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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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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Band Gap Engineering and Metastable Phase Discovery in Cu 2 BaSnS 4– x Se x Nanocrystals via Topotactic Anion Exchange

Earth-abundant multinary inorganic chalcogenides, such as Cu 2 BaSnS 4–x Se x (CBTSSe), have emerged as promising absorbers for next-generation solar cells. However, accessing colloidal CBTSSe nanocrystals, including metastable polymorphs with more favorable optoelectronic properties, remains a critical synthetic challenge. Herein, we report the first use of topotactic anion exchange to access metastable crystal structures in soft chemistry syntheses. By varying the stoichiometry of selenium precursors, we directly synthesize Cu 2 BaSnS 4–x Se x (0 ≤ x ≤ 4) nanocrystals with a broad band gap tunability of 1.51–2.04 eV. At x = 4, a new metastable trigonal phase of Cu 2 BaSnSe 4 is isolated. This phase arises via an in-situ topotactic anion exchange from sulfur-rich Cu 2 BaSnS 4–x Se x intermediates, while retaining both the cation sublattice and overall nanocrystal morphology. The trigonal Cu 2 BaSnSe 4 exhibits a nearly direct band gap that is 280 meV lower than its thermodynamically stable orthorhombic counterpart, aligning more closely with the optimal band gap for single-junction solar cells. In contrast, a post-synthetic anion exchange route leads to the thermodynamically preferred orthorhombic polymorph, pointing to the critical nature of the in-situ transformation. Our findings open a versatile pathway for both polymorphic and morphological control in colloidal nanocrystals, expanding the synthetic design space for new optoelectronic materials.

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

Opening pathways for the conversion of woody biomass into sustainable aviation fuel via catalytic fast pyrolysis and hydrotreating

Meeting aggressive decarbonization targets set by the International Civil Aviation Organization (ICAO) will require the rapid development of technologies to produce sustainable aviation fuel (SAF). Catalytic fast pyrolysis (CFP) can support these efforts by opening pathways for the conversion of woody biomass into an upgraded biogenic oil that can be further processed to SAF and other fuels. However, the absence of end-to-end experimental data for the process leads to uncertainty in the yield, product quality, costs, and sustainability of the pathway. The research presented here serves to address these needs through a series of integrated experimental campaigns in which real biomass feedstocks are converted to a final SAF product using large bench-scale continuous reactor systems. For these campaigns, the degree of catalytic upgrading during CFP was varied to produce CFP-oils with oxygen contents of 17 and 20 wt% on a dry basis. The CFP-oils were then hydrotreated and distilled into gasoline, diesel, and SAF fractions. Detailed yield and compositional data were obtained for each step of the process to inform technoeconomic and lifecycle analyses, and the fuel properties of the SAF fraction were evaluated to provide first-of-its-kind insight into the quality of the final product. This research reveals opportunities to optimize process carbon efficiency by tuning the degree of catalytic upgrading during the CFP step and highlights routes to produce a high-quality cycloalkane-rich SAF with 85–92% reduction in greenhouse gas emissions compared to fossil-based pathways.

09 BIOMASS FUELS↗
NASA NTRS1991

Overconfidence, preview, and probability in strategic planning

The performance of eight subjects in a 'rescue' video game requiring choices as to which node they should fly to in order to rescue the simulated casualties is presently studied with a view to biases and display support criteria in strategic planning. After each choice, the subjects needed to fly a challenging tracking dynamic along a path to reach the next node. The results obtained indicate that the choices of the subjects were less optimal when full preview was offered, perhaps due to subjects' reliance on the simple strategy of choosing routes with the greatest number of casualties.

