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Article

Rare Earth Element Detection and Quantification in Coal and Rock Mineral Matrices

by
Chet R. Bhatt
1,2,*,
Daniel A. Hartzler
1 and
Dustin L. McIntyre
1,*
1
National Energy Technology Laboratory, 3610 Collins Ferry Road, Morgantown, WV 26505, USA
2
NETL Support Contractor, 3610 Collins Ferry Road, Morgantown, WV 26505, USA
*
Authors to whom correspondence should be addressed.
Chemosensors 2025, 13(8), 270; https://doi.org/10.3390/chemosensors13080270
Submission received: 4 June 2025 / Revised: 17 July 2025 / Accepted: 18 July 2025 / Published: 23 July 2025
(This article belongs to the Special Issue Application of Laser-Induced Breakdown Spectroscopy, 2nd Edition)
Browse Figures
Versions Notes

Abstract

As global demand for rare earth elements (REEs) increases, maintaining the production and supply chain is critical. Technologies capable of being used in the field and in situ in the subsurface for rapid REE detection and quantification facilitates the efficient mining of known resources and exploration of new and unconventional resources. Laser-induced breakdown spectroscopy (LIBS) is a promising technique for rapid elemental analysis both in the laboratory and in the field. Multiple articles have been published evaluating LIBS for detection and quantification of REEs; however, REEs in their natural deposits have not been adequately studied. In this work, detection and quantification of two REEs, La and Nd, have been studied in both synthetic and natural mineral matrices at concentrations relevant to REE extraction. Measurements were performed on REE-containing rock and coal samples (natural and synthetic) utilizing different LIBS instruments and techniques, specifically a commercial benchtop instrument, a custom benchtop instrument (single- and double-pulse modes), and a custom LIBS probe currently being developed for in situ, subsurface, borehole wall detection and quantification of REEs. Plasma expansion, emission intensity, detection limits, and double-pulse signal enhancement were studied. The limits of detection (LOD) were found to be 10/14 ppm for La and 15/25 ppm for Nd in simulated coal/rock matrices in single-pulse mode. Signal enhancement of 3.5 to 6-fold was obtained with double-pulse mode as compared to single-pulse operation.

