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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Critical Minerals and Materials Matchmaker-CM3

This is the Critical Minerals and Materials Matchmaker (CM3) survey form. CM3 is an online information resource created to help connect users across the critical minerals and materials supply chain. The survey is designed to allow organizations to self-identify their critical minerals and materials-aligned activities and interests, and an interactive map that displays those on-going activities in a dynamic way. To include your critical minerals management activity or activities in CM3, please open and fill out the Critical Minerals and Materials Survey. If your organization has many ongoing or planned activities that would be onerous to enter in the form, or if your activities are difficult to geolocate (such as a transport network), please email the team at edxspatial@netl.doe.gov. This initiative is aligned with the approach of DOE’s H2 Matchmaker and Carbon Matchmaker. Read more information on H2 Matchmaker and Carbon Matchmaker. Below are some questions to help understand if you should fill out the CM3 survey: Does your company work with elements such as lithium, cobalt, copper, graphite, nickel, rare earth minerals, or platinum group metals? Does your organization have research and development activities related to critical materials or their supply chains? Does your company currently work in the critical minerals or materials supply chain? Do you have prospective work in critical minerals or materials in the next 5 years? Does your company mine, process, refine or distribute critical minerals or materials? Do you want to network with other facilities or organizations working in the same areas? Are you curious about the critical mineral and material activity in your surrounding area? Are you interested in aligning your potential needs across the supply chain to different geographic areas within the U.S? For more information, please see the CM3 website (https://www.energy.gov/fecm/articles/critical-minerals-materials-matchmaker-cm3) or email our team at edxspatial@netl.doe.gov.

CM3↗
DOE OSTI2025

An assessment of the global cooling supply chain and implications for critical minerals

The global room air conditioner (AC) industry has undergone rapid growth, and the U.S. and India now face new supply chain risks due to China’s dominant role in manufacturing and driving demand. As India’s room AC market and related electricity consumption increases with continued economic development, it faces dual challenges of supply chain vulnerabilities and power grid blackouts from higher peak loads. To mitigate supply chain vulnerabilities for high-efficiency cooling equipment and critical mineral inputs to AC components, there is an increasing need to diversify the supply chain and address energy security and peak load risks to both the U.S. and India. This report assesses the potential for diversifying supply chains and scaling up manufacturing of highly efficient cooling equipment technologies and related critical mineral inputs in the U.S. and India. Successful cooperation on these technologies will enable the U.S., India, and their Quadrilateral Security Dialogue (Quad) partners to diversify AC supply chains while meeting India’s large and growing demand for efficient cooling. In the similarly concentrated supply chain for critical minerals, the U.S. and India can work with Japan and Australia to leverage each country’s strengths in diversifying mining, processing, and refining while pursuing alternatives to materials with high supply chain risks and supporting increased recycling and circular economy approaches.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Computationally Guided and Experimentally Validated Design of Custom Chelators for Critical Mineral Recovery

Selective, high throughput separation of target critical metals from complex environments such as fly ash leachates and mining process streams presents a significant challenge for economical production. Custom chelators and sorbents are an attractive technology for selective metal extraction, however it can be difficult to predict their performance, and significant experimental efforts are often required to develop chelating technologies. Here, we present a computational strategy focused on modelling chelator-metal binding interactions and benchmark these results versus experimental data. A computational pipeline combining forcefield, semiempirical, and meta-GGA methods with a thermodynamic framework optimized for error cancellation has been developed to predict binding energies of chelator complexes towards critical mineral recovery applications. This approach, originally validated on [2.2.2] cryptates binding mono- and divalent cations, demonstrated robust predictive capabilities with an R2 of 0.850 against experimental aqueous binding energies. The workflow includes metadynamics for exploring high-dimensional potential energy surfaces and a cluster-continuum model for accurate yet computationally efficient solvation modeling. Error cancellation between solvation energies of free and chelator-coordinated ions enables faster convergence, even with finite cluster sizes. Initial studies on the cryptates revealed consistent metal-ligand coordination patterns, with systematic variations influenced by ion size and charge, highlighting key structural features linked to binding selectivity. Further studies of a proprietary chelator have resulted in identification of previously unreported selectivity towards economically significant metals, which in-house experiments have confirmed, demonstrating the feasibility of this approach. By applying this methodology to new chelators targeting critical minerals such as lithium, cobalt, nickel and other strategic metals, we aim to accelerate the discovery of next-generation chelators for efficient recovery, recycling, and separation processes. This computational framework serves as the backbone of a high-throughput design pipeline tailored for sustainable resource utilization and may be applied to a wide range of systems to meet experimental needs.

