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DOE OSTI2025

Contributions of vegetation heterogeneity within tower footprint to CO 2 flux estimations through graph neural network modeling

Net ecosystem exchange of CO 2 (Fc) measured directly by eddy covariance towers is based on various assumptions, including large, flat and homogenous land cover type. In reality, often a tower site is not large enough for flux measurements, and landscapes consist of patches of different land cover types within the flux footprint. In addition, some portions of fluxes are contributed by different cover types when a footprint exceeds the size of the target ecosystem. The contributions of non-dominant patches to Fc are often ignored. Here, in this study, we propose a novel integrated modeling framework that combines random forest (RF) and XGBoost with a residual correction module based on a deep graph convolutional network (DeeperGCN) to simulate Fc for seven flux measurement sites in southwest Michigan. High-resolution remote sensing vegetation indices, soil properties, meteorological variables, and footprint-weighted spatial features were used as model inputs at three spatial resolutions (10 m, 20 m, 30 m), and their importance in predicting Fc with DeeperGCN was assessed. We found that residual correction using DeeperGCN significantly improved prediction accuracy, with the R 2 increasing from 0.9098 to 0.9479 for RF and from 0.9235 to 0.9433 for XGBoost. At site level, the maximum improvement in R 2 reached 0.1617. Paired t-tests confirmed that these improvements were statistically significant (p < 0.05). Among all predictors, leaf area index and incoming shortwave radiation emerged as the dominant drivers of spatial residual variation, followed by precipitation, relative humidity, and selected vegetation indices. The 20 m resolution yielded the best balance between model performance and computational efficiency. In conclusion, our modeling framework effectively captures both spatial heterogeneity and nonlinear interactions, offering a robust solution for spatially explicit flux modeling in structurally diverse ecosystems beyond the study sites.

footprint model↗

DOE OSTI2021

Fabrication of Six Degrees-of-Freedom Hexflex Positioner With Integrated Strain Sensing Using Nonlithographically Based Microfabrication

In this study, a process flow is described for the low cost, flexible fabrication of metal micro-electromechanical systems (MEMS) with high performance integrated sensing. The process is capable of producing new designs in ≈1 week at an average unit cost of <$1 k/device even at batch sizes of ≈1–10, with expected sensing performance limits of about 135 dB over a 10 kHz sensor bandwidth. This is a ≈20× reduction in cost, ≈25× reduction in time, and potentially >30× increase in sensing dynamic range over comparable state-of-the-art compliant nanopositioners. The nonlithographically based microfabrication (NLBM) process is uniquely suited to create high performance nanopositioning architectures which are customizable to the positioning requirements of a range of nanoscale applications. These can significantly reduce the cost of nanomanufacturing research and development, as well as accelerate the development of new processes and the testing of fabrication process chains without excess capital investment. A six degrees-of-freedom (6DOF) flexural nanopositioner with integrated sensing for all 6DOF was fabricated using the newly developed process chain. The fabrication process was measured to have ≈30 μ m alignment. Sensor arm, flexure, and trace widths of 150 μ m, 150 μ m, and 800 μ m, respectively, were demonstrated. Process capabilities suggest lower bounds of 25 μ m, 50 μ m, and 100 μ m, respectively. Dynamic range sensing of 52 dB was demonstrated for the nanopositioner over a 10 kHz sensor bandwidth. Improvements are proposed to approach sensor performance of about 135 dB over a 10 kHz sensor bandwidth.

42 ENGINEERING↗

DOE OSTI2024

Assessing microbial systems and process configurations for improved ethanol production from sugary stovers by integrating soluble sugars and holocellulose

