Prompt Gamma Neutron Activation Analysis Research Articles

Capture enhanced neutron irradiation for the treatment of the Alzheimer Disease: Micro-nanodosimetric characterization of beta amyloid peptide samples

Alzheimer Disease (AD) is the most common cause of dementia. 46.8 million people live with AD worldwide, with numbers projected to almost double every 20 years. The etiological mechanisms underlying the neuropathological changes in AD remain unclear. The beta amyloid peptide Aβ is considered to be the main culprit of the pathological processes. NECTAR (NEutron Capture-enhanced Treatment of neurotoxic Amyloid aggRegates) project proposes an alternative and revolutionary strategy to address AD, investigating the safety, feasibility and effectiveness of a Capture-Enhanced Neutron Irradiation to structurally damage Aβ aggregates, by exploiting capture agent vectors containing B-10 and Gd-157.Among many others, one of the goals of the project is to perform calculations and measurements of microdosimetric and nanodosimetric quantities to characterize the effect of reaction products at local level and to link it to the fibrils depolymerization process. In this framework, a new low-pressure tissue-equivalent proportional counter (TEPC) was designed and developed. The system is a wall-free TEPC able to measure micro-nanometric dose deposition for different biological samples. It is characterized by two main features: i) a wall-less structure allows the charged particles produced by the sample to access the sensitive volume, where their effects are studied; ii) a trigger system enables the assessment of the number of ionizations induced in the sensitive volume related to a single primary particle for obtaining nandosimetric information.The prototype system was tested in the PGNAA (Prompt Gamma Neutron Activation Analysis) facility of the LENA reactor (Pavia) to directly study secondary particles from Aβ fibrils, loaded with Boron. Results of the micro-nanodosimetric characterization of high LET secondaries from Aβ-fibrils were compared to a reference Boron sample. For a 500 nm simulated site size, the presence of Boron in the Aβ sample greatly populates microdosimetric spectra at high lineal energy values. Instead, lower LET particles (photons, protons) from reactions on the other Aβ aggregate solution molecules populate the low lineal energy portion of the distribution.The nanodosimetric distributions highlights the presence of different field components on the nanodosimetric spectra: low LET particles (protons/electrons) produce small ionization clusters, while alpha and Lithium ions induce larger clusters, thus have a higher biological effect. The nanodosimetric measurements have been compared with Monte Carlo simulations performed with a track-structure code, showing a good agreement.This multi-level approach provides a deeper physical understanding of the mixed secondaries field coming from B-enriched target, as well as a reference for the biological effect on Aβ fibrils at nanometer level. These preliminary measurements represent the first microdosimetric and nanodosimetric characterization of the secondary mixed field directly emitted by a biological sample irradiated with neutrons.

Numerical solution of a parameter estimation problem arising in Prompt-Gamma Neutron Activation Analysis

We study a parameter estimation problem which arises from measurements with QUANTOM, a measurement facility utilizing Prompt-Gamma Neutron Activation Analysis (PGNAA) to analyze elemental masses in 200-l nuclear waste drums. The presence of neutron flux measurements in QUANTOM offers additional information for elemental composition reconstruction, motivating an alternative to the standard iterative approach. Therefore, the measurement is modelled using approximate models for neutron and photon transport. We introduce a mathematical formulation for solving the resulting parameter estimation problem of QUANTOM measurements and employ an adjoint-based method to determine the elemental composition. Uniqueness of solutions in this context is studied, and the results offer novel insights for PGNAA applications.

Online analysis of iron ore slurry using PGNAA technology with artificial neural network

Real-time analysis of metallic mineral grade and slurry concentration is significant for improving flotation efficiency and product quality. This study proposes an online detection method of ore slurry combining the Prompt Gamma Neutron Activation Analysis (PGNAA) technology and artificial neural network (ANN), which can provide mineral information rapidly and accurately. Firstly, a PGNAA analyzer based on a D-T neutron generator and a BGO detector was used to obtain a gamma-ray spectrum dataset of ore slurry samples, which was used to construct and optimize the ANN model for adaptive analysis. The evaluation metrics calculated by leave-one-out cross-validation indicated that, compared with the weighted library least squares (WLLS) approach, ANN obtained more precise and stable results, with mean absolute percentage errors of 4.66% and 2.80% for Fe grade and slurry concentration, respectively, and the highest average standard deviation of only 0.0119. Meanwhile, the analytical errors of the samples most affected by matrix effects was reduced to 0.61 times and 0.56 times of the WLLS method, respectively.

