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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Metal-organic chemical vapor deposition of MgGeN2 films on GaN and sapphire

MgGeN2 films were synthesized using metal-organic chemical vapor deposition on GaN/c-sapphire templates and c-plane sapphire substrates. Energy-dispersive x-ray spectroscopy was used to estimate the cation composition ratios. To mitigate magnesium evaporation, the films were grown at pyrometer temperature 745 °C with a wafer rotation speed of 1000 rpm. Growth rates were determined by fitting energy-dispersive x-ray spectroscopy spectra to film thicknesses using NIST DTSA-II software. The thickness estimates determined by this method were consistent with scanning transmission electron microscopy measurements done for selected samples. Scanning electron microscopy images revealed faceted surfaces indicative of a tendency toward three-dimensional growth. X-ray diffraction spectra confirmed that the films were highly crystalline and exhibited preferential orientation in alignment with the substrate. Atomic force microscopy measurements show that film thicknesses are consistent across samples grown on both GaN templates and sapphire substrates, with typical roughnesses around 10 nm. Transmittance spectra of films grown on double-side-polished sapphire substrates yielded band gaps of 4.28 ± 0.06 eV for samples exhibiting close-to-ideal stoichiometry. Comparison of the measured spectra with ab initio calculations are in good agreement both near the bandgap and at higher energies where excitation is into higher-lying bands. These findings provide insight into the growth and characterization of MgGeN2, contributing to the development of this material for potential applications in optoelectronics and power electronics.

Hu, Chenxi (ORCID:0000000236299660)↗
DOE OSTI2025

Active Learning Surrogates for Integrating Electron Microscopy and Computational Insights from Simulations in Autonomous Experiments

Artificial Intelligence (AI) combined with simulations and experiments has great potential to accelerate scientific discovery across technology and pharmaceuticals. However, the gap between simulations and experiments is challenging due to disparities in time and scale, making it difficult to estimate properties like energy and electronic states from experiments, and to provide feedback based on theoretical insights.Our research addresses the challenge by developing unique deep kernel based surrogate models that learns from microscopic images, mapping structural features to energy differences from defect formation. We start with full-training using simulated images to determine optimal settings, establishing a baseline for active learning. Using these settings from the baseline, active learning is trained, and predicts structures along simulation trajectories based on uncertainty and energetic stability, thus reducing data requirements, simulation time and computational costs. The results demonstrate that the model achieves a low average error margin of approximately 0.03 meV, indicating good performance. To enhance feature extraction and reconstruction capabilities, we developed an autoencoder-decoder as additional surrogate to create latent space to capture essential features, enabling precise comparisons between simulations and experiments. The results from this model achieved a reconstruction loss of around 0.2 and accurately reconstructed molecular structures.Overall, this work advances the steering of experiments through computational simulations by employing a surrogate models that actively predicts the trajectories of structural evolution, achieving time-to-solution comparable to experimental measurements.

Saranathan, Gayathri [Hewlett-Packard]↗
DOE OSTI2024

The Dark Energy Survey Supernova Program: Light Curves and 5 Yr Data Release

We present griz photometric light curves for the full 5 yr of the Dark Energy Survey Supernova (DES-SN) program, obtained with both forced point-spread function photometry on difference images (DiffImg) performed during survey operations, and scene modelling photometry (SMP) on search images processed after the survey. This release contains 31,636 DiffImg and 19,706 high-quality SMP light curves, the latter of which contain 1635 photometrically classified SNe that pass cosmology quality cuts. This sample spans the largest redshift (z) range ever covered by a single SN survey (0.1 < z < 1.13) and is the largest single sample from a single instrument of SNe ever used for cosmological constraints. We describe in detail the improvements made to obtain the final DES-SN photometry and provide a comparison to what was used in the 3 yr DES-SN spectroscopically confirmed Type Ia SN sample. We also include a comparative analysis of the performance of the SMP photometry with respect to the real-time DiffImg forced photometry and find that SMP photometry is more precise, more accurate, and less sensitive to the host-galaxy surface brightness anomaly. The public release of the light curves and ancillary data can be found at github.com/des-science/DES-SN5YR and doi:10.5281/zenodo.12720777.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Mapping the Spatial Footprint of Sea Breeze Winds in the Southeastern United States