Wickens, Christopher D.↗
DOE OSTI2024

Non-Electricity Based Renewable Fuels: Theory and Computation for Solar Thermochemical Hydrogen

Dominated by photovoltaics and wind, current renewable energy sources generate mostly electricity, but 80% of the global final energy consumption occurs in form of fuels. Therefore, direct solar fuel generation would be a major breakthrough for the energy transition. Solar thermochemical hydrogen (STCH) is one of the very few potential routes towards scalable renewable fuels, but currently suffers from lack of an oxide working material that could optimally perform energy conversion within the thermodynamic boundary conditions. Theory and computation can contribute in two distinct ways, through materials search and discovery, but also by providing detailed mechanistic models for specific systems so to advance our understanding of possible design strategies. To enable high-throughput materials screening, we developed a defect graph neural network (dGNN) machine learning approach,[1] which accelerates the prediction of defect formation energies by replacing the tedious density functional theory (DFT) supercell calculations for all possible defect sites. This approach enables high-throughput database screening of oxides, which was integrated with thermodynamic modeling to extract the reduction entropies as additional selection criterion for STCH. Once potential candidate materials are identified, detailed models can guide materials design by predicting performance characteristics. One challenge is to quantitatively predict thermochemical equilibria at high concentrations when the redox active defects start to interact with each other, thereby impeding the formation of additional defects. Introducing a model for the free energy of defect interaction, parametrized on the basis of DFT data, we simulated the complete STCH redox cycle for (Sr,Ce)MnO3 alloys, achieving near-quantitative agreement with experimental data.[2] The analysis of these simulations reveals how defect interactions diminish the reduction entropy and H2 yield, suggesting to include these interactions in design considerations. Finally, we revisit the popular van't Hoff method for analyzing reduction enthalpies and entropies. This method is not ideal, as it involves a temperature-dependent convolution of gas-phase and solid-state entropies, causing uncertainties in the same order of magnitude as the physical quantities of interest. To avoid this problem, we suggest a simple alternative approach which can be applied to experimental and simulated data alike.

first-principles calculations↗
DOE OSTI2024

A Method for Producing Hierarchical and Statistically Calibrated Predictions of Nuclear Material Properties from Existing Models

Computer vision-based analysis of micrographs of nuclear materials is an emerging technique for property prediction, synthetic route identification, and other material analysis tasks. These analysis tasks play a pivotal role in many material characterization applications such as signature development for treaty verification, process optimization, etc. The backbone in many of the recent computer vision-based techniques is a deep learning model, which takes a fixed-size set of pixels and provides a class prediction for that set of pixels. For example, previous work developed a deep convolutional neural network (CNN) to predict the synthetic route from a 256 px x 256 px patch taken from a larger image of uranium ore concentrates. In this work, we present several methods for first calibrating these models in a manner that they can provide accurate probabilities of their predictions’ veracity, and several methods of combining these probabilities. Overall, the combination of these two steps into a pipeline allows for full-image and even full-sample (where a sample has many images) predictions with associated confidence values. Finally, we show that one can also use the patch predictions and confidence to produce a visualization to map predicted constituents through the image. Results and examples for predicting and mapping uranium ore concentrates’ synthetic process from imagery will be presented.

artificial intelligence↗
DOE OSTI2026

Humins-Derived Hard Carbon as a Low-Cost Material for Sodium-Ion Battery Anodes

The growing demand for sodium-ion batteries (SIBs) in grid storage underscores the need for electrode materials that balance performance and cost, including sustainable and robust carbon sources. The equitable and abundant distribution of materials for SIBs, along with their superior low-temperature performance, safety, and fast-charging capability, further distinguishes them from lithium-ion batteries (LIBs). Hard carbon (HC) is the state-of-the-art anode for SIBs, but current commercial HC production is localized mostly to one region of the world, raising concerns about supply chain vulnerability, critical material dependency, and environmental aspects. Here, the first demonstration of humins, an abundant biorefinery byproduct, as a precursor for HC anodes for sodium-ion storage is reported. Humins were carbonized at 1100 degrees C-1300 degrees C, and the resulting materials were subjected to comprehensive materials and electrochemical characterization. Among the temperatures studied, humins-derived hard carbon synthesized at 1200 degrees C delivers an initial reversible capacity 270mA h g-1 , with stable cycling performance up to 500 cycles and excellent rate capability, representing the optimal performance. This study establishes humins as a promising and low-cost carbon source that provides a route to mitigate supply chain risks and valorizes an underutilized biorefinery waste stream for high-performance SIB anodes.

