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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Simulation of PV Variability as a Function of PV Generation and Plant Size

The deployment of photovoltaic (PV) systems continues to show significant expansion; however, this growth has brought added attention to issues around the variability of the solar resource. Both spatial and temporal variability exist. Temporal scales can range from the sub-second to multiyear, whereas spatial scales can range from a few meters to tens of kilometers. There are multiple methods described in the literature to quantify PV variability at various spatial and temporal scales. This study focuses on short-term temporal variability and uses similar approaches with the addition of PV plant size a parameter to quantify variability. The method employed here incorporates the normalization of clear-and cloudy-sky conditions and PV plant size to quantify nominal variability metrics. The distribution and fluctuations of these metrics provide relevant information that is useful for system operations. The National Solar Radiation Database (NSRDB) is used to simulate PV variability as a function of PV generation and plant size. Hypothetical but realistic system information at 33 locations is used to model PV generation by feeding NSRDB solar irradiance data to the National Renewable Energy Laboratory’s System Advisor Model (SAM). Over the selected region, it is found that the aggregated ramp rates for the 1-minute data are associated with standard deviations ranging from 0.002–0.055 on a daily basis; however, hourly intervals induce higher aggregated ramp rates than the other timescales. Even though minute-to-minute variations are significant for the 1-minute time-scale, the standard deviation aggregated into a daily metric is smaller because of the cancellation of values.

irradiance↗
DOE OSTI2020

Electrically driven photon emission from individual atomic defects in monolayer WS 2

Quantum dot–like single-photon sources in transition metal dichalcogenides (TMDs) exhibit appealing quantum optical properties but lack a well-defined atomic structure and are subject to large spectral variability. Here, we demonstrate electrically stimulated photon emission from individual atomic defects in monolayer WS 2 and directly correlate the emission with the local atomic and electronic structure. Radiative transitions are locally excited by sequential inelastic electron tunneling from a metallic tip into selected discrete defect states in the WS 2 bandgap. Coupling to the optical far field is mediated by tip plasmons, which transduce the excess energy into a single photon. The applied tip-sample voltage determines the transition energy. Atomically resolved emission maps of individual point defects closely resemble electronic defect orbitals, the final states of the optical transitions. Inelastic charge carrier injection into localized defect states of two-dimensional materials provides a powerful platform for electrically driven, broadly tunable, atomic-scale single-photon sources.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Simulation of PV Variability as a Function of PV Generation and Plant Size: Preprint

The deployment of photovoltaic (PV) systems continues to show significant expansion; however, this growth has brought added attention to issues around the variability of the solar resource. Both spatial and temporal variability exist. Temporal scales can range from the sub-second to multiyear, whereas spatial scales can range from a few meters to tens of kilometers. There are multiple methods described in the literature to quantify PV variability at various spatial and temporal scales. This study focuses on short-term temporal variability and uses similar approaches with the addition of PV plant size a parameter to quantify variability. The method employed here incorporates the normalization of clear- and cloudy-sky conditions and PV plant size to quantify nominal variability metrics. The distribution and fluctuations of these metrics provide relevant information that is useful for system operations. The National Solar Radiation Database (NSRDB) is used to simulate PV variability as a function of PV generation and plant size. Hypothetical but realistic system information at 33 locations is used to model PV generation by feeding NSRDB solar irradiance data to the National Renewable Energy Laboratory’s System Advisor Model (SAM). Over the selected region, it is found that the aggregated ramp rates for the 1-minute data are associated with standard deviations ranging from 0.002–0.055 on a daily basis; however, hourly intervals induce higher aggregated ramp rates than the other timescales. Even though minute-to-minute variations are significant for the 1-minute timescale, the standard deviation aggregated into a daily metric is smaller because of the cancellation of values.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2022

First Measurement Of Charged-current 1 Muon 2 Proton 0 Pion Single Differential Cross-sections On Argon-40 At 0.8 Gev Average Neutrino Energy With The MicroBooNE Detector