Here, this study evaluated microbial systems and technological approaches to configure the whole slurry co-fermentation process for ethanol biosynthesis from a novel stover system rich in sugars. Two approaches, namely separate and simultaneous hydrolysis and co-fermentation (SHCF and SSCF, respectively), were investigated using Escherichia coli monoculture and E. coli-yeast coculture. The SSCF with E. coli monoculture produced 32.75 g/L ethanol, representing only 44.87% yield, which left 65.13 g/L of total sugars unconverted and exhibited limited xylose consumption. Subsequently, a coculture-based SHCF significantly enhanced sugar consumption, leading to increase in ethanol yield and concentration to 66.94% and 48.86 g/L, respectively. Nevertheless, xylose utilization remained minimal due to the preference for glucose and the inhibitory effects of certain compounds. Thereafter, modification of the medium composition by supplementing betaine and sodium metabisulfite improved ethanol production to 53.18 g/L by reducing the toxic effects of inhibitors. Finally, a dual-phase SSCF (DP-SSCF) was explored by allowing the consumption of sugars from the pretreated slurry in the first phase, followed by concurrent cellulose hydrolysis and utilization of the resulting glucose in the second phase. This strategy increased ethanol titer to 63.14 g/L, with 84.3% yield and 0.88 g/L/h productivity.

09 BIOMASS FUELS↗

DOE OSTI2024

Flow and Performance Characterization of Rotating Detonation Combustor Integrated with Various Convergent Nozzles

In this study, convergent nozzles of various area ratios (ARs) are used downstream of an annular rotating detonation combustor (RDC) to increase the operating pressure and approach sonic conditions at the nozzle throat. Reactant methane and oxygen-enriched air (67% [Formula: see text] and 33% [Formula: see text] by volume) are supplied in counterflow arrangement from two separate plenums located at the base of the RDC annulus. Based on experimentation, a total mass flow rate of [Formula: see text] was chosen to achieve stable, single-wave mode RDC operation for all test cases, allowing for one-to-one comparisons. The internal performance of the RDC was characterized by ion probes and pressure measurements (wall static and oscillating) in supply plenums and across different axial locations of the combustor. Particle image velocimetry (PIV) at 100 kHz was utilized to measure axial and circumferential velocity components within a two-dimensional region of interest located downstream of the converging nozzle exit. Results show higher internal performance of the RDC with increasing AR of the convergent nozzle. PIV measurement illustrated that the flow oscillation amplitudes decrease with an increasing AR of the converging nozzle. The exit flow contained significant nonuniformity and unsteadiness even with a converging nozzle of AR 2.0, indicating incomplete choking of the flow at the nozzle throat.

Engineering↗

DOE OSTI2026

Explaining System-Level Prognostics with Established Machine Learning Methods

System-level prognostics is crucial for ensuring reliability and enabling predictive maintenance in complex systems with interconnected components. This study presents a framework that integrates data-driven methods to predict the remaining useful life (RUL) of a subsystem under multiple and concurrent faults within a nuclear power plant system with explainable artificial intelligence (XAI). A nuclear power plant (NPP) operation was simulated to model the degradation behavior of NPP components, and four machine learning models—Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), Fully Connected Neural Network (FCNN), and Long Short-Term Memory (LSTM)—were evaluated for prognostics with a novel system RUL parameter. The LSTM model demonstrated potential superior repeatability, while SHAP (SHapley Additive exPlanations) for explainability provided consistent and trustworthy global explanations. In contrast, LIME (Local Interpretable Model-agnostic Explanations) offered localized interpretability but showed reduced stability for sequential data. Key findings include the interplay between component-level degradation and system-wide performance, with LSTM effectively capturing these dynamics through sequence-level predictions. The XAI techniques enhanced transparency by identifying critical features influencing model predictions and aligning with domain knowledge. Furthermore, this framework has significant implications for improving trust and understanding in predictive maintenance, particularly in safety-critical industries like nuclear energy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Spin crossovers and superdiffusion in the one-dimensional Hubbard model

We use tools from integrability and generalized hydrodynamics to study finite-temperature dynamics in the one-dimensional Hubbard model. First, we examine charge, spin, and energy transport away from half-filling and zero magnetization, focusing on the strong coupling regime where we identify a rich interplay of temperature and energy scales, with crossovers between distinct dynamical regimes. We identify an intermediate-temperature regime analogous to the spin-incoherent Luttinger liquid, where spin degrees of freedom are hot but charge degrees of freedom are at low temperature. We demonstrate that the spin Drude weight exhibits sharp features at the crossover between this regime and the low-temperature Luttinger liquid regime, which are absent in the charge and energy response, and rationalize this behavior in terms of the properties of Bethe ansatz quasiparticles.We then turn to the dynamics along special lines in the phase diagram corresponding to half-filling and/or zero magnetization where on general grounds we anticipate that the transport is subballistic but superdiffusive. We provide analytical and numerical evidence for Kardar-Parisi-Zhang (KPZ) dynamical scaling (with length and time scales related via x ~ t 2/3 ) along both lines and at the SO(4)-symmetric point where they intersect. Furthermore, our results suggest that both spin-coherence crossovers and KPZ scaling may be accessed in near-term experiments with optical lattice Hubbard emulators.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Duality defect in a deformed transverse-field Ising model