Abstract Sea breeze winds are observed at various locations worldwide, but the spatially continuous mapping of sea breeze winds is rare. We have developed a scheme to map the areas of the southeastern United States (SEUS) coast influenced by sea breeze winds using a range of surface re‐analysis data to identify their occurrence. Changes in wind direction and dew point temperature are both used to detect a potential sea breeze signature, which is then confirmed by cumuliform clouds seen in satellite images or coastal fronts shown as cohesive lines in radar reflectivity images. Filters are employed to remove onshore winds not induced by the temperature difference between land and sea. From March to September 2019, this scheme identified 134 days with sea breeze occurrence somewhere in the SEUS, a frequency of 63 percent. The number of sea breezes increased from March to July and then decreased to September. Deep inland propagation of sea breezes during this period left footprints in a band parallel to the coastline up to about 220 km inland, after which the sea breeze winds quickly diminished. Comparisons show that the findings using the scheme are consistent with site observations, theoretical estimates, and idealized and semi‐idealized numerical model simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Examining the Characteristics of the Cropland Data Layer in the Context of Estimating Land Cover Change

The United States Department of Agriculture (USDA) Cropland Data Layer (CDL) provides spatially explicit information about crop production area and has served as a prevalent data source for characterizing cropland change in the U.S. in the last decade. Understanding the accuracy of the CDL is paramount because of the reliance on it for management and policy making. This study examined the characteristics of the CDL from 2007 to 2017 using comparisons to other USDA datasets. The results showed when examining the cropland area for the same year, the CDL produced comparable trends with other datasets (R 2 > 0.95), but absolute area differed. The estimated area of cropland changes from 2007 to 2012, 2008 to 2012 and 2012 to 2017 varied from weak to moderate correlation between the CDL and the tabular data (R 2 = 0.005~0.63). Differences in area of cropland change varied widely between data sources with the CDL estimating much larger change area. A series of image processing techniques designed to improve the confidence in cropland change estimated using the CDL reduced the area of estimated cropland change. The techniques also, unexpectedly, lowered the correlation in change estimated between the CDL and the tabular datasets. Estimated land cover change area varied widely based on analyses applied and could reverse from increasing to declining area in cropland. Further analyses showed unlikely change scenarios when comparing different year combinations. The authors recommend the CDL only be used for land cover change analysis if the error can be estimated and is within change estimates.

58 GEOSCIENCES↗
DOE OSTI2025

Improved Gas Plume Identification Using Nearest Neighbor Methods for Background Estimation

Longwave infrared (LWIR) hyperspectral imaging (HSI) can be used for many tasks in remote sensing, including detecting and identifying effluent gases by LWIR sensors on airborne platforms. Identification is used after detection to increase confidence in weakly detected plumes, reduce false positives from detection, and distinguish between similar and confounding material signatures. Background estimation is an important step used to reveal the unique spectral characteristics of the detected gas, allowing the identification model to determine what the gas is specifically. The importance of proper background estimation increases when dealing with weak signals, large libraries of gases of interest, and uncommon or heterogeneous backgrounds. In this article, we propose two methods for background estimation: a novel k-nearest segments (KNS) algorithm and the standard k-nearest neighbors (KNN) algorithm. We test our methods and three existing background estimation methods for comparison against global background estimation to determine which performs best at estimating the true background radiance under a plume and for increasing identification confidence using a neural network classification model. We compare the different methods using 640 simulated weak plumes in an urban environment. For identification, our KNS algorithm improves median neural network identification confidence by 53.2%. For background radiance estimation, the KNN algorithm provides a median of 49 times less RMSE than global background estimation. Furthermore, KNN is the easiest method to tune for different plumes, making it an excellent “out of the box” background estimator.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

A Comparison of Emerging and Industry Benchmark Photovoltaic Backsheets Between Different Outdoor Locations