Constraining systematic uncertainties that arise from neutrino-nucleon cross-section modeling is critical for upcoming neutrino oscillation experiments. Neutrino interactions that lead to the ejection of 2 particles from the target nucleus, leaving the nucleus with 2 holes, are one cross-section of interest. These instances, known as 2-particle 2-hole (2p2h) states, can be produced either by neutrino interaction modes or from the influence of nuclear physics on the neutrino interaction. While there are many theoretical models that attempt to describe the nature of 2p2h states, very few experimental measurements of this process exist, making it difficult to benchmark theoretical calculations. Furthermore, no high statistic cross-section measurements of 2p2h states exist on $^{40}$Ar, the target nucleus of many current and next generation neutrino oscillation experiments. This analysis aims to address this issue by providing the first high-statistic single differential cross-section me asurements of 2p2h states. Using data collected from the first 3 years of running of the MicroBooNE experiment, we select $\nu_\mu$ charged-current interaction events that produce a single muon, 2 protons, and no pions in the final state (CC1$\mu$2p0$\pi$). The single differential cross-section is then extracted as a function of a variety of physics variables and compared to theoretical predictions from several event generator model sets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Evidence for Coupling Between the Subseasonal Oscillations in the Southern Hemisphere Midlatitude Ocean and Atmosphere

Abstract A regional perspective is taken to investigate the intriguing ∼25‐day periodicity in the Southern Hemisphere (SH) baroclinic annular mode (BAM) and the possible underlying mechanisms. Results show that the 25‐day periodicity exists not only in the zonal average but also in the regional average of eddy activity as long as the domain of the average is wide enough to accommodate a wave packet. The favorable domain for the ∼25 days periodicity is located downstream of the largest baroclinicity in midlatitudes. The wave activity developed there can travel circumglobally several times with a typical group velocity of 25 m s −1 , and reenter the domain every 12.5 days, setting the base periodicity. A slower oceanic oscillation at a time scale of ∼100 days in the same region appears to select and reinforce the multiples of 12.5 days, giving rise to a pronounced ∼25‐day periodicity in the wave activity.

Xue, Daokai↗
DOE OSTI2024

Impact of survey spatial variability on galaxy redshift distributions and the cosmological 3 × 2-point statistics for the Rubin Legacy Survey of Space and Time (LSST)

We investigate the impact of spatial survey non-uniformity on the galaxy redshift distributions for forthcoming data releases of the Rubin Observatory Legacy Survey of Space and Time (LSST). Specifically, we construct a mock photometry data set degraded by the Rubin OpSim observing conditions, and estimate photometric redshifts of the sample using a template-fitting photo-z estimator, BPZ, and a machine learning method, FlexZBoost. We select the Gold sample, defined as $i\lt 25.3$ for 10 yr LSST data, with an adjusted magnitude cut for each year and divide it into five tomographic redshift bins for the weak lensing lens and source samples. We quantify the change in the number of objects, mean redshift, and width of each tomographic bin as a function of the coadd i-band depth for 1-yr (Y1), 3-yr (Y3), and 5-yr (Y5) data. In particular, Y3 and Y5 have large non-uniformity due to the rolling cadence of LSST, hence provide a worst-case scenario of the impact from non-uniformity. We find that these quantities typically increase with depth, and the variation can be $10\!-\!40~{{\rm per\ cent}}$ at extreme depth values. Using Y3 as an example, we propagate the variable depth effect to the weak lensing $3\times 2$ pt analysis, and assess the impact on cosmological parameters via a Fisher forecast. We find that galaxy clustering is most susceptible to variable depth, and non-uniformity needs to be mitigated below 3 per cent to recover unbiased cosmological constraints. There is little impact on galaxy–shear and shear–shear power spectra, given the expected LSST Y3 noise.