1. Introduction

The United States Geological Survey (USGS) and U.S. Department of Energy (DOE) have declared rare earth elements (REEs) to be critically important materials. According to the Energy Act of 2020, any non-fuel materials that contribute to energy technology, economic and national security, and face a risk of supply chain disruption are considered critical materials and minerals [1]. In the periodic table, REEs are the 15 lanthanides (elements with atomic number ranging from 57 to 71) along with two transition elements scandium (21) and yttrium (39). Geologically, REEs are available in minerals mostly as trace constituents, though multiple REEs may occur in a single mineral. Major sources of minable REEs are carbonatites, alkaline igneous systems, ion-absorption clay deposits, and monazite-xenotime-bearing placer deposits, typically in low concentrations, with carbonatites (igneous rocks made of mostly carbonate) considered to be the largest sources of REEs [2,3]. REEs are supremely important due to their diverse applications in modern technology. Critical optical and magnetic properties of REEs are useful for smart phones, televisions, petroleum refining, catalysts, power generation, military aircraft, computer memory drives, automotive systems, etc. [4,5,6,7,8,9]. Energy technologies such as electrical generators and motors use rare earth magnets as they are stronger than other types of magnets [10]. According to USGS Rare Earths Statistics and Information, mine production in the U.S. in 2024 was 45 thousand metric tons with an increase of 2000 metric tons compared to 2023 [11]. While the import decreased by 11% in its estimated value of rare earth compounds and metals in the year 2024 compared to the previous year, it still imported 8000 metric tons of these critical metal compounds. Global demand of rare earth oxides is approximately 118 thousand tons per year, which is estimated to reach 200 thousand tons per year by 2025 with a price estimated to be in the range of $110–150 thousand per ton [12,13,14]. Though a major share of rare earth deposits are located in China and other counties, the U.S. possesses significant reserves, estimated to be approximately 1.8 million metric tons [11]. With increasing demand and price, more REE resources are being explored and undiscovered resources are also possible due to advancements in engineering technologies for ore processing.
Sensing technologies enable exploration and evaluation of new or known REE resources, such as natural deposits or waste products. Major techniques currently used for REE measurement are inductively coupled plasma mass spectrometry (ICP-MS), inductively coupled plasma optical emission spectroscopy (ICP-OES), X-ray fluorescence (XRF), neutron activation analysis (NAA), etc. [15,16,17,18,19]. These are mostly laboratory-based techniques, requiring materials that need to be brought to the test centers for analysis. To evaluate REE content at the test sites, field-deployable, in situ techniques are more useful. Therefore, there is a critical need to develop sensors capable of rapid REE detection and quantification in non-laboratory environments. Recent studies have demonstrated LIBS as a good technique for field measurements [20,21,22,23,24,25,26,27,28]. Martin et al. have studied seven REEs (Eu, Gd, La, Nd, Pr, Sm, and Y) using their pure oxides [29]. While LIBS studies are mostly based on atomic and ionic emissions, application of laser-induced molecular emissions of the rare earth oxides is presented by Gaft et al. [30]. A review on REE determination by various spectroscopic techniques was published by Zawisza et al. [18]. Reported studies are mostly based on laboratory tests with standard samples; however, in real-world applications, REEs must be measured in natural ores which have complex matrices of multiple elemental species [8,31,32,33,34]. Therefore, it is valuable to study the behavior of laser-induced plasma emissions in various complex matrices. In our previous studies, multiple REEs, in both solids and aqueous solutions, have been studied using single- and double-pulse LIBS [35,36,37,38].
As our next aim is to develop a LIBS sensor for subsurface REE measurements by interrogating natural materials in situ within a borehole wall, in this study, selected REEs have been studied in both simulated and natural materials. The simulated samples were used to calibrate and optimize the LIBS system parameters, enabling the detection of La and Nd in test samples obtained from a natural REE deposit. Note that, in this study, the following definitions are used: “natural material” is material obtained from core samples of a coal seam and surrounding rock; “simulated material” is material produced in the lab to represent the approximate composition of the natural materials; “mineral phase” is all materials not lost on sample combustion (i.e., dry ash); “combustible” material is all materials lost on sample combustion; “coal-based” materials are materials that contain 0–10 wt.% dry ash; “rock-based” materials are 90–100% dry ash; “intermediate” or “mixed” materials are 10–90 wt.% dry ash (see Figure 1); “coal/rock mineral phase simulant” are the lab-produced materials approximating the corresponding natural mineral phases; and “simulated coal/rock” or “coal/rock simulant” are lab-produced materials approximating the natural coal/rock-based materials.

2. Materials and Methods

Natural coal and rock samples were obtained from a coal seam core known to contain REEs. The composition of these samples was determined through thermogravimetric analysis (TGA), ICP-MS, and ICP-OES (see the Supporting Information for the methods used). TGA was used to determine weight percentage (wt.%) moisture and dry ash, from which the wt.% of combustibles were calculated (wt.% combust = 100% − (% moisture) − (% dry ash)). For simplicity, it is assumed that all combustible material is 100% carbon. ICP-MS and ICP-OES were used to perform elemental analysis on the mineral phases contained within the samples, with aluminum (Al) determined via ICP-OES and the remainder determined via ICP-MS. The dry ash content of all 121 samples was between 3.1 and 95.2 wt.%, with 52 samples belonging to the “coal-based” category (defined earlier as materials containing 0–10 wt.% ash), 26 samples in the “rock-based” category (90–100 wt.% ash), and the remaining 43 samples belonging to the “intermediate” or “mixed” category. These categories were selected based on the distribution of ash within these samples (see Figure 1). Six samples named S-35, S-335, S-347, S-34, S-116, and S-154 were tested for the detection of selected REEs with four coal-based samples S-34 (5.54 wt.% ash), S-35 (6.72 wt.%), S-335 (6.46 wt.%), S-347 (5.44 wt.%); one rock-based sample S-116 (92.5 wt.% ash); and one mixed sample S-154 (37.28 wt.% ash). See Table 1 for their composition.
Simulated coal/rock-based samples resembling the natural test samples matrix were produced for calibration and system optimization. Based on the composition of all 121 natural samples, eight elements, all elements with an average concentration greater than 5000 ppm (Al, Na, Mg, Si, K, Ca, Fe) plus Ti (~4000 ppm), were selected to represent the mineral phases. Titanium was included because it is known to possess multiple emission lines throughout the spectral region of interest and, thus, has a high potential spectral interference. To produce the simulated coal and rock samples, mineral phase simulants were designed using the eight elements mentioned above to represent the approximate mineral phase composition of the natural-coal- and rock-based samples (Table 2). The mineral phase simulants were then diluted with graphite (Table 3) to produce the final simulated coal and rock sample stock (Table 4). These two stock materials, “simulated coal” and “simulated rock,” formed the basis of all simulant/calibration samples. Calibration samples for La and Nd were prepared by doping simulated coal/rock material with La2O3 and Nd2O3, with final dopant concentrations set to 125, 250, 500, and 1000 ppm (metal analyte concentration). One calibration set was produced for each analyte. Both calibration and natural samples were ground with a mortar and pestle to make a fine, homogeneous powder and pressed into pellets using 13 mm diameter die with a pellet press (Carver). While no binder was needed to press simulated coal samples, 20% starch was used as a binder for the simulated rock samples.