Physical quantities with long lifetimes have both theoretical significance in the study of quantum many-body systems and practical implications for quantum technologies. In this manuscript, we investigate the roles played by topological defects in the construction of quasiconserved quantities, using as a prototypical example the Kramers-Wannier duality defect in a deformed one-dimensional quantum transverse-field Ising model. We construct the duality defect Hamiltonian in three different ways: half-chain Kramers-Wannier transformation, utilization of techniques in the Ising fusion category, and defect-modified weak integrability breaking deformation. The third method is also applicable for the study of generic integrable defects under weak integrability breaking deformations. We also work out the deformation of defect-modified higher charges in the model and study their slower decay behavior. Furthermore, we consider the corresponding duality defect twisted deformed Floquet transverse-field Ising model and investigate the stability of the isolated zero mode associated with the duality defect in the integrable Floquet Ising model, under such weak integrability breaking deformation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2026

GPM IMERG V07B and V06B: Evaluation Using Ground-Based Radar Observations and Application in Global Mesoscale Convective System Tracking

This study evaluates the latest Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG V07B) against its predecessor V06B, for studying mesoscale convective systems (MCSs). Both versions are compared using ground-based radar and rain gauge data from five meteorologically diverse regions: the contiguous United States (including eastern coastlines), Amazon rainforest, central Argentina mountains, equatorial Indian Ocean, and northern Australia across multiple temporal (0.5–6 hours) and spatial scales (0.1°–0.25°). An updated global MCS tracking dataset is developed by integrating satellite-observed infrared brightness temperature with IMERG V07B. Comparation of IMERG against radar observations reveals that IMERG demonstrates better performance in capturing the probability distribution and quantitative contributions of rainfall (from no-rain to intense-rain conditions) over tropical oceans than over land, with marked improvements in IMERG V07B for heavy-to-intense rain (> 10 mm h-1). Over land, systematic biases persist: IMERG tends to overestimate light-to-moderate rain (1–10 mm h-1) while underestimating heavy-to-intense rain. Additionally, aggregating IMERG to coarser resolutions (3-hourly or 0.25°) improves consistency with radar observations, outperforming the 1-hourly/0.1° resolution. The new IMERG V07B-based global MCS dataset exhibits consistent statistical characteristics with the V06B-based dataset, despite lower mean rain rates and reduced heavy precipitation contributions. These findings offer valuable insights for utilizing IMERG V07B in global precipitation studies, MCS characterization, and model evaluation.

Zhang, Sihan↗

DOE OSTI2025

Electrically Reconfigurable Liquid Metal Nanophotonic Platform for Color Display and Imaging

Dynamically tunable optical materials and device architectures are essential for future photonic technologies, yet conventional solid metals lack the intrinsic tunability required for advanced functionalities. Gallium-based liquid metals (LMs) present an appealing alternative thanks to their distinctive mechanical and optical properties; however, their practical integration in tunable photonic elements remains largely unexplored. Here, in this study, an electrochemically controlled nanophotonic platform is demonstrated that integrates a dynamically reconfigurable LM ground plane with gold nanoantenna arrays within a microfluidic system, enabling precise and reversible modulation of optical resonances across the visible to mid-infrared spectral ranges. By employing moderate operational voltages (1.5–3.0 V), real-time tuning of high-resolution structural color patterns is achieved through nanoscale control of the interfacial gap between the LM and Au nanoantennas. This innovative platform facilitates electrically programmable, high-contrast color patterns suitable for dynamic optical displays, secure anti-counterfeiting labels, and imaging applications. Additionally, this platform enables tunable mid-infrared spectral responses, which may be utilized for chemical and biological sensing applications. This versatile integrated LM-based nanophotonic platform opens new paths toward multifunctional, actively tunable/reconfigurable photonic device and system technologies.