Recent interest within the photovoltaic (PV) module industry is largely directed toward enhanced lifetimes in the field, balanced with improved recyclability. Traditionally, fluoropolymer-based backsheets have been used, however, are difficult to recycle. Emerging polyolefin (PO)-based backsheets are more recyclable and can be formulated to be robust. Properties of different fluoropolymer- and non-fluoropolymer-based backsheet coupons and in encapsulated silicon mini modules that have been fielded in Albuquerque, NM and Cocoa, FL are reported here. Seven backsheets were examined: two novel PO's, TPT, APO, PPE, AAA, and KPf. Methods of examination include module electrical performance (I - V flash test), surface morphology (optical microscope and gloss), polymer chemical structure (FTIR), EL imaging, mechanical tensile testing, DC breakdown voltage, DSC (phase transitions), and optical performance (reflectance spectra).

industries↗
DOE OSTI2023

Additive manufacturing process parameter determination for a new Fe-C-Cu alloy

As a potential replacement for stainless steel alloys commonly used to print parts with laser powder bed fusion, a new Cu precipitation strengthened ferrous alloy, with composition Fe-0.2C-6Cu (wt%), was recently developed. This material is Co- and Ni-free, printable, has mechanical properties comparable to that of high strength stainless steels (approximately 1300 MPa UTS), and is cost-advantaged relative to existing low-alloy steels for additive manufacturing of parts with complex shapes. To gain traction for broader application, optimal laser powder bed fusion (LPBF) processing conditions to produce nearly defect-free parts without compromising mechanical strength are needed. Here, an optimal processing window based upon laser speeds that achieve minimal porosity was quantified via comparisons of measured melt pool penetration, scan speeds, printed material density and laser beam energy density on a commercial LPBF unit operating at 350 W, 80 μm hatch spacing, and 50 μm layer thickness based upon optimal parameters for 17-4PH. Furthermore, the window is 300 to 500 mm/s to achieve >99.8 % dense Fe-0.2C-6Cu (wt%) specimens. Other defects such as burning, spatter, and lack of fusion were also avoided in this window. The window was validated with in-situ synchrotron X-ray imaging that enabled visualization of the vapor cavity, melting, and solidification during a single laser track scan on a miniature powder bed sample. In addition, in-situ infrared imaging provided temperature fields, cooling rate and solidification range, and confirmed minimal printing defects and spatter within the processing window.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Microscopic analysis of copper current collectors and mechanisms of fragmentation under compressive forces

Extensive fragmentation of copper current collectors was observed after spherical indentation on prismatic and large-format pouch Li-ion cells by 3D X-ray computed tomography (XCT). Microscopic analysis including scanning electron microscopy (SEM), scanning transmission electron microscopy (STEM) and x-ray photoelectron microscopy (XPS) was carried out on copper current collectors from used commercial cells and pristine anodes. The copper-graphite cross-section images showed rough interface areas affected by reactions and diffusion in the used cell. Electron probe micro-analyzer (EPMA) element mapping showed the interface area was rich in oxygen and phosphorus. A detectable amount of phosphorus was also uniformly distributed inside the current collector. The same oxygen and phosphorus distributions were confirmed by STEM/EDS analysis. XPS depth profiles on multiple elements revealed the interface area of the aged anode was rich in Li, F, P, O and C and diffused at least 50 nm into the copper. In comparison, the pristine anode showed a very smooth C/Cu interface. No other elements were detected. For commercial cells, the reactions in the interface area and diffusion of multiple elements into the lattice and grain boundaries were responsible for the embrittlement of the copper current collectors. Finally, permanent cell capacity loss was observed in electrochemical performance of the indented cells.

36 MATERIALS SCIENCE↗
DOE OSTI2023

HamLib: A Library of Hamiltonians for Benchmarking Quantum Algorithms and Hardware

For a considerable time, large datasets containing problem instances have proven valuable for analyzing computer hardware, software, and algorithms. One notable example of the value of large datasets is ImageNet [1], a vast repository of images that has been instrumental in testing numerous deep learning packages. Similarly, in the domain of computational chemistry and materials science, the availability of extensive datasets such as the Protein Data Bank [2], the Materials Project [3], and QM9 [4] has greatly facilitated the evaluation of new algorithms and software approaches, while also promoting standardization within the field. These well-defined datasets and problem instances, in turn, serve as the foundation for creating benchmarking suites like MLPerf [5] and LINPACK [6], [7]. These suites enable fair and rigorous comparisons of different methodologies and solutions, fostering continuous advancements in various areas of computer science and beyond.

