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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Advanced Electron Microscopy of Nanophased Synthetic Polymers and Soft Complexes for Energy and Medicine Applications

After decades of developments, electron microscopy has become a powerful and irreplaceable tool in understanding the ionic, electrical, mechanical, chemical, and other functional performances of next-generation polymers and soft complexes. The recent progress in electron microscopy of nanostructured polymers and soft assemblies is important for applications in many different fields, including, but not limited to, mesoporous and nanoporous materials, absorbents, membranes, solid electrolytes, battery electrodes, ion- and electron-transporting materials, organic semiconductors, soft robotics, optoelectronic devices, biomass, soft magnetic materials, and pharmaceutical drug design. For synthetic polymers and soft complexes, there are four main characteristics that differentiate them from their inorganic or biomacromolecular counterparts in electron microscopy studies: (1) lower contrast, (2) abundance of light elements, (3) polydispersity or nanomorphological variations, and (4) large changes induced by electron beams. Since 2011, the Center for Nanophase Materials Sciences (CNMS) at Oak Ridge National Laboratory has been working with numerous facility users on nanostructured polymer composites, block copolymers, polymer brushes, conjugated molecules, organic–inorganic hybrid nanomaterials, organic–inorganic interfaces, organic crystals, and other soft complexes. This review crystalizes some of the essential challenges, successes, failures, and techniques during the process in the past ten years. It also presents some outlooks and future expectations on the basis of these works at the intersection of electron microscopy, soft matter, and artificial intelligence. Machine learning is expected to automate and facilitate image processing and information extraction of polymer and soft hybrid nanostructures in aspects such as dose-controlled imaging and structure analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Plastic additives in the ocean: Use of a comprehensive dataset for meta-analysis and method development

In excess of 13,000 chemicals are added to plastics (‘additives’) to improve performance, durability, and production of plastic products. They are categorized into numerous chemical classes including flame retardants, light stabilizers, antioxidants, and plasticizers. While research on plastic additives in the marine environment has increased over the past decade, there is a lack of methodological standardization. To direct future measurement of plastic additives, we compiled a first-of-its-kind dataset of literature assessing plastic additives in marine environments, delineated by sample type (plastic debris, seawater, sediment, biota). Using this dataset, we performed a meta-analysis to summarize the state of the science. Currently, our dataset includes 217 publications published between 1978 and May 2023. The majority of publications analyzed plastic additives in biota collected from Europe and Asia. Analyses concentrated on plasticizers, brominated flame retardants, and bisphenols. Common sample preparation techniques included Solvent - Agitation extraction for plastic, sediment, and biota samples, and Solid Phase Extraction for seawater samples with dichloromethane and solvent mixtures including dichloromethane as the organic extraction solvent. Finally, most analyses were performed utilizing gas chromatography/mass spectrometry. There are a variety of data gaps illuminated by this meta-analysis, most notably the small number of compounds that have been targeted for detection compared to the large number of additives used in plastic production. The provided dataset facilitates future investigation of trends in plastic additive concentration data in the marine environment (allowing for comparison to toxicity thresholds) and acts as a starting point for optimizing and harmonizing plastic additive analytical methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Entanglement of excitons by acoustic guiding

A system is described that exhibits the functionality of a beam-splitter, typically an optical device that splits a beam of light in two. In this case, the beams are acoustic pulses and can lead to the creation of a Wannier-Mott exciton: a bound state of an electron and an electron hole whose attraction to each other is maintained by the electrostatic Coulomb force. This exciton beam-splitter is lossy (i.e., involves the dissipation of electrical or electromagnetic energy). Half of the time the exciton is radiated away. Nevertheless, if the exciton is not lost, the exciton is now in a superposition of two states that can be well separated in position. Four such beam-splitters can be used to make an exciton interferometer that uses the interference patterns from the interacting acoustic pulses to extract information.

