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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 OSTI2023

Kinetic model-based group contribution method for derived cetane number prediction of oxygenated fuel components and blends

A four-step autoignition model was used to derive an expression for the ignition delay (ID) measured in ignition quality testers (IQT) with the derived cetane number (DCN) determined from this ID using the ASTM D6890 standard correlation. The model predicts DCN for individual compounds and blends as a function of each compound's global initiation and net chain branching rate constants. Expressions for these values were determined assuming they could be related to the functional groups present in each compound. Measurement data for 125 compounds and 94 binary and ternary blends (including oxygenates: alcohols, aldehydes, esters, ethers, and ketones), from literature and from measurements performed in our lab, were used to obtain the dependence of the measured ignition delay on each functional group. The new kinetic model-based group contribution method was able to predict the ignition delay of both pure compounds and blends, with an average DCN error of 4.4 (19%) and 2.8 (11%), respectively. Here, the blend model was also used to develop an ID mixing rule by incorporating existing IQT ignition delay data for each compound. Use of the mixing rule gave an average DCN error of 3.6 (16%) for blends. Both the blend model and mixing rule were found to be superior compared to standard linear by volume fraction or mole fraction mixing rules commonly used to estimate the DCN of mixtures.

42 ENGINEERING↗
DOE OSTI2020

Non-monotonic behavior of the lateral diffusivity in an adsorbate as a function of the surface coverage

Diffusion dynamics of adsorbate particles confined on a surface strongly depends on the adsorbate surface coverage. Our molecular dynamics simulation reveals that the average lateral diffusivity of the Lennard-Jones adsorbate on a flat substrate varies non-monotonically with the surface coverage. Given that the adsorbate becomes stratified into distinct layers, we propose a novel approach to analyze the lateral diffusion of the adsorbate particles in different layers, via dividing the total lateral diffusion into consecutive lateral diffusion processes, separated by the layer boundary crossing events. A new definition of the lateral diffusivity in the specific layer has been introduced, and it is revealed that the total lateral diffusivity is equal to the sum of lateral diffusivities in different layers. Furthermore, the non-monotonic behavior of the average lateral diffusivity with the surface coverage is found to result from the difference in behavior of the lateral diffusivity between the first and other layers.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Rapid generation of optimal generalized Monkhorst-Pack grids

In this report computational modeling of the properties of crystalline materials has become an increasingly important aspect of materials research, consuming hundreds of millions of CPU-hours at scientific computing centers around the world each year, if not more. A routine operation in such calculations is the evaluation of integrals over the Brillouin zone. We have previously demonstrated that performing such integrals using generalized Monkhorst-Pack k-point grids can roughly double the speed of these calculations relative to the widely-used traditional Monkhorst-Pack grids. However the generation of optimal generalized Monkhorst-Pack grids is not implemented in most software packages due to the computational cost and difficulty of identifying the best grids. To address this problem, we present new algorithms that allow rapid generation of optimal generalized Monkhorst-Pack grids on the fly. We demonstrate that the grids generated by these algorithms are on average significantly more efficient than those generated using existing algorithms across a range of grid densities. For grids that correspond to a real-space supercell with at least 50 Å between lattice points, which is sufficient to converge density functional theory calculations within 1 meV/atom for nearly all materials, our algorithm finds optimized grids in an average of 0.19 s on a single processing core. To facilitate the widespread adoption of this approach, we present new open-source tools including a library designed for integration with third-party software packages.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine learning elastic constants of multi-component alloys