Sawaya, Nicolas PD↗
DOE OSTI2022

Electronic Spectroscopy and Dissociation Dynamics of Vinyl-Substituted Criegee Intermediates: 2-Butenal Oxide and Comparison with Methyl Vinyl Ketone Oxide and Methacrolein Oxide Isomers

The 2-butenal oxide Criegee intermediate [(CH 3 CH=CH)CHOO], an isomer of the four-carbon unsaturated Criegee intermediates derived from isoprene ozonolysis, is characterized on its first π * ←π electronic transition and by the resultant dissociation dynamics to O ( 1 D) + 2-butenal [(CH 3 CH=CH)CHO] products. The electronic spectrum of 2-butenal oxide under jet-cooled conditions is observed to be broad and unstructured with peak absorption at 373 nm, spanning to half maxima at 320 and 420 nm, and in good accord with the computed vertical excitation energies and absorption spectra obtained for its lowest energy conformers. The distribution of total kinetic energy released to products is ascertained through velocity map imaging of the O ( 1 D) products. About half of the available energy, deduced from the theoretically computed asymptotic energy, is accommodated as internal excitation of the 2-butenal fragment. A reduced impulsive model is introduced to interpret the photodissociation dynamics, which accounts for the geometric changes between 2-butenal oxide and the 2-butenal fragment, and vibrational activation of associated modes in the 2-butenal product. Application of the reduced impulsive model to the photodissociation of isomeric methyl vinyl ketone oxide reveals greater internal activation of the methyl vinyl ketone product arising from methyl internal rotation and rock, which is distinctly different than the dissociation dynamics of 2-butenal oxide or methacrolein oxide.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Multiscale Data-Driven Seismic Full-Waveform Inversion With Field Data Study

Seismic full-waveform inversion (FWI), which uses iterative methods to estimate high-resolution subsurface models from seismograms, is a powerful imaging technique in exploration geophysics. In recent years, the computational cost of FWI has grown exponentially due to the increasing size and resolution of seismic data. Moreover, it is a nonconvex problem and can encounter local minima due to the limited accuracy of the initial velocity models or the absence of low frequencies in the measurements. To overcome these computational issues, we develop a multiscale data-driven FWI method based on fully convolutional networks (FCNs). In preparing the training data, we first develop a real-time style transform method to create a large set of synthetic subsurface velocity models from natural images. We then develop two convolutional neural networks with encoder-decoder structures to reconstruct the low- and high-frequency components of the subsurface velocity models, separately. To validate the performance of our data-driven inversion method and the effectiveness of the synthesized training set, we compare it with conventional physics-based waveform inversion approaches using both synthetic and field data. Finally, these numerical results demonstrate that, once our model is fully trained, it can significantly reduce the computation time and yield more accurate subsurface velocity models in comparison with conventional FWI.

58 GEOSCIENCES↗
DOE OSTI2025

Shock propagation in aerogel and TPP foams for inertial fusion energy target design

Achieving practical inertial fusion energy (IFE) requires the development of target designs with well-characterized microstructure and compression response. We measured shock dynamics in low-density (17.5–500 mg/cm 3 ) aerogel and two-photon polymerization (TPP) foams using x-ray phase contrast imaging (XPCI) methods and the Velocity Interferometer System for Any Reflector. By analyzing shock front evolution, we examined how target type and density influence shock propagation and energy dissipation. Talbot-XPCI shows that aerogels support a smooth, bowed shock front due to their homogeneous nanometer-scale pore network. In contrast, TPP foams exhibit irregular, stepwise propagation driven by interactions with their periodic micrometer-scale lattice. Shock velocity follows a power-law relation: aerogels deviate from classical ρ −1/2 scaling due to pore-collapse dissipation, while TPP foams follow the trend with larger uncertainties from density variations. Comparisons with xRAGE simulations reveal systematic underestimation of shock speeds. These results provide the first experimental constraints on shock propagation in TPP foams over a wide density range and highlight the influence of internal structure on anisotropic shock behavior. Our findings support improved benchmarking of EOS and hydrodynamic models and inform the design of foam architectures that promote implosion symmetry in IFE capsules.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Nanoscale heterogeneity of arsenic and selenium species in coal fly ash particles: analysis using enhanced spectroscopic imaging and speciation techniques

