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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Data-Driven Understanding of Low-to-Moderate Income Customers’ Adoption and Financial Qualification in Community Solar (Final Technical Report)

When Solstice began working in the community solar industry, it quickly became clear that there was a wide disparity along race and class lines in terms of what kind of household traditional solar offerings benefited. Solstice is an organization that aligns itself to the principles of a Just Transition, or the concept that a transition to a green economy should benefit and prioritize historically marginalized groups of people. Given that low-income communities are disproportionately burdened by our current polluting fossil fuel economy, Solstice set out to investigate the root causes of the exclusion of low to moderate income (LMI) households in the community solar market. As we worked more and more with financiers, developers, and LMI households, we recognized that significant barriers to entry in a developer-owned project were credit threshold requirements. A FICO score of 750 and higher discriminates against LMI households that are not financially stable enough to take on multiple lines of credit (or who may not have been deemed credit worthy enough to access a line of credit) yet who may have been reliable utility customers. As Solstice began looking further into preliminary data and speaking to community-based organizations (CBOs) that serve LMI households, we found our hypothesis to be worth investigating; there was indeed a subset of LMI households with poor FICO scores (or no FICO score at all) that had perfect bill payment history. Though initially Solstice was intent on gathering a body of data to prove or disprove this hypothesis, we were also concerned that all this proof of a financially stable and reliable customer was not being incorporated into FICO, and wondered if such a score would be possible. After preliminary research into the existence of alternative scores, Solstice realized that many mission-driven lending institutions rely on alternative metrics that are formulated for their specific industry. However, we also realized that community solar did not have an alternative credit underwriting mechanism, though there was great need from solar financiers for one. We anticipated that the creation of an EnergyScore would contribute to financiers’ need to qualify more people, as these entities are desperately looking for ways to lower the cost of customer acquisition. FICO turns away nearly 50% of potential customers (according to our own acquisition experience), and much of the community solar industry is realizing that FICO is not the perfect qualifying mechanism. Additionally, with a grounding in energy justice, just transitions, and climate justice, Solstice recognizes the need for a solution that addresses a more urgent need for LMI households to access renewable energy savings and relieve energy burdens. With funding from the Department of Energy, we gathered the data necessary to build the EnergyScore, reached out to mission-driven developers willing to test the metric, and secured several demonstration projects to pilot the EnergyScore. After acquiring customers for these demo projects using the EnergyScore, we will be continuously collecting customer payment behavior data. Though it goes beyond the scope of this project, we plan to disseminate this de-identified data with the wider community solar industry, which includes not only financiers and developers, but other mission-driven nonprofits, solar cooperatives, community-based organizations, environmental justice activists, and academics. While we intend to disprove the notion that LMI households cannot be included in projects without acutely increasing the risk to project finances, we also hope the data can be used to negotiate better pricing of systems and terms of ownership for community groups seeking to build inclusive projects.

14 SOLAR ENERGY↗
DOE OSTI2023

The endpoint of partial deconfinement

We study the matrix quantum mechanics of two free hermitian N × N matrices subject to a singlet constraint in the microcanonical ensemble. This is the simplest example of a theory that at large N has a confinement/deconfinement transition. In the microcanonical ensemble, it also exhibits partial deconfinement with a Hagedorn density of states. We argue that the entropy of these configurations, based on a combinatorial counting of Young diagrams, are dominated by Young diagrams that have the VKLS shape. When the shape gets to the maximal depth allowed for a Young diagram of SU(N), namely N, we argue that the system stops exhibiting the Hagedorn behavior. The number of boxes (energy) at the transition is N 2 /4, independent of the charge of the state.

