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At least 253 records · Page 14

Measurement of hybrid rocket solid fuel regression rate for a slab burner using deep learning

This study presents an imaging-based deep learning tool to measure the fuel regression rate in a 2D slab burner experiment for hybrid rocket fuels. The slab burner experiment is designed to verify mechanistic models of reacting boundary layer combustion in hybrid rockets by the measurement of fuel regression rates. A DSLR camera with a high intensity flash is used to capture images throughout the burn and the images are then used to find the fuel boundary to calculate the regression rate. A U-net convolutional neural network architecture is explored to segment the fuel from the experimental images. Here, a Monte-Carlo Dropout process is used to quantify the regression rate uncertainty produced from the network. The U-net computed regression rates are compared with values from other techniques from literature and show error less than 10%. An oxidizer flux dependency study is performed and shows the U-net predictions of regression rates are accurate and independent of the oxidizer flux, when the images in the training set are not over-saturated. Training with monochrome images is explored and is not successful at predicting the fuel regression rate from images with high noise. The network is superior at filtering out noise introduced by soot, pitting, and wax deposition on the chamber glass as well as the flame when compared to traditional image processing techniques, such as threshold binary conversion and spatial filtering. U-net consistently provides low error image segmentations to allow accurate computation of the regression rate of the fuel.

42 ENGINEERING↗

Selective oxidation and nickel enrichment hinders the repassivation kinetics of multi-principal element alloy surfaces

Robust and sustained corrosion resistance in multi-principal element alloys (MPEA) requires rapid repassivation, i.e. regrowth of the passive layer once it is damaged or destroyed at the surface. In this study, we show that the repassivation of Al 0.1 CrCoFeNi MPEA in 0.6 M NaCl solution is hindered at pH of ~ 2.4 - 6.8 due to the formation of a Ni-enriched subsurface layer as a result of selective oxidation and dissolution of several principal elements, which can be fully restored at pH of ~ 14 from the oxidation of all principal elements. Specifically, surface characterization via X-ray photoelectron spectroscopy (XPS), high-angle annular dark-field (HAADF) imaging in scanning transmission electron microscopy (STEM), and atom probe tomography (APT), are coupled with density functional theory (DFT) calculations to determine surface composition, oxidation state, and electron work function to uncover the structural origin of the pH-dependent repassivation mechanisms. It was found that selective oxidation of Cr, Co, and Fe in the acidic to neutral solutions altered the surface composition to be significantly enriched in Ni as compared to the bulk. Once the original passive film is destroyed locally by either pitting or tribocorrosion, this altered surface composition exhibited a much poorer repassivation capability due to the increased electron work function and reduced surface reactivity at higher Ni concentration. Finally, these understandings could shed light on the future compositional design of non-equiatomic MPEAs towards sustained repassivation and corrosion resistance over a wide pH range.

36 MATERIALS SCIENCE↗

Thermal Analysis of a Solid Particle Light-Trapping Planar Cavity Receiver Using Computational Fluid Dynamics

Concentrated solar power (CSP) is one of the most effective ways of harnessing solar power to create efficient, durable, and resilient energy systems. This study entails thermal modeling and analysis of a novel central tower receiver configuration. This receiver uses solid particles as the heat transfer fluid (HTF), a promising option for third-generation CSP systems. The configuration considered here is the light-trapping planar cavity receiver (LTPCR) introduced by the National Renewable Energy Laboratory. While heat transfer studies of various LTPCR subsystems have been done, system-level thermal analysis of the LTPCR receiver has not been attempted. This study also presents important sensitivity analyses of the operating parameters of the CSP system, which can help guide the design of future central tower receivers. This study employs Ansys Fluent as a computational fluid dynamics (CFD) tool to model fluid dynamics and heat transfer in the receiver, intending to quantify its thermal performance. The model seamlessly integrates Monte Carlo ray tracing data, which generates absorbed solar flux profiles from the heliostat field design, with the heat transfer characteristics of the fluidized particle bed. This unified model is designed to accurately predict the thermal behavior of the LTPCR. Analysis of preliminary results reveals that the primary loss mechanisms are radiative and natural convective losses, in that order. Based on observations from a baseline case, several strategies are suggested and numerically tested. These solutions include selective cooling of high-temperature regions and manipulation of particle bed parameters. Selective cooling of high-temperature regions reduced the peak temperature by 151 degrees C and decreased thermal losses by 0.9%. Improving the particle-wall heat transfer coefficient (P-W HTC) of the particle bed decreased the thermal losses by 1.7% and decreased the peak temperatures by 57 degrees C. Decreasing the particle inlet temperature (PIT) also reduced thermal losses by 3.5% and decreased peak temperatures by 29 degrees C. Compounding these strategies improved the thermal losses of the receiver from 13.5% in the baseline case to 7.5%. Additionally, the study explores the variation in thermal performance across different locations of the receiver, where a variation of thermal losses from 12.9% to 17.3% is found. This allows a comprehensive evaluation of potential improvements in efficiency and temperature management.

computational fluid dynamics↗

Photometric redshifts for the S-PLUS Survey: Is machine learning up to the task?