Yurke, Bernard↗
DOE OSTI2025

Time-dependent phenomena in correlated materials

Understanding time-dependent processes and light-matter interaction in strongly correlated materials, and the interplay between electronic, orbital, vibrational, and spin degrees of freedom, is a cornerstone of condensed matter. These mechanisms can be proven by measuring the response of the systems to time-dependent perturbations. The corresponding time scales are dictated by the way light couples to the different excitations, and how these excitations exchange energy and momentum. Our research advances our understanding of these processes, and the interpretation of different equilibrium and time-resolved spectroscopies. Our project encompasses two main themes: (i) developing and refining computational techniques to study non-equilibrium spectroscopies including non-perturbative effects and (ii) applications to non-equilibrium phenomena. We have developed a new computational approach that works directly in the time domain: by including all the degrees of freedom involved in the scattering process (e.g. electrons, photons, neutrons), we solve the time dependent problem: a faithful numerical simulation of the experiment. By measuring the energy and momentum of the outgoing particles, we can extract information about the energy and momentum absorbed by the system. Prior to our work, people attempting to model and calculate non-equilibrium spectral functions relied on a description of the scattering cross section based on a formulation in the frequency domain, a treatment that is extremely cumbersome and complex. Our technique works in and out of equilibrium and can reproduce spectra by several spectroscopic techniques, such as time-resolved photoemission, neutron scattering, Raman, X-ray spectroscopies (RIXS, Auger, XAS, XMCD), and, by not relying on analytical approximations, yields results that reveal novel overlooked transient mechanisms. These tools provide sorely needed intuition for understanding the phenomenology of strongly correlated materials and will help experimentalists in identifying signatures of relevant excitations in pump-probe experiments, such as those conducted in DOE supported facilities.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Baryon–Antibaryon Photoproduction off the Proton

The GlueX Experiment at Jefferson Lab uses linearly polarized photon beam to study the photoproduction of light mesons and baryons off a proton target with unprecedented precision. We have observed p¯p photoproduction and, for the first time, ?¯? photoproduction from thresholds up to E = 11.6 GeV. We reproduce the observed kinematic distributions with a phenomenological model that includes t-channel Regge exchange and double Regge exchange. The model uses few parameters to match all the kinematic spectra via an optimization algorithm developed specifically for this purpose. We report the total cross sections of the two reactions, as well as differential cross sections with respect to various momentum transfers and invariant mass spectra of p¯p, ?¯?, p¯? systems which show enhancements right above production thresholds. Furthermore, the beam asymmetries of the p¯p system are extracted from the GlueX data to further probe the nature of the t-channel Regge exchanges. We also report preliminary measurements of the spin polarizations of the individual hyperons. No prominent resonance structure was found in these reaction channels, but the angular distributions and the correlation of longitudinal momenta indicate the existence of multiple production mechanisms. Besides a dominant forward peak in the polar angle of the baryon-antibaryon pair in t-channel production, we see wide-angle antibaryon distributions different from that of the baryon. In the hyperon channel, we see a clear separation between the photoproduction of the ?¯?, p¯? systems.

Li, Hao↗
DOE OSTI2021

Reaction losses of charged particles in CsI(Tl) crystals

To efficiently detect energetic light charged particles, it is common to use arrays of energy-loss telescopes involving two or more layers of detection media. As the energy of the particles increases, thicker layers are usually needed. However, carrying out measurements with thick-telescopes may require corrections for the losses due to nuclear reactions induced by the incident particles on nuclei within the detector and for the scattering of incident particles out of the detector, without depositing their full energy in the active material. In this paper, we develop a method for measuring such corrections and determine the reaction and out-scattering losses for data measured with the silicon-CsI(Tl) telescopes of the newly developed HiRA10 array. Here, the extracted efficiencies are in good agreement with model predictions using the GEANT4 reaction loss algorithm for Z = 1 and Z = 2 isotopes. After correcting for the HiRA10 geometry, we obtain a general function that describes the loss of efficiency due to reaction losses in CsI(Tl) crystals as a function of range.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Hybrid Machine Learning for Scanning Near-Field Optical Spectroscopy