1/N Expansion↗
DOE OSTI2022

Universal machine learning framework for defect predictions in zinc blende semiconductors

Our article introduces a universal predictive framework for point defect formation energies and charge transition levels in a wide chemical space of zinc blende semiconductors and possible impurity atoms selected from across the periodic table. This framework was developed by leveraging high-throughput quantum mechanical simulations benchmarked using some experimental data from the literature, as well as machine learning (ML)-based regressions techniques that map unique materials descriptors to computed defect properties and yield optimized and generalizable models. Furthermore, the power and utility of these models is revealed through quick predictions for thousands of new defects and screening of low-energy impurities, which may tune the equilibrium conductivity in the semiconductor. This work presents, to our knowledge, the largest density functional theory (DFT) dataset of defect properties in semiconductors and the largest DFT+ML-based screening of point defects in semiconductors to date.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Effects of electron beam irradiation on CrMnV and CrMnTiV high entropy alloys: Nano-mechanical, structural, and thermodynamic perspectives

Beam exit windows are crucial components of any particle accelerator as they provide an interface between the beamline vacuum and target material at atmospheric media. For high beam power machines, special materials and designs are required to withstand high radiation and mechanical loads, while minimizing energy loss during transition and maximizing window lifetime. This research investigates the impact of electron beam exposure to bulk CrMnV and CrMnTiV high entropy alloys (HEAs) with the primary goal of identifying suitable candidate materials for the design of robust and durable exit window settings. The selection criteria include intrinsic characteristics, power dissipation, and mechanical responses. According to the thermodynamic calculations, both equiatomic CrMnV and the addition of 7% of Ti with equiatomic CrMnV yield solid-solutions phases. The structural and mechanical properties of CrMnV and CrMnTiV samples were tested using field emission scanning electron microscopy, atomic force microscopy, scanning electron microcopy with energy dispersive x-ray spectroscopy, x-ray diffraction, and nanoindentation before and after exposure to a dose of ~66 kGy from a 10 MeV e-beam accelerator. Despite exhibiting beam transmission characteristics comparable to Cr and V, the indentation hardness of HEAs exceeded that of the Cr and V samples by five to six times. The examination of the CrMnTiV irradiated samples revealed organized deformation patterns depicting new features, which we suspect twinning and twin boundaries due to the addition of Ti to CrMnV. Ti, a hexagonal-close-packed crystal structure, is commonly known for deformation twinning behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Optimizing Neutrino Flavor Conversion Measurements through Machine Learning

The phenomenon of neutrino flavor conversion whereby the flavor of a neutrino particle can change between its time of production and later detection was the first definitive evidence of physics beyond the Standard Model. Some of the oscillation parameters used to describe this conversion are not yet well measured, leaving important questions still open regarding flavor conversion both in vacuum and as neutrinos travel through matter. NOvA is a long-baseline neutrino oscillation experiment that uses Fermilab's predominantly $\nu_\mu$ NuMI beam. A 14 kton oil-based liquid scintillator far detector 810 km away is used to measure neutrino oscillation through the $\nu_\mu$ disappearance and $\nu_e$ appearance channels. Super-K is a 50 kton water Cherenkov detector, which measures the disappearance of $\nu_e$ produced during solar fusion. The high density environment of the sun decreases the $\nu_e$ survival probability at higher energies observable in Super-K compared to the vacuum-dominated oscillations at lower energies. However, the transition region is overshadowed by radioactive background in the detector. In both of these experiments, the separation of neutrino detection events from background and classification of neutrino flavor are crucial tasks that benefit from the introduction of machine learning. Chapter 1 gives an overview of neutrinos and the context under which flavor conversion is measured in this dissertation. Chapters 2-7 present the results of a Bayesian sampling approach for the latest NOvA 3-flavor oscillation analysis with $26.6 \times 10^{20}$ protons on target in neutrino mode and $12.5 \times 10^{20}$ in antineutrino mode collected over 10 years. Chapters 8-13 present the results of extending the Super-K solar analysis to lower energies during its fourth phase with 2970 days of livetime.