25 ENERGY STORAGE↗
DOE OSTI2024

Solvent-Dependent Dynamics of Cellulose Nanocrystals in Process-Relevant Flow Fields

Flow-assisted alignment of anisotropic nanoparticles is a promising route for the bottom-up assembly of advanced materials with tunable properties. While aligning processes could be optimized by controlling factors such as solvent viscosity, flow deformation, and the structure of the particles themselves, it is necessary to understand the relationship between these factors and their effect on the final orientation. In this study, we investigated the flow of surface-charged cellulose nanocrystals (CNCs) with the shape of a rigid rod dispersed in water and propylene glycol (PG) in an isotropic tactoid state. In situ scanning small-angle X-ray scattering (SAXS) and rheo-optical flow-stop experiments were used to quantify the dynamics, orientation, and structure of the assigned system at the nanometer scale. The effects of both shear and extensional flow fields were revealed in a single experiment by using a flow-focusing channel geometry, which was used as a model flow for nanomaterial assembly. Due to the higher solvent viscosity, CNCs in PG showed much slower Brownian dynamics than CNCs in water and thus could be aligned at lower deformation rates. Moreover, CNCs in PG also formed a characteristic tactoid structure but with less ordering than CNCs in water owing to weaker electrostatic interactions. The results indicate that CNCs in water stay assembled in the mesoscale structure at moderate deformation rates but are broken up at higher flow rates, enhancing rotary diffusion and leading to lower overall alignment. Albeit being a study of cellulose nanoparticles, the fundamental interplay between imposed flow fields, Brownian motion, and electrostatic interactions likely apply to many other anisotropic colloidal systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Ketjenblack-Supported and Unsupported ZrO 2 –ZrN Nanoparticle Systems for Enabling Efficient Electrochemical Nitrogen Reduction to Ammonia

Artificial N 2 fixation via the electrocatalytic nitrogen (N 2 ) reduction reaction (NRR) has been recently promoted as a rational route toward reducing energy consumption and CO 2 emission as compared with the traditional Haber–Bosch process. Nevertheless, optimizing NRR relies on developing highly efficient electrocatalysts. Herein, we report on the reliable and reproducible synthesis of two promising electrocatalysts in either the presence or absence of Ketjenblack (KB), namely, ZrO 2 –ZrN@KB and ZrO 2 –ZrN systems, synthesized through the nitriding of Zr. Both materials had never previously been considered for NRR, to the best of our knowledge. Nevertheless, both of these electrocatalysts incorporated a combination of tetragonal ZrO 2 , ZrON, and cubic ZrN and showed excellent activity and durability toward NH 3 formation. Moreover, the maximum NH3 production rate of 84.1 μg h –1 mg –1 at -0.7 V vs a reversible hydrogen electrode (RHE) was achieved with the ZrO 2 –ZrN electrocatalyst with an impressive Faradaic efficiency of 21.2% at -0.6 V vs RHE, indicating a high selectivity associated with the NRR. Additionally, the catalysts demonstrated excellent stability during the electrolysis process and recycling tests. Here we postulate that the combination of exposed active sites of ZrN and ZrO 2 likely contributes to the enhanced NRR performance attributed to ZrO 2 –ZrN.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Detect-and-Avoid Maneuver Planning: Benefits of Including Route Recapture

Development of Detect-and-avoid (DAA) systems’ maneuver guidance requirements at RTCA has centered on tactical maneuvers away from the intruder. Recapturing the flight-plan route the initial maneuver is not directly taken into account by most DAA maneuver guidance algorithms. This work demonstrates potential challenges and inefficiencies that can arise in recapturing the route after resolving a conflict. Horizontal resolution trajectories for a test matrix of encounters with varying aircraft speeds and geometries are computed by minimizing either flight time or deviation to serve as a baseline. Turn directions computed from a reference DAA algorithm coupled with a pilot selection model and a second algorithm called Autoresolver (AR) are compared to these baseline resolution trajectories. Baseline results show that turning into the intruder yields favorable cost for most encounters. Additional analysis of pilot response data shows that only three-fourths of pilots’ horizontal maneuvers turn into intruders, a percentage much lower than the baseline results. Improvement to a DAA guidance algorithm based on findings in this work is discussed.