3. Principal Component Analysis of Test Samples

Broad spectrum data (185–900 nm) were acquired using a benchtop LIBS instrument (Applied Spectra J200, 266 nm laser, West Sacramento, CA, USA) from all samples; natural-coal- and rock-based samples and their simulants. Images of the sample pellets taken with the instrument’s built-in microscope are shown in Figure 2. Note that coal-based samples were slightly darker than the rock-based samples. Principal Component Analysis (PCA) was performed using open-source software (Python 3.13.3) to evaluate if the samples can be distinguished using LIBS spectral data. Spectra for PCA were collected from 25 different spots on each pellet at 10 acquisitions per spot. Ten principal components were computed for PCA, the first two of which accounted for more than 90% of the total variance of the data. The clustering demonstrated by PCA clearly shows that the coal and rock simulants (Figure 3a) and the natural-coal- and rock-based samples (Figure 3b) cluster around similar values and, thus, can be categorized into their respective types. It must be noted that the mixed sample, S-154 (32.28 wt.% ash) was classified as “rock-based,” which is likely due to the coal mineral phase being diluted within the carbon matrix; thus, the rock minerals provided a higher signal.

4. LIBS Benchtop Experimental Setup

A custom benchtop LIBS setup was also used for the analysis (Figure 4). A 1064 nm Nd: YAG laser (New Wave Research, Fremont, CA, USA) producing pulses of 5 ns width was used as the excitation source and was focused with a 3.8 cm focal length lens on the surface of the sample pellets, which were placed on a rotating platform. A rotating platform was used so that each laser pulse would ablate fresh material from the sample pellet. The pulse energy delivered to the sample was adjusted using a half-wave plate and a polarizing beam splitter, and was kept fixed at 5 mJ per pulse. The plasma emission was collected in a confocal mode through the same focusing lens and directed using a dichroic mirror toward the collection lens (5 cm focal length) and fiber (Andor SR-OPT-8024, Thessaloniki, Greece). Plasma emissions were analyzed using a Czerny-Turner spectrograph (Andor Shamrock SR303i-A, Thessaloniki, Greece) with an intensified charge-coupled device (ICCD) camera (Andor iStar DH320T-25F-03, Thessaloniki, Greece).

5. Detection and Selection of Characteristic Emission Lines

Selection of emission lines in LIBS spectra is critical to develop a sensing system for detection and measurement of REEs in natural sources. When LIBS spectra are obtained from materials with a complex composition, a number of emission lines of species present in the material may appear depending upon the matrix material and experimental parameters used. Ionic emission lines appear prominently with shorter gate time delays, while neutral emission lines are better detected at comparatively long gate time delays. Moreover, as different emission lines correspond to different excitation energies, the laser energy used for material ablation also plays an important role; however, higher laser energy carries a greater possibility of causing saturation in emission intensity. When a material has a complex composition (such as coal/rock), the emission lines of different elements might overlap with each other and cause spectral interference. As detection of elements of interest in the test materials primarily depend upon identification of the emission lines, interference-free emission lines are needed, particularly for trace elements which can be easily overwhelmed by matrix element emissions. This study aimed to analyze two trace REEs, specifically La and Nd. Based on the reported literature and the NIST database, the observed La and Nd emission lines in the 400–440 nm wavelength range are shown in Figure 5 [39,40,41,42,43]. After evaluating LIBS spectra recorded at various experimental, interference-free emission lines at 433.4 nm for La and 401.2 nm for Nd were chosen for the analysis of La and Nd in coal and rock matrices.

