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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Approach to Evaluating Reorganization Energies of Interfacial Electrochemical Reactions

Reaction rate coefficients for electron-transfer processes at the electrode–electrolyte interface are commonly estimated by using the Butler–Volmer equation, but their values are inaccurate beyond a few tenths of volts of overpotential. The Marcus–Hush–Chidsey (MHC) formalism yields correct asymptotic behavior of the rate coefficients vs applied overpotential but has complex dependencies on the redox system’s intrinsic parameters, which can be difficult to model or measure. In this work, we bridge the two kinetics formalisms to estimate the reorganization energy, one of the important parameters for the MHC formalism, and investigate its dependence on other intrinsic parameters such as activation barriers, electronic coupling strength, and the density of states of the electrode surface. We examine the sensitivity of the reorganization energy to these parameters, establish some general relationships for accurately predicting rate coefficients using the MHC formalism over a wide range of applied overpotentials, and compare this approach to calculating MHC rate constants with other empirical approaches for the mechanisms of CO 2 reduction on different metal electrode surfaces.

Butler−Volmer↗
DOE OSTI2021

Synthesis of (hemi)cellulosic lubricant base oils via catalytic coupling and deoxygenation pathways

The past decade has witnessed a significant growth in bioproduct development due to the strong interest in creating a more diverse energy supply, environmental sustainability, and a carbon neutral bioeconomy. The initial biorefinery strategy, which focused mainly on the production of low carbon number drop-in bioproducts, has recently shifted its focus on the production of high carbon number and high performance bioproducts with unique properties and value propositions. One such product is lubricant base oil (LBO), which represents over $150 billion global lubricant market and is used for various industrial, automotive, marine, metal-working, consumer, and specialized applications. The application landscape of lubricants has changed significantly over the past decade because of environmental regulations and the growing demand for sustainable lubricants to mitigate regulatory challenges and improve carbon footprint. Currently, only 3% LBOs are produced from bio-based triglycerides/fatty acids. Because of low oxidation stability and high hydrolytic susceptibility, these bio-based LBOs have limited applications. Thus, research and development and commercialization efforts for (hemi)cellulosic LBOs to meet the market demand have received momentum over the past few years. This review describes strategies to produce (hemi)cellulosic LBOs via carbon–carbon coupling and hydrodeoxygenation chemistries. We emphasize the coupling strategies to create LBOs with a branched architecture to surrogate petroleum-based poly-α-olefins. Here, a structure–property relationship for tuning the key specifications of LBOs as a function of molecular architecture is illustrated. Mechanistic understanding and molecular interactions of multi-furan substrates with the active sites of complex multifunctional catalysts are described. Furthermore, techno-economic and life-cycle analysis is summarized along with standing challenges and future opportunities.

09 BIOMASS FUELS↗
DOE OSTI2024

Effect of pretreatment conditions on Fe-ZSM-5 properties and performance for Fischer–Tropsch synthesis

Iron supported on ZSM-5 is a widely studied catalyst for Fischer–Tropsch synthesis (FTS). Iron is activated with H 2 , CO, or a mixture of CO and H 2 prior to FTS, resulting in phase transformations that make it challenging to understand structure–property relationships. Furthermore, in this work, we demonstrate that increasing the pretreatment temperature of Fe–Na-ZSM-5 reduces CO conversion irrespective of the reductant, but the product selectivity, iron particle size, composition, CO adsorption properties, and zeolite structure is dependent on both the pretreatment temperature and reductant. Pretreatment of Fe–Na-ZSM-5 in H 2 induces sintering of iron particles, increasing C 2 –C 4 olefins and C 5+ hydrocarbons selectivity from 19.0% and 14.0% at 350 °C to 28.2% and 25.4% at 770 °C, respectively. Conversely, CO pretreatment facilitates carbide formation, coke deposition, and CH 4 formation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Impedance-Based Prediction of SSR-Generated Harmonics in Doubly-Fed Induction Generators

