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DOE OSTI2019

Surface evolution of synthetic bilgewater emulsion

Bilgewater is oil-in-water emulsion emitted from shipboard and it is a source of pollution to the ocean. Wastewater treatment needs fundamental knowledge of the bilgewater surface changes; however, limited information is available. We have reported the first surface characterization of synthetic bilgewater emulsion using time-of-flight secondary ion mass spectrometry (ToF-SIMS) coupled with optical microscopy. A NAVY synthetic bilgewater emulsion consisting of three oils and a detergent mixture is used as the synthetic bilgewater model. Both fresh and aged emulsion samples are analyzed to determine their droplet size distributions (DSDs) and surface chemical compositions. Our results show that fresh emulsions are largely mono-modal with hydrocarbon fragments as the main surface composition. Aged emulsions are also monomodal with slightly larger size. Both SIMS spectral comparison and Principal Component Analysis (PCA) show that some surfactant components appear on the fresh emulsion surface immediately while larger molecular weight components appear at the aged bilge droplet surface. Our results indicate that the oil-water interface evolves after emulsion droplet forms. More importantly, surface evolution not only changes the bilgewater DSD but also surface chemical composition and reactivity.

bilgewater, droplet size distribution, ToF-SIMS, s↗

DOE OSTI2025

Chemistry imaging and distribution analysis of rare earth elements in coal using LIBS and LA-ICP-MS instruments

Currently, demand for rare earth elements (REEs) increased significantly. Coal is actively evaluated as potential economic sources for extraction of REEs. Here, in this work, laser-induced breakdown spectroscopy (LIBS) was evaluated for rapid estimation of REEs content and their distribution in the natural coal samples. The results were compared with similar laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MS) measurements. Thirteen coal samples (nine standard samples and five natural samples) were used in this study. Powder samples were pressed into pellets while coal chunks were directly ablated for data recording. Pellets of the powder standard samples were used to optimize the data acquisition system and then data recorded with this optimized system was used to identify the proper data acquisition and analysis models. After establishing the proper data acquisition system and analysis model using the standard samples, natural coal samples in powder form and their chunks were utilized to record LIBS and LA-ICP-MS spectra. Multivariate calibration models were developed using four of the natural samples, which were evaluated by predicting the REE content in the fifth sample. Principal component analysis was performed on the LIBS data obtained from the natural samples and it classified all the samples with high accuracy. Two-dimensional (2D) elemental mapping on coal chunk samples was also performed using both LIBS and LA-ICP-MS to study the distribution of REEs in the samples. The resulting elemental images and their correlations can be used to infer mineral distributions.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2025

Silver diamine fluoride differentially affects dentin and hypomineralized enamel permeabilities

OBJECTIVES: To investigate the physicochemical effect of silver diamine fluoride (SDF) by correlating permeability with mineral density and elemental composition of hypomineralized enamel and carious dentin. METHODS: Enamel and dentin from human carious primary teeth with and without SDF treatment in-vivo, and hypomineralized enamel from permanent molars with and without SDF treatment in-vitro were scanned using micro X-ray computed tomography. Spatial maps of biometals (calcium, zinc), phosphorus, and silver were generated using X-ray fluorescence microprobe. Permeabilities were computed using Porous Microstructure Analysis software. RESULTS: The intrinsic permeability of SDF-treated carious dentin was 14.3 % lower than untreated sound dentin (6.39e-15 ± 3.01e-15 m² vs 7.46e-15 ± 1.82e-15 m²; P < 0.0001), while untreated carious dentin was 98.4 % higher (1.48e-14 ± 7.11e-15 m²; P < 0.0001). SDF-treated and untreated transparent dentin showed similar reduced permeabilities (75.6 % and 78.4 % lower than untreated sound dentin, respectively; P = 0.93). Severely hypomineralized enamel showed permeability reaching 108.1 % of adjacent sound dentin (5.71e-15 ± 2.04e-15 m² vs 5.28e-15 ± 1.30e-15 m²; P = 0.1409) and was significantly higher than mildly hypomineralized enamel (1.39e-15 ± 1.04e-15 m²; P < 0.0001). SDF treatment did not significantly impact the permeability of severely hypomineralized enamel (12.4 % reduction; P = 0.07). Principal component regression identified Zn level as a significant effector of tissue permeabilities in carious primary teeth (P < 0.0001). SIGNIFICANCE: This study introduces a computational method to measure dental tissue permeability, and demonstrates that SDF significantly reduces permeability in carious dentin but not intact hypomineralized enamel. The study reveals biometal Zn localization can alter dentin and enamel permeabilities, providing new insights into pathobiological mechanisms underlying caries and hypomineralization.

