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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2026

Development, Characterization, and Validation of Elevated-Temperature Constitutive Models: Deformation and Damage

This report provides a brief review of experimentally observed hereditary and nonhereditary material behavior along with background information on standard as well as advanced internal state variable constitutive modeling at elevated temperature. A description of exploratory, characterization, and validation testing is presented along with a detailed outline of what constitutes “sufficient” data content (i.e., quality and quantity) for developing or enhancing, characterizing, and validating a particular sophisticated nonlinear time- and history-dependent (hereditary) class of constitutive models known as GVIPS (generalized viscoplasticity with potential structure). The tests described are necessary to reveal a material’s behavior in the reversible (or viscoelastic) and irreversible (or viscoplastic) regimes, for the identification of both deformation and damage model parameters. Results presented are primarily for metallic materials. In all cases, both uniaxial and multiaxial tests are described, and the linkage between the specific tests and the parameters within the model that can be characterized from the results of these tests are also discussed. Discussion is also provided relative to the role information management must play relative to material data collection, analysis, maintenance, and dissemination. The need for such an information system is particularly important as both analyst and designer move toward utilizations of sophisticated, nonlinear time- and history dependent (hereditary) constitutive models. Lastly, the concept of state space and its utility in understanding and establishing constitutive models is addressed throughout. The intent behind this document is to help both the modeler and experimentalist understand each other’s specific points of view and provide guidance for both model development and characterization. Emphasis has been placed on providing the mechanician with information regarding how tests are performed and what issues to be aware of when interpreting results. It is hoped that experimentalists will take away a new perspective on the types of information that modelers and analysts are looking for from them.

Constitutive Modeling↗
DOE OSTI2024

Revolutionizing Waste Management: AI-Powered Real-Time Characterization for Efficient Handling of Non-Recyclable Municipal Solid Waste

According to EPA, -300 MM tons of municipal solid waste (MSW) was available in the US as of 2018. Of that total material, nearly 50% was landfilled resulting in a significant loss for the potential to convert its energy value into cost effective and sustainable biofuels. Redirecting this material away from the landfill and into conversion ready feedstock for energy generation can directly address DOE's selling price < $2.50/GGE while securing the US national energy independence [1]. However, the paramount challenges in any rational fuel conversion strategy are understanding the chemical makeup, quality and associated calorific value of the MSW. Understanding these parameters is critical in achieving any acceptable fuel conversion and requires rapid characterization followed by accurate separation technologies. Therefore, we are proposing to address the rapid characterization by building a non-invasive, rapid, and highly accurate Artificial Intelligence (AI)-enabled spectrometric/optical approach augmented with multi-sensory information for advanced characterization of domestic heterogeneous MSW. North Carolina State University (NCSU) and the National Renewable Energy Laboratory (NREL), in partnership with strong support from the Town of Cary and IBM, Inc., will closely work together to implement this ground-breaking technology for the effective characterization of MSW for sustainable and affordable production of conversion-ready feedstocks, while solving the environment issue of planetary proportions.

artificial intelligence↗
DOE OSTI2025

Automating Sensor Characterization with Bayesian Optimization

The development of novel instrumentation requires an iterative cycle with three stages: design, prototyping, and testing. Recent advancements in simulation and nanofabrication techniques have significantly accelerated the design and prototyping phases. Nonetheless, detector characterization continues to be a major bottleneck in device development. During the testing phase, a significant time investment is required to characterize the device in different operating conditions and find optimal operating parameters. The total effort spent on characterization and parameter optimization can occupy a year or more of an expert's time. In this work, we present a novel technique for automated sensor calibration that aims to accelerate the testing stage of the development cycle. This technique leverages closed-loop Bayesian optimization (BO), using real-time measurements to guide parameter selection and identify optimal operating states. We demonstrate the method with a novel low-noise CCD, showing that the machine learning-driven tool can efficiently characterize and optimize operation of the sensor in a couple of days without supervision of a device expert.