Route Recapture↗
NASA NTRSDate not supplied

Detect-and-Avoid Maneuver Planning: Benefits of Including Route Recapture

Development of Detect-and-avoid (DAA) systems’ maneuver guidance requirements at RTCA has centered on tactical maneuvers away from the intruder. Recapturing the flight-plan route the initial maneuver is not directly taken into account by most DAA maneuver guidance algorithms. This work demonstrates potential challenges and inefficiencies that can arise in recapturing the route after resolving a conflict. Horizontal resolution trajectories for a test matrix of encounters with varying aircraft speeds and geometries are computed by minimizing either flight time or deviation to serve as a baseline. Turn directions computed from a reference DAA algorithm coupled with a pilot selection model and a second algorithm called Autoresolver (AR) are compared to these baseline resolution trajectories. Baseline results show that turning into the intruder yields favorable cost for most encounters. Additional analysis of pilot response data shows that only three-fourths of pilots’ horizontal maneuvers turn into intruders, a percentage much lower than the baseline results. Improvement to a DAA guidance algorithm based on findings in this work is discussed.

Route Recapture↗
NASA NTRSDate not supplied

Aerial Vehicle Routing and Scheduling for UAS Traffic Management: A Monte Carlo Tree Search Approach

Numerous unmanned aircraft systems operating at low altitudes to deliver goods and services may one day become ubiquitous in our cities. In the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) framework, such a concept is envisioned, where aerial vehicles operate beyond visual line of sight (BVLOS) within specifically reserved and time stamped “corridors” in the airspace. For example, these corridors or operational intent volumes can connect an aerial vehicle’s origin site to its destination site for package delivery operations. There may also be more than one corridor available for an aerial vehicle to choose from and often different corridors may intersect with one another. Thus, it is imperative to ensure flight trajectories belonging to different aerial vehicles are not in conflict. Per the UTM CONOPs, we assume that a vehicle almost always stays inside its corridor or operational volume. This work provides a framework for strategic deconfliction of UTM or package delivery drones, where we schedule the departure time of all vehicles subject to various temporal constraints (including the corridor deconfliction at the intersections). We present the “multi-route weighted package delivery problem” which serves as an exemplifying model for strategic deconfliction in UTM. In the multi-route weighted package delivery problem, a graph network is given which consists of a set of depots (source) and drop-off (destination) nodes, with multiple routes (defined as a sequence of waypoints) connecting the depots to drop-off nodes. In addition, routes are weighted by the associated ground risk and total travel distance for package delivery. The goal is for a known set of aerial vehicles to depart from the depots, choose a route and take off time, while avoiding conflicts with other aerial vehicles, and minimizing both risk and distance traveled. We provide a mixed integer linear programming (MILP) formulation of the problem, as well as a heuristic solution based on Monte Carlo Tree Search (MCTS) – a method used in game theory and artificial intelligence – to overcome limitations inherent to optimal solvers. Computational results show the advantages of using MCTS over the MILP formulation; the former can provide a sub-optimal solution quickly, and may sometimes even reach an optimal solution, whereas the latter may not even produce a solution in reasonable time. Furthermore, results from both the MILP formulation and MCTS methods were validated using a preliminary agent-based simulator implementing the UTM concept of operations. Thus, the MCTS method can be seen as a scalable solution to the complex multi-route weighted package delivery problem and may possibly be extended to similar complex optimization problems.

Kenny Chour↗
DOE OSTI2026

Blueprint for DOE Quantum Supercomputing: Ensuring U.S. Leadership in the Quantum Decade