The underlying physics behind an experimental observation often lacks a simple analytical description. This is especially the case for scanning probe microscopy techniques, where the interaction between the probe and the sample is nontrivial. Realistic modeling to include the details of the probe is always exponentially more difficult than its "spherical cow" counterparts. On the other hand, a well-trained artificial neural network based on real data can grasp the hidden correlation between the signal and sample properties. In this work, we show that, via a combination of model calculation and experimental data acquisition, a physics-infused hybrid neural network can predict the tip-sample interaction in the widely used scattering-type scanning near-field optical microscope. This hybrid network provides a long-sought solution for accurate extraction of material properties from tip-specific raw data. The methodology can be extended to other scanning probe microscopy techniques as well as other data-oriented physical problems in general.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Radiation-resilient InAs quantum dot lasers

Displacement damage from particle radiation increases the threshold current of semiconductor lasers, impeding the deployment of photonic systems in harsh radiation environments. Replacing conventional quantum well (QW) gain regions with quantum dots (QDs) has been shown to suppress the radiation-induced increase in threshold current owing to three-dimensional carrier confinement, which effectively segregates charge carriers away from radiation-induced defects. However, radiation-induced change in threshold current is also affected by extrinsic design elements of a laser, such as photon lifetime, and therefore provides only a partial assessment of the radiation hardness. In this work, we advance the understanding of the radiation resiliency of InAs QD lasers by extracting the change in the nonradiative recombination rate after radiation exposure, which provides a more fundamental assessment of the intrinsic radiation hardness of InAs QD gain regions. We find that carriers that populate InAs QD states have a nonradiative recombination rate that is over an order-of-magnitude less sensitive to neutron radiation relative to carriers in InGaAsP QWs. InAs QD lasers must be designed such that carriers primarily occupy QD states, preventing carrier spillover into surrounding barrier layers to obtain the maximum benefit. Furthermore, we show that enhanced radiation hardness is observed in InAs QD lasers grown on both highly lattice-mismatched silicon substrates and native GaAs substrates, further broadening the appeal of InAs QD lasers as reliable light sources for photonic systems operating in harsh radiation environments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Optimizing Simulation Parameters for Weak Lensing Analyses Involving Non-Gaussian Observables

We performed a series of numerical experiments to quantify the sensitivity of the predictions for weak lensing statistics obtained in ray-tracing dark matter (DM)-only simulations, to two hyper-parameters that influence the accuracy as well as the computational cost of the predictions: the thickness of the lens planes used to build past light cones and the mass resolution of the underlying DM simulation. The statistics considered are the power spectrum (PS) and a series of non-Gaussian observables, including the one-point probability density function, lensing peaks, and Minkowski functionals. Counterintuitively, we find that using thin lens planes (< 60 h {sup −1} Mpc on a 240 h {sup −1} Mpc simulation box) suppresses the PS over a broad range of scales beyond what would be acceptable for a survey comparable to the Large Synoptic Survey Telescope (LSST). A mass resolution of 7.2 × 10{sup 11} h {sup −1} M {sub ⊙} per DM particle (or 256{sup 3} particles in a (240 h {sup −1} Mpc){sup 3} box) is sufficient to extract information using the PS and non-Gaussian statistics from weak lensing data at angular scales down to 1′ with LSST-like levels of shape noise.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Southwest Pacific tropical cyclone development classification utilizing machine learning and synoptic composites