Coal combustion byproducts are known to be enriched in arsenic (As) and selenium (Se). This enrichment is a concern during the handling, disposal, and reuse of the ash as both elements can be harmful to wildlife and humans if mobilized into water and soils. The leaching potential and bioaccessibility of As and Se in coal fly ash depends on the chemical forms of these elements and their association with the large variety of particles that comprise coal fly ash. The overall goal of this research was to determine nanoscale and microscale solid phase mineral associations and oxidation states of As and Se in fly ash. We utilized nanoscale 2D imaging (30–50 nm spot size) with the Hard X-ray Nanoprobe (HXN) in combination with microprobe X-ray capabilities (~5 μm resolution) to determine the As and Se elemental associations. Speciation of As and Se was also measured at the nano- to microscale with X-ray absorption spectroscopy. The enhanced resolution of HXN showed As and Se as either diffusely located around or comingled with Ca- and Fe-rich particles. The results also showed nanoparticles of Se attached to the surface of fly ash grains. Overall, a comparison of As and Se species across scales highlights the heterogeneity and complexity of chemical associations for these trace elements of concern in coal fly ash.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Discovering vanishing objects in POSS I red images using the Virtual Observatory

ABSTRACT In this paper, we report a search for vanishing sources in POSS I red images using virtual observatory (VO) archives, tools, and services. The search, conducted in the framework of the VASCO project, aims at finding POSS I (red) sources not present in recent catalogues like Pan-STARRS DR2 (limiting magnitude r = 21.4) or Gaia EDR3 (limiting magnitude G = 21). We found 298 165 sources visible only in POSS I plates, out of which 288 770 had a cross-match within 5 arcsec in other archives (mainly in the infrared), 189 were classified as asteroids, 35 as variable objects, 3592 as artefacts from the comparison to a second digitization (Supercosmos), and 180 as high proper motion objects without information on proper motion in Gaia EDR3. The remaining unidentified transients (5399) as well as the 172 163 sources not detected in the optical but identified in the infrared regime are available from a VO compliant archive and can be of interest in searches for strong M-dwarf flares, high-redshift supernovae, asteroids, or other categories of unidentified red transients. No point sources were detected by both POSS-I and POSS-II before vanishing, setting the rate of failed supernovae in the Milky Way during 70 yr to less than one in one billion.

Solano, Enrique↗
DOE OSTI2022

Extension of the bright high-harmonic photon energy range via nonadiabatic critical phase matching

The concept of critical ionization fraction has been essential for high-harmonic generation, because it dictates the maximum driving laser intensity while preserving the phase matching of harmonics. In this work, we reveal a second, nonadiabatic critical ionization fraction, which substantially extends the phase-matched harmonic energy, arising because of the strong reshaping of the intense laser field in a gas plasma. We validate this understanding through a systematic comparison between experiment and theory for a wide range of laser conditions. In particular, the properties of the high-harmonic spectrum versus the laser intensity undergoes three distinctive scenarios: (i) coincidence with the single-atom cutoff, (ii) strong spectral extension, and (iii) spectral energy saturation. We present an analytical model that predicts the spectral extension and reveals the increasing importance of the nonadiabatic effects for mid-infrared lasers. These findings are important for the development of high-brightness soft x-ray sources for applications in spectroscopy and imaging.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Impact of atomic substitution on core-hole relaxation dynamics: A study of Br 2 and IBr

Understanding inner-shell decay processes in heavy-element molecules is essential for unraveling x-ray-induced photodynamics and advancing molecular imaging techniques. Here, in this study, we investigate the influence of atomic substitution on core-hole relaxation dynamics and molecular fragmentation in Br 2 and IBr, initiated by x-ray ionization absorption at the Br K-edge. Using a combination of x-ray/ion coincidence measurements and Monte Carlo/molecular dynamics simulations, we track the charge distribution and the kinetic energy release (KER) of fragment ions with a total charge from 2+ to 8+. For both molecules, the simulated KER values show good agreement with experiment across different fragmentation channels. Our comparison reveals that substituting Br with the heavier I atom in IBr has a minimal impact on the inner-shell electronic decay process but significantly influences nuclear motion, leading to slower dissociation and thus a KER close to the Coulomb limit—an effect attributed to the atomic mass. These findings highlight the interplay between electronic and nuclear effects in molecular fragmentation, particularly in heavy-element species, and provide new insights into medical therapies, structural biology, and astrophysics.