Cuevas-Zepeda, Julian [Chicago U., KICP; Chicago U↗
DOE OSTI2022

Two–Photon Polymerized Shape Memory Microfibers: A New Mechanical Characterization Method in Liquid

Two-photon polymerization (TPP) is widely used to create 3D micro- and nanoscale scaffolds for biological and mechanobiological studies, which often require the mechanical characterization of the TPP fabricated structures. To satisfy physiological requirements, most of the mechanical characterizations need to be conducted in liquid. However, previous characterizations of TPP fabricated structures are all conducted in air due to the limitation of conventional micro- and nanoscale mechanical testing methods. In this study, a new experimental method is reported for testing the mechanical properties of TPP-printed microfibers in liquid. The experiments show that the mechanical behaviors of the microfibers tested in liquid are significantly different from those tested in air. By controlling the TPP writing parameters, the mechanical properties of the microfibers can be tailored over a wide range to meet a variety of mechanobiology applications. In addition, it is found that, in water, the plasticly deformed microfibers can return to their predeformed shape after tensile strain is released. The shape recovery time is dependent on the size of microfibers. The experimental method represents a significant advancement in mechanical testing of TPP fabricated structures and may help release the full potential of TPP fabricated 3D tissue scaffolds for mechanobiological studies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

LX-17 Thermal Decomposition-Characterization of Solid Residues from Cook-Off in a Small-Scale Vessel Under Confinement

Concerns surround whether insensitive (or any) energetic materials are more dangerous to handle when exposed to abnormal thermal environments. This study characterizes the residual material remaining after LX-17 (92.5 % 1,3,5-triamino 2,4,6-trinitro benzene (TATB) and 7.5 % Kel-F) is exposed to various thermal environments in a sealed small-scale vessel cook-off test reactor (heated at 0.1 to 100 °C/min until the reactor opened at 3000 psi (20.7 MPa)). Previous work has shown no additional sensitivity of these residues as evaluated by small-scale safety analysis, but characterization on the molecular scale indicates the TATB is transformed to more reactive compounds as well as the residue could be precursors to toxic gases. The solids and chars were characterized by various analytical methods. Heat-flow measurements indicated exothermic release is due to a mixture of residual TATB and related decomposition products (which may be more energetic). The N/C and O/N ratios indicated a material much more degraded than TATB. Primarily, the solids were a network of amorphous C inter-dispersed with N and O. Types of bonding include C–C, C–N, N–H, N–C, N=C, N≡C, C–O=, and –OH. Solvent extracts of the solids showed TATB decomposition intermediates benzo-furazans and benzo-furoxans, substituted TATB (mono-nitroso, hydroxyl, and chlorinated) along with several unidentified smaller molecules. These results indicate thermal treatment produces an amorphous carbon residue with heteroatoms incorporated through differing functionality, varying depending upon the thermal severity of exposure. These structures also could further decompose producing toxic light gases (such as cyanide).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

Additive manufacturing↗
DOE OSTI2022

Importance of Standardizing Analytical Characterization Methodology for Improved Reliability of the Nanomedicine Literature

Understanding the interaction between biological structures and nanoscale technologies, dubbed the nano-bio interface, is required for successful development of safe and efficient nanomedicine products. The lack of a universal reporting system and decentralized methodologies for nanomaterial characterization have resulted in a low degree of reliability and reproducibility in the nanomedicine literature. As such, there is a strong need to establish a characterization system to support the reproducibility of nanoscience data particularly for studies seeking clinical translation. Here, we discuss the existing key standards for addressing robust characterization of nanomaterials based on their intended use in medical devices or as pharmaceuticals. We also discuss the challenges surrounding implementation of such standard protocols and their implication for translation of nanotechnology into clinical practice. We, however, emphasize that practical implementation of standard protocols in experimental laboratories requires long-term planning through integration of stakeholders including institutions and funding agencies.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Seismicity-constrained fault detection and characterization with a multitask machine learning model

Geological fault detection and characterization are crucial for understanding subsurface dynamics across scales. While methods for fault delineation based on either seismicity location analysis or seismic image reflector discontinuity are well-established, a systematic approach that integrates both data types remains absent. We develop a novel machine learning model that unifies seismic reflector images and seismicity location information to automatically identify geological faults and characterize their geometrical properties. The model encodes a seismic image and a seismicity location image separately, and fuses the encoded features with a spatial-channel attention fusion module to improve the learning of important features in both inputs. We design an automated strategy to generate high-quality synthetic training data and labels. To improve the realism of the seismicity location image, we include random seismicity noise and missing seismicity location associated with some of the faults. We validate the model’s efficacy and accuracy using synthetic data examples and two field data examples. Moreover, we show that fine-tuning the trained model with a small, domain-specific dataset enhances its fidelity for field data applications. The results demonstrate that integrating seismicity location and seismic images into a unified framework allows the end-to-end neural network to achieve higher fidelity and accuracy in delineating subsurface faults and their geometrical properties compared with image-only fault detection methods. Our approach offers an adaptive data-driven tool for geological fault characterization and seismic hazard mitigation, bridging the gap between seismicity location and image-based fault detection methods.