Imaging↗

DOE OSTI2024

INTEGRATION OF CONDUIT HYDROPOWER AND BATTERIES INTO IRRIGATION INFRASTRUCTURE

Irrigation districts, ditch companies, and other agricultural water providers across the West operate and maintain canals, ditches, and reservoirs that store and deliver water for agricultural production, municipal needs, and other purposes. Co-locating energy generation and storage with this infrastructure provides opportunities to improve resilience and reduce energy costs. This memo discusses two case studies of co-located infrastructure and their contexts. The first case study discusses the development of an integrated microgrid, hydropower, solar, and battery storage project in North Unit Irrigation District (NUID) in Oregon. The second case study discusses the development of a battery storage project in Tulelake Irrigation District (TID) in northern California. Together, these two projects demonstrate the potential for co-located energy and water infrastructure.

13 - HYDRO ENERGY↗

DOE OSTI2025

Red Light-Powered Nanowire Photochemical Diodes for Unassisted Hydrogen Evolution and Glycerol Valorization

Artificial photosynthesis via unassisted photochemical diodes is a promising approach for generating solar fuels. However, photoelectrochemical systems often degrade under solar diurnal cycling, and reliance on solar insolation exacerbates land use. Narrow-band LED illumination offers continuous and constant operation, enabling the vertical integration of stable photochemical diodes. In this study, 740 nm red light was investigated as the illumination source for silicon-based photochemical diodes integrating the hydrogen evolution reaction with the glycerol oxidation reaction (GOR), where replacing the oxygen evolution reaction with GOR increases attainable bias-free photocurrents. Increasing photovoltages, saturation current densities, and power conversion efficiencies were observed with increasing incident red light intensity. Under a red light intensity of 60 mW cm –2 , these systems are stable above 7.5 mA cm –2 for 6 days with periodic electrolyte refreshing. In conclusion, these results offer a promising prospect for red light-powered photochemical diodes that integrate other significant reduction reactions, such as CO 2 reduction.

Alcohols↗

DOE OSTI2024

Utilization of microalgae for agricultural runoff remediation and sustainable biofuel production through an integrated biorefinery approach

Generally wastewater such agricultural runoff is considered a nuisance; however, it could be harnessed as a potential source of nutrients like nitrates and phosphates in integrated biorefinery context. In the current study, microalgae Chlorella sp. S5 was used for bioremediation of agricultural runoff and the leftover algal biomass was used as a potential source for production of biofuels in an integrated biorefinery context. The microalgae Chlorella sp. S5 was cultivated on Blue Green (BG 11) medium and a comprehensive optimization of different parameters including phosphates, nitrates, and pH was carried out to acquire maximum algal biomass enriched with high lipids content. Dry biomass was quantified using the solvent extraction technique, while the identification of nitrates and phosphates in agricultural runoff was carried out using commercial kits. The algal extracted lipids (oils) were employed in enzymatic trans-esterification for biodiesel production using whole-cell biomass of Bacillus subtilis Q4 MZ841642. The resultant fatty acid methyl esters (FAMEs) were analyzed using Fourier transform infrared (FTIR) spectroscopy and gas chromatography coupled with mass spectrometry (GC–MS). Subsequently, both the intact algal biomass and its lipid-depleted algal biomass were used for biogas production within a batch anaerobic digestion setup. Interestingly, Chlorella sp. S5 demonstrated a substantial reduction of 95% in nitrate and 91% in phosphate from agricultural runoff. The biodiesel derived from algal biomass exhibited a noteworthy total FAME content of 98.2%, meeting the quality standards set by American Society for Testing and Materials (ASTM) and European union (EU) standards. Furthermore, the biomethane yields obtained from whole biomass and lipid-depleted biomass were 330.34 NmL/g VS added and 364.34 NmL/g VS added , respectively. In conclusion, the findings underscore the potent utility of Chlorella sp. S5 as a multi-faceted resource, proficiently employed in a sequential cascade for treating agricultural runoff, producing biodiesel, and generating biogas within the integrated biorefinery concept.