MCNP simulation and experimental study in situ low-grade copper analysis based on PGNAA

A prompt gamma neutron activation analysis (PGNAA) facility utilizing 252Cf source has been developed for in situ analysis of copper samples. Monte Carlo simulation is employed to determine optimal sizes for neutron moderator, gamma-ray shielding material, and thermal neutron absorber. Subsequently, based on the parameters optimized by MCNP, the PGNAA facility was constructed. Five sets of experimental samples containing low-grade copper concentration of 0 %, 0.5 %, 1 %, 1.5 % and 2 % are measured with the PGNAA facility. The results show that the minimum detectable concentration of copper is 0.218 %. The maximum relative deviation of copper is 8.53 %.

Effects of natural leaching on electronic properties of common lithium manganese oxide LiMn2O4

The effects of degradation and possible leaching of lithium in the natural conditions were tested by subjecting LiMn2O4 to 3-month degradation in an open air environment. The starting material was characterized structurally using X-ray (XRD) and neutron (NPD) powder diffraction and found to crystallize in the nominal Fd-3m space group. The initial chemical composition was verified using X-ray photoelectron spectroscopy (XPS), electron microscopy (EDS) and neutron prompt gamma activation analysis (PGAA). The effects of degradation were studied by reinvestigating the samples using XRD, XPS and X-ray absorption (XAS). The studies did not find any chemical shift of manganese caused by the removal of Li or any statistically significant degradation of the starting composition suggesting that the spinel cathode material is resistant to possible Li removal due to medium term natural exposure.

Analysis of mixture samples of uranium dioxide and boron compounds by using thermal neutron prompt gamma activation analysis

Analysis of mixture samples of uranium dioxide and boron compounds by using thermal neutron prompt gamma activation analysis

Design of an out-of-tank neutron beam line for prompt gamma neutron activation analysis at low-power Miniature Neutron Source Reactor

Research reactors are strong and stable neutrons sources, and various researches and applications were carried out using their neutron and gamma beamlines. Some research reactors, such as Miniature Neutron Source (MNSR) Reactors, do not have beam tube in their original design, which can be considered as a disadvantage. The design and installation of beamlines can develop their applications. In this research, the design and optimization of a suitable neutron beamline for PGNAA is done at Isfahan MNSR. Designs are done to reach the appropriate neutron beam parameters and taking into account the geometrical limitations of the MNSR. Neutron beam parameters including thermal neutron flux, thermal to total neutron flux, and thermal neutron flux to gamma dose ratio are considered. For this, vertical beamlines with different diameters are simulated and the optimal diameter is determined. Also, for optimization of beam parameters, a thermal column and gamma filter are considered and optimal conditions are determined.

Design, construction, and characterization of a Prompt Gamma Neutron Activation Analysis (PGNAA) system at Isfahan MNSR

In this research, a prompt gamma neutron activation analysis (PGNAA) system is designed and constructed based on the use of a low power research reactor. For this purpose, despite the fact that this reactor did not include beam tubes, a thermal neutron beam line is installed inside the reactor tank. The extraction of the beam line from inside the tank made it possible to provide the neutron flux from the order of 106 n.cm-2.s-1. Also, because the beam line is installed in a tangential position to the reactor core, its gamma level has been minimized. Also, a suitable radiation shield is considered for the detector to minimize the background radiation and prevent radiation damage to the detector. Calculations and measurements are done in order to characterize this system, as well as spectrometry of several samples. The results of evaluations and experiments show that this system is suitable for performing PGNAA.

Void fraction prediction using prompt gamma neutron activation analysis and artificial intelligence

This paper presents the simulation of a water-gas biphasic system in annular and stratified flow regimes using a neutron beam from a241Am–Be radiation source. The objective is to evaluate the potential of prompt gamma neutron activation analysis (PGNAA) and artificial intelligence-based algorithms in flow measurements. A measurement geometry consisted of a neutron flat source with parallel emission and a spherical detector was developed using the MCNP6 code. Prompt gamma rays spectra were used as input data of a feed-forward multilayer perceptron (MLP) artificial neural network aiming to predict void fraction and to identify the investigated flow regimes. The results showed that the MLP was able to predict void fraction, for both flow regimes, with mean absolute percentage error within 1.58%. In addition, the same MLP achieved 100% of success in flow regime identification. Therefore, it is possible to combine PGNAA with MLP in order to investigate flow characteristics.