Doubly-fed induction generators (DFIGs) are prone to subsynchronous resonance (SSR) with series-compensated transmission lines and experiencing SSR-generated harmonics of wide-ranging magnitudes. This paper presents a large-signal impedance-based approach for the prediction of the magnitude of SSR-generated harmonics in DFIGs. Note that the large-signal impedance of a network shows variation in its impedance response with the magnitude of perturbation at its terminals. It is discovered in this paper that the impedance of a series-compen- sated transmission line starts changing with the magnitude of SSR because of the metal-oxide varistor-based protection of series capacitors in the line. The changing line impedance halts the growth of the SSR beyond a certain point, where the DFIG forms a limit-cycle mode and emits harmonics at the SSR frequency. The discovered role of the protection of series capacitors in SSR not only enables the prediction of SSR-generated harmonics but also can be used for the timely detection of an SSR event to avoid system disruption and sudden loss of bulk generation. The large- signal impedance-based prediction of SSR-generated harmonics in DFIG-based wind and hydro generators is demonstrated using numerical simulations of a 2.5-MVA, 690-V DFIG supplying to a series-compensated transmission line.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene

The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials.

Krantz, Joshua↗
DOE OSTI2020

Exploring the relationship between solvent-assisted ball milling, particle size, and sintering temperature in garnet-type solid electrolytes

Garnet-type solid electrolytes, such as Li 6.4 La 3 Zr 1.4 Ta 0.6 O 12 (LLZTO), are promising materials for solid-state batteries, but processing remains a challenge, in part due to the high sintering temperature required for densification. Additionally, this temperature can be lowered by decreasing the initial particle size via solvent-assisted ball milling, but the relationship between solvent choice, particle properties, sintering behavior, and ionic conductivity is not well understood. In this work, we systematically explore these parameters, showing that milling in commonly used protic solvents, such as alcohols, effectively decreases the particle size but results in lithium loss (through Li + /H + exchange) that leads to poor sintering. By contrast, milling in aprotic solvents with surfactant reduces the particle size to ~220 nm without lithium loss, enabling the fabrication of dense samples (5.1 g/cm 3 ) with good ionic conductivity (0.43 mS/cm at 25 °C) at a lower sintering temperature (1000 °C). We compare ionic conductivities and activation energies for samples prepared with different particle sizes and sintering temperatures and use multiphase-field simulations to identify the mass transport and microstructural mechanisms responsible for the observed sintering dependence on particle size. These results further clarify the relationship between processing parameters and performance and represent important progress toward overcoming fabrication challenges for these materials.

25 ENERGY STORAGE↗
DOE OSTI2020

Why Conventional Design Rules for C–H Activation Fail for Open-Shell Transition-Metal Catalysts

The design of selective and active C–H activation catalysts for direct methane-to-methanol conversion is challenging. Bioinspired complexes that form high-valent metal–oxo intermediates capable of hydrogen abstraction and rebound hydroxylation are promising candidates. This promise has made them a target for computational high-throughput screening, typically simplified through the use of linear free energy relationships (LFERs). However, their mid-row transition-metal centers have numerous accessible spin and oxidation states that increase the combinatorial scale of design efforts. Here, we carry out a computational design screen of over 2500 mid-row 3d transition-metal complexes with four metals in numerous spin and oxidation states. We demonstrate the importance of spin/oxidation state in dictating design principles, limiting the generalization of strategies derived for widely studied high-spin Fe(II) catalysts to other metals or spin/oxidation states. Combined assessment of the effect of ligand-field tuning on reaction step energetics and on the identity of the ground state allows us to propose refined design strategies for spin-allowed methane-to-methanol catalysis. We observe weak coupling of energetics and design principles between reaction steps (e.g., oxo formation vs methanol release), meaning that LFERs do not generalize across our larger catalyst set. To rationalize relative reactivity in known catalysts, we instead compute independent reaction energies and propose strategies for further improvements in catalyst design.