The Southern Photometric Local Universe Survey (S-PLUS) is a novel project that aims to map the Southern Hemisphere using a twelve filter system, comprising five broad-band SDSS-like filters and seven narrow-band filters optimized for important stellar features in the local universe. In this paper we use the photometry and morphological information from the first S-PLUS data release (S-PLUS DR1) cross-matched to unWISE data and spectroscopic redshifts from Sloan Digital Sky Survey DR15. We explore three different machine learning methods (Gaussian Processes with GPz and two Deep Learning models made with TensorFlow) and compare them with the currently used template-fitting method in the S-PLUS DR1 to address whether machine learning methods can take advantage of the twelve filter system for photometric redshift prediction. Using tests for accuracy for both single-point estimates such as the calculation of the scatter, bias, and outlier fraction, and probability distribution functions (PDFs) such as the Probability Integral Transform (PIT), the Continuous Ranked Probability Score (CRPS) and the Odds distribution, we conclude that a deep-learning method using a combination of a Bayesian Neural Network and a Mixture Density Network offers the most accurate photometric redshifts for the current test sample. In conclusion, it achieves single-point photometric redshifts with scatter (σ NMAD ) of 0.023, normalized bias of -0.001, and outlier fraction of 0.64% for galaxies with r_auto magnitudes between 16 and 21.

79 ASTRONOMY AND ASTROPHYSICS↗

Direct observation of C 3 S particle dissolution using fast nano X-ray computed tomography

Tricalcium silicate (C 3 S) occupies 50 % to 70 % of ordinary portland cement (OPC) by mass and it is an important component affecting the hydration of OPC [1], [2], [3], [4], [5], [6], [7]. Generally, the hydration of C 3 S is described by two processes: the dissolution of C 3 S particles and the precipitation of hydration products. While it is understood that the dissolution rates of C 3 S vary with time, more precise measurements are needed to understand this process. Many mechanisms have been proposed to explain the time-evolving dissolution rates of C 3 S [2]. The metastable barrier hypothesis suggests that a thin metastable layer of hydrates forms around the C 3 S particle surface and prohibits the access of grains to the aqueous solution [8], [9], [10], [11], [12]. The slow dissolution step hypothesis suggests that the increased ion concentration from the initial reaction delays the C 3 S dissolution [2], [13], [14], [15], [16], [17]. More recent publications suggest that C 3 S may react differently depending on the existence of crystallographic defects [18], [19], [20]. Etch pits are thought to open on the particle surface during the initial reaction and this contributes to the C 3 S dissolution [21], [22]. As hydrates precipitate and cover these highly reactive surfaces, hydration slows down and the induction period starts [18], [23], [24], [25], [26]. Many experiments have been conducted to test the aforementioned mechanisms. Some hydration studies utilize bulk measurements, such as isothermal calorimetry [27], [28], [29], pore solution analysis [30], quasi-elastic neutron scattering [31], and nuclear magnetic resonance spectroscopy [32], [33]. One limitation of these measurements is that they do not provide direct and detailed information on the individual C 3 S particles. Some other studies utilize imaging techniques, such as scanning electron microscopy (SEM) [34], [35], [36] and transmission electron microscopy (TEM) [37]. However, SEM/TEM cannot track the evolution of individual particles throughout hydration [34], [35], [38], [39], [40] and they do not give insights into the microstructure of materials before hydration [34], [35], [39]. This makes it challenging to draw strong conclusions from only SEM or TEM observations. Synchrotron X-ray tomography techniques have been used more broadly in recent years to study cement hydration. They are not only non-destructive but also able to image a sample in full 3D with resolutions that can reach from micron to nanoscale. Nano computed tomography (nCT) is one technique that has been applied to study cement hydration at the nanoscale [26], [41]. A typical nCT can reach a pixel size from 15 to 65 nm, providing enough detail for observing features <1 μm. However, nCT often takes >0.5 h to finish one scan. This makes the application of this technique on continuous scans for in-situ observations challenging. Fast X-ray computerized tomography (fCT) is another technique that has shown success in studying the time-evolving cement microstructures [20], [42], [43], [44], [45], [46], [47], [48], [49]. Due to the high flux of the X-ray beam from the synchrotron ring, fCT allows a scan to be captured within 1 min at a pixel size of 1 μm. This allows a paste sample to be continuously scanned during the hydration process. However, the micron-sized resolutions limit does not provide detailed insights for particles <5 μm [20], [49]. Fortunately, the combination of nCT and fCT has allowed the development of fast nano X-ray computed tomography (fnCT). fnCT can capture a 3D data set in <2 min at a pixel size of 50 nm. This makes this procedure an exciting method to evaluate hydrating pastes. fnCT collects multiple X-ray radiographs at various rotation angles and generates a 3D model of the scanned sample, which is also referred to as a 3D tomography [50], [51]. In one tomography, the X-ray absorptions of different components (e.g., C 3 S and hydrates) differ as functions of density and chemistry [52], [53]. These X-ray absorption contrasts can be used to extract detailed information about the 3D microstructure [26], [54], [55]. In this paper, fnCT is used to collect time-lapse tomographs of hydrating C 3 S paste from 18 min after mixing to 7 h of hydration. The bulk measurements of anhydrous C 3 S, as well as the microstructural changes of individual C 3 S particles, are directly observed, quantified, and discussed. The dissolution behavior of C 3 S particles at various size scales is systematically analyzed and compared. This work aims to find the relationship between the size of C 3 S particle sizes and their dissolution rates. This provides significant insights into the early-age hydration of C 3 S on length and time scales not previously possible. Because of the magnitude of the data and the substantial amount of observations, this work will solely focus on the change in the anhydrous particles. Changes in the hydration products will be reported in future work.