6. Temporal Evolution of Plasma Emission

When plasma is produced with laser-induced ablation, continuum emission is dominantly observed in the beginning due to bremsstrahlung and recombination processes in the plasma. As the plasma expands and cools, emission lines from ions, atoms, and then molecules appear subsequently. Therefore, to obtain ionic or atomic emission lines, a time delay after the plasma formation is needed to permit the plasma to cool sufficiently to obtain the desired lines and optimize signals. The temporal evolution of the plasma emissions in both coal and rock matrices for the selected La and Nd emission lines was studied. LIBS spectra were recorded at varying gate delay times at a fixed gate width/integration time (5 μs) and a fixed laser energy (5 mJ). Pulse energy was kept low to eliminate possible saturation and self-absorption seen at higher pulse energies. Gate delay was varied in the 0.1–10 μs range and emission signal peak intensity of both the La II 433.4 nm and Nd II 401.2 nm emission lines were monitored in simulated coal and rock matrices. Both the La and Nd signal peak intensity decreased almost exponentially with the increasing gate delay in both coal and rock matrices as shown in Figure 6a,c. When the signal peak intensity-to-background ratio (SBR) was plotted against the gate delay, it initially rapidly increased with increasing gate delay before slowly decreasing after reaching the maximum value as depicted in Figure 6b,d. While the absolute signal strength is high during the early stages of plasma emission, the background is also elevated due to the continuum emission, resulting in a low SBR. As time passes following plasma creation, the continuum emission decreases much more rapidly than the analyte emission intensity, causing the rise in, and eventual peak of, the SBR, as seen in Figure 6b,d. Interestingly, it was also noted that signal strength of both lines was found to be higher in the coal matrix compared to that in the rock matrix which might be attributed to the higher carbon content compared to other metals in the coal samples. For these samples, the peak SBR was found at a gate delay in the range of 0.5–3 μs for La II 433.4 nm and 0.5–4 μs for Nd II 401.2 nm lines. Therefore, this range of gate time delay was considered favorable to take LIBS spectra.

7. Calibration Curves & Limits of Detection (LOD)

After finding the optimum experimental parameters (gate delay and analyte emission lines), univariate regression models were developed. These regression models were used to estimate limits of detection (LOD) of La and Nd in both coal and mineral simulants. As mentioned in Section 2, stock mixtures of the coal and rock simulants were doped with La and Nd oxides so that La and Nd concentrations were in the range of 125 ppm to 1000 ppm. These powder samples were then pressed into pellets to perform measurements. Using the benchtop LIBS instrument (Figure 4), spectra were recorded from each of these sample pellets using a laser pulse of energy 5 mJ, a gate delay of 1 µs, and gate width of 5 µs. From each sample, 10 spectra were recorded with each spectrum consisting of 200 acquisitions. Background subtracted peak intensities of the emission lines, La II 433.4 nm and Nd II 401.2 nm, were plotted against the concentration to obtain linear regressions as shown in Figure 7. The coefficients of determination (R2) values were found to be in the range 0.98–0.999 for all four calibration curves. LOD were estimated by using Equation (1):
L O D = 3 σ S  
where σ is the standard deviation of the background—in this case it was taken from the blank sample—and S is the slope of the linear regression curve. The estimated LOD values (Table 5) were 10 ppm/14 ppm for La and 15 ppm/25 ppm for Nd in the simulated coal/rock materials, respectively. For both the analytes, La and Nd, estimated LOD values are lower in coal simulant compared to the rock simulant which can be attributed to the difference in their matrices. More precisely, physical properties like melting point, density, ionization energy, etc., of the species in the matrices with their different concentrations have a role in plasma formation, composition, and expansion which ultimately affects the plasma emission and hence detection limit. The possibility of a lower detection limit in the coal mineral simulant can also be expected by analyzing temporal evolution demonstrated in Figure 6 in the section “Temporal Evolution of Plasma Emission.” For all the gate delays, the intensity of the analyte lines in the coal simulant is higher compared to the rock simulant.