Yankelevich, Alejandro Jaime [UC, Irvine]↗
DOE OSTI2026

Optimizing Neutrino Flavor Conversion Measurements through Machine Learning

The phenomenon of neutrino flavor conversion whereby the flavor of a neutrino particle can change between its time of production and later detection was the first definitive evidence of physics beyond the Standard Model. Some of the oscillation parameters used to describe this conversion are not yet well measured, leaving important questions still open regarding flavor conversion both in vacuum and as neutrinos travel through matter. NOvA is a long-baseline neutrino oscillation experiment that uses Fermilab's predominantly $\nu_\mu$ NuMI beam. A 14 kton oil-based liquid scintillator far detector 810 km away is used to measure neutrino oscillation through the $\nu_\mu$ disappearance and $\nu_e$ appearance channels. Super-K is a 50 kton water Cherenkov detector, which measures the disappearance of $\nu_e$ produced during solar fusion. The high density environment of the sun decreases the $\nu_e$ survival probability at higher energies observable in Super-K compared to the vacuum-dominated oscillations at lower energies. However, the transition region is overshadowed by radioactive background in the detector. In both of these experiments, the separation of neutrino detection events from background and classification of neutrino flavor are crucial tasks that benefit from the introduction of machine learning. Chapter 1 gives an overview of neutrinos and the context under which flavor conversion is measured in this dissertation. Chapters 2-7 present the results of a Bayesian sampling approach for the latest NOvA 3-flavor oscillation analysis with $26.6 \times 10^{20}$ protons on target in neutrino mode and $12.5 \times 10^{20}$ in antineutrino mode collected over 10 years. Chapters 8-13 present the results of extending the Super-K solar analysis to lower energies during its fourth phase with 2970 days of livetime.

Yankelevich, Alejandro Jaime [UC, Irvine]↗
DOE OSTI2026

Optimizing Neutrino Flavor Conversion Measurements through Machine Learning

The phenomenon of neutrino flavor conversion whereby the flavor of a neutrino particle can change between its time of production and later detection was the first definitive evidence of physics beyond the Standard Model. Some of the oscillation parameters used to describe this conversion are not yet well measured, leaving important questions still open regarding flavor conversion both in vacuum and as neutrinos travel through matter. NOvA is a long-baseline neutrino oscillation experiment that uses Fermilab's predominantly $\nu_\mu$ NuMI beam. A 14 kton oil-based liquid scintillator far detector 810 km away is used to measure neutrino oscillation through the $\nu_\mu$ disappearance and $\nu_e$ appearance channels. Super-K is a 50 kton water Cherenkov detector, which measures the disappearance of $\nu_e$ produced during solar fusion. The high density environment of the sun decreases the $\nu_e$ survival probability at higher energies observable in Super-K compared to the vacuum-dominated oscillations at lower energies. However, the transition region is overshadowed by radioactive background in the detector. In both of these experiments, the separation of neutrino detection events from background and classification of neutrino flavor are crucial tasks that benefit from the introduction of machine learning. Chapter 1 gives an overview of neutrinos and the context under which flavor conversion is measured in this dissertation. Chapters 2-7 present the results of a Bayesian sampling approach for the latest NOvA 3-flavor oscillation analysis with $26.6 \times 10^{20}$ protons on target in neutrino mode and $12.5 \times 10^{20}$ in antineutrino mode collected over 10 years. Chapters 8-13 present the results of extending the Super-K solar analysis to lower energies during its fourth phase with 2970 days of livetime.

Yankelevich, Alejandro Jaime [UC, Irvine] (ORCID:0↗
DOE OSTI2021

The critical role of synthesis conditions on small polaron carrier concentrations in hematite—A first-principles study

Achieving highly efficient energy conversion with transition metal oxides necessitates overcoming conductivity limitations due to the formation of small polarons. Detailed understanding of the interplay among intrinsic defects, dopants, and electron polarons can help devise strategies for achieving higher carrier concentrations, therefore improving carrier conductivity. This work employs first-principles calculations to reliably predict electron polaron concentrations in a prominent polaronic oxide, hematite (Fe 2 O 3 ), by resolving interactions between charged defects and electron polarons and keeping charge neutrality condition among all charged species. This work addresses that both V O and Fe i can be primary donors in undoped hematite depending on the synthesis conditions, such as synthesis temperature and oxygen partial pressure, despite the fact that V O owns an extremely high ionization energy compared to k B T. Furthermore, from calculations of a plethora of n-type dopants (group IV and V elements), we find that Ti, Ge, Sb, and Nb are able to raise electron polaron concentrations in hematite significantly without considering dopant clustering. However, the magnitude of electron polaron concentration increase would be smaller if the dopant has a high tendency of clustering, such as Ti. Here we reveal the critical role of synthesis conditions on tuning electron polaron concentrations of both undoped and doped hematite. Our theoretical analysis provides important insights and general design principles for engineering more conductive polaronic oxides.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Electrochemical Imaging of Interfaces in Energy Storage via Scanning Probe Methods: Techniques, Applications, and Prospects