09 BIOMASS FUELS↗

DOE OSTI2023

Integration of Biorelevant Pediatric Dissolution Methodology into PBPK Modeling to Predict In Vivo Performance and Bioequivalence of Generic Drugs in Pediatric Populations: a Carbamazepine Case Study

This study investigated the impact of gastro-intestinal fluid volume and bile salt (BS) concentration on the dissolution of carbamazepine (CBZ) immediate release (IR) 100 mg tablets and to integrate these in vitro biorelevant dissolution profiles into physiologically based pharmacokinetic modelling (PBPK) in pediatric and adult populations to determine the biopredictive dissolution profile. Dissolution profiles of CBZ IR tablets (100 mg) were generated in 50–900 mL biorelevant adult fasted state simulated gastric and intestinal fluid (Ad-FaSSGF and Ad-FaSSIF), also in three alternative compositions of biorelevant pediatric FaSSGF and FaSSIF medias at 200 mL. This study found that CBZ dissolution was poorly sensitive to changes in the composition of the biorelevant media, where dissimilar dissolution (F2 = 46.2) was only observed when the BS concentration was changed from 3000 to 89 μM (Ad-FaSSIF vs Ped-FaSSIF 50% 14 BS). PBPK modeling demonstrated the most predictive dissolution volume and media composition to forecast the PK was 500 mL of Ad-FaSSGF/Ad-FaSSIF media for adults and 200 mL Ped-FaSSGF/FaSSIF media for pediatrics. A virtual bioequivalence simulation was conducted by using Ad-FaSSGF and/or Ad-FaSSIF 500 mL or Ped-FaSSGF and/or Ped-FaSSIF 200 mL dissolution data for CBZ 100 mg (reference and generic test) IR product. The CBZ PBPK models showed bioequivalence of the product. This study demonstrates that the integration of biorelevant dissolution data can predict the PK profile of a poorly soluble drug in both populations. Further work using more pediatric drug products is needed to verify biorelevant dissolution data to predict the in vivo performance in pediatrics.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Sorption-induced polymer rearrangement: approaches from molecular modeling

With the growing need for chemical separation and chemical storage solutions, polymeric adsorbents have emerged as a promising class of candidate materials because of their potentially tunable sorption properties, membrane structure and relatively cost consciousness. Moreover, the developing field of polymeric membrane materials has shown particular success at integrating both experimental and computational studies. However, these material systems are known to suffer from varying degrees of induced membrane structural rearrangement upon adsorbate uptake, and thus many polymeric membrane performance metrics are often considered to degrade with an increasing number of ‘guest’ species. In this mini–review, we highlight methodology tradeoffs and provide insights into atomistic molecular simulations used to study adsorption with flexible frameworks, which have the potential to predict separation, storage or catalytic capabilities a priori to experimental efforts. Specifically, molecular simulation methods that have been applied to provide predictions of polymeric membrane properties that have included consideration for sorbate–induced polymer chain rearrangement, swelling and/or plasticization are reviewed. Here, the examples and methodologies described provide demonstrations of the applicability of simulations as an approach to understand adsorption–based phenomena at an atomistic/molecular level, and as a tool to carry out screening studies aimed at efficiently providing analysis for a diversity of polymeric adsorbent–adsorbate systems.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Dependence of inclusive jet production on the anti-k$_{T}$ distance parameter in pp collisions at $ \sqrt{\mathrm{s}} $ = 13 TeV

The dependence of inclusive jet production in proton-proton collisions with a center-of-mass energy of 13 TeV on the distance parameter R of the anti-k$_{T}$ algorithm is studied using data corresponding to integrated luminosities up to 35.9 fb$^{−1}$ collected by the CMS experiment in 2016. The ratios of the inclusive cross sections as functions of transverse momentum p$_{T}$ and rapidity y, for R in the range 0.1 to 1.2 to those using R = 0.4 are presented in the region 84 < p$_{T}$< 1588 GeV and |y|< 2.0. The results are compared to calculations at leading and next-to-leading order in the strong coupling constant using different parton shower models. The variation of the ratio of cross sections with R is well described by calculations including a parton shower model, but not by a leading-order quantum chromodynamics calculation including nonperturbative effects. The agreement between the data and the theoretical predictions for the ratios of cross sections is significantly improved when next-to-leading order calculations with nonperturbative effects are used.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Empirical evidence that glucan-interacting amino acid side chains within the transmembrane channel collectively facilitate cellulose synthase function