8. REE Detection in Natural Samples

LIBS measurements were performed on six natural test samples to detect the presence of La and Nd. Four of these samples were coal-based (S-34/35/335/347), one was rock-based (S-116), and one was intermediate/mixed (S-154). See Table 1 for mineral phase REE concentrations. Samples consisted of finely powdered material which was subsequently pressed into 13 mm pellets. It must be noted that, while the REE concentration was highest in the mineral phase of the coal-based samples, these samples only contained between 5.44–6.72 wt.% ash (Table 1). Therefore, one should expect the REE concentration in the as-extracted, natural material to be diluted by ~94 wt.% combustibles + moisture. LIBS spectra were recorded using a gate delay of 1 μs, a gate width of 5 μs, and a laser pulse energy of 5 mJ using the highest resolution setting (Grating 2400 l/mm) of the spectrometer. As mentioned before, samples were placed on a rotating platform and 10 spectra consisting of 200 acquisitions each were recorded. Spectral widths of 420–450 nm and 390–420 nm were used for La and Nd, respectively. La and Nd emission lines can be identified in the spectra of several samples (Figure 8). The La II 433.4 nm was detected in three coal-based samples, S-35/335/347, and the intermediate/mixed sample, S-154. Similarly, the Nd II 401.2 nm line was clearly identified in sample S-35 (coal-based), but only a very weak signal was detected (in the shoulder of a line from some other species) in the samples S-335/347 (coal-based) and the sample S-154 (mixed). Neither the La nor Nd emission lines were detected in the remaining two samples, S-37 (coal-based) and S-116 (rock-based).

9. Double Pulse LIBS for Signal Enhancement

For very low analyte concentrations, traditional single-pulse LIBS may not be adequate for analysis. In this situation, the double-pulse LIBS (DP-LIBS) signal enhancement technique might be useful, where two sequential laser pulses are used, increasing both the plasma volume and lifetime which attributes to stronger emission signals [44,45,46,47,48,49]. In this study, a collinear excitation mode DP-LIBS was applied to obtain stronger emission signals from the test samples. A second laser, identical to the one used in single-pulse benchtop setup (New Wave Research 1064 nm Nd: YAG laser, 5 ns width, Fremont, CA, USA), was used to reheat the plasma formed using the first laser after a short, inter-pulse delay. Both lasers were set to 5 mJ per pulse and the inter-pulse delay between the two lasers was controlled using a signal generator (Stanford Research System Inc DG535, Sunnyvale, CA, USA) with spectral acquisition being triggered together with the second laser. The inter-pulse delay was optimized to produce maximum signal-to-background ratio. For both La and Nd, an inter-pulse delay of 500 ns was found to contribute maximum signal enhancement compared to single-pulse LIBS and hence was chosen for all the measurements with the double-pulse setup. With respect to the second laser, gate delay was kept at 1 μs and gate width was kept at 5 μs. Using DP-LIBS, a significant enhancement of the analyte emission lines was observed. With sample S-35 (Figure 9), the La and Nd emission lines were found to experience an intensity enhancement of 3.5 to 6-fold compared to the single-pulse LIBS technique. Additionally, the Nd emission signal in samples S-335/347/154 was also enhanced using the DP-LIBS technique; however, these lines are still partially obscured and appear as a shoulder on an emission line from some other species in the sample matrix as shown in Figure 10.