Developing a deeper understanding of dynamic chemical, electronic, and morphological changes at interfaces is key to solving practical issues in electrochemical energy storage systems (EESSs). To unravel this complexity, an assortment of tools with distinct capabilities and spatiotemporal resolutions have been used to creatively visualize interfacial processes as they occur. This review highlights how electrochemical scanning probe techniques (ESPTs) such as electrochemical atomic force microscopy, scanning electrochemical microscopy, scanning ion conductance microscopy, and scanning electrochemical cell microscopy are uniquely positioned to address these challenges in EESSs. We describe the operating principles of ESPTs, focusing on the inspection of interfacial structure and chemical processes involved in Li-ion batteries and beyond. We discuss current examples, performance limitations, and complementary ESPTs. Finally, we discuss prospects for imaging improvements and deep learning for automation. We foresee that ESPTs will play an enabling role in advancing EESSs as we transition to renewable energies.

25 ENERGY STORAGE↗
DOE OSTI2024

Analysis of differential scanning calorimetry data for aged plutonium

Differential scanning calorimetry data for samples of a 52 year old plutonium alloy with 3.3 at. % Ga that were heated beyond the melting point is analyzed using transition state theory to find activation energies for the δ to ε and ε to liquid phase transitions. A Bayesian statistical method involving a Gaussian process model is used to find mean values and confidence intervals for the activation energies. The activation energy for the δ to ε phase transition increases by 3.3 ± 3.8% per decade, relative to the case when all age related plutonium lattice point defects have been removed through annealing. The corresponding increase in activation energy for the ε to liquid transition is shown to be 7.1 ± 1.8% per decade. It is postulated that the change in activation energy with age for both phase transitions is caused, in part, by the accumulation of the same type of lattice point defects associated with the observed increase in elastic bulk modulus over time.

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

The LSU-Argonne conversion electron spectrometer: A new detector for the X-Array and SATURN decay station

In this study, a new conversion electron detector has been commissioned at the ATLAS/ CARIBU facility at Argonne National Laboratory. The LSU-Argonne Conversion Electron Spectrometer (LACES) is a LN 2 -cooled Si(Li) detector system designed to be incorporated into a decay station that comprises the dedicated HPGe clover array with a box geometry (X -Array) and the Scintillator and Tape Using Radioactive Nuclei (SATURN) device. This integration enables simultaneous measurements of conversion electrons and gamma-rays in decay experiments, yielding novel information on transition multipolarities, electric monopole transitions, and isomeric states that decay mostly via conversion electrons. A measurement of the energy resolution of LACES yielded 2.3-keV FWHM at 975 keV for electrons and 1.3-keV FWHM at 75 keV for X-rays. A detailed study of the absolute detection efficiency (at 5 mm from the source) was performed, where this quantity was determined experimentally in the range of electron transition energies between 25.5 keV and 1047.8 keV and subsequently simulated using the GEANT4 code. Measurement and simulations are found to be in excellent agreement. A precise characterization, for this type of detector system, of the absolute detection efficiency for such a wide energy range is reported for the first time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Thermodynamics and its prediction and CALPHAD modeling: Review, state of the art, and perspectives