computational materials↗
DOE OSTI2024

Summary: Nuclear Energy Critical Material Waste Reduction and Supply Chain Solutions Enabled by Advanced Manufacturing

In September 2020, the U.S. government issued an executive order to address the threat to the domestic supply chain from its reliance on critical minerals (CMs) from foreign competitors and to support the domestic mining and processing industry. A national strategy on CMs with impact on the U.S. Department of Energy’s (DOE’s) vision for 2021–2031 was developed. This vision embraces science and technology to re-establish U.S. competitiveness in the CM and material supply chains by (a) scientific innovation and technologies to ensure resilient and secure CMs and maintain a domestic material supply chain, (b) building a long-term minerals and materials innovation ecosystem to foster new capabilities to mitigate CM supply chain challenges, (c) increasing private sector adoption for sustaining the domestic CM supply chain, and (d) coordinating with international partners and federal agencies to diversify global supply chains and ensure the adoption of best practices for sustainable mining and processing (DOE 2021).

36 MATERIALS SCIENCE↗
DOE OSTI2023

NEWTS Coal Mine Drainage Dataset from Cravotta Brady (2015)

Data from Cravotta, Brady, "Priority pollutants and associated constituents in untreated and treated discharges from coal mining or processing facilities in Pennsylvania, USA". Applied Geochemistry, 2015. https://doi.org/10.1016/j.apgeochem.2015.03.001 Dataset includes information on water quality composition including inorganic compounds from untreated and treated streams of coal-mine discharge from coal mining and coal processing locations. Data is provided in the original version as well as in a summarized version for easy input into aqueous chemistry modeling software.

Aqueous Chemistry↗
DOE OSTI2022

Prospective Life Cycle Assessment of Synthetic Graphite Manufactured via Electrochemical Graphitization

Lithium-ion batteries (LIBs) are expected to play a crucial role in meeting many of the clean energy-related goals. Due to its electrical properties such as good conductance, chemical inertness, and corrosion resistance, graphite is a very popular anode for LIBs. Traditional methods of producing battery-grade graphite (high purity >99%) include processing naturally mined graphite or manufacturing synthetic graphite via the Acheson process, which converts soft amorphous carbons such as petroleum coke into graphite by subjecting it to high temperature (up to 3000 °C) for prolonged periods of times (3–5 days). However, due to a lack of abundant high purity natural graphite sources, synthetic graphite is the preferred choice for many LIBs. A new synthetic electrochemical graphitization method that subjects the amorphous carbon precursor submerged in a molten salt mixture to a constant cathodic polarization (against a graphitic anode) has been discovered that has significantly lower graphitization temperatures (~800 °C) and reduced graphitization time (3–6 h). Furthermore, the method can accept a higher variety of carbon precursors compared to the Acheson process. A prospective life cycle assessment (LCA) is conducted on this new method and compared against the traditional processes. The laboratory-scale demonstration of the method is used to build an inventory, which through various assumptions is scaled up to a commercial scale. An additional scenario is also considered with a biomass-derived carbon precursor for the graphite. The results from the LCA show that while the laboratory scale process is similar to the Acheson process and natural flake graphite in terms of impact, the scaled-up process is drastically better than the Acheson process in all environmental categories. Using a coconut shell-derived biomass precursor has a higher impact due to its manufacturing in Indonesia, as the Indonesian energy grid is highly fossil fuel dependent. Therefore, the biomass carbon may have a higher impact than petroleum coke dependent on the location of production of biomass-derived carbon black. The LCA has identified the molten salt—CaCl 2 —as a potential hotspot and suggests other salts should be considered. Accordingly, the new method shows promise in this early stage LCA in improving the environmental performance of graphite (and by relation LIBs) and therefore needs to be explored more in terms of its commercial viability.