Quantum computing stands at the threshold of a transformative decade, where the field will evolve from small-scale demonstrations toward practical scientific computing at scale. This Blueprint identifies fault-tolerant quantum computers (FTQCs) as a viable, scalable, and broadly applicable path to achieving “quantum scientific utility,” defined as solving scientifically valuable problems beyond the reach of conventional, classical computers. This capability is expected to show scientific demonstrations in the late 2020s and to mature in the early-to-mid 2030s. This Blueprint outlines a strategy to prepare the U.S. Department of Energy (DOE) for FTQCs and their integration into the U.S. national scientific computing infrastructure. Its purpose is to identify the steps, milestones, and research directions necessary for DOE to enable initial deployment of FTQCs in 2028 as a scientific tool for the nation and mature this capability into the 2030s. DOE has a long history of supporting quantum information science and technology, contributing significantly to research advancements, training a quantum-ready workforce, and providing access to early small-scale quantum hardware. Given recent demonstrations of logical operations on error-corrected logical qubits and the advancement of commercial hardware roadmaps, DOE should begin preparations for large-scale, fault-tolerant quantum computing deployment for DOE science missions. This Blueprint proposes that DOE focus on (1) deploying first-generation scientifically relevant quantum computers with at least 100 logical qubits and performing at least 10,000 to 100,000 hard logical operations in scientifically relevant calculations; (2) developing essential FTQC programming competencies, system software, and facility readiness; and (3) investing in cutting edge focused R&D that fosters breakthroughs in scientific applications, algorithms, and logical architectures needed to accelerate the advent of scientific utility. This effort will position DOE to transition to larger systems: production-scale quantum computers that comprise 1,000 to 10,000 logical qubits, perform 1 to 10 billion hard logical operations, and execute scientifically useful computations at scale. Achieving these goals will require DOE facilities to evolve with urgency to support scientific campaigns that integrate quantum and classical computing resources into efficient workflows, novel software and firmware environments for compiling and routing quantum programs on FTQC machines, and suitable infrastructure for quantum hardware. It will also require further development and optimization of scientific applications from the fields of materials science, quantum chemistry, and high-energy and nuclear physics. The Blueprint calls for transformative R&D and collective action to accelerate the advent of scientific quantum utility and bring it within reach by 2028.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1985

Design of an advanced flight planning system

The demand for both fuel conservation and four-dimensional traffic management require that the preflight planning process be designed to account for advances in airborne flight management and weather forecasting. The steps and issues in designing such an advanced flight planning system are presented. Focus is placed on the different optimization options for generating the three-dimensional reference path. For the cruise phase, one can use predefined jet routes, direct routes based on a network of evenly spaced grid points, or a network where the grid points are existing navaid locations. Each choice has its own problem in determining an optimum solution. Finding the reference path is further complicated by choice of cruise altitude levels, use of a time-varying weather field, and requiring a fixed time-of-arrival (four-dimensional problem).

Sorensen, J. A.↗
NASA NTRS1993

Cycler orbit between Earth and Mars

A periodic orbit between Earth and Mars has been discovered that, after launch, permits a space vehicle to cycle back and forth between the planets with moderate maneuvers at irregular intervals. A Space Station placed in this cycler orbit could provide a safe haven from radiation and comfortable living quarters for astronauts en route to Earth or Mars. The orbit is largely maintained by gravity assist from Earth. Numerical results from multiconic optimization software are presented for a 15-year period from 1995 through 2010.

Byrnes, Dennis V.↗
NASA NTRSDate not supplied

Optical Modem Enabling Broadband Datacom Links for Crewed Cis-Lunar Missions

We report on the design, development, and testing of our high-power broadband optical modem supporting NASA’s crewed Artemis-2 mission. The O2O modem will be mounted in the crewed Orion module and provide a broadband 505,000 km bi-directional optical link back to earth while en route to the moon. The full-duplex modem consists of a high-power optical transmitter and receiver optimized for serially-concatenated pulse-position modulation (SCPPM). The transmitter is a master-oscillator power-amplifier optical architecture using efficient cladding-pumped amplification in erbium-ytterbium co-doped fiber. The transmitter outputs up to 1 W at ≈1550 nm (limited for eye safety) and supports 6 different user-rates ranging from 20.39 Mb/s to 260.95 Mb/s using PPM16 and PPM32 modulation formats. The optical receiver supports two user-rates: 10.19 Mb/s and 20.39 Mb/s with both rates employing PPM32. The narrowband receiver filtering is optimized to simultaneously accept four separate wavelength channels to mitigate atmospherics through spatial diversity. A configurable interleaver provides additional protection against atmospherics-based signal fading and a powerful soft-decision error correction scheme enables highly sensitive detection. The measured sensitivities at the two bit-rates are -73.8 and -71.8dBm, respectively. The architecture was designed for reliable operation in space, featuring automatic hardware interlocks, pump sparing for the amplifiers, and autonomous operation of all internal hardware and software control loops. The protoflight unit (PFU) was put through rigorous environmental testing which included pyroshock, vibration, electromagnetic interference/compatibility, and thermal-vacuum testing. The modem successfully passed all the environmental screening and has been declared at Technology Readiness Level (TRL) 6.