Thermodynamics is a science concerning the state of a system, whether it is stable, metastable, or unstable, when interacting with its surroundings. The combined law of thermodynamics derived by Gibbs about 150 years ago laid the foundation of thermodynamics. In Gibbs combined law, the entropy production due to internal processes was not included, and the 2nd law was thus practically removed from the Gibbs combined law, so it is only applicable to systems under equilibrium, thus commonly termed as equilibrium or Gibbs thermodynamics. Gibbs further derived the classical statistical thermodynamics in terms of the probability of configurations in a system in the later 1800's and early 1900's. With the quantum mechanics (QM) developed in 1920's, the QM-based statistical thermodynamics was established and connected to classical statistical thermodynamics at the classical limit as shown by Landau in the 1940's. In 1960's the development of density functional theory (DFT) by Kohn and co-workers enabled the QM prediction of properties of the ground state of a system. On the other hand, the entropy production due to internal processes in non-equilibrium systems was studied separately by Onsager in 1930's and Prigogine and co-workers in the 1950's. In 1960's to 1970's the digitization of thermodynamics was developed by Kaufman in the framework of the CALculation of PHAse Diagrams (CALPHAD) modeling of individual phases with internal degrees of freedom. CALPHAD modeling of thermodynamics and atomic transport properties has enabled computational design of complex materials in the last 50 years. Our recently termed zentropy theory integrates DFT and statistical mechanics through the replacement of the internal energy of each individual configuration by its DFT-predicted free energy. The zentropy theory is capable of accurately predicting the free energy of individual phases, transition temperatures and properties of magnetic and ferroelectric materials with free energies of individual configurations solely from DFT-based calculations and without fitting parameters, and is being tested for other phenomena including superconductivity, quantum criticality, and black holes. Those predictions include the singularity at critical points with divergence of physical properties, negative thermal expansion, and the strongly correlated physics. Furthermore, those individual configurations may thus be considered as the genomic building blocks of individual phases in the spirit of the materials genome®. This has the potential to shift the paradigm of CALPHAD modeling from being heavily dependent on experimental inputs to becoming fully predictive with inputs solely from DFT-based calculations and machine learning models built on those calculations and existing experimental data through newly developed and future open-source tools. Furthermore, through the combined law of thermodynamics including the internal entropy production, it is shown that the kinetic coefficient matrix of independent internal processes is diagonal with respect to the conjugate potentials in the combined law, and the cross phenomena that the phenomenological Onsager flux and reciprocal relationships are due to the dependence of the conjugate potential of a molar quantity on nonconjugate molar quantities and other potentials, which can be predicted by the zentropy theory and CALPHAD modeling.

42 ENGINEERING↗
DOE OSTI2025

Exploring Anomalous Photoelectron Angular Distributions in the Photoelectron Spectra of Gd 3 O 3 – : Study of Gd 3 O 2 – and Gd 3 O 3 – Using Photoelectron Spectroscopy and Density Functional Theory Calculations

Anion photoelectron (PE) spectra of lanthanide oxide clusters obtained previously have exhibited anomalous photoelectron angular distributions which were attributed to strong PE–valence electron (PEVE) interactions. Here, to further explore this effect, we have obtained the PE spectra of Gd 3 O 2 – and Gd 3 O 3 – , two clusters that have similarly complex electronic structures but contrasting symmetries. The spectra exhibit manifolds of detachment transitions at similar binding energies in a 0.5 eV window of energy. The electron affinity of Gd 3 O 2 is measured to be 1.29 ± 0.05 eV, and that of Gd 3 O 3 is 1.31 ± 0.05 eV. As seen in previous studies on lanthanide oxide cluster anions in lower than conventional oxidation states, transitions in spectra obtained lower photon energies are more congested than those obtained with higher photon energy, a signature of strong PEVE interactions. While the detachment transitions have predominantly parallel photoelectron angular distributions (PAD), the PAD varies across the manifold of transitions in the PE spectrum of Gd 3 O 3 – in a way that suggests four different subgroups of transitions. Results of calculations on Gd 3 O 2 – suggest kite or V-shape structures with antiferromagnetic coupling between one of the 4f 7 subshells with the two others. Calculations on Gd 3 O 3 – more definitively point to ring structures with a nearly isoenergetic ferromagnetically coupled high spin (24-tet) state and a dectet state in which one of the 4f 7 subshells is antiferromagnetically coupled with the other two. Taking these results as qualitative, we propose that strong mixing between the unperturbed states predicted computationally leads to overlapping transitions with different PADs.