25 ENERGY STORAGE↗
DOE OSTI2021

Metal-Organic Framework-based Luminescent Sensors for Rare Earth Elements

Rare earth elements (REEs) are crucial for many applications and are particularly important in wind power and electric vehicle technologies, spurring projected demand increases for REEs in coming decades. However, the REE supply is restricted by a monopolistic global marketplace, tedious and costly processing steps for REE production, and environmental hazards associated with mining and processing. Coal and coal-utilization byproducts have emerged as potential domestic sources for REEs, however REE production from these sources is currently hindered by slow and expensive characterization techniques. Here, we demonstrate that metal-organic framework (MOF)-based sensitizers can effectively serve as luminescent sensors capable of rapidly detecting and distinguishing part-per-billion concentration levels of emissive REEs, including terbium, europium, dysprosium, samarium, neodymium, and ytterbium. Importantly, we demonstrate that the MOF structure plays a significant role in tuning the selectivity of the MOF for REEs in acidic environments, an essential step towards deployment in various REE-rich process streams. As a proof-of-concept, we demonstrate sensitive and reversible detection of REEs using a portable fiber-optic spectrometer

Crawford, Scott↗
DOE OSTI2022

Recent advances in the global rare-earth supply chain

The current global rare-earth element (REE) supply chain is highly imbalanced and tightly controlled by just a few countries. Such an imbalance of the critical metals supply chain poses a significant challenge to the energy-transition strategies and the national security of many countries. As such, this issue of MRS Bulletin delves into the materials science aspects of the REE supply chain, including fundamental REE mineralogy, REE separation and extraction, REE mining economics, the environmental impacts of REE mining and processing, and circular economy potential for REEs. This issue of MRS Bulletin is meant to inform the materials science community of some of the constraints on REE production from the mining of ore deposits, through processing technologies, and then finally, the possibility of recycling.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Upstream Extraction of Tellurium from Copper Concentrates

A major barrier to increasing CdTe PV module production is the availability of tellurium (Te), an element necessary to manufacture CdTe PV cells that currently has a very limited supply. Most Te is obtained from ore extracted for copper mining, but the current processing methods only extract 3-4% of the available Te in this ore, with the rest being discarded during other processing steps. This project aims to increase Te supply by developing an innovative extraction method that will recover more Te from the copper ore earlier in the process. This method will also extract toxic elements like antimony and arsenic, making the copper mining process more environmentally friendly.

14 SOLAR ENERGY↗
DOE OSTI2024

Multi-functional Sorbent Development for Separation of Critical Minerals and Pollutants

Critical Metals (CMs), including Rare Earth Elements (REEs), aluminum, manganese, cobalt, and others outlined by the U.S. Department of the Interior’s Geological Survey are essential to the national and economic security of the US, and whose supply chain is susceptible to disruption. Because of the negative environmental impacts inherent with the conventional mining and processing of solid ores for CMs, adsorption-based recovery of naturally dissolved species from coal wastewaters is appealing. Acid mine drainage (AMD) remains a relatively untapped source that can be rich in REEs, and especially in Al, Mn, and other CMs. NETL’s Multi-functional Sorbent Technology (MUST) has been developed originally from the functionalized silica sorbents for carbon capture. Ongoing work aims to find AMD sites enriched with either REEs or other CM, and to perform additional field tests at these sites. Overall, our work shows the viability of recovering CMs from AMD and other coal waste streams, using a fixed-bed adsorption system that can also employ a selective elution technique to achieve highly purified metal resources.