42 ENGINEERING↗

Atomic-scale identification of defects in alite

Crystallographic defects play a crucial role in cement hydration, with initial dissolution being dominated by the formation of etch pits that rely on the intersection of defects on surfaces. However, the defects present in cement particles have remained a mystery due to the lack of detailed direct observation at the atomic scale. In this study, we used scanning transmission electron microscopy to unravel the defects in alite particles at the single-atom level. Our observations identified different types of defects, including vacancies, doping, dislocations, rough surfaces, and grain boundaries. Atomic ordering in the alite crystal was further examined based on our single-atom recognition method. Our findings indicate that defects in cement particles may serve as reactive sites during the early hydration stage, facilitating the initial dissolution and providing nucleation sites for hydration products. Finally, this work provides insights into cement defect formation at the single-atom level and offers new opportunities in tuning the hydration process through defect engineering of cement particles.

36 MATERIALS SCIENCE↗

Relating storm-snow avalanche instabilities to data collected from the Differential Emissivity Imaging Disdrometer (DEID)

Storm-snow avalanches are challenging to forecast due to complex alpine terrain and during rapidly changing weather conditions. They can result in loss of lives and significant economic impact. We describe how a new device that continuously measures with high-frequency snowflake mass, size, density, and type, the Differential Emissivity Imaging Disdrometer (DEID), and show how the DEID can be used to aid avalanche forecasting when coupled with a storm-snow stability model. DEID measurements of snow accumulation, snow water equivalent (SWE), and snow density obtained during seventeen storms taken at the mid-Collins Snow-Study Plot at Alta Ski Area in Utah's Central Wasatch mountain range during winter 2020–2021 show excellent agreement with infrequent manual measurements. Additionally, two new variables, the Shape Density Index (SDI) and Complexity, are proposed and used to classify snowflake habit and estimate storm-snow shear strength. We illustrate how these DEID-derived data can be used to identify layers of concern in the storm snow such as density inversions, in real-time without digging snow pits. Furthermore, the DEID-data are used to run four variations of the SNOw Slope Stability model (SNOSS) for the storms investigated. The results are evaluated with data collected from tilt-board tests, infrasound measurements, and visual observations of avalanches. For a total fourteen storms analyzed, the DEID-driven SNOSS-modeled minimum stability index predicts the general stability of the storm-snow as indicated by observed avalanches, both natural and of unknown cause. Finally, the results provide a promising approach for nowcasting instabilities within storm-snow layers with a single instrument.