Chou, Conrad↗

DOE OSTI2022

Chemical mixture exposure patterns and obesity among U.S. adults in NHANES 2005–2012

The effect of chemical exposure on obesity has raised great concerns. Real-world chemical exposure always imposes mixture impacts, however their exposure patterns and the corresponding associations with obesity have not been fully evaluated. To discover obesity-related mixed chemical exposure patterns in the general U.S. population. Sparse Decompositional Regression (SDR), a model adapted from sparse representation learning technique, was developed to identify exposure patterns of chemical mixtures with exclusion (non-targeted model) and inclusion (targeted model) of health outcomes. We assessed the relationships between the identified chemical mixture patterns and obesity-related indexes. We also conducted a comprehensive evaluation of this SDR model by comparing to the existing models, including generalized linear regression model (GLM), principal component analysis (PCA), and Bayesian kernel machine regression (BKMR). Eight core exposure patterns were identified using the non-targeted SDR model. Patterns of high levels of MEP, high levels of naphthalene metabolites (ΣOH-Nap), and a pattern of high exposure levels of MCOP, MCNP, and MCPP were positively associated with obesity. Patterns of high levels of BP3, and a pattern of higher mixed levels of MPB, PPB, and MEP were found to have negative associations. Associations were strengthened using the targeted SDR model. In the single chemical analysis by GLM, BP3, MBP, PPB, MCOP, and MCNP showed significant associations with obesity or body indexes. The SDR model exceeded the performance of PCA in pattern identification. Both SDR and BKMR identified a positive contribution of ΣOH-Nap and MCOP, as well as a negative contribution of BP3 and PPB to obesity. Our study identified five core exposure patterns of chemical mixtures significantly associated with obesity using the newly developed SDR model. The SDR model could open a new avenue for assessing health effects of environmental mixture contaminants.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experiments

The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physical faults (e.g., HVAC -heating, ventilation, and air-conditioning faults) may have similar adverse impacts as those from the cyber-attacks on building energy systems, such as occupant discomfort, energy wastage, and equipment downtime. However, current physical behavior-based anomaly detection methods fail to differentiate between cyber-attacks and physical faults in building energy systems. Moreover, the challenge in collecting real-world threat data with ground truth has led researchers to rely on numerical models with user-defined assumptions, which may not accurately reflect real-world conditions due to the lack of in-situ experimental datasets. To address these challenges and gaps, this paper presents a flexible hardware-in-the-loop (HIL) testbed for generating cyber-attack and physical fault datasets and demonstrating threat detection algorithms in a real building automation system (BAS) environment. This testbed combines hardware (i.e., real BAS with local HVAC controllers and a physical network) with software (i.e., high-fidelity models to represent behaviors of building envelope and HVAC energy systems), enabling emulations of realistic threats. Five HIL experiments, including one baseline without any threats, two with physical faults, and two with cyber-attacks, were conducted to generate datasets containing detailed network traffic and system states. A joint classification framework, incorporating a network analyzer and a physical HVAC fault detector, was proposed to automatically detect cyber-physical abnormalities on BAS at both the network and the physical HVAC levels. The network analyzer comprises a conditional random fields (CRF) based command validator and a statistics-based detection strategy. The fault detector employs a weather and schedule-based pattern matching and feature-based principal component analysis (WPM-FPCA) method. Evaluation of the classification using four metrics from the multi-class confusion matrix revealed an average accuracy of 90.2%, recall of 89.7%, precision of 88.5% and F1-score of 89.2%. Finally, these results demonstrate that the proposed joint classification framework can effectively differentiate between specific types of cyber-attacks (e.g., device reinitialization attack, network Denial-of-Service attack) and physical faults (e.g., air handling unit operational fault, cooling coil valve stuck) in real time for improved building energy management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Data analytics for leak detection in a subcritical boiler