Design and optimization of thermal neutron activation device based on 5 MeV electron linear accelerator

The optimized design of a Neutron Activation Analysis (NAA) system, including Delayed Gamma NAA (DGNAA) and Prompt Gamma NAA (PGNAA), has been proposed in this research based on Mevex Linac with 5 MeV electron energy and 50 kW power as a neutron source. Based on the MCNPX 2.6 simulation, the optimized configuration contains; tungsten as an electron-photon converter, BeO as a photoneutron target, BeD2 and plexiglass as moderators, and graphite as a reflector and collimator, as well as lead as a gamma shield. The obtained thermal neutron flux at the beam port is equal to 2.06×109(#/cm2.s).In addition, using the optimized neutron beam, the detection limit has been calculated for some elements such as H-1, B-10, Na-23, Al-27, and Ti-48. The HPGe Coaxial detector has been used to measure gamma rays emitted by nuclides in the sample. By the results, the proposed system can be an appropriate solution to measure the concentration and toxicity of elements in different samples such as food, soil, and plant samples.

Feasibility study on PGNAA experiments using a prototype neutron beam at Isfahan MNSR

In this study, the feasibility study of Prompt Gamma Neutron Activation Analysis (PGNAA) experiments at Isfahan Miniature Neutron Source Reactor (MNSR) has been done using a prototype neutron beam. This neutron beam was not originally created for this purpose, but previous studies on this neutron beam have shown that it has enough neutron flux to carry out some irradiations outside the reactor core. This prototype neutron beam provided a neutron flux of order 105 n·cm-2·s-1 and a circular diameter of 5 cm for the experiments. In addition, an appropriate setup for High Purity Germanium detector (HPGe) was done at the beam outlet, where the sample is irradiated. A simple shield was also installed to prevent radiation damage to the detector. Experiments were conducted for bulk samples of salt solution with different concentrations and the prompt gamma spectrums have been obtained and analyzed.

Design of an in-tank thermal neutron beam for PGNAA application at Isfahan MNSR

Calculations have been done in order to achieve a thermal neutron beam for Prompt Gamma Neutron Activation Analysis (PGNAA) applications using Miniature Neutron Source Reactor (MNSR) as neutron source. The MNSR is a 30 kW tank-in-pool reactor and there are ten small occupied in-tank irradiation positions. Also, there are four positions in order to install or remove aluminum Regulating Rod Tubes (RRTs) that are bigger than that ten irradiation positions. In this study, the eastern RRT has been selected for more simulations of thermal neutron beam. Thermal neutron flux (φth), the ratio of thermal neutron flux to gamma dose rate (φthDγ), and the ratio of thermal neutron flux to total neutron flux (φthφtot) have been considered in calculations as important neutron beam parameters. Some designs have been done and simulated using MCNP code. The effect of the height of beam inlet, the effect of the different materials and thickness of moderators, and the effect of different thickness and height of gamma filters on the beam parameters at the beam outlet have been investigated. The results of different designs showed that using appropriate heights and thicknesses, by using bismuth as gamma filter and heavy water as moderator, φth, φthφtot and φthDγ, are 1.64E + 06n.cm−2.s−1, 0.789, and 4.36E + 05n.cm−2.μSv−1 respectively. Also, by using bismuth as gamma filter and graphite as moderator, these parameters are 1.57E + 06n.cm−2.s−1, 0.761, and 4.69E + 05E + 05n.cm−2.μSv−1, respectively.