cosmology↗
DOE OSTI2025

Constrained or unconstrained? Neural-network-based equation discovery from data

Throughout many fields, practitioners often rely on differential equations to model systems. Yet, for many applications, the theoretical derivation of such equations and/or the accurate resolution of their solutions may be intractable. Instead, recently developed methods, including those based on parameter estimation, operator subset selection, and neural networks, allow for the data-driven discovery of both ordinary and partial differential equations (PDEs), on a spectrum of interpretability. The success of these strategies is often contingent upon the correct identification of representative equations from noisy observations of state variables and, as importantly and intertwined with that, the mathematical strategies utilized to enforce those equations. Specifically, the latter has been commonly addressed via unconstrained optimization strategies. Representing the PDE as a neural network, we propose to discover the PDE (or the associated operator) by solving a constrained optimization problem and using an intermediate state representation similar to a physics-informed neural network (PINN). The objective function of this constrained optimization problem promotes matching the data, while the constraints require that the discovered PDE is satisfied at a number of spatial collocation points. We present a penalty method and a widely used trust-region barrier method to solve this constrained optimization problem, and we compare these methods on numerical examples. Our results on several example problems demonstrate that the latter constrained method outperforms the penalty method, particularly for higher noise levels or fewer collocation points. This work motivates further exploration into using sophisticated constrained optimization methods in scientific machine learning, as opposed to their commonly used, penalty-method or unconstrained counterparts. For both of these methods, we solve these discovered neural network PDEs with classical methods, such as finite difference methods, as opposed to PINNs-type methods relying on automatic differentiation. Here, we briefly highlight how simultaneously fitting the data while discovering the PDE improves the robustness to noise and other small, yet crucial, implementation details.

Data-driven discovery↗
DOE OSTI2020

A combined machine learning and density functional theory study of binary Ti-Nb and Ti-Zr alloys: Stability and Young’s modulus

The multicomponent Ti alloys, specifically the -phase, have experienced a strong growth over the last decades, due to their outstanding properties of ultra-high strength and low Young’s modulus. These properties play a significant role in many aerospace and biomedical applications. Selection and optimization of multicomponent alloys is challenging due to the vast chemical and compositional space. Here we investigate the use of machine learning techniques informed by density functional calculations to guide the selection of Nb- and Zr-based Ti binary alloys. From the cubic structures obtained from high throughput calculations and literature, we identify several structures with Young’s moduli below 40 GPa. The multivariant decision tree methods provide efficient surrogate models to identify structure variables have high influences on the energetic stability and Young’s modulus. Furthermore, we implement a workflow of incorporating DFT provided results and machine learning method to explore the chemical and composition space of other binary and multicomponent alloys, to eventually accelerate the material design via taking advantages of identified key variables.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Ensemble variational Fokker-Planck methods for data assimilation

Particle flow filters solve Bayesian inference problems by smoothly transforming a set of particles into samples from the posterior distribution. Particles move in state space under the flow of an McKean-Vlasov-Itˆo process. This work introduces the Variational Fokker-Planck (VFP) framework for data assimilation, a general approach that includes previously known particle flow filters as special cases. The McKean-Vlasov-Itˆo process that transforms particles is defined via an optimal drift that depends on the selected diffusion term. It is established that the underlying probability density - sampled by the ensemble of particles - converges to the Bayesian posterior probability density. For a finite number of particles the optimal drift contains a regularization term that nudges particles toward becoming independent random variables. Based on this analysis, we derive computationally-feasible approximate regularization approaches that penalize the mutual information between pairs of particles, and avoid particle collapse. Moreover, the diffusion plays a role akin to a particle rejuvenation approach that aims to alleviate particle collapse. The VFP framework is very flexible. Different assumptions on prior and intermediate probability distributions can be used to implement the optimal drift, and localization and covariance shrinkage can be applied to alleviate the curse of dimensionality. A robust implicit-explicit method is discussed for the efficient integration of stiff McKean- Vlasov-Itˆo processes. Here, the effectiveness of the VFP framework is demonstrated on three progressively more challenging test problems, namely the Lorenz ’63, Lorenz ’96 and the quasi-geostrophic equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Facets control charge separation during photoelectrochemical water oxidation with strontium titanate (SrTiO 3 ) single crystals