The present manuscript explores application of machine learning methods for determining elastic constants and other derived mechanical properties of multi-component alloys. Here, a number of machine learning models, including linear regression, neural network and random forest based models, are trained and tested on a dataset of binary alloys generated using density functional theory (DFT) calculations and spanning over a large number of elemental species in the periodic table. Starting with a wide range of simple and easily accessible compositionally-averaged elemental features, a correlation-based feature selection strategy was used to systematically down-select a set of most relevant features towards the prediction of the elasticity tensor components. The true predictive performance and the associated uncertainties of the models were established by testing on unseen data and bootstrapping, respectively. A single and pair-wise feature partial dependence analysis was performed to visualize the average property trends in the multi-dimensional feature space in order to further understand the achieved predictive performance. The utility of the trained model is further demonstrated by obtaining sufficiently accurate yet highly efficient approximations for bulk modulus, Young’s modulus, shear modulus and Poisson’s ratio for alloys beyond the binary space (i.e., two-component alloys) on which the model was originally trained. More importantly, we test and validate the predictive performance of the developed model directly against the experimentally measured elastic constants of technologically relevant multi-component alloys (such as, Ni- and Ti-based alloys). Finally, utility of such a data-enabled route is demonstrated by predicting the possible range of various elastic properties for vast composition space available within the five component Ni-Cr-Fe-Mo-W alloy system in a high-throughput manner.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Out-of-distribution detection with non-parametric density estimation for models predicting processing history of uranium ore concentrates

The rapid advancement in machine learning (ML) and computer vision (CV) coincides with the growth of interest in deploying these ML/CV models in numerous fields from medicine to social science. Similar to those areas, we have witnessed a great number of works in materials science employing ML/CV models – neural networks in particular – in their studies in recent years. These models have proven to obtain accurate performance in various tasks. However, these models struggle to attain a similar performance when encountering test samples coming from a distribution that is different from the training set. More importantly, they fail without providing any warning to the users. Therefore, we propose a framework for detecting out-of-distribution (OOD) samples to alert users when a human intervention might be necessary in this work. Specifically, we explore the use of a non-parametric density estimation method to detect OOD samples. Here, we assess OOD detection capability of the proposed framework on ML models developed for categorizing precipitation routes of U 3 O 8 when encountering OOD datasets that contain samples (1) undergone different imaging acquisition process, (2) undergone different material synthesis process, and (3) different materials than ID set. Through those experiments, we achieve an average area under the receiver operating characteristic (AUROC) of at least 91% on average in detecting OOD samples. With minimal overhead cost and superior performance, the proposed framework enables a reliable and safe system when deploying in real-world scenarios.

Convolutional neural networks↗
DOE OSTI2022

Plasma surface modification coupled with thermal and step-over distance effects on significant fracture improvement of adhesively-bonded metal-$\mathrm{CFRTP}$ dissimilar materials

Here, this work proposes an approach to improve the interfacial bonding between carbon-fiber-reinforced thermoplastic polymers (CFRTP) and thermoset adhesive by using the surface modification with the combination of air plasma, thermal, and step-over effects. Thanks to the proper heating which causes the exposure of carbon fibers on the CFRTP surface, both polymer matrix and these exposed carbon fibers can have improved bonding with adhesive due to air plasma treatment. By conducting the Double Cantilever Beam (DCB) tests performed on adhesively-bonded AA5052/CFRP-PA66 dissimilar joints, it was shown in this work that the average Mode I specific fracture energy can be improved up to about 180% compared to non-treated counterparts. The foregoing improvement can be further increased by considering the step-over distance of the surface treatment, which triggers the tortuous damage path in the failure process of adhesively-bonded structures. By manipulating this treatment parameter, often overlooked in the literature, and other parameters (i.e., treatment speed, nozzle tip-to-surface distance, etc.) investigated in this study, the average Mode I specific fracture energy of adhesively-bonded AA5052/CFRP-PA66 dissimilar joints can be improved reaching up to about 410% compared to non-treatment counterparts.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

$\mathrm{D}$ark$\mathrm{F}$lux: A new tool to analyze indirect-detection spectra of next-generation dark matter models