54 ENVIRONMENTAL SCIENCES↗

Corrosion characteristics of Mo and TZM alloy for plasma facing components in molten lithium at 623 K

Here, in this study, the corrosion behaviours of molybdenum (Mo) and a Mo-based alloy (TZM) were investigated using a static immersion corrosion technique. Weight loss, surface microstructure, and corrosion depth of Mo and the TZM alloy were correlated with elements. The compatibility of Mo and the TZM alloy in static liquid Li was suitable. Mo demonstrated uniform corrosion with a homogenous dissolution of free C and Mo, and TZM alloy exhibited a nonuniform corrosion behaviour with a preferential grain boundary attack caused by the selective dissolution of free C, Ti, and Zr. When the surface oxidation layer of the samples was consumed, free C from Mo and TZM diffused into molten Li to form Li 2 C 2 and then was captured by Zr, Ti, and Mo to form thermodynamically stable carbides, which resulted in the enrichment of the C layer near the sample surfaces. In addition, Ti and Zr acted as N-trappers in liquid Li; the formation of Zr and Ti nitrides resulted in the enrichment of N, Ti, and Zr elements on the surface and led to TZM corrosion increase. Thus, Zr and Ti depletion, pitting, and grain boundary corrosion were problematic for TZM under long-term exposure to liquid Li. Reducing the content of free C and nonmetallic N and increasing the amount of Ti and Zr carbides on the surface of and inside the raw TZM alloy helped improve the corrosion resistance of TZM in liquid Li.

36 MATERIALS SCIENCE↗

Corrosion characteristics of monolithic SiC materials in beryllium-bearing molten fluoride salt

In this work, chemical vapor deposited SiC single crystal and polycrystals were exposed to a static beryllium-bearing molten fluoride salt, 2LiF–BeF 2 for 500 and 1000 h at 750 °C. Overall, the single crystal and polycrystalline SiC materials experienced slight weight loss and nominal surface etching regardless of the material type. Localized pitting and nonuniform Si removal from surface regions were associated with trace metal impurities and oxygen. Cross-sectional observation revealed minimal subsurface changes in the microstructure. The corrosion damage processes were similar to those reported for molten LiF–KF–NaF salt with trace impurities, indicating that the impurities significantly affect SiC corrosion.

36 MATERIALS SCIENCE↗

Hot corrosion behavior of 304 & P91 graded composite transition joint under molten sulfate salts

A novel graded composite transition joint (GCTJ) between AISI 304 stainless steel and ASTM A335 P91 steel, has been demonstrated remarkablely superior creep performance compared to the conventional dissimilar metal weldment (DMW) under equivalent conditions. However, this advance is challenged by hot corrosion under salt deposition at elevated temperatures. Here, this study investigates the hot corrosion behavior of 304&P91 GCTJ exposed to sulfate salts at 700°C. Compared to the 304 steel, corrosion attacks initiate in the P91 triangle, where the dual-phase microstructure of ferrite and tempered martensite significantly influences pitting initiation. Anodic dissolution mainly occurs within the tempered martensite due to more vulnerable sites within the tempered martensite. With prolonged exposure, corrosion propagates across both phases, and the corrosion depth within the P91 triangle is related to the exposed surface area ratio between 304 and P91. Electrochemical analysis reveals the occurrence of galvanic corrosion between the 304 and P91, with a positive linear relationship between anodic dissolution current density (I a ) and the exposed surface area ratio between 304 and P91 in molten salts, further emphasizing the critical impact of this ratio on the corrosion severity in the P91 triangle. These findings underscore the importance of transition zone design optimization in mitigating localized corrosion.

Galvanic corrosion↗

Combined density functional theory/kinetic Monte Carlo investigation of surface morphology during cycling of Li-Cu electrodes

In this work, we employed a combined density functional theory and kinetic Monte Carlo approach in order to investigate the nanoscale behavior of lithium stripping and plating on lithium-copper nanoalloys. Density functional theory calculations were used to develop an atom-by-atom understanding of the nucleation behavior of lithium on copper surfaces with different surface morphologies. We found that nanometric pits resulted in significantly more favorable lithium deposition when compared to pristine or lightly-defective Cu surfaces, which is in agreement with previous experimental studies. We then used density functional theory and constrained ab initio molecular dynamics to develop interaction and reaction parameters for a larger-scale kinetic Monte Carlo model in order to provide insight into the stripping and plating behavior of nanostructured Li-Cu nanoalloy anodes. Substantial losses in capacity and strong indications of morphological change were observed for a wide range of Cu:Li ratios over 100 cycles. We observed a complex relationship between the nanoparticle composition and the properties of the formed solid-electrolyte interphase.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Liquid metal compatibility of pre-oxidized FeCrAl in flowing Sn