A conceptual strategy for effective bulk ore sorting of copper porphyries: Exploiting the synergy between two sensor technologies

Bulk ore sorting is a preconcentration technique that may address the operational and environmental challenges of extracting low-grade ore deposits, such as copper porphyries. The technique allows for the early detection and diversion of sub-economic material by sensor-diverter systems before milling, thereby improving mill feed quality and reducing environmental impact. Grade heterogeneity in ore and sensor capability to measure grade accurately are prerequisites to applying bulk ore sorting. The technical synergy between the responses of multiple bulk ore sorting technologies can aid in taking full advantage of the grade heterogeneity existing in sorter feed while providing accurate and precise grade measurements. This study is aimed at quantitatively demonstrating the potential benefits of a conceptual strategy of integrating the responses from two sensor technologies for bulk ore sorting of copper porphyry deposits. Results show that a more significant enhancement in the Net Smelter Return (NSR) of the ore can be achieved by the simultaneous employment of the magnetic resonance (MR) and prompt gamma neutron activation analysis (PGNAA) sensors compared to the bulk ore sorting scenarios in which the sensor technologies are utilized separately. Results are encouraging and can prompt the collaboration of sensor manufacturers and mining companies to prove the actual value of the presented concept by performing on-site trials.

PGNAA Spectral Classification of Metal With Density Estimations

For environmental, sustainable economic and political reasons, recycling processes are becoming increasingly important, aiming at a much higher use of secondary raw materials. Currently, for the copper and aluminium industries, no method for the non-destructive online analysis of heterogeneous materials are available. The Prompt Gamma Neutron Activation Analysis (PGNAA) has the potential to overcome this challenge. A difficulty when using PGNAA for online classification arises from the small amount of noisy data, due to short-term measurements. In this case, classical evaluation methods using detailed peak by peak analysis fail. Therefore, we propose to view spectral data as probability distributions. Then, we can classify material using maximum log-likelihood with respect to kernel density estimation and use discrete sampling to optimize hyperparameters. For measurements of pure aluminium alloys we achieve near perfect classification of aluminium alloys under 0.25 second.

Characterization of thermal neutron distribution of an Am–Be neutron source setup by CdZnTe detector

Owing to the developments in prompt gamma neutron activation analysis (PGNAA) and prompt gamma ray activation imaging (PGAI), it is necessary to develop an online thermal neutron distribution measurement method. The CdZnTe detector is considered as an alternative thermal neutron detector because of its high thermal neutron capture cross section. In this study, the thermal neutron field of an 241Am–Be neutron source was determined by CdZnTe detector. The intrinsic neutron detection efficiency of CdZnTe detector was calculated by using indium foil activation, and the value was 3.65%. Then the characteristics of neutron source were conducted with the calibrated CdZnTe detector. The thermal neutron fluxes in front of beam port were measured at several distances ranging from 0 cm up to 28 cm. Thermal neutron field at distances of 1 cm and 5 cm were also measured. The experimental data were then compared with Monte Carlo simulation. The results demonstrated that the simulated data agree well with experimental measurements.

Development of the neutron salt-meter RANS-μ for non-destructive inspection of concrete structure at on-site use

We have started the development of a transportable neutron salt-meter, we call it RANS-μ, combining a 252Cf neutron source and a prompt gamma neutron activation analysis. Trials of chloride detection measurement with RANS-μ were performed outdoor using removed bridges damaged by chloride attack at an outdoor yard in Public Works Research Institute (PWRI) and a test bridge in Fukushima Robot Test Field. For the measurement at PWRI, the results obtained by RANS-μ were compared with those of the automatic potentiometric titration by drilled powder, and then consistent results were obtained.

Identification of Pb–Zn ore under the condition of low count rate detection of slim hole based on PGNAA technology

The grade analysis of lead-zinc ore is the basis for the optimal development and utilization of deposits. In this study, a method combining Prompt Gamma Neutron Activation Analysis (PGNAA) technology and machine learning is proposed for lead-zinc mine borehole logging, which can identify lead-zinc ores of different grades and gangue in the formation, providing real-time grade information qualitatively and semi-quantitatively. Firstly, Monte Carlo simulation is used to obtain a gamma-ray spectrum data set for training and testing machine learning classification algorithms. These spectra are broadened, normalized and separated into inelastic scattering and capture spectra, and then used to fit different classifier models. When the comprehensive grade boundary of high- and low-grade ores is set to 5%, the evaluation metrics calculated by the 5-fold cross-validation show that the SVM (Support Vector Machine), KNN (K-Nearest Neighbor), GNB (Gaussian Naive Bayes) and RF (Random Forest) models can effectively distinguish lead-zinc ore from gangue. At the same time, the GNB model has achieved the optimal accuracy of 91.45% when identifying high- and low-grade ores, and the F1 score for both types of ores is greater than 0.9.