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

Understanding the deformation behavior of the γ rich transformative Fe 38.5 Mn 20 Co 20 Cr 15 Si 5 Cu 1.5 complex concentrated alloy using in situ synchrotron diffraction

In situ tensile testing coupled with synchrotron x-ray diffraction was used to study the deformation behavior of metastability-engineered Fe 38.5 Mn 20 Co 20 Cr 15 Si 5 Cu 1.5 complex concentrated alloy. Monitoring the evolution of phase fraction and strain hardening response allowed the determination of true critical stress for the onset of the transformation-induced plasticity (TRIP) to be ∼375 MPa, preceded by slip starting at ∼255 MPa. In situ EBSD was used to validate the critical stress for transformation at the microstructure level and observe slip traces to confirm prior slip activity before the transformation. Further, a modeling framework based on stacking fault energy (SFE) was developed to predict the critical stress for transformation. Modeling suggested the SFE of the alloy to fall nearly 15 mJ/m 2 , which agrees well with SFE values calculated using synchrotron peak shifting (12 mJ/m 2 ) and thermodynamic calculation (11 mJ/m 2 ). As a result of γ-fcc to ε-hcp phase transformation, new {0002} ε planes emerged parallel to unaligned {111} γ planes with the tensile loading following S-N orientation relationship. Such selective emergence of new diffraction rings corresponding to ε phase is understood based on the reorientation of γ crystals with reference to tensile axis. In conclusion, this approach can be extended to effectively design alloys based on critical stress required for activating different deformation mechanisms to further push the limits of the strength-ductility envelope.

Complex concentrated alloy↗
DOE OSTI2025

The structural basis for the broad aldehyde specificity of the aminoaldehyde dehydrogenase PauC from the human pathogen Pseudomonas aeruginosa

Abstract Despite significant differences in size and formal charge, the aldehyde dehydrogenasePaPauC (PA5312) fromPseudomonas aeruginosaPAO1 efficiently catalyzes the NAD + ‐dependent oxidation of the aminoaldehydes formed in polyamines degradation. We report here thatPaPauC also oxidizes 4‐guanidinebutyraldehyde, formed in one arginine degradation pathway, trimethylaminobutyraldehyde, of unknown metabolic origin, and indole‐3‐acetaldehyde, a precursor of the plant growth‐promoting hormone indoleacetic acid.PaPauC has been proposed as a potential target for combatingP. aeruginosa. However, understanding its structure–function relationships, crucial for developing specific inhibitors, is lacking. Using X‐ray crystallography, we identified the structural characteristics that determinePaPauC broad aldehyde specificity: a spacious aldehyde‐entrance tunnel and six active‐site residues. Docking simulations, site‐directed mutagenesis, and kinetic analyses support the interactions of Lys479 with glutamylated aminoaldehydes; Phe169, Trp176, and Phe467 with amino and guanidinium groups through cation–π interactions and with the indole group via NH–π and CH–π interactions; Asp459 with amino and indole groups; and Thr303 with amide and guanidinium groups. Exploiting the distinctive structural features of thePaPauC active site could aid in developing specific inhibitors to combatP. aeruginosainfections in humans and animals, as well as in preventing its colonization of plants, which are abundantP. aeruginosareservoirs and, therefore, a significant source of human infections.

Biochemistry & Molecular Biology↗
DOE OSTI2022

Locus coeruleus catecholamines link neuroticism and vulnerability to tau pathology in aging

Higher neuroticism is a risk factor for Alzheimer's disease (AD), and is implicated in disordered stress responses. The locus coeruleus (LC)-catecholamine system is activated during perceived threat and is a centerpiece of developing models of the pathophysiology of AD, as it is the first brain region to develop abnormal tau. We examined relationships among the "Big 5" personality traits, LC catecholamine synthesis capacity measured with [ 18 F]Fluoro-m-tyrosine PET, and tau burden measured with [ 18 F]Flortaucipir PET in cognitively normal older adults (n = 47). β-amyloid (Aβ) status was determined using [ 11 C]Pittsburgh compound B PET (n = 14 Aβ positive). Lower LC catecholamine synthesis capacity was associated with higher neuroticism, more depressive symptoms as measured by the Geriatric Depression Scale, and higher amygdala tau-PET binding. Exploratory analyses with other personality traits revealed that low trait conscientiousness was also related to both lower LC catecholamine synthesis capacity, and more depressive symptoms. A significant indirect path linked both high neuroticism and low conscientiousness to greater amygdala tau burden via their mutual association with low LC catecholamine synthesis capacity. Together, these findings reveal LC catecholamine synthesis capacity to be a promising marker of affective health and pathology burden in aging, and identifies candidate neurobiological mechanisms for the effect of personality on increased vulnerability to dementia.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Evidence That Sharp Interfaces Suppress Recombination in Thick Organic Solar Cells