For decades, boiler leaks have been the leading cause of forced outages in the coal-fired unit. The leak occurrences are currently escalating since the existing plants must satisfy faster-ramping rates to support grid operation. Data analytics including Principal Component Analysis, Canonical Variate, and Fisher Discriminant Analysis were combined for detecting and characterizing the leak in a commercial 650 MW subcritical coal-fired power plant. The combined approach was shown to be highly effective in the fault investigation that would not have been easily achieved by an individual technique. The variability in both training and validation datasets was first evaluated using PCA. Then, the CV-FDA was employed to discriminate among faults, and to categorize the processed data into two main groups: no-leak (0) and leak (1), providing the timeframe and location of the leak occurrence. Furthermore, about 8,014 observations from 81 process variables were initially included in the calculation, while the variable count was reduced to 4 with less than 1% misclassification rate in total observations. Finally, the leak was isolated in the waterwall section. Thus, the outcome of this research may provide early detection and isolation of faulty operations in the coal-fired power plant that involves a considerable number of process variables.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2021

Distribution of rare earth elements and other critical elements in beneficiated Pennsylvania anthracites

The Pennsylvania Anthracite Fields are in a complex tectonic and metamorphic terrain, historically hosting one of the largest concentrations of coal mining in the USA. Anthracite mining now largely consists of the surface mining of the pillars remaining from the prime years of underground mining. The geochemical study of the sized coal products and the refuse (largely rock) from three preparation plants (breakers) demonstrates that Principal components analysis (PCA) of select major oxide, minor element, and rare earth elements illustrates some differentiation among the products from the individual plants. The rock samples, with abundant quartz and metamorphic Al-Si minerals and with a lower ash-basis REE concentration than the coals, were distinctly separated from the coal samples on the PCA plots. Plots of Gd N /Gd N * vs. Eu N /Eu N * and Ce N /Ce N * vs. Eu N / Eu N * showed differentiation between the individual suites of coals showed that the refuse samples had distinct REE distributions compared to the associated coals. Further, several minor and trace elements show enrichments in the coal samples. Lithium, with concentrations of up to 314 ppm on an ash basis, is among the most promising of the critical elements, exceeding the enrichment of the REY and Sc.

58 GEOSCIENCES↗

DOE OSTI2022

Past fires and post-fire impacts reconstructed from a southwest Australian stalagmite

Speleothem records of past environmental change provide an important opportunity to explore fire frequency and intensity in the past, and the antecedent climatic conditions leading to fire events. Here, fire sensitive geochemical signals in a stalagmite from Yonderup Cave, a shallow cave in Western Australia, are compared to well-documented wildfire events that occurred in recent decades. The results are extended to identify wildfires during the growth interval of the speleothem (1760 CE–2005 CE). Principal component analysis of the stalagmite time-series revealed distinct peaks in a combination of phosphorus and metal (aluminium, zinc, copper and lead) concentrations in response to known fire events, which are interpreted to have come from ash. Varying responses in the geochemical signal in the pre- and post-European colonisation period are likely linked to changes in land management, fire frequency and fire intensities. Of note is what we infer to be a particularly intense fire event concluded to have occurred in 1897 ± 5 CE, which climate sensitive proxies (δ18O and bedrock-derived elements) indicate was preceded by a multi-decadal dry period that began in the late 1860s. The intensity of the deduced fire event is supported by a peak in P that is 6.3 times higher than elsewhere in the speleothem, accompanied by a peak in Zn. It was also inferred that changes to the surface-cave hydrology occurred as a result of this event, indicated by increased input of colloidal organic material onto the stalagmite that was interpreted to be caused by shallow karst bedrock fracturing from the heat-induced deformation that is observed after intense fires. These findings show the potential for speleothems to provide dated records of fire intensity and recurrence intervals. Further development could lead to a better understanding of the climate-fire relationship and the effects of land-management practices on wildfire frequency and intensity.