This study evaluates the ability of machine learning algorithms to classify tropical depressions (TDs) and tropical storms (TSs) in the western region of the southwest Pacific Ocean (SWPO). Decision rules are generated to predict the environment required for a depression to fully develop into a mature storm, and the most influential predictors in the classification decision are ranked. TD and TS are discriminated based on a maximum sustained wind speed threshold (≥17 ms -1 ). Various aerosol, thermodynamic, and dynamic parameters are extracted closest to the initiation point of each non-developing and developing sample. The covariates associated with each labelled sample are used to train a decision tree and random forest model. Results using a testing dataset suggest the random forest approach more accurately distinguishes between non-developing and developing samples. The classification accuracy of the decision tree and random forest are 72% and 91%, respectively. Random forest outperformed the decision tree by providing higher accuracy in test data. The most important variables for binary classification are sea salt aerosol optical depth (AOD), 1,000 mb relative humidity, and sea surface temperature. AOD is a quantitative estimate of the aerosols presents in the air through the extinction of a ray of light as it passes through the atmosphere. Mean composite maps constructed in an unsupervised manner have been created for the most important variables identified by the random forest classifier during TD and TS events to highlight the difference in geophysical and aerosol variables' climatology during the two different classifications. This work will advance the risk management strategies for northeastern Australia and other SWPO basin islands to control their tropical cyclone related losses through prioritizing forecasting variables that are the strongest predictors of the strengthening of tropical depressions into tropical cyclones.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Ultrafast-induced coherent acoustic phonons in the two-dimensional magnet CrSBr

Magnetism in two-dimensional (2D) van der Waals (vdW) crystals offers promising new directions for low-dimensional physics and devices. In this work, mega-electron volt (MeV) ultrafast electron diffraction was employed to investigate the ultrafast atomic dynamics of a novel, 2D vdW magnetic single-crystal CrSBr. Femtosecond (fs) optical pump pulses excited non-equilibrium atomic displacements shown to be coherent acoustic phonons (CAPs). Phonon frequencies were extracted by analyzing oscillations of different Bragg peak (BP) intensities and were determined to be GHz acoustic disturbances that propagated as strain waves. Phonon modes exhibit anisotropy with respect to the a and b crystal axes. Subharmonic phonon frequencies were also observed, and this provided a signature of nonlinear oscillatory coupling between the laser-induced pumping phonon frequency and secondary phonon frequencies. Thus, CrSBr was found to serve as a nonlinear phononic frequency converter. The ultrafast time dependence of the Bragg intensity was simulated by incorporating an oscillating deviation parameter ansatz into expressions for the dynamical scattering intensity yielded excellent modeling of the ultrafast structural dynamics of the photo-excited 2D crystal. Our work provides a foundation for exploring how fs light pulses can influence phonon dynamics in materials with strong spin-lattice coupling. These results suggest that CAPs can match the magnon frequencies and show the promise of CrSBr for use in optical-to-microwave transducers and phononic devices.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Modulating metal-centered dimerization of a lanthanide chaperone protein for separation of light lanthanides

Elucidating details of biology’s selective uptake and trafficking of rare earth elements, particularly the lanthanides, has the potential to inspire sustainable biomolecular separations of these essential metals for myriad modern technologies. Here, we biochemically and structurally characterize Methylobacterium (Methylorubrum) extorquens LanD, a periplasmic protein from a bacterial gene cluster for lanthanide uptake. This protein provides only four ligands at its surface-exposed lanthanide-binding site, allowing for metal-centered protein dimerization that favors the largest lanthanide, La III . However, the monomer prefers Nd III and Sm III , which are disfavored lanthanides for cellular utilization. Structure-guided mutagenesis of a metal-ligand and an outer-sphere residue weakens metal binding to the LanD monomer and enhances dimerization for Pr III and Nd III by 100-fold. Selective dimerization enriches high-value Pr III and Nd III relative to low-value La III and Ce III in an all-aqueous process, achieving higher separation factors than lanmodulins and comparable or better separation factors than common industrial extractants. Finally, we show that LanD interacts with lanmodulin (LanM), a previously characterized periplasmic protein that shares LanD’s preference for Nd III and Sm III . Our results suggest that LanD’s unusual metal-binding site transfers less-desirable lanthanides to LanM to siphon them away from the pathway for cytosolic import. The properties of LanD show how relatively weak chelators can achieve high selectivity, and they form the basis for the design of protein dimers for separation of adjacent lanthanide pairs and other metal ions.