10. Measurements with LIBS-Prototype

A prototype of a LIBS-based sensor (see Hartzler et al. [36] for a description) under development for use in the subsurface was also evaluated for measurements of natural coal-based samples [50,51,52]. This sensor system splits the traditional benchtop LIBS system into two parts: (1) an all-optical sensor-head built around a passively Q-switched (PQSW) microchip (MC) laser (see Figure 11a,b)) which is connected via a fiber optic umbilical to (2) a control unit containing the spectrograph, pump-laser, etc. The sensor head’s MC laser is pumped via fiber optic using a quasi-continuous wave (QCW) 808 nm diode laser (Lumibird Q1406-APF10, Lannion, France) and produces a 3 ns, 4 mJ laser pulse at 1064 nm. The MC laser output goes through beam expander, long-wave pass dichroic mirror (DCM), and then a focusing lens to interact with the test material to form a plasma. This laser-induced plasma emits light which is collected confocally and redirected using the DCM to a second optical fiber connected to the spectrograph. As with the benchtop setup, sample pellets were placed on a rotating platform for measurement. The laser pulse had 4 mJ energy, and gate time delay and integration time were set to be 1μs and 5μs, respectively. For each sample, 10 spectra consisting of 200 acquisitions each were recorded. Emission lines La II 433.4 nm and Nd II 401.2 nm were both detected in samples S-35 and S-335 but were not detected in S-347.
This demonstrates the feasibility of using this type of instrument for REE detection, and potentially quantification, in natural materials. Given that the most recent iteration of the subsurface LIBS sensor head has an outer diameter (OD) of 2”/50.8 mm and is able to operate at well (and water) depths of up to 100 ft/30 m (having demonstrated operation at 30 ft/9 m well and 18 ft/5.5 m water depth, see reference [51]), it is feasible to develop a LIBS sensor capable of operating directly within a borehole to perform in situ measurements of the borehole wall. Work is underway to reduce the sensor head OD to permit operation in 2” boreholes.

11. Conclusions

LIBS was evaluated for the determination of REEs in their natural deposits, with La and Nd studied as a proof of concept. Two different sample matrices were tested, namely naturally occurring (and simulated) coal-based and rock-based material obtained from a natural REE deposit. Six natural samples and four sets of calibration samples (La/Nd doped simulated coal and La/Nd doped simulated rock) were tested to evaluate the performance of LIBS for detection of REEs in coal and rock matrices. Using the simulants, experimental parameters were evaluated to optimize La and Nd emission signals, and these signals were then detected in four of the six natural sample materials. PCA was performed, demonstrating the ability of LIBS + PCA to distinguish rock from coal-based materials and validating that the simulants replicated the natural materials. These experiments were performed with a variety of instrumentation, including a commercial benchtop LIBS system, a custom benchtop LIBS system capable of operating in single-pulse or double-pulse modes (SP/DP-LIBS), and a prototype LIBS sensor system under development for subsurface operation. The commercial instrument performed well for PCA classification due to its broad spectral acquisition window. The custom benchtop instrument (in single-pulse mode) demonstrated LODs of 10/14 ppm for La and 15/25 ppm for Nd in simulated coal/rock matrices, while double-pulse mode was used to demonstrate a 3.5 to 6-fold signal enhancement (in natural materials) as compared to single-pulse operation. Finally, the prototype subsurface LIBS sensor head demonstrated detection of La and Nd in natural coal-based material. Since versions of this prototype system have already been operated in the subsurface for over two weeks, this demonstrates the feasibility of using a LIBS-based sensor directly as a borehole for in situ detection and possibly for quantification of REEs.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/chemosensors13080270/s1, LiBO2 Fusion Method; LECO 701 Thermogravimetric analysis (TGA) (Moisture& Ash); ICP-MS; and ICP-OES used by Pittsburgh analytical lab for the samples study.

Author Contributions

Conceptualization, C.R.B., D.A.H. and D.L.M.; methodology, validation, formal analysis, writing—original draft preparation, C.R.B. and D.A.H.; writing—review and editing, C.R.B., D.A.H. and D.L.M.; supervision, project administration, funding acquisition, D.L.M. All authors have read and agreed to the published version of the manuscript.

Funding

This project was funded by the United States Department of Energy, National Energy Technology Laboratory’s Resource Sustainability Program.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Acknowledgments

This work was performed in support of the U.S. Department of Energy’s (DOE) Office of Fossil Energy and Carbon Management’s Minerals Sustainability Program and executed through the National Energy Technology Laboratory (NETL) Research & Innovation Center’s Critical Mineral Characterization Technologies Multi-Year Research Plan. Sample analysis by TGA, ICP-MS and ICP-OES were performed by NETL’s Pittsburgh Analytical Laboratory (PAL).