58 GEOSCIENCES↗

DOE OSTI2025

Optical vibrational spectroscopic signatures of ammonium diuranate process parameters

Ammonium diuranate (ADU) is commonly encountered in the nuclear fuel cycle; however, previous investigations have shown that ADU is a complex mixture of distinct compounds. Moreover, production parameters are known to heavily influence the composition of the resulting ADU. Here, we examine four samples of ADU prepared at Oak Ridge National Laboratory (ORNL), and one sample of ADU made at Pacific Northwest National Laboratory (PNNL), with the goal of further characterizing and elucidating the effect of processing parameters such as stir rate, strike direction, and temperature on material composition. Process parameters during ADU precipitation at ORNL and PNNL were well documented, and we relate process variables to optical vibrational spectroscopic signatures observed using Raman and infrared (IR) spectroscopy. In addition, powder X-ray diffraction (PXRD) reveals differences in the solid-phase composition of ADU precipitates, but we find that the primary phase is similar to the uranyl oxyhydroxyhydrate mineral metaschoepite. Despite the significant phase contributions of a metaschoepite-like phase, spectroscopic evidence of both nitrate and ammonium are observed for all samples. To gain a more holistic understanding of spectroscopic features of process parameters in ADU, principal component analysis (PCA) is employed and results in observable signatures that relate to the stir rate used during synthesis. These results provide further information about the process-dependence of ADU precipitate composition.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Using real-time data analysis to conduct next-generation synchrotron fatigue studies

Next-generation experimental techniques, like high energy X-ray diffraction microscopy (HEDM), usher in new opportunities to collect the grain-scale data necessary for understanding the evolving processes that drive fatigue failure. In this study, we present a framework for monitoring the evolution of a deforming polycrystal, in real-time, by applying principal component analysis (PCA) to raw X-ray diffraction image data. We applied this framework to inform in-situ HEDM measurements of a cyclically loaded Inconel-718 superalloy. Further, we discovered correlations between PCA of the diffraction data and the physical processes in the polycrystal. Lastly, we discuss extending this framework in future HEDM fatigue studies.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Aerosol emissions from water-lean solvents for post-combustion CO 2 capture

Advanced water-lean solvents (WLS) for post-combustion CO 2 capture have been gaining interest due to their ability to reduce the parasitic penalty from energy needed for solvent regeneration. Commercial implementation of these novel CO 2 capture technologies hinges on successful control of amine emissions. RTI conducted a parametric study of fundamental and operational variables influence on overall amine aerosol and vapor emissions from our water-lean solvent eCO 2 Sol™ using our 6-kW equivalent bench-scale gas absorption system. The parametric testing used a simulated flue gas with 15 % CO 2 , 2.3–4.2 % H 2 O, and 0–6 ppm sulfite (SO 3 ) to examine the impact of the presence of aerosols to the capture performance and amine emissions from the system. The SO 3 reacts with water in the flue gas to create H 2 SO 4 , which forms liquid aerosol droplets and provide nucleation sites for growth of aerosols. Scanning Mobility Particle Sizer and Aerodynamic Particle Sizer instruments monitored the aerosol particle size distribution. Parametric testing results suggested that the presence of the aerosols in the flue gas could increase the overall amine emissions by 10X compared to the baseline emissions from WLS’s vapor pressure. Principal component analysis (PCA) and projection to latent squares (PLS) developed models to predict the aerosol-based amine emissions from process data. The predictive PLS model had a correlation coefficient (Q 2 ) of 0.92 and could predict the aerosol-based emissions from the NAS process with ±15 % accuracy (average absolute deviation, AAD). The PLS regression model also identified key variables affecting aerosol-based emissions from WLS.

42 ENGINEERING↗

DOE OSTI2021

A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media

This paper presents a new deep learning data-driven model for predicting structure dependent pore-fluid velocity fields in rock. The model is based on a Convolutional Auto-Encoder (CAE) artificial neural network capable of learning from image data generated by direct numerical simulations of fluid flow through pore-structures, such as by Lattice Boltzmann or molecular dynamics methods. The main novelty of the model in comparison to previous CAE-based data-driven approaches consists of three parts. The first is a methodology for decomposing the full-domain of the porous media into sub-regions, or “sub-domains”, in order to reduce the overall size of the CAE, batch process the sub-domains in parallel, and enable the CAE to learn local and generalizable nonlinear mappings of pore-fluid velocities. The second consists of embedding the finite difference solutions of the incompressible Navier-Stokes and continuity equations into convolutional layers prior to the CAE in order to provide the CAE with knowledge of fluid dynamics physics (PhyFlow). The third main novelty is that the training of the CAE is regularized with a hierarchical loss function that encourages the learning of fluid flow patterns (in a way similar to ranked modes in principal component analysis), ranking from most to least important. This is shown to increase the stability in learning, reduce over-fitting, and promote interpretability of the CAE neural network layers (HierCAE). The comprehensive new data-driven model, which we call the PhyFlow-HierCAE model, is shown to exhibit improved accuracy and generalizability of flow field predictions over conventional CAE models, attributable to the embedded physical knowledge and the hierarchical regularization, as well as realize orders of magnitude speed-ups in computation times as a surrogate for the direct numerical simulations. Examples of training and forward predictions on unseen pore-structures are provided and evaluated for data from Lattice Boltzmann and molecular dynamics simulations of pore-fluid flow. The model is shown to be a fast and accurate emulator (or “surrogate”) for predicting effective permeability of unseen pore-structures based on learning from relatively small direct numerical simulation datasets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Cantor-derived medium-entropy alloys: bridging the gap between traditional metallic and high-entropy alloys