Ultra-repeatability measurement of calorific value of coal by NIRS-XRF.

Calorific value is an important indicator to evaluate the comprehensive quality of coal, and its real-time and rapid analysis is of great significance for optimizing the coal blending process and improving boiler combustion efficiency. Traditional assays are time-consuming, and prompt gamma neutron activation analysis (PGNAA) and laser-induced breakdown spectroscopy (LIBS) have certain limitations. In this paper, a novel technique for ultra-repeatability measurement of coal calorific value by combining near-infrared spectroscopy (NIRS) and X-ray fluorescence (XRF) is proposed. In this NIRS-XRF technology, the former can stably measure organic components such as C-H and N-H that are positively correlated with the calorific value, while the latter can stably measure inorganic elements such as Na, Al, Si, Ca, Fe, and Mn that are negatively correlated with the calorific value. The combination of the two can greatly improve the measurement repeatability of coal calorific value. In the quantitative analysis algorithm, a holistic-segmented prediction model based on partial least squares (PLS) is proposed, that is, the holistic model is used to roughly predict the calorific value and determine the segment accordingly, and then the corresponding segmented model is used to accurately predict the calorific value. The experimental results show that the root mean square error of prediction (RMSEP), the average relative error (ARE), and the standard deviation (SD) of this method for predicting the calorific value of coal are 0.71 MJ kg-1, 1.18% and 0.07 MJ kg-1 respectively. The measurement repeatability meets the requirements of the Chinese national standard. This calorific value measurement technology based on NIRS-XRF is safe, fast, and stable, providing a new way to optimize and control the utilization process of coal in coal washing plants, power plants, coking, and other industries.

Prompt gamma activation analysis as a means for quantitative, non-destructive, non-invasive measurement of mercury in steel from oil and gas operations

Prompt Gamma Neutron Activation Analysis (PGAA or PGNAA) is a non-destructive elemental analysis method potentially applicable for the non-invasive measurement of wall-bound mercury (Hg) deposited at trace levels on the process wetted surface of steel pipe and vessels in oil and gas production and processing. Total wall-bound mercury, on 17 mercury-impacted steel coupon samples was measured by two non-destructive methods: PGAA and XRF (X-ray Fluorescence spectroscopy). Following the non-destructive measurement methods, the samples were digested and analyzed by an Atomic Fluorescence Spectroscopy method based on the principles of EPA 1631 method (Acid Digest-AFS), generally considered state of the art for accurate mercury in steel measurements. PGAA and XRF results were compared to those from the modified EPA 1631 method. Comparative analysis of the results indicated that the PGAA measurements exhibited a trend relatively like that of the acid digest-AFS method measurements. Measurement accuracy, as determined by comparison to a “standard method” (Modified EPA 1631) was not yet adequate for quantitative measurement. However, the consistency in the tracking of the PGAA results with results from the “standard method” along with derivation of a self-attenuation correction factor suggests the potential for improved accuracy and correlation. Details of the intercomparison are provided herein.

Lower-Weight Landmine Detection Under Various Buried Conditions Based on PGNAA and Machine Learning

The performance of a prompt gamma neutron activation analysis (PGNAA) system for lower-weight landmine detection is investigated in this study. A total of 2880 characteristic gamma-ray spectra of 10 buried samples (five explosives and five nonexplosives), within a weight range of 0.01 to 10 kg and a hidden depth of 2.5 to 15 cm, under 0%, 10%, and 20% soil moisture conditions, were generated using Monte Carlo N-Particle Code 5 (MCNP5). The conventional characteristic peak analysis method was not applicable to lower-weight sample detection. The discrimination accuracy was acceptable only under 0% soil moisture when explosives exceeded 2 kg with the discrimination accuracy exceeding 80%. Four machine learning models, including radial basis function (RBF) neural network, fully connected neural network, XGBoost, and LightGBM, were used to perform whole-spectrum analysis, and better performance was demonstrated. The discrimination accuracy exceeded 90% in most cases, and the RBF neural network was demonstrated to be the best performance (96.6% for explosives and 95.1% for nonexplosives). All four of these models were insensitive to soil moisture. The minimum detectable weight of 0.02 kg for the simulation data provided valuable reference for experimental applications. These results indicate that machine learning was an effective method for lower-weight landmine detection using PGNAA under complicated conditions.