space communications↗
DOE OSTI2026

Carbon dioxide pipeline network transportation cost model: evaluating economic and geographic factors for efficient carbon capture, storage, and utilization

This study presents a comprehensive pipeline network modeling framework to estimate the CO 2 delivery cost for CO 2 utilization and geologic CO 2 storage across the United States. We developed a Python-based CO 2 pipeline transportation cost model leveraging Argonne National Laboratory’s pipeline engineering expertise and detailed natural gas transmission pipeline cost data across U.S. regions. Using existing road corridors as practical routing guides, the model designs pipeline networks that aggregate CO 2 from one or multiple sources and deliver it to selected destinations. It then minimizes the total transportation cost by optimizing pipeline diameters and incorporating booster pumps. A key contribution is the incorporation of up-to-date, region-specific cost factors with itemized components for materials, labor, miscellaneous construction expenses, and right-of-way acquisition. Results emphasize that regional variation and economies of scale associated with CO 2 pipeline costs are significant and should be explicitly accounted for in screening and planning studies. By combining realistic routing constraints with regionalized cost inputs, the model provides transparent design methodology and location-specific insights into source–destination delivery costs, including the effects of routing complexity along existing road networks. We demonstrate the model with two illustrative case studies – one for CO 2 storage and one for CO 2 utilization – in which the model designs pipeline networks spanning hundreds of miles across the states, collecting CO 2 from multiple sources and delivering it to designated endpoints while minimizing levelized cost of delivery via diameter and compression optimization. The model offers a practical, scalable approach for alternative design option screening and early-stage CO 2 transportation planning.

CCS↗
DOE OSTI2026

SimH 2 : an integrated techno-economic modeling framework for hydrogen pipeline infrastructure and network optimization

Large-scale hydrogen (H 2 ) pipeline transport design and network optimization have seldom been reported due to the lack of a cost model accounting for the relationship between transport cost and hydrogen mass flow rate. Here, this work introduced a system-level cost model for hydrogen pipeline transport at supercritical state and integrated it with an existing CO 2 pipeline network tool, SimCCS, for hydrogen-specific pipeline design and optimization. The Intermountain West (I-West) region of the U.S., historically dependent on fossil fuel-based economies, is chosen to demonstrate the capabilities of our H 2 pipeline cost model and transport network optimization platform called SimH 2 . Two scenarios are examined: one where the pipeline is not allowed to pass through disadvantaged communities and the other where it is permitted. The results highlight that incorporating disadvantaged-community constraints lead to longer pipeline routes and increased transport costs, reflecting the trade-offs involved in equitable infrastructure development. It is demonstrated that the newly developed SimH 2 tool not only enables the efficient design of H 2 transportation pipelines but also optimizes the network by accounting for local terrain and the presence of disadvantaged areas.

08 HYDROGEN↗
DOE OSTI2026

Decomposition of methane diluted with inert gas in an RF discharge cell

Decomposition of methane using non-thermal plasmas is an attractive route for producing hydrogen-rich gases and valuable carbon nanomaterials. Understanding how plasma discharge modes influence methane decomposition in optimizing plasma-assisted chemical conversion remains unexplored. This study explores the coupling between the discharge structure and product selectivity in RF capacitively coupled discharges operating in methane/inert gas mixtures in the pressure range of 2–3 torr. Under our experimental conditions the discharge exhibits mode transitions from uniform to striated in Ar and Kr and from diffuse to contracted in Ar and Kr with <5% CH 4 . The discharges in He and Ne remained uniform under our operating conditions, and their mixtures with CH 4 remained diffuse. A 0-d model for Ar/CH 4 discharge established a threshold for contraction while also asserting the importance of Ar$^{*}_{\textrm{m}}$ in the dissociation and ionization processes. The highest degree of methane decomposition, >99.7% with the main products of acetylene and graphitized solid carbon was achieved in the contracted discharge mode for both Kr or Ar with ⩽5% CH 4 . We demonstrate that contraction can play a crucial role in the effective decomposition of methane with value-added products and that both the electronic and thermal properties of plasma gas are responsible for this effect.

RF plasma↗