58 GEOSCIENCES↗
DOE OSTI2023

Advances in imaging, scattering, spectroscopy, and machine learning-aided approaches for multiscale characterization of cementitious systems

Recent progress in methods used in the multiscale characterization of cementitious systems is reviewed, focusing on advances in imaging, scattering, and spectroscopy. The review includes relevant applications and developments in machine learning and other data analytics approaches to enhance characterization. Developments in imaging using light and electron microscopy as well as x-ray (i.e., from synchrotron) methods are summarized. Updates include scanning electron microscopy (SEM), transmission electron microscopy (TEM), tomography, and holography. A critical overview of spectroscopy (e.g., MAS NMR, Raman) and scattering (e.g., neutron, x-ray, synchrotron x-ray) methods is provided, and the intersection of these with imaging is developed (e.g., Raman imaging). Additionally, the paper summarizes recent developments in and implementations of state-of-the-art machine-learning algorithms and data analytics methods for automated, systematic, and/or quantitative analyses of image data sets. The review considers but is not limited to the application of these methods for investigating the hydration and microstructure development of cement phases, low-carbon-footprint cements (e.g., limestone calcined clay cements, LC3), environmental interactions (e.g., ASR) and model systems. Finally, the present work provides a critical presentation of advances in characterization methods that link together the composition and multiscale structure of cementitious materials.

42 ENGINEERING↗
DOE OSTI2026

AI-Based Analytics and Energy Modeling Framework for Characterizing Urban Energy Systems

Developing location-specific district energy models is essential for understanding energy patterns and supporting efficient management and planning decisions. However, accurately characterizing these models remains challenging due to gaps in building characteristics and labor-intensive traditional modeling workflows. To address these challenges, we develop an AI-based framework that integrates top-down and bottom-up building energy data to automate urban energy model characterization. The framework trains multimodal deep learning models using heterogeneous ResStockTM datasets to infer missing building characteristics from varying levels of known information and generate simulation-ready inputs for district-scale energy modeling. It also employs a conditioning-based injection approach to generate ”what-if” scenarios, enabling users to explore retrofit, efficiency, and technology-upgrade pathways. Integrated within URBANoptTM, a bottom-up district energy modeling platform for simulating co-located buildings, the framework infers detailed building-level inputs required for bottom-up simulations. Both localized and generalized AI models are developed to learn relationships across categorical, numerical, and time-series data, enabling reconstruction of missing attributes and generation of targeted upgrade scenarios. We demonstrate this methodology on a residential neighborhood in Baltimore, MD, assessing internal consistency against ResStock reference data and URBANopt simulation, and comparing selected attributes against real-world building characteristics. Results show strong overall predictive accuracy in data completion and scenario generation, with localized and generalized models offering complementary trade-offs between precision and scalability. Overall, our automated framework streamlines energy modeling and provides a reliable framework for urban building energy characterization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Thermal property characterization of phase change materials in building applications: A systematic review of fundamentals, recent progress, and future directions

Phase change materials (PCMs) can reduce building peak loads and enable demand-responsive thermal energy storage (TES), but their deployment depends on reliable measurement and interpretation of thermal properties across laboratory, intermediate, and application scales. Here, this review systematically examines characterization methods, testing protocols, and recent advances for neat PCMs and PCM composites, emphasizing thermal conductivity, enthalpy-related properties (phase change temperature, latent heat, specific heat), and cycling stability. For thermal conductivity, we compare steady-state and transient techniques and note limitations when phase transition and contact resistance affect measurements. For enthalpy–temperature characterization, we discuss differential scanning calorimetry together with intermediate- and bulk-scale methods, including T-history, heat flow meter testing, and three-layer calorimetry (3LC), to generate application-relevant enthalpy–temperature profiles. Cycling stability is organized into four experimental families: thermoelectric–air, fully thermoelectric, water-bath, and in situ chamber approaches, with attention to separating reversible supercooling from true degradation such as phase segregation. We highlight emerging noncontact diagnostics, including infrared thermography and embedded sensing, for spatially resolved validation and multiscale interpretation. Finally, we review the growing use of AI and machine learning for property prediction, inverse characterization from experimental signals, and real-time state estimation in building-integrated TES. Key needs include harmonized protocols, interlaboratory benchmarking, uncertainty reporting, and metadata-rich datasets to accelerate reproducible PCM qualification for grid-flexible buildings.