Conflicts of Interest

The authors declare no conflicts of interest. This project was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

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Figure 1. Distribution of ash (by wt.%) within the 121 natural samples. Based on this data, “coal-based” samples are defined as those with between 0–10 wt.% ash while “rock-based” samples are defined as having between 90–100 wt.% ash.
Figure 1. Distribution of ash (by wt.%) within the 121 natural samples. Based on this data, “coal-based” samples are defined as those with between 0–10 wt.% ash while “rock-based” samples are defined as having between 90–100 wt.% ash.
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Figure 2. Images of the samples recorded using Applied Spectra’s J200 in micro mode where S-34/35/335/347 are coal-based, S-116 is rock-based, and S-154 is intermediate/mixed. The coal and rock simulants pictured here are undoped materials.
Figure 2. Images of the samples recorded using Applied Spectra’s J200 in micro mode where S-34/35/335/347 are coal-based, S-116 is rock-based, and S-154 is intermediate/mixed. The coal and rock simulants pictured here are undoped materials.
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Figure 3. PCA showing the clustering of (a) coal and rock simulants, and (b) natural samples. Note that the analysis method identified the intermediate/“mixed” sample, S-154 (37.28 wt.% ash), as “rock-based” together with S-116 (92.50 wt.% ash) rather than “coal-based” like S-34/35/335/347 (5.44–6.72 wt.% ash).
Figure 3. PCA showing the clustering of (a) coal and rock simulants, and (b) natural samples. Note that the analysis method identified the intermediate/“mixed” sample, S-154 (37.28 wt.% ash), as “rock-based” together with S-116 (92.50 wt.% ash) rather than “coal-based” like S-34/35/335/347 (5.44–6.72 wt.% ash).
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Figure 4. Experimental setup for benchtop LIBS measurements.
Figure 4. Experimental setup for benchtop LIBS measurements.
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Figure 5. LIBS spectra showing La and Nd emission lines in the 400–440 nm wavelength range.
Figure 5. LIBS spectra showing La and Nd emission lines in the 400–440 nm wavelength range.
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Figure 6. Variation of signal intensity and signal-to-background ratio (SBR) with increasing gate time delays. (a,b) for La II 433.4 nm and (c,d) for Nd II 401.2 nm emission lines.
Figure 6. Variation of signal intensity and signal-to-background ratio (SBR) with increasing gate time delays. (a,b) for La II 433.4 nm and (c,d) for Nd II 401.2 nm emission lines.
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Figure 7. Calibration curves for La in (a) coal, (b) rock mineral simulants, and Nd in (c) coal, (d) rock mineral simulants.
Figure 7. Calibration curves for La in (a) coal, (b) rock mineral simulants, and Nd in (c) coal, (d) rock mineral simulants.
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Figure 8. LIBS spectra showing La II 433.4 nm and Nd II 401.2 nm emission lines in the coal-based (S-34/35/335/347), rock-based (S-116), and intermediate (S-154) samples. (a,c) showing S-35/335/347 and (b,d) showing S-34/116/154.
Figure 8. LIBS spectra showing La II 433.4 nm and Nd II 401.2 nm emission lines in the coal-based (S-34/35/335/347), rock-based (S-116), and intermediate (S-154) samples. (a,c) showing S-35/335/347 and (b,d) showing S-34/116/154.
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Figure 9. LIBS spectra showing signal enhancement with double-pulse LIBS. (a) La II emission line at 433.4 nm and (b) Nd II emission line at 401.2 nm.
Figure 9. LIBS spectra showing signal enhancement with double-pulse LIBS. (a) La II emission line at 433.4 nm and (b) Nd II emission line at 401.2 nm.
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Figure 10. Nd II emission line at 401.2 nm detected with the double-pulse setup in samples (a) S-335, (b) S-347, and (c) S-154.
Figure 10. Nd II emission line at 401.2 nm detected with the double-pulse setup in samples (a) S-335, (b) S-347, and (c) S-154.
