Control co-design of commercial building chiller plant using Bayesian optimization
Not Available
Bhattacharya, Arnab↗
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Co-optimization of fuel properties, combustion system geometry, and injection strategy for conventional diesel fuel
Here, studies have shown that fuel properties can impact an engine’s operation in several ways, including ignition delay, sooting tendency, mixture formation, and combustion temperature. In mixing-controlled compression ignition (MCCI) engines, the fuel system design and piston bowl geometry significantly affect combustion performance and emissions. Based on current information, it is difficult to draw conclusions about fuel property effects and sensitivities. The central fuel hypothesis approach used in the US Department of Energy Co-Optima program has worked well for spark ignition fuels: identifying critical fuel property ranges is sufficient to screen fuel blends that are expected to maximize efficiency and reduce pollutant emissions. However, for MCCI-relevant fuels, the information gained from past studies is not sufficient to build such a merit function or to allow for performing a similar screening of fuel blends. It is hypothesized that a co-optimization of a fuel’s physical and chemical properties, combustion system geometry, and injection strategy could leverage synergies between the effects of the fuel properties and geometries, resulting in improved performance over state-of-the-art. A machine learning–assisted unconstrained global optimization algorithm was used to explore a design space comprising 23 independent variables. The results show that physical property effects were minimal even for large variations in fuel properties, and the only interaction effect that was observed was the effect of varied fuel density parameters on fuel/air mixture formation. Nevertheless, these interactions were not sufficient in magnitude to significantly affect optimization results. Therefore, analysis of the results suggests that fuel physical properties cannot be leveraged in a co-optimization context to increase engine efficiency.
33 ADVANCED PROPULSION SYSTEMS↗
Disruption modelling for engineering and physics design of ST-E1 fusion power plant
Plasma disruptions represent a critical challenge for high-performance tokamak operations, as they can compromise machine integrity and reduce operational availability. Although future fusion devices essentially need to incorporate strategies to minimise disruption occurrence, complete avoidance remains unattainable. Consequently, assessing and characterising unmitigated disruption consequences is fundamental for the design and qualification of next-generation fusion power plants. This work supports the pre-conceptual design of ST-E1, a low aspect-ratio Tokamak Fusion Power Plant developed by Tokamak Energy Ltd., by presenting a comprehensive disruption modelling approach applied across different design stages. The methodology integrates both physics and engineering considerations to evaluate the impact of disruptions on machine performance and structural integrity. From an engineering perspective, several ST-E1 layout options were analysed to investigate the electromagnetic response of key components under disruption-induced loads, enabling comparison between alternative design solutions. On the physics side, a broad set of disruption scenarios was explored, scanning operational space parameters, plasma-material interactions, and associated thermal loads. Furthermore, the study examined variations in disruption behaviour arising from different reference equilibria, focusing on a range starting from Double Null to Single Null configurations, reflecting the increasing up-down asymmetry consequences. The results reveal significant contrasts in plasma dynamics and structures electromagnetic behaviour between configurations, highlighting the importance of disruption modelling in guiding design choices. These analyses have proven instrumental in shaping ST-E1 development, offering critical insights for mitigating risks and optimising future fusion power plant designs.
Borowiec, Katarzyna [ORNL] (ORCID:0000000335911739↗
Optimizing fluvial flood mitigation strategies: A multi-objective approach for cost-effective and socially-aware infrastructure feasibility analysis
Effective levee planning must balance capital cost, risk reduction, and community priorities. These objectives are rarely optimized together. This study presents a feasibility phase, simulationin-the-loop framework that couples terrain-based flood modeling with a socially aware multiobjective optimizer. Flood risk is measured as Expected Annual Exposed Population (EAEP), obtained by integrating exposure over Annual Exceedance Probability (AEP) nodes, mirroring the Hydrologic Engineering Center's Flood Damage Reduction Analysis (HEC-FDA) expected-annual formulation but with people rather than dollars. Exposure per scenario is computed by overlaying binary inundation masks with a population surface at the tract level. Distributional fairness is encoded through a Group Benefit Share (GBS) constraint that requires high-SVI tracts to receive at least a baseline share of annualized benefits. Capital cost is represented by a height-dependent unit-cost model suitable for screening. This study addresses the two-objective problem, minimize cost and expected annual exposure subject to the GBS constraint, using Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and leveraging Pareto front for feasibility phase decision making. Implemented with terrain-based flood modeling, GeoFlood, for rapid scenario evaluation, the framework is demonstrated in Southeast Texas. The results reveal clear trade-offs among cost, risk, and social benefits and identify non-dominated levee height configurations that satisfy the benefit-share floor. The contributions are a scalable decision support method that operationalizes expected annual population-based risk, embeds enforceable benefit-sharing guarantees, and uses lightweight simulation to explore large design spaces before higher fidelity design stages.