AI↗
DOE OSTI2024

CO 2 storage site characterization using ensemble-based approaches with deep generative models

Estimating spatially distributed properties such as permeability from available sparse measurements is a great challenge in efficient subsurface CO 2 storage operations. In this paper, a deep generative model that can accurately capture complex subsurface structure is tested with an ensemble-based inversion method for accurate and accelerated characterization of CO 2 storage sites. We chose Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for its realistic reservoir property representation and Ensemble Smoother with Multiple Data Assimilation (ES-MDA) for its robust data fitting and uncertainty quantification capability. WGAN-GP are trained to generate high-dimensional permeability fields from a low-dimensional latent space and ES-MDA then updates the latent variables by assimilating available measurements. Several subsurface site characterization examples including Gaussian, channelized, and fractured reservoirs are used to evaluate the accuracy and computational efficiency of the proposed method and the main features of the unknown permeability fields are characterized accurately with reliable uncertainty quantification. Furthermore, the estimation performance is compared with a widely-used variational, i.e., optimization-based, inversion approach, and the proposed approach outperforms the variational inversion method in several benchmark cases. We explain such superior performance by visualizing the objective function in the latent space: because of nonlinear and aggressive dimension reduction via generative modeling, the objective function surface becomes extremely complex while the ensemble approximation can smooth out the multi-modal surface during the minimization. This suggests that the ensemble-based approach works well over the variational approach when combined with deep generative models at the cost of forward model runs unless convergence-ensuring modifications are implemented in the variational inversion.

42 ENGINEERING↗
DOE OSTI2020

Characterization of flow and transport in a fracture network at the EGS Collab field experiment through stochastic modeling of tracer recovery

Energy extraction from subsurface reservoirs is important for addressing the increasing energy demand and environmental concerns such as global warming. However, the characterization of subsurface reservoirs, particularly reservoirs dominated by fracture networks remains a challenge due to the lack of means to directly observe subsurface processes. This study explores the feasibility and efficacy of characterizing fracture flow and transport processes in an enhanced geothermal system (EGS) testbed through stochastic tracer modeling. There are two enabling factors that allow application of stochastic modeling to characterize a subsurface reservoir. First, an abundance of geological and geophysical measurements enables the development of a high-fidelity and well-constrained fracture network model. Second, high-performance computing (HPC) allows running massive realizations efficiently. Six conservative tracer tests were stochastically modeled and produced satisfactory realizations that successfully reproduce field tracer recovery data from each tracer test. The evolution of flow and transport processes in the fracture network was then analyzed from these satisfactory realizations. This work demonstrates that stochastic tracer modeling on a high-fidelity fracture network model is feasible and can provide important insights regarding flow and transport characteristics in subsurface fractured reservoirs.

58 GEOSCIENCES↗
DOE OSTI2021

Evaluating the state-of-the-art in remote volcanic eruption characterization Part II: Ulawun volcano, Papua New Guinea

Retrospective eruption characterization is valuable for advancing our understanding of volcanic systems and evaluating our observational capabilities, especially with remote technologies (defined here as a space-borne system or non-local, ground-based instrumentation which include regional and remote infrasound sensors). In June 2019, the open-system Ulawun volcano, Papua New Guinea, produced a VEI 4 eruption. We combined data from satellites (including Sentinel-2, TROPOMI, MODIS, Himawari-8), the International Monitoring System infrasound network, and GLD360 globally detected lightning with information from the local authorities and social media to characterize the pre-, syn- and post-eruptive behaviour. The Rabaul Volcano Observatory recorded ~24 h of seismicity and detected SO 2 emissions ~16 h before the visually-documented start of the Plinian phase on 26 June at 04:20 UTC. Infrasound and SO2 detections suggest the eruption started during the night on 24 June 2019 at 10:39 UTC ~38 h before ash detections with a gas-dominated jetting phase. Local reports and infrasound detections show that the second phase of the eruption started on 25 June 19:28 UTC with ~6 h of jetting. The first detected lightning occurred on 26 June 00:14 UTC, and ash emissions were first detected by Himawari-8 at 01:00 UTC. Post-eruptive satellite imagery indicates new flow deposits to the south and north of the edifice and ash fall to the west and southwest. In particular, regional infrasound data provided novel insight into eruption onset and syn-eruptive changes in intensity. We conclude that, while remote observations are sufficient for detection and tracking of syn-eruptive changes, key challenges in data latency, acquisition, and synthesis must be addressed to improve future near-real-time characterization of eruptions at minimally-monitored or unmonitored volcanoes.