Here we present DarkFlux, a software tool designed to analyze indirect-detection signatures for next-generation models of dark matter (DM) with multiple annihilation channels. Version 1.0 of this tool accepts user-generated models with 2→2 tree-level dark matter annihilation to pairs of Standard Model (SM) particles and analyzes DM annihilation to γ rays. The tool consists of three modules, which can be run in a loop in order to scan over DM mass if desired: The annihilation fraction module calls an internal installation of MadDM, a dark matter phenomenology plugin for the Monte Carlo event generator MadGraph5 _ aMC@NLO, to compute the thermally averaged cross section $\langle σv\rangle_i$ for each annihilation channel $χχ$ ($\bar{χ}$,$χ^†$)→ i $\∈{SM, SM}. . The module then computes the fractional annihilation rate (annihilation fraction) into each channel. The flux module combines the flux spectrum from each annihilation channel, weighted by the appropriate annihilation fractions, to compute the total flux at Earth due to DM annihilation. In DarkFlux v1.0, this module specifically computes the γ-ray flux for each channel using the publicly available PPPC4DMID tables. The analysis module compares the total flux to observational data and computes the upper limit at 95% confidence level (CL) on the total thermally averaged DM annihilation cross section. In DarkFlux v1.0, this module compares the total γ-ray flux to a joint-likelihood analysis of fifteen dwarf spheroidal galaxies (dSphs) analyzed by the Fermi-LAT collaboration. DarkFlux v1.0 automatically provides data tables and can plot the output of these three modules. In this manual, we briefly motivate this indirect-detection computer tool and review the essential DM physics. We then describe the several modules of DarkFlux in greater detail. Finally, we show how to install and run DarkFlux and provide two worked examples demonstrating its capabilities.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Development of an anti-clogging perforated plate atomizer for a zero liquid discharge humidification-dehumidification desalination system

An anti-clogging perforated plate atomizer is designed for high temperature and salinity applications in a novel solar-powered zero liquid discharge humidification-dehumidification desalination system (US Patent Application US62882953). Herein this paper presents a detailed discussion on its design, and operation. Experiments are performed on seven atomizers to study the effect of design, and operating parameters on spray cone angle, and average droplet diameter. Spray cone angle remains constant with change in air mass flux, and increases with increasing orifice diameter, and manifold diameter. It also increases initially with water mass flow rate, and manifold height, before attaining a constant value. Average droplet diameter increases with increasing water mass flow rate, and decreasing air mass flux, orifice diameter, and manifold diameter. Further, anti-clogging performance of the atomizer is tested with hypersaline water of 100,000 ppm total dissolved solid (NaCl) at elevated temperatures such as air at 175 °C and saline water at 45 °C. Results show no clogging for 2–13 h of operation. Near-complete suppression of atomizer clogging makes our new additively-manufactured perforated plate atomizer an ideal fit for high salinity, and zero liquid discharge humidification-dehumidification desalination systems. Additionally, its open-surface design allows additional surface modifications to further reduce clogging, and enhance self-cleaning characteristics.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Performance and lifetime of intercalative water deionization cells for mono- and divalent ion removal

We report intercalative deionization (IDI) uses two cation intercalation electrodes separated by an anion exchange membrane in a symmetric cell design that has the potential to deliver electrochemically desalinated water in an energy- and water-efficient way. Here, we define and measure metrics to describe the performance and lifetime of IDI cells and compare them for NaCl and CaCl 2 feed solutions. With 20 mM NaCl, NiHCF/AEM/NiHCF flow cells achieve 10 mM average concentration change at a productivity of 20 l/h/m 2 and 5 mM average concentration change at 130 l/h/m 2 . In both cases the cells are operated at a 3C current rate and consume ~30 Wh/m 3 of energy. With 10 mM CaCl 2 , the specific capacity and salt removal of IDI flow cells is ~4 times lower. NiHCF/NiHCF beaker cells with CaCl 2 electrolyte suffer from strong capacity fade, while the same cells with NaCl electrolyte achieve 500 cycles without any capacity fade. Our post-mortem analysis using X-ray diffraction, secondary electron microscopy, energy dispersive X-ray spectroscopy, synchrotron-based X-ray absorption spectroscopy and micro X-ray fluorescence mapping reveals that NiHCF dissolves upon repeated intercalation with Ca 2+ , releasing residual K + , Ni 2+ and Fe(CN) 6 3 , which precipitates as a crystalline decomposition product on the electrodes. This side reaction deprives the active material NiHCF of charge compensating Fe, and thus accounts for the observed capacity fade.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Superhydrophobic composite asymmetric electrospun membrane for sustainable vacuum assisted air gap membrane distillation