The fundamental mechanism of cellulose synthesis is widely conserved across Kingdoms and depends on cellulose synthases, which are processive, dual-function, family 2 glycosyltransferases (GT-2). These enzymes polymerize glucose on the cytoplasmic side of the plasma membrane and export the glucan chain to the cell surface through an integral transmembrane (TM) channel. Structural studies of active plant cellulose synthases (CESAs) have revealed interactions between the nascent glucan chain and the side chains of polar, charged, and aromatic amino acid residues that line the TM channel. However, the functional consequences of modifying these side chains have not been tested in vivo in CESAs or other processive GT-2s. To test this, we used an established in vivo assay based on genetic complementation of CESA5 in the moss, Physcomitrium patens. For accurate prediction of glucan-interacting amino acid residues, we generated a complete homotrimeric molecular model of PpCESA5 using a combination of homology and de novo modeling. All-atom molecular dynamics-based analyses of contact metrics and interaction energy identified 23 amino acid residues with high propensity to interact with the nascent glucan chain within the TM channel or on the apoplastic surface of PpCESA5. Mutating any one of 18 of these amino acid residues to alanine, thereby removing their side chains, abolished or impaired CESA function, with the strongest effects observed upon the loss of charged amino acid side chains. This provides direct evidence to support the hypothesis that multiple amino acid residues collectively maintain a smooth energy landscape within the TM channel to facilitate glucan translocation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

A semantics-driven framework to enable demand flexibility control applications in real buildings

Decarbonising and digitalising the energy sector requires scalable and interoperable Demand Flexibility (DF) applications. Semantic models are promising technologies for achieving these goals, but existing studies focused on DF applications exhibit limitations. These include dependence on bespoke ontologies, lack of computational methods to generate semantic models, ineffective temporal data management and absence of platforms that use these models to easily develop, configure and deploy controls in real buildings. This paper introduces a semantics-driven framework to enable DF control applications in real buildings. The framework supports the generation of semantic models that adhere to Brick and SAREF while using metadata from Building Information Models (BIM) and Building Automation Systems (BAS). The work also introduces a web platform that leverages these models and an actor and microservices architecture to streamline the development, configuration and deployment of DF controls. The paper demonstrates the framework through a case study, illustrating its ability to integrate diverse data sources, execute DF actuation in a real building, and promote modularity for easy reuse, extension, and customisation of applications. The paper also discusses the alignment between Brick and SAREF, the value of leveraging BIM data sources, and the framework's benefits over existing approaches, demonstrating a 75% reduction in effort for developing, configuring, and deploying building controls.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Thermodynamic modeling of countercurrent chemical looping reverse water gas shift process for redox material screening

The reverse water gas shift (RWGS) reaction is a key pathway for CO 2 utilization, particularly within Power-to-X process chains aimed at sustainable fuel and chemical production. Countercurrent chemical looping (CL-RWGS) using non-stoichiometric oxides can overcome equilibrium limitations of conventional RWGS reactors, enabling significantly higher CO 2 conversions. However, modeling the limiting performance of such systems is challenging due to their multiphase nature and coupled spatial and temporal variation in chemical composition. In this work, we present a discretized batch equilibrium model that simulates CL-RWGS reactors as a series of localized equilibrium exchanges between gas and solid elements. The model is numerically stable, computationally efficient, and free of kinetic source terms, making it well-suited for parametric studies and system-level integration. It is validated against established convection–diffusion models and shown to predict reasonable upper bounds on experimental results. Application of the model to a range of oxygen carrier materials identifies cerium–zirconium solid solutions, particularly Ce 0.80 Zr 0.20 O 2 , as a promising class offering superior oxygen storage characteristics compared to state-of-the-art La 0.6 Sr 0.4 FeO 3 . This framework provides a robust platform for materials screening, reactor sizing, and performance optimization in chemical looping systems. The model implementation is available as open-source software to support further research and development.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