Shi, Fan↗
DOE OSTI2021

Applying Waveform Correlation to Reduce Seismic Analyst Workload Due to Repeating Mining Blasts

Agencies that monitor for underground nuclear tests are interested in techniques that automatically characterize mining blasts to reduce the human analyst effort required to produce high–quality event bulletins. Waveform correlation is effective in finding similar waveforms from repeating seismic events, including mining blasts. We report the results of an experiment to detect and identify mining blasts for two regions, Wyoming (U.S.A.) and Scandinavia, using waveform templates recorded by multiple International Monitoring System stations of the Preparatory Commission for the Comprehensive Nuclear–Test–Ban Treaty Organization (CTBTO PrepCom) for up to 10 yr prior to the time of interest. We discuss approaches for template selection, threshold setting, and event detection that are specialized for characterizing mining blasts using a sparse, global network. We apply the approaches to one week of data for each of the two regions to evaluate the potential for establishing a set of standards for waveform correlation processing of mining blasts that can be generally applied to operational monitoring systems with a sparse network. Here, we compare candidate events detected with our processing methods to the Reviewed Event Bulletin of the International Data Centre to assess potential reduction in analyst workload.

58 GEOSCIENCES↗
DOE OSTI2021

CONVERSION OF COAL WASTES AND MUNICIPAL SOLIDS MIXTURES BY PYROLYSIS TORREFACTION AND ENTRAINED FLOW GASIFICATION

Hundreds of millions of tons of underutilized high-ash, low-energy-density bituminous and anthracite waste coal can be found in the U.S. These stockpiles contribute to water pollution from leaching that is harmful to waterways downstream of the piles. Conventional circulating fluidized combustion power plants use this coal to generate energy but emit toxic trace metals into groundwater (e.g., lead, mercury, arsenic). In addition, refuse-derived fuel (RDF) including municipal solid waste (MSW), biomass, waste plastics, and industrial waste are an enormous and untapped resource that is currently filling landfills or incinerated for low-quality energy with poor emissions. Mainstream Engineering has developed a combined pyrolysis torrefaction entrained flow gasification (PT-EFG) process that utilizes combined waste coal and RDF to create an alternative energy source, increasing energy independence and security and reducing the environmental burden from coal mines and processing facilities. Mainstream’s self-sustaining PT process converts RDF into feedstocks that can be pulverized and handled like conventional coal, enabling combined RDF-waste coal co-feeding into an EFG. The EFG operates at temperatures high enough to slag the ash completely, destroying any residual polycyclic aromatic hydrocarbons (PAHs), converting ash into nonporous vitrified slag or non-leachable ash, and generating high-hydrogen syngas for power generation or liquid fuels. During Phase II, Mainstream successfully demonstrated PT of biomass and MSW into a coal-like feedstock, which was co-gasified with waste coal in an EFG in a combined PT EFG process

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Applying Waveform Correlation to Mining Blasts Using a Global Sparse Network

Agencies that monitor for underground nuclear tests are interested in techniques that automatically characterize mining blasts to reduce the human analyst effort required to produce high-quality event bulletins. Waveform correlation is effective in finding similar waveforms from repeating seismic events, including mining blasts. We report the results of an experiment that uses waveform templates recorded by multiple International Monitoring System stations of the Comprehensive Nuclear-Test-Ban Treaty for up to 10 years prior to detect and identify mining blasts that occur during single weeks of study. We discuss approaches for template selection, threshold setting, and event detection that are specialized for mining blasts and a sparse, global network. We apply the approaches to two different weeks of study for each of two geographic regions, Wyoming and Scandinavia, to evaluate the potential for establishing a set of standards for waveform correlation processing of mining blasts that can be effective for operational monitoring systems with a sparse network. We compare candidate events detected with our processing methods to the Reviewed Event Bulletin of the International Data Centre to develop an intuition about potential reduction in analyst workload.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

Applying Waveform Correlation to Mining Blasts Using a Global Sparse Network

Agencies that monitor for underground nuclear tests are interested in techniques that automatically characterize mining blasts to reduce the human analyst effort required to produce high-quality event bulletins. Waveform correlation is effective in finding similar waveforms from repeating seismic events, including mining blasts. We report the results of an experiment that uses waveform templates recorded by multiple International Monitoring System stations of the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization for up to 10 years prior to the time period of interest to detect and identify mining blasts that occur during single weeks of study. We discuss approaches for template selection, threshold setting, and event detection that are specialized for mining blasts and a sparse, global network. We apply the approaches to two different weeks of study for each of two geographic regions, Wyoming and Scandinavia, to evaluate the potential for establishing a set of standards for waveform correlation processing of mining blasts that can be effective for operational monitoring systems with a sparse network. We compare candidate events detected with our processing methods to the Reviewed Event Bulletin of the International Data Centre to develop an intuition about potential reduction in analyst workload.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Critical Minerals from Waste Streams in the Powder River Basin of Wyoming and Montana, USA