Membrane distillation (MD) relies on superhydrophobic membranes that must achieve the competing goals of high liquid entry pressure (LEP) and high permeability. Here, in this study, we developed the first superhydrophobic composite electrospun asymmetric membrane using fluorinated graphite (FG). We also provided among the first comparisons of the relative benefits of membrane asymmetry and composite coatings via 3D electrospinning and spraying. FG particles increased the roughness and reduced the zeta potential of the membranes. Superhydrophobic composite membranes had a contact angle of 157° (sliding angle of 7°) and an LEP of 327 kPa. Long-term MD testing revealed that FG particles could increase MD's sustainability while decreasing membrane wetting. Vacuum-assisted air gap membrane distillation (V-AGMD) showed that the composite asymmetric membrane outperformed the non-asymmetric version (composite nanofiber) membrane by 21 % in average water flux and 25 % in average thermal efficiency because of the asymmetric structure of the membrane. The numerical model developed for the V-AGMD system also successfully predicted the water flux generated experimentally from the composite nanofiber and composite asymmetric membranes. Our results confirmed that electrospun asymmetric FG membranes with high contact angle, low sliding angle, high permeability, and high gained output ratio (GOR) would be promising candidates for sustainable MD.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Computational flow modeling of triply periodic minimal surfaces as feed channel spacers in ultra-high pressure reverse osmosis applications

Triply periodic minimal surfaces (TPMS) are a special class of mathematical surfaces characterized by a high surface area-to-volume ratio. They have generated considerable interest in fields such as acoustics, heat transfer, and membrane-based filtration processes. This study evaluates the performance of four different TPMS designs—Schoen Gyroid, Schoen Crossed Layers of Parallels (CLP), Schoen Transverse Crossed Layers of Parallels (tCLP), and Schwarz-Primitive—when used as feed channel spacers under ultra-high pressure reverse osmosis (UHPRO) conditions, at approximately 200 bar. Our experimentally validated computational fluid dynamics model reveal different flow patterns within the feed channels for each of the four TPMS designs, leading to varying hydrodynamic and permeation properties. Under the simulated UHPRO conditions, the Gyroid and tCLP designs yield up to a 23% increase in average permeate velocity and a 14% reduction in average membrane-surface concentration relative to a non-woven spacer of the same porosity. Furthermore, the enhanced performance comes with an increased feed channel pressure drop, although it only constitutes less than 4% of the operating pressure when extrapolated for a meter-long membrane module. Additionally, the study analyzes the effects of varying inlet velocity and spacer porosity on membrane performance. Overall, this research provides valuable insights into the potential use of TPMS spacers in UHPRO applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

From observation to replication: machine-learning-driven quantification and replication of fine-scale fish kinematics and behavior

Long-term quantification of fish behavior is essential for aquatic ecology, wildlife telemetry, and biomechanical device development. However, the observation duration required to obtain reliable behavioral and kinematic metrics remains unclear, and few tools exist to physically reproduce natural swimming motion for controlled experimentation. We address these challenges by developing a generalizable framework that models behavioral reliability (Spearman–Brown reliability index) as a function of observation duration and derives metric-specific monitoring thresholds. Using juvenile white sturgeon (Acipenser transmontanus) as a case study, we demonstrate that the minimum duration needed for reliable estimates varies substantially across kinematic features: to exceed a reliability of 0.8, total distance traveled requires 12 days, average curvature (mm?¹) 15 days, tail-beat frequency (Hz) 8 days, and average speed (body length/s) 17 days. We further bridge digital analysis and physical testing by developing a hardware-in-the-loop simulator that reconstructs machine-learning-derived swimming kinematics with high fidelity (correlation coefficient 0.98–0.99, RMSE 1.22–1.27 mm over a 5-minute segment). This platform enables realistic, repeatable motion stimuli for evaluating aquatic sensing technologies and bio-integrated devices under controlled conditions. Together, these contributions provide a scalable approach for designing long-term behavioral studies and a data-driven connection between ecological observation and robotic experimentation.