Flood mitigation↗
In situ synchrotron X-ray imaging and mechanical properties characterization of additively manufactured high-entropy alloy composites
Laser beam directed energy deposition has become an increasingly popular advanced manufacturing technique for materials discovery as a result of the in situ alloying capability. In this study, we leverage an additive manufacturing enabled high throughput materials discovery approach to explore the composition space of a graded W x (CoCrFeMnNi) 100–x sample spanning 0 ≤ x ≤ 21 at%. In addition to microstructural and mechanical characterization, synchrotron high speed x-ray computer aided tomography was conducted on a W 20 (CoCrFeMnNi) 80 composition to visualize melting dynamics, powder-laser interactions, and remelting effects of previously consolidated material. Results reveal the formation of the Fe 7 W 6 intermetallic phase at W concentrations> 6 at%, despite the high configurational entropy. Unincorporated W particles also occurred at W concentrations> 10 at% accompanied by a dissolution band of Fe 7 W 6 at the W/matrix interface and hardness values greater than 400 HV. In this work, the primary strengthening mechanism is attributed to the reinforcement of the Fe 7 W 6 and W phases as a metal matrix composite. The in situ high speed x-ray imaging during remelting showed that an additional laser pass did not promote further mixing of the Fe 7 W 6 or W phases suggesting that, despite the dissolution of the W into the Fe 7 W 6 phase being thermodynamically favored, it is kinetically limited by the thickness/diffusivity of the intermetallic phase, and the rapid solidification of the laser-based process.
36 MATERIALS SCIENCE↗
Emerging materials and design principles for wurtzite-type ferroelectrics
Not Available
Lee, Cheng-Wei↗
Autonomous alloy composition optimization using molecular dynamics guided by a large language model
Here, we present an autonomous materials discovery framework that couples a large language model (LLM) with molecular dynamics (MD) simulations to optimize Fe–Cr–Mn alloy compositions for tensile strength. Starting from six distinct compositions, the LLM operated as an intelligent agent, iteratively proposing changes based on prior simulation results and constraints. Over 50 iterations per case, the LLM adaptively explored the composition space, identifying high-strength regions, not easily accessible by conventional methods. The highest strength, 18.7 GPa, was achieved with Fe 71 Cr 25 Mn 4 composition, identified from a Fe 75 Cr 20 Mn 5 starting point. The LLM autonomously adjusted its strategy in real time, demonstrating closed-loop decision-making using commodity hardware. This approach showcases the potential of LLMs as scientific co-pilots, capable of accelerating materials discovery and generalizable to other domains like biology and drug design.
Autonomy↗
Surrogate-assisted optimization under uncertainty for design for remanufacturing considering material price volatility
Remanufacturing is a well-established end-of-life (EOL) strategy that promises significant savings in energy and carbon emissions. However, the current design practices are not remanufacturing-inclusive, i.e., the majority of products are designed for a single life cycle. As a result, potential products that can sustain multiple life cycles are deprived of additional benefits of being designed for remanufacturing, such as reduced material usage, lower cost, and improved environmental impact. Moreover, the uncertainty in design, material selection, and economics are not considered to produce remanufacturable designs. Accordingly, this research proposes a design for remanufacturing (DfRem) framework that accounts for design uncertainty and material price volatility. The framework systematically explores the design space, performs design optimization under uncertainty, followed by topology optimization to provide additional mass savings, and finally, a price volatility analysis for plausible design material choices. The candidate designs are evaluated based on their design mass, material price volatility, failure mode characteristics, carbon footprint, and embodied energy impacts. The proposed framework's utility is demonstrated via the use of an engine cylinder head case study subjected to thermo-mechanical loads along with fatigue and wear failure. Considering grey cast iron and aluminum alloy as the design material choices, it was found that the cast iron design reduced the initial design mass by 6% as opposed to a 5% decrease for aluminum. On the other hand, about 8% area of the cast iron design failed due to fatigue, compared to 3% for aluminum. Here, we further observed that although the aluminum design provided better mechanical performance than the cast iron design, this material was more expensive and volatile in price.