Background/Objectives. Critical minerals (CM) are essential for numerous industrial and defense applications, including green technologies that will help to meet carbon emission reduction goals. Many CM are currently mined and processed in countries that lack stringent environmental and labor regulations. Unconventional sources such as existing industrial waste streams could play a part in building an ethical CM supply chain. Additionally, waste streams created in the extraction and processing of CM could be used for other industrial or commercial purposes, thereby reducing waste and advancing a circular economy. Approach/Activities. The Department of Energy funded Powder River Basin (PRB) CORE-CM project is exploring all aspects of the carbon ore, rare earth element (REE), and CM value chain, including the potential for extraction of CM from industrial waste streams in the basin. Waste streams that have potential as CM feedstocks are being inventoried to assess the concentration of CM in each waste stream and to estimate volume and accessibility. Although basin-specific technologies for extraction of CM are still in development, waste streams that may be produced during these processes are being cataloged and mapped to potential secondary uses. Results/Lessons Learned. Initial studies of the CM potential of PRB coal ash show that this waste stream contains concentrations of greater than 300 ppm total REE (Bagdonas et al., 2022, Renewable and Sustainable Energy Reviews). Moreover, calcium-rich PRB coal ash is amenable to REE extraction (Taggart et al., 2016, Environmental Science and Technology). Wyoming coal is shipped to 28 states, meaning that coal ash produced at power stations across the US represents a potential widespread resource for the extraction of REE. In addition to coal mining and coal fired electricity generation, the PRB is home to other energy industries including oil and gas production, in-situ uranium mining, and bentonite mining. Historically, precious and base metal mining has taken place on the perimeter of the PRB. Waste streams from these industries are currently being evaluated for their CM potential. Assessing the CM resource potential of waste streams could contribute to the development of an ethical CM supply chain. Results from these studies can be replicated for other waste streams, increasing the likelihood of successful creation of circular economies.

Phillips, Erin↗
DOE OSTI2020

Argonne’s global critical materials agent-based model (GCMat)

Several studies have identified rare earths as critical materials. Although reasonably abundant in the Earth’s crust, rare earths typically occur in low concentrations of mined ores. Processes for recovering rare earth concentrates from these ores are complex and capital intensive. Further, lead times for deposit development, licensing, and construction are long, with reports of 10- 15 years. China is a major player in the rare earths supply chain, both in production capacity and technology innovation. In 2017, China supplied more than 80% of global rare earth oxide demand. Rare earth elements (REEs) have unique magnetic, catalytic, and phosphorescent properties that significantly improve performance of a wide range of technologies. These technologies span aerospace, energy, telecommunications, electronics, transportation, defense, and other diverse applications. Consequently, disruptions in rare earth supply can have a significant societal impact. Estimating that impact requires understanding of the dynamics across the supply chain, from rare earth oxide extraction to end use application. This report documents Argonne’s Global Critical Materials model (GCMat), first described by Riddle et al. 2015. GCMat provides capabilities to explore supply chain dynamics and uncertainty under scenarios of demand growth or shrinkage, technology adoption, supply disruptions, and trade policies and mitigation strategies of new supply sources, product substitution, consumer thrifting, and stockpiling. Supply chain participants from rare earth mining through final demand are modeled as interacting agents who make market decisions independently as time progresses. Since the version documented in Riddle et al. 2015, GCMat has been expanded to cover additional REEs, derived products and supply chains that use these REEs, and includes new agent behaviors and modeling capabilities. Section 2 provides a summary of the GCMat model design, including the structure of the model and key assumptions, section 3 summarizes methods used for model calibration and sensitivity analysis, and section 4 provides examples of model results.

36 MATERIALS SCIENCE↗