The year 2004 marked the beginning of a new era in the design of metallic materials, as the concept of multiple principal component alloys, commonly known as High-Entropy Alloys (HEAs), was proposed by Cantor and Yeh. The unexpected single-phase microstructure, instead of the expected brittle intermetallic compounds, was attributed to the large entropy of mixing and immediately caught the attention of the scientific community. Today, HEAs are considered important advanced materials and a broad range of alloys using nominally the same design principle have been investigated. Despite that, the CrMnFeCoNi (Cantor) alloy stands out as the most successful HEA due to its outstanding mechanical properties and microstructure. In this scenario, variants of the Cantor alloy, named medium-entropy alloys (MEAs), are gaining significant interest as they display a better industrial potential than both HEAs and traditional alloys. These variants of the Cantor alloy with only three or four main elements result in 15 possible combinations. The microstructure of these alloys is discussed in terms of advanced characterization as well as thermodynamic parameters and computational simulation. Their phase stability is addressed over a wide range of temperatures and strain rates. The mechanical properties, especially the fracture toughness, of the CrFeCoNi and CrCoNi alloys have been reported to be even superior to those of the Cantor alloy and most modern engineering alloys. This is associated with the formation of a continuous sequence of strengthening mechanisms, including hierarchical twin networks, which serve to prolong the strain hardening. The present article reviews and critically assesses, for the first time, recent advances in these Cantor-derived MEAs.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems

Here we present a data-driven method for monitoring machine status in manufacturing processes. Audio and vibration data from precision machining are used for inference in two operating scenarios: (a) variable machine health states (anomaly detection); and (b) settings of machine operation (state estimation). Audio and vibration signals are first processed through Fast Fourier Transform and Principal Component Analysis to extract transformed and informative features. These features are then used in the training of classification and regression models for machine state monitoring. Specifically, three classifiers (K-nearest neighbors, convolutional neural networks and support vector machines) and two regressors (support vector regression and neural network regression) were explored, in terms of their accuracy in machine state prediction. It is shown that the audio and vibration signals are sufficiently rich in information about the machine that 100% state classification accuracy could be accomplished. Data fusion was also explored, showing overall superior accuracy of data-driven regression models.

42 ENGINEERING↗

DOE OSTI2021

Optical vibrational spectra and proposed crystal structure of ε-UO 3

ε-UO 3 is an exotic polymorph in the uranium trioxide system with an undetermined crystal structure and limited optical vibrational spectroscopic data. To improve understanding of this compound, we synthesize and investigate the crystal structure and optical vibrational spectra of ε-UO 3 . Infrared spectra collected for ε-UO 3 are in good agreement with previously published results, and our studies extend the available data into the low-energy (600–100 cm –1 ) regime. For the first time, Raman spectra are presented for ε-UO 3 using both 785 and 532 nm excitation wavelengths. Previous reports suggest an impurity phase may be present in ε-UO 3 produced by calcination of U 3 O 8 ; however, spectral center-of-mass calculations, principal component analyses, and Raman spectroscopic mapping employed to investigate this possibility indicate that the product of U 3 O 8 calcined in O 3 (g) in this work is likely phase-pure. A possible novel structure solution for ε-UO 3 is determined via Rietveld refinement of powder X-ray diffraction data and is triclinic, P-1, with a = 4.01 Å, b = 3.85 Å, c = 4.18 Å, and α = 98.26°, β = 90.41°, γ = 120.46° (R wp = 8.30%). The asymmetric unit of ε-UO 3 consists of U(VI) in hexagonal bipyramidal coordination with displaced equatorial oxygen. Further analysis reveals that the structure of ε-UO3 is best described by a 2 × 1 × 2 supercell structure in P-1 with a = 8.03 Å, b = 3.86 Å, c = 8.37 Å with α = 98.26°, β = 90.41°, and γ = 120.46°, although a higher-symmetry structure is possible. Optical vibrational spectroscopic and structural measurements of ε-UO 3 presented here furthers our understanding of this complex uranium oxide and clarifies the origin of reported structural similarity to U 3 O 8 .