58 GEOSCIENCES↗
DOE OSTI2025

Ultrasonic characterization of material heterogeneities in stainless steel components produced by laser powder bed fusion

We introduce pulse-echo ultrasound as a method for characterizing the impact of powder bed fusion parameters on the properties of additively manufactured stainless-steel components, their material anisotropy, and location-dependent heterogeneity. Our results indicate that accurate characterization requires careful selection of ultrasonic propagation paths, which must consider the direction of additive layering, variations in processing parameters, and the component's geometry. We employed two distinct methods to estimate material properties from ultrasonic data: One assumes isotropy, while the other accounts for anisotropic interactions during the propagation of elastic waves. When applied to samples fabricated with laser energy densities ranging from 24 to 42 J/mm³ , these methods revealed transverse isotropy and weak anisotropy (quantified by small Thomsen parameters, ε = 0.0651 and γ = 0.0092) and less than a ∼6 % change in acoustic impedance. The assumption of isotropy, in this case, leads to small errors (less than 4 % or 1 % for Young's modulus in the build or transverse directions) when estimating orthotropic material properties using ultrasonic data measured along just two orthogonal directions, one of which must align with the build direction. By comparing ultrasonic measurements — which aggregate the spatial variability in material properties along the length of elastic wave propagation into a single value — with localized measurements obtained from surface nanoindentation, we uncovered and spatially profiled significant differences between the surface and interior properties. Specifically, the surface Young's modulus decreased from approximately 210 GPa to 180 GPa within a depth of about 3 mm. We attribute this surface-localized heterogeneity in PBF-fabricated components to distinct thermal histories experienced by the surface and interior regions. Collectively, the results of this study establish a framework for the ultrasonic characterization of material heterogeneity and anisotropy in material properties and demonstrate its application in additively manufactured metal components.

36 MATERIALS SCIENCE↗
DOE OSTI2023

CLC: A cross-level program characterization method

Characterization of program execution plays a key role in performance improvement. There are numerous transformations applied to each step that a program takes on its lowering from source code to a compiler intermediate representation to machine language to microarchitecture-specific execution. The unpredictable benefit of each transformation step could lead a notionally superior algorithm to exhibit inferior performance once actually run, and it can be hard to discern which step in the transformation path contradicted the code developer’s assumptions. Conventional approaches to program-execution characterization consider the behavior after only a single one of those steps, which limits the information that can be provided to the user. To help address the issue of myopic views of program execution, this paper presents a novel cross-level characterization approach for understanding the behavior of program execution at different levels in the process of writing, compiling, and running a program. We show that this approach provides a richer view of the sources of performance gains and losses and helps identify program execution in a more accurate manner.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Characterization and verification of a two-body wave energy converter with a novel power take-off

The lack of high efficient, predictable and reliable power take-off (PTO) systems limits developments of ocean wave energy technology. In this paper, a two-body self-reacting wave energy converter (WEC) with a novel PTO is designed, modelled and implemented for efficiency enhancement. A novel mechanical motion rectifier (MMR) using a ball screw mechanism and an enclosed gear set is integrated to improve the energy harvesting efficiency and reliability by rectifying the oscillatory wave motion into unidirectional rotation of the generator. Detailed design and dynamic modelling for the proposed WEC are presented. A prototype of the PTO is tested in a dry lab to characterize and refine the dynamic modelling. The characterized PTO model is combined with the WEC model to create an overall system model. A water tank test is conducted to verify the overall system dynamics, which proves the accuracy of the model and shows the advantages of the proposed WEC on efficiency and predictability. Following the method of how the WEC system is characterized, performance prediction of the proposed WEC with MMR PTO can be achieved with high accuracy.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Rapid characterization of MSW and RDF feedstocks for waste-to-energy process using LIBS and ML techniques

The heterogeneity in the composition of municipal solid wastes (MSW) poses significant challenges in the production of biofuel and bioproducts. This research aims to enhance the accuracy and efficiency of waste analysis and characterization by introducing a fast characterization approach for MSW-derived refuse-derived fuels (RDF) by combining Laser-Induced Breakdown Spectroscopy (LIBS) with advanced machine learning (ML) techniques. The approach combines data pre-processing of LIBS spectra of RDF, and the development of ML models trained on domain and theory-based spectral features for predicting process parameters. These models are adept at predicting key process parameters like High Heating Value (HHV), carbon content, and volatile matter. This approach can achieve an average RRMSE of 2.13% and R 2 of 0.98 or higher for all considered parameters on testing data. This work demonstrates significant potential for improving waste sorting, processing efficiency, and environmental compliance over traditional labor- and time-intensive laboratory waste analysis and characterization.

09 BIOMASS FUELS↗