lanthanide biochemistry↗
DOE OSTI2020

Effects of pore size on water dynamics in mesoporous silica

Water confined in mesoporous silica plays a central role in its many uses ranging from gas sorption to nanoconfined chemical reactions. Here in this paper, the influence of pore diameter (2.5–5.4 nm) on water hydrogen bond (H-bond) dynamics in MCM41 and SBA15 mesoporous silicas is investigated using femtosecond infrared vibrational spectroscopy and molecular dynamics simulations on selenocyanate (SeCN-) anions dissolved in the pores. As shown recently, SeCN- spectral diffusion is a reliable probe of surrounding water H-bond structural motions. Additionally, the long CN stretch vibrational lifetime facilitates measurement of the full range of confined dynamics, which are much slower than in bulk water. The simulations shed light on quantitative details that are inaccessible from the spatially averaged observables. The dependence of SeCN- orientational relaxation and that of spectral diffusion on the distance from the silica interface are quantitatively described with an exponential decay and a smoothed step-function, respectively. The distance-dependence of both quantities is found to be independent of the diameter of the pores, and the spatial distribution of SeCN- is markedly non-uniform, reaching a maximum between the interface and the pore center. The results indicate that the commonly invoked two-state, or core–shell, model is a more appropriate description of spectral diffusion. Using these insights, we model the full time-dependence of the measured dynamics for all pore sizes and extract the “core” and “shell” dynamical correlation functions and SeCN- spatial probability distributions. The results are critically compared to those for water confined in reverse micelles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Leaching Kinetics of Rare Earth Elements from Fire Clay Seam Coal

Recovery of rare earth elements (REEs) from coal samples collected from the Fire Clay coal seam using diluted mineral acid solutions was investigated. The initial processing step was coal recovery using conventional froth flotation which concentrated the REEs in tailing material resulting in an upgrade to values around 700 ppm on a dry whole mass basis. Leaching experiments were performed on the flotation tailings material using a 1.2 M sulfuric acid solution adjusted to a temperature of 75 °C to study the extractability of REEs from coal material. The effect of particle size, leaching time, leaching temperature, and solid concentration on REE leaching recovery were evaluated. The kinetic data obtained from leaching over a range of temperatures suggested that the leaching process follows the shrinking core model with possibly a mixed control mechanism that may be a result of several heterogenous materials leaching simultaneously. Leaching recovery increased rapidly at the beginning of the reaction then slowed as the system reached equilibrium. The apparent activation energy determined from test data obtained over a range of temperatures using 1 M sulfuric acid was 36 kJ/mol for the first 20 min of reaction time and 27 kJ/mol for the leaching period between 20 and 120 min. The leaching of light REEs during the initial stage was determined to be driven by a chemical reaction, followed by the formation of a product layer, which required lower activation energy in the later stage of leaching. In regards to the heavy REEs, the major mechanism for leaching is desorption and the product layer formation does not affect the heavy REEs significantly.