-UO3↗

DOE OSTI2024

Characterization of Fuel Cladding Chemical Interaction on a High Burnup U-10Zr Metallic Fuel via Electron Energy Loss Spectroscopy Enhanced by Machine Learning

Fuel cladding chemical interaction (FCCI) is one of the main performance limiting factors for metallic nuclear fuels. The interaction destabilizes the martensitic microstructure and deteriorates mechanical properties of HT-9 cladding. The detection of low atomic number elements (Z<10) and overlapping of elemental peaks can be problematic in interpreting energy dispersive X-ray spectroscopy (EDS) data. Electron energy loss spectroscopy (EELS) provides precise elemental edge energy values and can detect elements with a low atomic number. This work utilizes EELS to study the distribution of lanthanides and light elements at the interaction region. The sample was prepared from the FCCI region of a U-10Zr (wt.%) solid fuel with HT-9 cladding, irradiated to a burnup of 13.2 at.%. Processing the EELS data included three major steps: 1) enhance the signal to noise ratio by denoising the spectrum with principal component analysis (PCA) method, removing background and performing deconvolution; 2) identify chemical elements with core energy loss edges; 3) confirm different phases using a popular machine learning method, K-means. This work presents qualitative assessment of lanthanides and light elements like carbon (C) and oxygen (O) enhanced by the application of machine learning algorithms. By comparing with EDS elemental maps, EELS provides higher resolution chemical maps, reveals the distribution of carbon at the interaction region supporting the formation of zirconium carbide, a rind-like microstructure feature that was proposed to mitigate the chemical interaction. Furthermore, the plasmon peak map was also found to indicate an energy shift associated with the formation of phases/compounds. K-means clustering method was used on the processed electron energy loss (EEL) spectrum to automatically reveal different phases. The resulting clustered maps from K-means clustering align well with elemental maps confirming certain phases, especially Fe-Ce and Zr-C, in the FCCI region.

EELS↗

DOE OSTI2026

Micro-photoluminescence mapping and Chemometrics for the rapid classification of rare earth materials

This article introduces advancements in chemically mapping rare earth materials using photoluminescence (PL) and chemometrics. By leveraging the high sensitivity and selectivity of PL compared to alternative optical techniques, as well as its compatibility with microscopy, we present enhanced capabilities for noninvasive material screening and characterization. Exemplary PL spectra of samarium(III) and europium(III) in oxide, nitrate, and chloride forms demonstrated the ability to extract detailed chemical information of diverse rare earth particles. Additionally, we introduced efficient PL mapping sequences capable of covering a 9 mm diameter carbon tab within minutes, which highlighted the benefits of rapid, large-area imaging. Furthermore, an integrated approach combining PL mapping with principal component analysis and a random forest classifier enabled the resolution of overlapping spectral peaks from different chemistries and provided accurate material classification. In conclusion, these advancements underscored the versatility and robustness of PL for chemically mapping rare earth materials, with the potential to support applications in mining, energy, environmental monitoring, isotope production and beyond.

Chemometrics↗

DOE OSTI2023

In-pixel AI for lossy data compression at source for X-ray detectors

Integrating neural networks for data compression directly in the Read-Out Integrated Circuits (ROICs), i.e. the pixelated front-end, would result in a significant reduction in off-chip data transfer, overcoming the I/O bottleneck. Our ROIC test chip (AI-In-Pixel-65) is designed in a 65 nm Low Power CMOS process for the readout of pixelated X-ray detectors. Each pixel consists of an analog front-end for signal processing and a 10b analog-to-digital converter operating at 100KSPS. Here, we compare two non-reconfigurable techniques, Principal Component Analysis (PCA) and an AutoEncoder (AE) as lossy data compression engines implemented within the pixelated area. The PCA algorithm achieves 50$×$ compression, adds one clock cycle latency, and results in a 21% increase in the pixel area. The AE achieves 70$×$ compression, adds 30 clock cycle latency, and results in a similar area increase.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