Hwang, SungJoo↗
DOE OSTI2023

Hydrocarbon-based membranes cost-effectively manage species transport and increase performance in thermally regenerative batteries

Low-temperature heat (T<130°C) can be utilized by thermally regenerative batteries (TRBs) for power production, allowing the thermal energy to be converted to storable chemical potential energy. However, TRBs suffer from high ohmic losses and ammonia crossover, which has slowed their development. In this study, we examined how the use of six different membranes influenced TRB performance, determined the most influential membrane parameters, and identified promising membrane candidates that cost-effectively increase TRB performance. Of the six membranes examined, an inexpensive, hydrocarbon CEM (Selemion CMVN) had low ammonia crossover without compromising resistance, resulting in good performance across all metrics studied. A thin anion exchange membrane (Sustainion, 50 microns) showed a high peak power density of 82 mW cm –2 due to low resistance, but the average power density and energy density were low due to high ammonia flux. Full discharge curves using Selemion CMVN provided an average power density of 26 ± 7 mW cm –2 with an energy density of 2.9 Wh L –1 , which were large improvements on previous TRBs. A techno-economic analysis showed that Selemion CMVN had the lowest levelized cost of storage ($410 per MWh) at an applied current density of 50 mA cm –2 .

25 ENERGY STORAGE↗
DOE OSTI2023

Dilated causal convolutional neural networks for forecasting zone airflow to estimate short-term energy consumption

Here this paper investigates the use of dilated causal convolutional neural networks for fine- grained temporal forecasting of building zone states. Specifically, we build and evaluate models using a small set of exogenous features (e.g., external temperature) to autoregressively predict zone airflow setpoints every minute for a 24-hour prediction window. We carefully explore the trade-off between generality and specificity in these models, training and evaluating them based on zone, zone type, month, season, and combinations thereof. When evaluated for a commercial office building in Eastern Washington with 16 zones served by variable air volume air handling units, we find that the highest performance comes from a zone-specific, season-agnostic approach; with it, we obtain an R 2 of 0.704 (averaged over zones) and an average normalized root mean square error (nRMSE) of 0.111. In contrast, the most general model (trained across all zones and seasons) yields an R 2 of only 0.416 and a nRMSE of 0.168, while a baseline zone-specific reduced order model obtains 0.443 R 2 and 0.159 nRMSE. We also report on factors affecting airflow forecasting performance, on the ability of models trained on a specific zone to generalize to other zones, and on the capability of those models trained on a specific month to generalize to other months.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Techno-economic feasibility of borehole thermal energy storage system connected to geothermal heat pumps for seasonal heating load of two buildings in Fairbanks, Alaska

Borehole thermal energy storage (BTES) is an effective solution for managing imbalanced heating and cooling loads in cold regions. This study evaluated the long-term feasibility of a BTES system in the Fairbanks area, Alaska, through building energy modeling, resource characterization, and numerical modeling. The system was designed to store waste heat from a nearby coal power plant during summer and provide thermal energy during winter to geothermal heat pumps supplying heating loads in two buildings. Heating load profiles were modeled for the buildings using EnergyPlus, and the results indicated the annual heating load was 5.6 times greater than the cooling load. 40 borehole heat exchangers were pre-designed approximately 100 m away from the two buildings in terms of land availability and regulatorily optimized depth. The 20-year performance of the designed BTES system under two operational scenarios—one with a 5-year preheating period and one without—was numerically modeled using subsurface temperature and properties characterized through the literature review and thermal response tests. Both scenarios demonstrated that the BTES has the capacity to fully cover the heating loads in the two buildings throughout the 20-year lifetime. Production temperatures at central wells were 33 % higher on average than at outer wells in both scenarios. The 5-year preheating period increased subsurface and extraction temperatures, and correspondingly annual average and total thermal energy production was higher for 8 years than in the scenario without the preheating period. These results highlight the long-term reliability and sustainability of the BTES system in meeting heating demands over its lifetime, with the preheating period offering potential performance improvements. Implementing the BTES system in cold regions with high heating demand, such as Fairbanks, Alaska, could provide a long-term, sustainable energy solution for managing imbalanced heating and cooling loads.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Surrogate model evaluation and building energy benchmarking for commercial buildings