Commercialization and scale-up of organic solar cells (OSCs) using industrial solution printing requires maintaining maximum performance at active-layer thicknesses >400 nm – a characteristic still not generally achieved in non-fullerene acceptor OSCs. NT812:PC71BM is a rare system whose performance increases up to these thicknesses due to highly suppressed charge recombination relative to the classic Langevin model. The suppression in this system, however, uniquely depends on device processing, pointing towards the role of nano-morphology. We investigate the morphological origins of this suppressed recombination by combining results from a suite of X-ray techniques. We are surprised to find that while all investigated devices are composed of pure, similarly aggregated nanodomains, Langevin reduction factors can still be tuned from ~2 to >1000. This indicates that pure aggregated phases are insufficient for non-Langevin (reduced) recombination. Instead, we find that large well-ordered conduits and, in particular, sharp interfaces between domains appear to help to keep opposite charges separated and percolation pathways clear for enhanced charge collection in thick active layers. To our knowledge, this is the first quantitative study to isolate donor/acceptor interfacial width correlated to non-Langevin charge recombination. Furthermore, this new structure-property relationship will be key to successful commercialization of printed OSCs at scale.

14 SOLAR ENERGY↗
DOE OSTI2025

Diurnal Trends and Meteorological Factors Influencing the Variability of Fluorescent Bioaerosol in Mt. Crested Butte, Colorado During SAIL

Abstract Despite the crucial role of bioaerosol particles (BAP) in our climate system, local ecosystems, and human health, our grasp on their atmospheric interactions is hampered by a lack of high‐resolution and long‐term data, which is essential for understanding their abundance and variability in response to meteorological conditions. To discern these relationships in a high‐altitude mountainous terrain is even less well‐studied. Therefore, we deployed a Wideband Integrated Bioaerosol Sensor (WIBS‐Neo) for three months during the first biologically active season of the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign in summer 2022 on Mt. Crested Butte in Colorado (elevation 3,140 m ASL). Here, we report real‐time fluorescent BAP (FBAP) data collected from 15 June to 13 September 2022, sampled from within the ARM Aerosol Observing System (AOS). To our knowledge, these are the first high altitude (>3,000 m ASL) continuous measurements of FBAP made in North America. During this deployment, we observed on average 21% and as many as 48% (hourly maximum) of particles within the detection size range ( to ) of the WIBS as FBAP. Our analysis presents the diurnal cycles for seven distinct types of FBAP, showing unique patterns and, in some cases, correlations with temperature and solar radiation cycles. Abundance and composition of FBAP varied with relative humidity and precipitation. Precipitation events appeared to both cause emission and removal of FBAP, whereas dust and smoke events had no significant effect, highlighting the critical role of meteorology on FBAP at high altitudes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Accelerated annealing of colloidal crystal monolayers by means of cyclically applied electric fields

Abstract External fields are commonly applied to accelerate colloidal crystallization; however, accelerated self-assembly kinetics can negatively impact the quality of crystal structures. We show that cyclically applied electric fields can produce high quality colloidal crystals by annealing local disorder. We find that the optimal off-duration for maximum annealing is approximately one-half of the characteristic melting half lifetime of the crystalline phase. Local six-fold bond orientational order grows more rapidly than global scattering peaks, indicating that local restructuring leads global annealing. Molecular dynamics simulations of cyclically activated systems show that the ratio of optimal off-duration for maximum annealing and crystal melting time is insensitive to particle interaction details. This research provides a quantitative relationship describing how the cyclic application of fields produces high quality colloidal crystals by cycling at the fundamental time scale for local defect rearrangements; such understanding of dynamics and kinetics can be applied for reconfigurable colloidal assembly.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Data-driven based coordinated smart inverter control for distributed energy resources

Smart inverters (SI) for distributed energy resources (DER) are becoming popular since they have the ability to stabilize as well as restore the voltage and frequency of power systems. Aiming at establishing the mathematical models combined with SI control methods, multiple optimization methods are developed. However, the computational complexity of solving such a mathematical model with various uncertainties limits the real-time application of the SI control. To conquer this challenge, a data-driven-based SI control approach is developed to achieve coordinated control in the high penetration DER system. First, an optimization problem for maximizing the active power generation and minimizing the power loss is designed using the Volt/VAR control. To reduce the time consumption, the recurrent neural network (RNN) is proposed to model the relationship between the uncertainties and control actions during the offline site. The RNN with different sub-structures such as the long short-term memory cell and gated recurrent unit cell are included to enrich the diversity of features. In the last stage, different experiment comparisons, including multiple uncertainties maps and stateof- art machine learning methods, are conducted to verify the effectiveness of the proposed method based on the IEEE 123 bus power system. The results demonstrate that the proposed method can effectively achieve a rapid and coordinated control with a lower error rate.