The photocatalytic overall water splitting reaction provides a pathway to hydrogen fuel from sunlight. Photocatalysts must achieve the reaction without the application of an external bias, which requires an effective charge separation mechanism. Photolabeling studies and electrostatic simulations for the well-known CoOOH/Al:SrTiO 3 /Rh/Cr 2 O 3 photocatalyst suggest that charge separation is driven by work function differences at the (100) and (110) facets of SrTiO 3 , which are electron and hole selective, respectively. Here we use hydrogen annealed SrTiO 3–x single crystals to obtain the first quantitative assessment of the charge separation ability of the (100), or (110), or (111) facets during oxygen evolution. Under UV illumination (60 mW cm –2 ), the crystals exhibit variable water oxidation photocurrents (0.34, 0.82, 1.36 mA cm –2 at 1.23 V versus RHE) and photovoltage values of 1.40, 1.52 and 1.52 V for (100), (110), and (111) SrTiO 3–x , respectively. A surface photovoltage increase in that same order (0.31 V < 0.57 V < 0.67 V) is confirmed independently with vibrating Kelvin probe surface photovoltage spectroscopy (VKP-SPV) under 375 nm (1.91 mW cm –2 ) illumination. Mott Schottky measurements in aqueous K 3/4 [Fe(CN) 6 ] reveal facet-dependent flatband positions of –0.58, –0.71, and –0.74 V RHE for the (100), (110), and (111) crystals respectively. This confirms that the photoelectrochemical water oxidation performance of SrTiO 3–x crystals is controlled by the work function of each facet, which determines the electron transfer barrier height of the respective solid–liquid junctions. After correcting for differences in electron donor concentrations, barriers are found to increase in the order (100) < (111) < (110) and differ by as much as 0.16 eV, similar to an earlier prediction. Altogehter, these results explain the charge separation mechanism in SrTiO 3 photocatalysts and highlight the need for faceted semiconductor crystals as light absorbers in particle-based photocatalysts.

08 HYDROGEN↗
DOE OSTI2025

Carbon Dioxide Removal Measurement, Reporting, and Verification Simulation toolkit (CDR MRVSim) v0.1

CDR MRVSim is a statistical software toolkit for techno-economic analysis of measurement, reporting, and verification of multiple carbon dioxide removal technologies. The current version applies Monte Carlo simulation to existing datasets estimate the cost and uncertainty of measuring the soil organic carbon (SOC) content of agricultural land, accounting for multiple sources of uncertainty, using data from field measurements. We are planning to incorporate enhanced rock weathering and other technologies into the software. The current model leverages existing data to characterize underlying variability in soil organic carbon and related parameters, including bulk density, in an agricultural field attempting to increase its SOC. We then simulate baseline and post-intervention "measurement campaigns" in which some number of SOC and bulk density measurements are conducted using a selected technology. This enables estimates of the cost and accuracy of measuring changes in SOC in the simulated field. The primary advantages over similar software are: 1) ability to quantify tradeoffs between cost and uncertainty in MRV across a range of possible MRV approaches 2) focus on guiding development of novel sensors by determining desirable sets of characteristics

Sherwin, Evan [Lawrence Berkeley National Laborato↗
DOE OSTI2026

NASA’s Pandora SmallSat Mission: Simulated Modeling and Retrieval of Near-infrared Exoplanet Transmission Spectra

Pandora is a SmallSat mission dedicated to understanding exoplanets and their host stars by disentangling the impact of stellar heterogeneity on exoplanet transmission spectra. Selected as a NASA Astrophysics Pioneers mission in 2021, Pandora will provide simultaneous long-term visible photometric monitoring (0.4–0.7 μm) and low-resolution near-infrared (NIR) spectroscopy (0.9–1.6 μm) of transiting systems for the purposes of monitoring host star variability and characterizing exoplanetary atmospheres. Pandora’s year-long prime mission from 2026 to 2027 coincides with the middle of a decade defined by targeted efforts for atmospheric characterization of exoplanets, offering a key opportunity to leverage this new resource to maximize science with JWST and other observatories. Here we investigate Pandora’s anticipated performance for the general exoplanet population accessible to transit spectroscopy, from hot Jupiters to temperate sub-Neptunes. By modeling the atmospheres of five test cases broadly consistent with the bulk properties of HD 209458b, HD 189733b, WASP-80 b, HAT-P-18 b, and K2-18 b, we find that Pandora may provide abundance constraints as precise as ∼1.0 dex for main atmospheric absorbers such as H 2 O and CH 4 . Then, we explore the synergies between Pandora and JWST. Our results suggest that targets with JWST data in the NIR can benefit from the addition of Pandora observations and result in more reliable abundance estimates than with JWST data alone. Moreover, Pandora can serve the community by providing precursory observations of targets of interest for JWST atmospheric characterization. We conclude by outlining strategies for the use of Pandora as a standalone observatory and in synergy with JWST.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Using automated machine learning for the upscaling of gross primary productivity