Building energy consumption benchmarking involves challenges associated with various energy patterns for different building types; heating, ventilating, and air-conditioning (HVAC) system types; and climates. Given significant variation in energy use patterns, accurate prediction of long-term energy use using surrogate models remains challenging. Multiple linear regression (MLR) is commonly used for building energy benchmarking because of its simple structure; however, it lacks accuracy compared to other black-box models. Although many studies have compared surrogate models and offer guidance on model selection based on metrics, they do not provide detailed analysis on improving the surrogate model accuracy. In this paper, we implement a surrogate model using polynomial ridge regression (i.e., MLR with interaction terms combined with ridge regularization) for small office and retail strip mall buildings across six HVAC system types and all climate zones, for electricity and natural gas in baseline and proposed scenarios. A simulation workflow is developed using OpenStudio TM /EnergyPlus TM to generate simulation data using measures over a wide range of efficiency inputs. Enhancements based on statistical insights are used for improving the model accuracy using filters, input transformations, and change points. Surrogate models achieved average coefficient of variation of the root mean squared error (CVRMSE) values of 2.17, 1.06, 2.05, and 3.26 for proposed electricity, proposed natural gas, baseline electricity, and baseline natural gas, respectively, with enhancements reducing CVRMSE by an average of 14.9% across all combinations. We provide model interpretation via Shapley additive explanations to determine which input variables most influence energy consumption and provide supportive arguments for enhancements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Opportunities for green hydrogen production with land-based wind in the United States

Hydrogen (H 2 ) is an efficient energy carrier and storage mechanism that can supply both stationary and transport energy demand. Rapidly declining renewable energy generation costs; technology innovations in wind, solar, battery storage, and electrolysis; and a global push for more sustainable and secure energy have driven increased interest in green H 2 production. In this study, we develop an H 2 scenario analysis tool to assist in rapid, high-resolution insights into future, green H 2 pathways to achieve policy goals and market competitiveness. Using this tool, we estimate H 2 production and costs for U.S., off-grid scenarios given varying policy and cost scenarios from 2025–2035. Results indicate that achieving economically competitive green H 2 production (below $\$$2/kg) is possible in 2030 with no policy incentives (one site achieves this target), while increasing policy support to include wind and green H 2 production tax credits enables widespread economic viability sooner, with sub-$\$$2/kg LCOH targets achieved by 2025 and 51.7% of sites achieving this target by 2035. Maximizing policy support through prevailing wage and apprenticeship credit multipliers enable widespread economic viability, including sub-$\$$2/kg of green H 2 by 2025 and even negative pricing by 2035. Regions with lowest LCOH values correspond to high wind resource areas and capacity factors. Achieving decarbonization goals with green H 2 depends on technology cost reductions and policy support, with a maximum average LCOH reduction of $\$$23.10 between no and maximum policy support scenarios, and a maximum average LCOH reduction of $\$$25.86 between current, conservative technology costs and 2035 projected technology cost assumptions.

08 HYDROGEN↗
DOE OSTI2020

Investigation of startup, performance and cycling of a residential furnace integrated with micro-tubular flame-assisted fuel cells for micro-combined heat and power

Solid Oxide Fuel Cells (SOFCs) offer advantages for micro-Combined Heat and Power (μCHP), but currently suffer from slow startup (>1 h) and limited thermal cycling which reduces the applications, energy savings and economics. In this work, a micro-Tubular SOFC stack is integrated into a residential furnace to create a micro-Tubular Flame-assisted Fuel Cell (mT-FFC) μCHP system. A high power density of 202 mW cm -2 is reported operating in synthesis gas generated from fuel-rich combustion of natural gas/air. Unlike previous reports, instabilities in the polarization are attributed to low temperature of the oxygen reduction reaction at the cathode. The mT-FFC stack achieved peak power density in 6 min after ignition. 200 thermal cycles at an average heating rate of 215 °C.min -1 and average cooling rate of 176 °C.min -1 were conducted and a low degradation rate of 0.0325 V per 100 cycles per fuel cell was achieved. Low NO x emissions (10 ppm) and high combined efficiency is reported.

25 ENERGY STORAGE↗