resonance (NMR) involves the study of nuclei immersed in a static magnetic field and exposed to a second oscillating field. Nuclei have two properties; spin properties and charge properties. Pyrolysis oil is created by dry heating biomass in a reactor without oxygen to around 500 degrees Celsius and then cooling it. Pyrolysis oil is a type of tar that includes too much oxygen to be classified as a pure hydrocarbon. One of the most fundamental methods in synthetic chemistry is using NMR to verify chemical structure. In the literature, little attention has been paid to the application of NMR in the authentication of chemical structures. In this study, we present a use case of NMR to characterize pyrolysis oil and authenticate chemical structures. Results show that the elucidation of chemical compositions of bio-oil is essential for the optimization of its processing technology and exploration of its potential application.


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