Qiu, Wei↗
DOE OSTI2022

Plant carbohydrate storage: intra- and inter-specific trade-offs reveal a major life history trait

Trade-offs among carbon sinks constrain how trees physiologically, ecologically, and evolutionarily respond to their environments. These trade-offs typically fall along a productive growth to conservative, bet-hedging continuum. How nonstructural carbohydrates (NSCs) stored in living tree cells (known as carbon stores) fit in this trade-off framework is not well understood. Here, we examined relationships between growth and storage using both within species genetic variation from a common garden, and across species phenotypic variation from a global database. We demonstrate that storage is actively accumulated, as part of a conservative, bet-hedging life history strategy. Storage accumulates at the expense of growth both within and across species. Within the species Populus trichocarpa, genetic trade-offs show that for each additional unit of wood area growth (in cm 2 yr –1 ) that genotypes invest in, they lose 1.2 to 1.7 units (mg g –1 NSC) of storage. Across species, for each additional unit of area growth (in cm 2 yr –1 ), trees, on average, reduce their storage by 9.5% in stems and 10.4% in roots. Our findings impact our understanding of basic plant biology, fit storage into a widely used growth-survival trade-off spectrum describing life history strategy, and challenges the assumptions of passive storage made in ecosystem models today.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Understanding Air-Sea Feedbacks to the MJO through Process Evaluation of Observations and Models (Final Report)

The goal of our project was to enhance our understanding of the role of coupling in the simulation of the Madden Julian Oscillation (MJO) in coupled climate models. We developed diagnostics of precipitation and Sea Surface Temperature variability in climate models and investigated how they are related to metrics of MJO simulation performance. We found a relationship between models with a high spatial coherence of precipitation and a good propagation of the MJO through the Maritime Continent region, but no relationship between metrics of precipitation organisation and intensity. Analysis of coupled and uncoupled model simulations from the Sixth Coupled Model Intercomparison Project (CMIP6) shows that coupling systematically improved the MJO simulation in both metrics that we investigated. We analysed the role of background moisture gradients in the MJO simulation performance following Lim et al. (2018) and found that although the model to model variation in moisture gradients to the west of Indonesia was related to model MJO performance, in general no single moisture gradient was as good at explaining a large fraction of MJO performance across the models as compared to a linear combination of those metrics, which was able to explain a significant fraction of the variance in the MJO performance. Those relationships were much stronger for coupled simulations than uncoupled simulations and stronger for those metrics that measured propagation of the MJO across the Maritime Continent than for overall MJO activity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Light Water Sustainability Program: Optimizing Information Automation Using a New Method Based on System-Theoretic Process Analysis