58 GEOSCIENCES↗
DOE OSTI2023

Full forward model of galaxy clustering statistics with AbacusSummit light cones

ABSTRACT Novel summary statistics beyond the standard 2-point correlation function (2PCF) are necessary to capture the full astrophysical and cosmological information from the small-scale (r < 30h−1Mpc) galaxy clustering. However, the analysis of beyond-2PCF statistics on small scales is challenging because we lack the appropriate treatment of observational systematics for arbitrary summary statistics of the galaxy field. In this paper, we develop a full forward modelling pipeline for a wide range of summary statistics using the large high-fidelity AbacusSummit light cones that account for many systematic effects as well as remain flexible and computationally efficient to enable posterior sampling. We apply our forward model approach to a fully realistic mock galaxy catalog and demonstrate that we can recover unbiased constraints on the underlying galaxy–halo connection model using two separate summary statistics: the standard 2PCF and the novel k-th nearest neighbour (kNN) statistics, which are sensitive to correlation functions of all orders. We will demonstrate its strong constraining power on extended galaxy–halo connection models and cosmology in follow up papers. We expect this to become a powerful approach when applying to upcoming surveys such as DESI where we can leverage a multitude of summary statistics across a wide redshift range to maximally extract information from the non-linear scales.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.

09 BIOMASS FUELS↗
DOE OSTI2024

Multifunctional entinostat enhances the mechanical robustness and efficiency of flexible perovskite solar cells and minimodules

Flexible perovskite solar cells (F-PSCs), prized for their nature of soft and high power-weight compatibility, have attracted intensive attention. However, inferior buried perovskite-substrate interfaces due to low interfacial adhesion between perovskites and substrates and large deformation of flexible substrates have greatly limited the performance of F-PSCs. Here, we add organic molecule Entinostat (ET) into hole extraction material Poly[bis(4-phenyl)(2,4,6-trimethylphenyl)amine] (PTAA) to enhance the adhesion at the perovskite/substrate interface using the interactions of ET with perovskites, PTAA and indium tin oxide (ITO) through its multiple function groups. Meanwhile, ET added into perovskites reduces the voids at the bottom perovskite film, due to its capability to tune the crystallization of perovskites by forming adduct with lead. Consequently, the inverted small area F-PSCs achieved an efficiency of 23.4%. The flexible perovskite minimodule with an area of 9 cm 2 achieved an aperture efficiency of ~19.0% certified by National Renewable Energy Laboratory. Furthermore, the optimized unencapsulated flexible minimodule retained 84% of the initial efficiency after 5000 bending cycles and retained 90% of the initial PCE (T90) after light soaking for >750 hours.

14 SOLAR ENERGY↗
DOE OSTI2023

Removal of Iron from Pyrite-Rich Coal Refuse by Calcination and Magnetic Separation for Hydrometallurgical Extraction of Rare Earth Elements

In the metallurgical extraction of rare earth elements (REEs), the ratio of contaminant ions to REEs in the leachate dictates the cost and operational efficiency of the downstream processes. The current study investigated the potential iron contamination removal from the feed to the hydrometallurgical process by calcination followed by magnetic separation. The 2.20 specific gravity sink fraction of Baker coal seam coarse refuse was pulverized to finer than 180 μm, calcined at various temperatures, and separated into magnetic and non-magnetic fractions using a wet high-intensity magnetic separator at different field strengths. The untreated feed, calcined products, and their subsequent magnetic and non-magnetic fractions were subjected to acid leaching tests with 1.2 M sulfuric acid at 75 °C and 1% w/v solids concentration. The recovery of light and heavy rare earth elements (LREEs and HREEs, respectively) along with the concentration of common contaminant ions (Al, Ca, and Fe) were measured as output variables. The weight percent of magnetic material was maximized at approximately 29% by calcination at a temperature of 400 °C. Magnetic removal of this fraction using a field strength of 1.15 Tesla resulted in the rejection of 81% of the iron. Leaching of the magnetic fraction provided significantly higher Fe recovery relative to untreated feed material and the non-magnetic fraction. The non-magnetic fraction was subsequently calcined at 600 °C to dehydroxylate the clays and released the REE minerals in the same manner as the treatment of the original coarse refuse material. A comparison of the leachate elemental concentrations resulting from the leaching of both the calcined non-magnetic and original coarse refuse showed only a slight reduction in the iron content from the non-magnetic material. This finding combined with the REE loss in the magnetic fraction resulted in the conclusion that the magnetic removal step was unfavorable.

58 GEOSCIENCES↗