Bhat, Nivedita [Argonne National Laboratory (ANL),↗
DOE OSTI2025

Characterization of Arsenic and Selenium in Coal Fly Ash to Improve Evaluations for Disposal and Reuse Potential (Final Technical Report)

Coal fly ash is a high volume waste material that is discarded in landfills and surface water impoundments across the U.S. and is also widely recycled for a variety of applications. The leaching of potential of contaminants of concern, such as arsenic (As) and selenium (Se), is often the driver of risk assessments for coal ash disposal and reuse. The extent of leachable As and Se depends on several factors related to environmental conditions and fly ash characteristics. Previous studies employed various methods to delineate the concentration, chemical form, and distribution of As and Se in fly ash materials. However, few studies have attempted to directly correlate these properties to mobilization parameters relevant to disposal and reuse. Instead, the coal residuals industries often rely upon standardized leaching protocols that can be laborious or involve hazardous chemicals. The goals of the project were to: 1) Develop and evaluate a characterization protocol that can be used to screen fly ash samples for leachability of As and Se; 2) Characterize As, Se, and associated constituents of fly ash particles at multiple length scales (nanometer to micrometer) to determine if elemental associations differ as a function of the resolution of characterization; and 3) Establish a predictive model for the chemical composition of coal ash produced annually at major U.S. coal fired power facilities on 50-year national coal supply records. For the first objective, we performed leaching experiments with 52 fly ash samples collected from 15 different U.S. power plants and representing coal feedstocks from the three major domestic coal regions. For this work, we assessed the mobilization potential of As and Se in fly ash based on standardized leaching protocols and performed multivariate and lasso regression analyses to explore correlations of leachable As and Se contents with characteristics such as major element contents, loss on ignition (LOI) and pH. The results of regression models indicated that major elements (Fe, Ca, Al) for a wide range of fly ashes can serve as predictor variables for the leaching potential of As, but not for Se. LOI and pH were not important predictive variables in the models. Both regression approaches resulted in relatively strong fits for leachable As (correlation coefficient R 2 = 0.78 for both models) compared to models for leachable Se (R 2 = 0.49). Overall, these results suggest that correlation models combined with on-site elemental analysis with portable analyzers may enable a screening method for leachable As in coal ash. For the second objective, we utilized nanoscale 2-D imaging (30-50 nm spot size) with the Hard X-ray Nanoprobe (HXN) in combination with microprobe X-ray capabilities (~5 µm resolution) to determine As and Se elemental associations in fly ash particles. Speciation of As and Se was also measured at the nano- to microscale with X-ray absorption spectroscopy. The enhanced resolution of HXN showed As and Se that were diffusely located around or comingled with Ca- and Fe-rich particles. The results also showed nanoparticles of Se attached to the surface of fly ash grains. Overall, a comparison of As and Se species across scales highlights the heterogeneity and complexity of chemical associations for these trace elements of concern in coal fly ash. For the final objective, we developed a predictive model for major element composition of coal ash in reserve at disposal sites of major U.S. coal fired power plants. This model was constructed from coal purchase records of 705 power stations from 1973-2022 and was trained on coal ash composition data showing that coal ash elemental composition is strongly associated with the source of feedstock coal. The model showed regional shifts in the major element contents of ash produced by power plants in the last 50 years, particularly for calcium and iron (expressed as %CaO and %Fe 2 O 3 ), as coal-fired power stations changed their source of coal over this time frame. Our approach enables an estimation of coal ash chemical composition that is stored in waste impoundments at individual power stations. Such information can help delineate the regional market potential for material applications that would utilize coal ash harvested from disposal sites across the U.S.

01 COAL, LIGNITE, AND PEAT↗