This report describes the interim progress for research supporting the design and optimization of information automation systems for nuclear power plants. Much of the domestic nuclear fleet is currently focused on modernizing technologies and processes, including transitioning toward digitalization in the control room and elsewhere throughout the plant, along with a greater use of automation, artificial intelligence, robotics, and other emerging technologies. While there are significant opportunities to apply these technologies toward greater plant safety, efficiency, and overall cost-effectiveness, optimizing their design and avoiding potential safety and performance risks depends on ensuring that human-performance-related organizational and technical design issues are identified and addressed. This report describes modeling tools and techniques, based on sociotechnical system theory, to support these design goals and their application in the current research effort. The report is intended for senior nuclear energy stakeholders, including regulators, corporate management, and senior plant management. We have developed and employed a method to design an optimized information automation ecosystem (IAE) based on the systems-theoretic constructs underlying sociotechnical systems theory in general and the Systems-Theoretic Accident Modeling and Processes (STAMP) approach in particular. We argue that an IAE can be modeled as an interactive information control system whose behavior can be understood in terms of dynamic control and feedback relationships amongst the system’s technical and organizational components. Up to this point, we have employed a Causal Analysis based on STAMP (CAST) technique to examine a performance- and safety-related incident at an industry partner’s plant that involved the unintentional activation of an emergency diesel generator. This analysis provided insight into the behavior of the plant’s current information control structure within the context of a specific, significant event. Our ongoing analysis is focused on identifying near-term process improvements and longer-term design requirements for an optimized IAE system. The latter analyses will employ a second STAMP-derived technique, System-Theoretic Process Analysis (STPA). STPA is a useful modeling tool for generating and analyzing actual or potential information control structures. Finally, we have begun modeling plantwide organizational relationships and processes. Organizational system modeling will supplement our CAST and STPA findings and provide a basis for mapping out a plantwide information control architecture. CAST analysis findings indicate an important underlying contributor to the incident under investigation, and a significant risk to information automation system performance, was perceived schedule pressure, which exposed weaknesses in interdepartmental coordination between and within responsible plant organizations and challenged the resilience of established plant processes, until a human caused the initiating event. These findings are discussed in terms of their risk to overall system performance and their implications for information automation system resilience and brittleness. We present two preliminary information automation models. The proactive issue resolution model is a test case of an information automation concept with significant near-term potential for application and subsequent reduction in significant plant events. The IAE model is a more general representation of a broader, plantwide information automation system. From our results, we have generated a set of preliminary system-level requirements and safety constraints. These requirements will be further developed over the remainder of our project in collaboration with nuclear industry subject matter experts and specialists in the technical systems under consideration. Additionally, we will continue to pursue the system analyses initiated in the first part of our effort, with a particular emphasis on STPA as the main tool to identify weak or weakening control structures that affect the resilience of organizations and programs. Our intent is to broaden the scope of the analysis from an individual use case to a related set of use cases (e.g., maintenance tasks, compliance tasks) with similar human-system performance challenges. This will enable more generalized findings to refine the Proactive Issue Resolution and IAE models, as well as their system-level requirements and safety constraints. We will use organizational system modeling analyses to supplement STPA findings and model development. We conclude the report with a set of summary recommendations and an initial draft list of system-level requirements and safety constraints for optimized information automation systems.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Beyond Thermal Limits: Manipulating Reactive Intermediate Coverages and Turnover Rates via Visible Photon-Mediated Catalysis on Rh-Doped Perovskite Oxides

Catalyst behavior depends on surface adsorbate energetics that are constrained by scaling relationships on metal surfaces. External stimuli (e.g., photons), however, can disrupt these limitations by modulating key intermediate coverages via non-thermal reaction pathways. Here, low-energy visible photon fluxes are utilized to selectively control coverages of strongly bound intermediates on isolated Rh active sites doped within a semiconductor perovskite oxide host (SrTiO 3 ). Red light (632 nm) facilitates selective photolytic CO desorption from rhodium gem-dicarbonyl (Rh(CO) 2 ) species that are ubiquitous reaction intermediates, including for the probe reaction studied herein CO oxidation to CO 2 . Thermochemical CO 2 formation rates (408 K) on Rh-doped SrTiO 3 are limited by adsorbed CO, exhibiting a negative apparent CO rate order (−0.6) and a positive O 2 rate order (+0.4). Arrhenius analyses, anaerobic CO oxidation measurements, and in situ spectroscopies assert that, thermochemically, lattice oxygens from the doped perovskite contribute to CO 2 formation rates. Notably, under red light illumination (0.76–2.02 W cm –2 ), the apparent CO rate order shifts to positive (+1). This, combined with decreasing apparent activation energies and CO coverages (wavelength-agnostic) with increasing photon flux, indicates that photons act selectively toward driving Rh(CO) 2 photolysis, even within complex reaction networks, thereby enhancing Rh accessibility, O 2 dissociation, and consequent rates. Finally, low-energy red light enables more stoichiometric feeds, leading to 650% higher CO 2 rates than those achieved thermally. Overall, this work elucidates how low-energy light can be leveraged, not just to improve reaction rates, but to selectively affect rates of individual elementary steps and key intermediate coverages, thereby breaking conventional scaling relationships that limit thermal catalyst performance.

catalysts↗