Estimating gross primary productivity (GPP) over space and time is fundamental for understanding the response of the terrestrial biosphere to climate change. Eddy covariance flux towers provide in situ estimates of GPP at the ecosystem scale, but their sparse geographical distribution limits larger-scale inference. Machine learning (ML) techniques have been used to address this problem by extrapolating local GPP measurements over space using satellite remote sensing data. However, the accuracy of the regression model can be affected by uncertainties introduced by model selection, parameterization, and choice of explanatory features, among others. Recent advances in automated ML (AutoML) provide a novel automated way to select and synthesize different ML models. In this work, we explore the potential of AutoML by training three major AutoML frameworks on eddy covariance measurements of GPP at 243 globally distributed sites. We compared their ability to predict GPP and its spatial and temporal variability based on different sets of remote sensing explanatory variables. Explanatory variables from only Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and photosynthetically active radiation explained over 70 % of the monthly variability in GPP, while satellite-derived proxies for canopy structure, photosynthetic activity, environmental stressors, and meteorological variables from reanalysis (ERA5-Land) further improved the frameworks' predictive ability. We found that the AutoML framework Auto-sklearn consistently outperformed other AutoML frameworks as well as a classical random forest regressor in predicting GPP but with small performance differences, reaching an r 2 of up to 0.75. We deployed the best-performing framework to generate global wall-to-wall maps highlighting GPP patterns in good agreement with satellite-derived reference data. This research benchmarks the application of AutoML in GPP estimation and assesses its potential and limitations in quantifying global photosynthetic activity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Optimization Algorithm for Criticality Experiment Design Using Whisper

Many criticality experiments performed to aid in nuclear data evaluation are designed to maximize the sensitivity of the system’s effective neutron multiplication factor to a certain nuclide reaction pair over an energy region of interest. This is typically done by evaluating possible designs in a transport code such as MCNP and selecting the one with the highest desired sensitivity. A designer has many tools to try to maximize this sensitivity such as different moderators, reflectors, fuels, and geometries. This balancing act of identifying a critical and maximally sensitive system become very computationally expensive as more variables are added and higher precisions are desired. In order to identify these optimal configurations more efficiently a Particle Swarm Optimization (PSO) algorithm coupled with MCNP has been developed by Los Alamos National Laboratory (LANL). This algorithm has been used to design two upcoming criticality experiments that will be performed at the National Criticality Experiments Research Center (NCERC), located at the Nevada National Security Site, and operated by LANL, the only general-purpose critical experiments laboratory in the United States. PSO uses a population (swarm) of candidate solutions (particles) on a search space of dimensions such as moderator and reflector thicknesses or enrichments and concentrations. These particles move around the search space from generation to generation according to simple rules. Eventually, the swarm converges on the configuration that is both critical and maximally sensitive to a piece of nuclear data. PSO is well suited for criticality experiments as the algorithm is agnostic to the underlying physics, meaning it is effective on many different experimental setups. This algorithm has been modified to maximize the nuclear data similarity coefficient between an application case and an experiment aimed at replicating the application case using WHISPER, a nuclear criticality safety analysis tool. This allows for the efficient design of critical experiments informed by nuclear data sensitives.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Quantifying Variability and Controls of Riverine Dissolved Organic Carbon Exported to Arctic Coastal Margins of North America (Final Report)

This project involved implementation and application of a coupled permafrost hydrology and dissolved organic carbon process models to investigate how spatial and seasonal variations in terrestrial hydrology and soil freeze/thaw dynamics influence the mobilization, loading, and export of organic carbon to the stream network for selected arctic basins across northern Alaska and northwest Canada. The model simulations were constrained by detailed observations of in- stream chemistry, soil active layer profile moisture and temperature dynamics, streamflow, soil carbon inventories and satellite microwave remote sensing based assessments of surface soil freeze-thaw dynamics. We developed and applied the numerical modeling and data analysis, incorporating observed data for calibration and validation, in order to investigate the terrestrial hydrology, permafrost dynamics, and associated DOC production and loading to rivers across a region encompassing watersheds draining to the coast. The project produced six publications and three datasets archived in public repositories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Multisublattice cluster expansion study of short-range ordering in iron-substituted strontium titanate