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Figure 11. Schematic (a) and real (b) version of the prototype sensor head, LIBS spectra showing La (c) and Nd (d) emission signals.
Figure 11. Schematic (a) and real (b) version of the prototype sensor head, LIBS spectra showing La (c) and Nd (d) emission signals.
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Table 1. Matrix composition and mineral phase REE composition of natural samples S-35, S-335, S-347, S-34, S-116, and S-154.
Table 1. Matrix composition and mineral phase REE composition of natural samples S-35, S-335, S-347, S-34, S-116, and S-154.
Sample
NameS-347S-335S-34S-35S-116S-154
TypeCoalCoalCoalCoalRockMixed
Matrix Composition (ppm)
Al †54,84043,690107,100125,900101,100148,500
Na12,47312,35833,81530,90310814959
Mg22,78932,75660,39746,090795113,064
Si697244,59590,935129,393326,740274,680
K124317093661633220,34415,203
Ca206,800263,307146,943101,67435016669
Ti131524722454345850834939
Fe69,95063,17729,70925,05832,34136,275
TGA (wt.%)
Moisture32.8817.3516.1919.781.9013.79
Dry Ash5.446.465.456.7292.5037.28
Combust. ‡61.6876.1978.3673.505.6048.93
Mineral Phase REE Composition (ppm)
Total Rare Earths
REE + Sc + Y1080198014402500310950
Selected Rare Earths
La22049013029052110
Nd18025023037050150
† Al determined through ICP-OES, remainder determined through ICP-MS. ‡ “Combust.”/loss on combustion = 100% − Moisture wt.% − Dry Ash wt.%.
Table 2. Composition of coal and rock mineral phase and their simulants.
Table 2. Composition of coal and rock mineral phase and their simulants.
Natural Mineral PhaseMineral Phase Simulant
Coal RockCoalRock
Matrix Element (ppm)
Al75,270111,59374,300100,000
Na18,289160029,6002620
Mg42,540856938,9008000
Si74,367344,34281,400320,000
K251620,983248020,000
Ca186,4835380101,0002870
Ti2840496930104600
Fe50,19523,83249,50022,000
Dry Matter (wt.%)
Ash †7%96%----
Combust. ‡93%4%----
†/‡ Weight % dry matter, where (†) Ash = (% Dry Ash)/(100% − % Moisture) and (‡) Combust. = 100% − % Dry Ash. Note that the average wt.% moisture of the coal-based samples was 17.5% and the rock-based samples was 3.3%.
Table 3. Recipe for simulated coal and rock matrices. Graphite is used to simulate the combustible component of the simulants.
Table 3. Recipe for simulated coal and rock matrices. Graphite is used to simulate the combustible component of the simulants.
Coal Mineral Phase
(wt.%)		Rock Mineral Phase
(wt.%)
Mineral Phase Simulant Component
Al2O314.0318.90
NaCl5.580.49
MgCO313.502.78
SiO217.4068.48
KCl0.473.81
CaCl2·2H2O41.431.62
TiO20.500.77
Fe2O37.083.15
Dry Matter
Mineral phase simulant693
Graphite947
Table 4. Simulated coal and rock matrix composition after dilution with graphite.
Table 4. Simulated coal and rock matrix composition after dilution with graphite.
Matrix ElementCoal Simulant
(ppm)		Rock Simulant
(ppm)
Al446093,000
Na17802430
Mg23307440
Si4880298,000
K14918,600
Ca60902670
Ti1804280
Fe297020,500
C941,000 †73,700 ‡
† 1200 ppm C and ‡ 3700 ppm C contributed by MgCO3.
Table 5. Detection limits (LODs) for La and Nd in simulated coal and rock materials.
Table 5. Detection limits (LODs) for La and Nd in simulated coal and rock materials.
AnalyteMatrix
Coal SimulantRock Simulant
La10 ppm14 ppm
Nd15 ppm25 ppm
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Bhatt, C.R.; Hartzler, D.A.; McIntyre, D.L. Rare Earth Element Detection and Quantification in Coal and Rock Mineral Matrices. Chemosensors 2025, 13, 270. https://doi.org/10.3390/chemosensors13080270

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Bhatt CR, Hartzler DA, McIntyre DL. Rare Earth Element Detection and Quantification in Coal and Rock Mineral Matrices. Chemosensors. 2025; 13(8):270. https://doi.org/10.3390/chemosensors13080270

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Bhatt, Chet R., Daniel A. Hartzler, and Dustin L. McIntyre. 2025. "Rare Earth Element Detection and Quantification in Coal and Rock Mineral Matrices" Chemosensors 13, no. 8: 270. https://doi.org/10.3390/chemosensors13080270

APA Style

Bhatt, C. R., Hartzler, D. A., & McIntyre, D. L. (2025). Rare Earth Element Detection and Quantification in Coal and Rock Mineral Matrices. Chemosensors, 13(8), 270. https://doi.org/10.3390/chemosensors13080270

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