Here, a flowing Sn thermal convection loop (TCL) experiment was conducted in FeCrAlMo (Kanthal alloy APMT) tubing for 1000 h with a peak temperature of 400 °C. Oxide dispersion strengthened (ODS) Fe-(10–12)Cr-6Al and APMT specimens were exposed and all were pre-oxidized to form an external alumina scale. Unlike static exposures at 400° and 500 °C where pre-oxidation prevented dissolution, large mass losses were observed in the TCL hot leg after the specimens were cleaned in Li. The observed pitting suggests that localized failure of the pre-formed alumina surface oxide caused significant metal loss. Specimen mass gains were not observed on the cold leg implying that mass transfer did not occur and the small levels of Fe and Cr in the Sn after the experiment implies that Sn-rich phases may have formed. Post exposure room temperature tensile testing showed limited impact of the Sn exposure on the 12Cr ODS FeCrAl alloy, comparable to a 1000 h anneal in Ar at 400 °C. These results suggest that pre-oxidation is not sufficient to protect FeCrAl alloys in contact with flowing liquid Sn at 350°-400 °C.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Effects of crystallographic orientation on calcite dissolution under alkaline conditions

Here, this study investigates the dissolution of calcite’s (104) and (100) surfaces under alkaline conditions (pH 8.8–13.1) using vertical scanning interferometry under flow-through conditions. The dissolution rate of (100) surfaces decreases with increasing pH. For example, the dissolution rate at pH 8.8 is more than 20 times higher than that at pH 13.1. In contrast, the dissolution rate of (104) surfaces is far less sensitive to pH. We explain these observations as being on account of the preferential adsorption of [OH – ] on (100) surfaces, inhibiting their dissolution. Particularly, this is because Ca-surface sites on (100) surfaces are less coordinated and have a stronger tendency for [OH – ] adsorption. As a result, (100) surfaces dissolve nearly twice as fast as (104) surfaces at pH 8.8, whereas (104) surfaces dissolved ~4.5 times faster than (100) surfaces at pH 13.1; indicating an inversion in dissolution behavior. The dissolution rate of (104) surfaces at pH 13.1 is 60 % of the rate at pH 10–12 because of the formation of slow dissolving microfacets of other orientations. The rhombohedral dissolution etch-pits formed on (104) surfaces, at lower pH’s, disappeared upon exposure to solutions with pH > 12 and were replaced by protrusions. The addition of up to 4m (molality) NaCl enhances the dissolution of (104) surfaces at pH 8.8–13.1 and (100) surfaces at pH 8.8–11.0, and inhibited the dissolution of (100) surfaces at pH = 12.0–13.1. In addition, the competing adsorption between OH – and CO 3 2– species mitigates the inhibition effects of CO 3 2– on both surfaces at pH 13.1 compared to pH 11, and the dissolution of (100) surfaces is far less affected by CO 3 2– than (104) surfaces. These results provide new insights into the rates and mechanisms of how crystallographic orientations affect calcite’s dissolution at alkaline pH conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Mechanisms of dissolution from gibbsite step edges elucidated by ab initio molecular dynamics with enhanced sampling

Surface reactions underpin our collective understanding of mineral dissolution. This impacts a range of predictive geochemical models (for weathering, the fate and transport of metals) as well as industrial utilization of minerals. Yet atomistic details are rarely known due to the complex mineral/fluid interfacial environment. There is significant need to understand the mechanistic details of dissolution reactions and how they depend on surface morphology and solution conditions. This work utilizes surface pit models in conjunction with changes to solution composition that mimic pH to explore surface detachment of aluminate from gibbsite, which is a primary source of Al in soils and within the industrial processing of aluminum. Ab initio molecular dynamics simulations with enhanced sampling has been used to explore the detailed process of the detachment, the results of which indicate two potential pathways that are differentiated based upon the extent of water hydration. Here, the heights of the energy barriers depend upon the local morphology which influence the number of bridges (quasi-)simultaneously broken (1 or 2) or the Al-O coordination of the neighboring aluminum atoms (5 or 6) at the armchair edge. pH effects are significant, with a nearly 50% reduction in barrier height under alkaline conditions that are relevant to geothermal fluids and Al extraction from minerals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Effects of transient thermal shock on the microstructures and corrosion properties of a reduced activation high-entropy alloy