Owing to the challenges in obtaining realistic atomic configurations in large chemical phase spaces, it is not straightforward to describe structure–property relations in materials exhibiting configurational disorder. One example is iron-substituted strontium titanate (SrTi 1–x Fe x O 3–d , STF), a promising perovskite-derivative cathode material in solid oxide fuel cells that exhibits full solid solubility 0 ≤ x ≤ 1 and a tendency to exhibit short-range order. Here we demonstrate a multisublattice cluster expansion (CE) framework and apply it to STF across the full composition range. The CE approach is distinct from more traditional CE formulations in that clusters are defined explicitly by the chemical species distributed among multiple sublattices, rather than via cluster functions of occupation variables with decoration. The modified CE approach makes it easy to distinguish meaningful chemical interactions that are harder to extract from conventional CE, since for the latter chemical identity in a cluster is expressed as a product of site occupations. The least absolute shrinkage and selection operator (LASSO) is implemented as a regression analysis tool to select key clusters and avoid overfitting. We demonstrate this formulation on STF, and show that it can accurately predict configurational energies in comparison to conventional CE. From the key clusters, we identify that short-range ordering between substitutional Fe and oxygen vacancies (V O ) results in the formation of Fe–VO strings. In addition, we consider the stability of STF through CE-based Monte Carlo (MC) simulations and confirm the presence of superstructures that were previously observed in transmission electron microscopy. In this work, analysis of atomic configurations from MC samples reveals variations in the oxidation state of Fe atoms, which can be explained by the ordering tendency of Fe and V O . The cluster description and selection formalism described here may be applied to other disordered multisublattice systems for accurate and efficient material modeling.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Many-Body Benchmark of Electronic Charge and Spin Densities for Li 1–x NiO 2

Accurate benchmarks are particularly important for highly correlated oxides as mean-field approximations often fail to describe the subtle balance of charge transfer and magnetism in these materials with an accuracy comparable to experimental needs. Here we present accurate diffusion Monte Carlo (DMC) results of the electronic charge and spin densities for the tunable highly correlated oxide Li 1–x NiO 2 for x = 0, 1/2, and 1. To enable quantitative comparisons, we introduce a robust density-partitioning scheme, extending Voronoi analysis to assign atomic charges from spatially noisy DMC densities. We then benchmark common approximations used in density functional theory (DFT). Comparison against DMC shows that r 2 SCAN delivers the most balanced performance across charge, spin, and radial density descriptors, nearly reproducing DMC results for LiNiO 2 and apical Ni sites in Li 0.5 NiO 2 . Hybrid functionals (PBE0, SCAN0) perform unexpectedly poorly, and PBE + U + V yields inconsistent trends between charge and spin densities. Therefore, the r 2 SCAN functional minimizes errors relative to DMC while capturing the variable valence of the Ni ion and also retaining the computational efficiency of DFT for large-scale simulations of the tunable structural and electronic phases of Li1−xNiO2. Our study highlights the importance of accurate benchmarking of the fundamental quantities involved in DFT to select appropriate DFT approximations in order to advance the predictive modeling of charge-transfer-driven phenomena in correlated electron systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Coal-Waste-Enhanced Filaments for Additive Manufacturing of High-Temperature Plastics and Ceramic Composites

In the United States, coal waste from over a century of mining and burning coal for heat and electricity has accumulated as mountains of coal fly ash and bottom ash and acre-size ponds, coal fines and gob. These materials can be a problem for local communities and water systems. A cost-effective process to utilize high volumes of these coal wastes in a high-value product would be beneficial to those communities by reducing the amount of waste and providing jobs, manufacturing components, and materials from the waste. Many coal-to-products technologies (e.g., carbon fibers, graphene, carbon foam) rely on carefully choosing the starting material and then altering it chemically or thermally to make the products work. Due to the wide variability of composition and coal content in typical coal waste streams, many high-volume coal waste streams are likely to be unsuitable for use in those technologies. Semplastics’ technology has been shown to utilize most types of coal waste successfully without any pre-selection or pre-processing requirements other than a nominal particle-size reduction for wastes like bottom ash. This characteristic of Semplastics’ solution may enable the use of much larger volumes of a wider range of coal wastes than other coal-to-products technologies. In this project, Semplastics leveraged its unique experience with both coal waste (fly ash or coal combustion residuals), resin materials, and 3D printing to develop 3D printer filaments using common coal wastes – bituminous coal fines and fly ash – and researched the feasibility of using other forms of coal waste as fillers. Simple 3D-printed parts were successfully produced from the coal waste enhanced filaments, which were found to have improved strength and stiffness.

01 COAL, LIGNITE, AND PEAT↗