anions↗
DOE OSTI2022

Understanding Low Level Jets in the US Atlantic Offshore

Low level jets (LLJs) in the atmosphere exhibit a local windspeed maximum in the boundary layer, with positive shear beneath the jet and negative shear above the jet. Wind turbines tend to experience increased loads with varying wake recovery characteristics in the presence of an LLJ, therefore understanding the mechanism and impact of LLJs is crucial to wind energy development. The US Mid-Atlantic offshore region is a huge potential wind energy resource, yet LLJs in this area are poorly understood. In particular, the coastal offshore environment does not exhibit the same diurnal cycle that leads to strong LLJs in the well-characterized Great Plains region. In this study, we use the Weather Research and Forecasting model (WRF) plus lidar buoy data to identify case studies for LLJ events in 2020 in the New York Bight. A reduced order model is presented to explain the onset of these events based on the competing effects of baroclinicity and eddy diffusivity. Finally, using the macro-scale WRF, we drive a micro-scale large eddy simulation (LES) to generate a more detailed characterization of the Marine Boundary Layer during an LLJ. Gaining a better understanding of LLJs and their impacts on offshore wind in the mid-Atlantic is crucial for a transition toward renewable energy.

atmospheric boundary layer↗
DOE OSTI2024

Ion diffusion retarded by diverging chemical susceptibility

Abstract For first-order phase transitions, the second derivatives of Gibbs free energy (specific heat and compressibility) diverge at the transition point, resulting in an effect known as super-elasticity along the pressure axis, or super-thermicity along the temperature axis. Here we report a chemical analogy of these singularity effects along the atomic doping axis, where the second derivative of Gibbs free energy (chemical susceptibility) diverges at the transition point, leading to an anomalously high energy barrier for dopant diffusion in co-existing phases, an effect we coin as super-susceptibility. The effect is realized in hydrogen diffusion in vanadium dioxide (VO 2 ) with a metal-insulator transition (MIT). We show that hydrogen faces three times higher energy barrier and over one order of magnitude lower diffusivity when it diffuses across a metal-insulator domain wall in VO 2 . The additional energy barrier is attributed to a volumetric energy penalty that the diffusers need to pay for the reduction of latent heat. The super-susceptibility and resultant retarded atomic diffusion are expected to exist universally in all phase transformations where the transformation temperature is coupled to chemical composition, and inspires new ways to engineer dopant diffusion in phase-coexisting material systems.

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

A biohybrid strategy for enabling photoredox catalysis with low-energy light

Natural systems drive the high-energy reactions of photosynthesis with efficient and broadband energy capture. Transition-metal photocatalysts similarly convert light into chemical reactivity, and yet suffer from light-limited operation and require blue-to-UV excitation. In photosynthesis, both light capture and reactivity have been optimized by separation into distinct sites. In this work, inspired by this modular architecture, we synthesized a biohybrid photocatalyst by covalent attachment of the photosynthetic light-harvesting protein R-phycoerythrin (RPE) to the transition-metal photocatalyst tris(2,2'-bipyridine)ruthenium(II) ([Ru(bpy) 3 ] 2+ ). Spectroscopic investigation found that absorbed photoenergy was efficiently funneled from RPE to [Ru(bpy) 3 ] 2+ . The utility of the biohybrid photocatalyst was demonstrated via an increase in yields for a thiol-ene coupling reaction and a cysteinyl-desulfurization reaction, including recovered reactivity at red wavelengths where [Ru(bpy) 3 ] 2+ alone does not absorb.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Design for Carbon Conversion Product Pathways with Nuclear Power Plant Integration (PCC)

Coal is a globally abundant resource that historically has been used for power generation via combustion. As the power industry replaces coal with cleaner methods of generation, energy rich coal could be used in other chemical and fuel applications. This study presents a coal utilization option in which coal combustion is replaced with a carbon-free nuclear power plant and the coal is upgraded to valuable products for a variety of markets. Coal is prepared for conversion first by the pyrolysis process, which will optimize solid, liquid, and gaseous products based on the market size and potential product value, maximizing the monetary value of coal. This process is designed using bituminous coal from the Appalachian region as a basis to provide a pathway to preserve or transition coal-related jobs and create new jobs associated with the clean energy transition. Process modeling will be used to determine each component’s sensitivities, costs, inputs, and outputs Advanced and light-water reactors are considerations to supply the heat, steam, and electricity to the process. This paper focuses on the technical and market analysis used to determine the optimal processes and product pathways for the carbon refinery. Product pathways are on activated carbon, formic acid synthesis, and methanol synthesis for further upgrading to marketable chemical and polymer products.

01 COAL, LIGNITE, AND PEAT↗