The devices would subject transient thermal shocks (TTS) during operation under extremely harsh conditions of nuclear fusion reactors, which inevitably exert significant impact on the microstructure and performance of structural materials. Here, a reduced activation VCrFeTa 0.2 W 0.2 high-entropy alloy (HEA) was developed by vacuum arc melting. The effects of electron beam induced TTS on its microstructure, microhardness, and corrosion properties were investigated. The results indicate that the weight fraction of each phase changes after TTS, showing a significant decrease in the content of BCC1 phase and an increase in the content of BCC2 and Laves phases. The content of BCC1 phase continues to decrease slightly with increasing the power of TTS. Besides, the microhardness of the alloy increases from ~673 HV to ~714 HV after TTS treatments. In the reduced activation HEA, TTS results in a relatively low corrosion current density of ~0.472 μA/cm 2 in 3.5 wt% NaCl solution, around one-third of the current density observed in the as-cast sample. Furthermore, the VCrFeTa 0.2 W 0.2 HEA after TTS exhibits a pitting potential of ~1.165 V SCE , which is much higher than that of the as-cast sample. The refined composite multiscale entropy method is employed to analyze the influence of TTS on current fluctuation behavior during the corrosion process. The reduced activation VCrFeTa 0.2 W 0.2 HEA exhibits excellent properties in harsh environments after TTS, thereby showing advantageous property in the field of nuclear structural materials. Furthermore, TTS is an efficient and controllable strategy for the improvement of the HEAs’ microstructures and performances.

36 MATERIALS SCIENCE↗

Rapid Coal-Ash Characterization using Geophysical Methods & Machine Learning

Coal combustion products (CCP) are challenging to delineate in heterogeneous field settings. Conventional methods (test pits, coring, and laboratory analyses) are labor-intensive, slow, invasive, and provide sparse spatial coverage. This study evaluates whether rapid non-invasive geophysical screening methods—induced polarization (IP), magnetic susceptibility, and nuclear magnetic resonance (NMR) —combined with surface colorimetry (RGB_24), can discriminate CCP-soil mixtures and provide reliable estimates of CCP content. Laboratory measurements were collected on five CCP-soil mixtures (series) and modeled using (i) a linear baseline, (ii) a calibrated non-linear (power-mean) model, and (iii) a machine-learning (ML) Random Forest approach, with validation via leave-one-series-out and site-specific tests. Across the five series, individual signals—particularly IP and magnetic susceptibility—were strongly predictive of ash content but were consistently outperformed by combined models. The pooled calibrated non-linear and ML models captured the observed non-linearity and achieved high accuracy and precision, improving on linear fits. Colorimetry showed the weakest direct relationship with ash content for the tested samples but improved performance when included in multi-signal models. At pre-selected 3.5% decision threshold, calibrated and ML approaches yielded near-perfect classification (Matthews correlation coefficient ˜ 1), suggesting strong practical operability for field screening. Additionally, field-analog tests highlighted the role of endmembers—accuracy declined without access to end-member measurements but was largely recovered by collecting a minimal labeled pair for local recalibration. With end members, accuracy remained high. Globally trained models performed well on three operational unknowns; however, series-specific refits provided the most accurate predictions. Overall, these results highlight the potential of combining rapid geophysics and minimal local calibration for improved coal-ash delineation.

Peshtani, Klaudio↗

Assessment of the grindability of robocast silicon carbide

The demand for high-performance lightweight optics has driven interest in silicon carbide (SiC) due to its exceptional thermal stability, hardness, and strength-to-weight ratio. This study investigates the potential of robocasting, an additive manufacturing process, as a viable method for producing lightweighted SiC components for optical applications. Four samples with varied starting powder phases (α and β) and sintering conditions were fabricated and evaluated. Post-sintering surface and form were assessed using coherence scanning interferometry (CSI) and coordinate measuring machine (CMM) techniques. A three-stage grinding process was applied to each sample, with surface roughness assessed at each stage. Results demonstrate that samples with predominantly α-phase SiC and smaller particle sizes achieved superior surface finish, particularly sample D2-α-2135 °C, which displayed the lowest post-grinding Sq value of 0.178 µm. The analysis also indicated no significant print-through effect from the lightweighting structure, or print artifacts, at this stage of grinding. However, β-phase samples showed poorer grindability, increased surface roughness, and pitting. These findings suggest that phase composition and particle size are critical for achieving the desired surface quality in robocast SiC optics. Future work will incorporate additional samples and finer grinding wheels to refine surface quality further, supporting the development of SiC for high-precision optical applications.

Barrett, Taylor [ORNL]↗