36 MATERIALS SCIENCE↗
Active and machine learning-based approaches to rapidly enhance microbial chemical production
In order to make renewable fuels and chemicals from microbes, new methods are required to engineer microbes more intelligently. Computational approaches, to engineer strains for enhanced chemical production typically rely on detailed mechanistic models (e.g., kinetic/stoichiometric models of metabolism)—requiring many experimental datasets for their parameterization—while experimental methods may require screening large mutant libraries to explore the design space for the few mutants with desired behaviors. To address these limitations, we developed an active and machine learning approach (ActiveOpt) to intelligently guide experiments to arrive at an optimal phenotype with minimal measured datasets. In this study, ActiveOpt was applied to two separate case studies to evaluate its potential to increase valine yields and neurosporene productivity in Escherichia coli. In both the cases, ActiveOpt identified the best performing strain in fewer experiments than the case studies used. This work demonstrates that machine and active learning approaches have the potential to greatly facilitate metabolic engineering efforts to rapidly achieve its objectives.
60 APPLIED LIFE SCIENCES↗
Metabolic flux optimization of iterative pathways through orthogonal gene expression control: Application to the β-oxidation reversal
Balancing relative expression of pathway genes to minimize flux bottlenecks and metabolic burden is one of the key challenges in metabolic engineering. This is especially relevant for iterative pathways, such as reverse β-oxidation (rBOX) pathway, which require control of flux partition at multiple nodes to achieve efficient synthesis of target products. Here, we develop a plasmid-based inducible system for orthogonal control of gene expression (referred to as the TriO system) and demonstrate its utility in the rBOX pathway. Leveraging effortless construction of TriO vectors in a plug-and-play manner, we simultaneously explored the solution space for enzyme choice and relative expression levels. Remarkably, varying individual expression levels led to substantial change in product specificity ranging from no production to optimal performance of about 90% of the theoretical yield of the desired products. We obtained titers of 6.3 g/L butyrate, 2.2 g/L butanol and 4.0 g/L hexanoate from glycerol in E. coli, which exceed the best titers previously reported using equivalent enzyme combinations. Since a similar system behavior was observed with alternative termination routes and higher-order iterations, we envision our approach to be broadly applicable to other iterative pathways besides the rBOX. Here, considering that high throughput, automated strain construction using combinatorial promoter and RBS libraries remain out of reach for many researchers, especially in academia, tools like the TriO system could democratize the testing and evaluation of pathway designs by reducing cost, time and infrastructure requirements.
59 BASIC BIOLOGICAL SCIENCES↗
Formation of transient high-β plasmas in a magnetized, weakly collisional regime
We present experimental data providing evidence for the formation of transient (~20 μs) plasmas that are simultaneously weakly magnetized (i.e. Hall magnetization parameter ωτ > 1) and dominated by thermal pressure (i.e. ratio of thermal-to-magnetic pressure β > 1). Particle collisional mean free paths are an appreciable fraction of the overall system size. These plasmas are formed via the head-on merging of two plasmas launched by magnetized coaxial guns. The ratio λ gun = μ 0 I gun /ψ gun of gun current I gun to applied magnetic flux ψ gun is an experimental knob for exploring the parameter space of β and ωτ. These experiments were conducted on the Big Red Ball at the Wisconsin Plasma Physics Laboratory. The transient formation of such plasmas can potentially open up new regimes for the laboratory study of weakly collisional, magnetized, high-β plasma physics; processes relevant to astrophysical objects and phenomena; and novel magnetized plasma targets for magneto-inertial fusion.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Access to Advanced Functional Materials through Postmodification of Biomimetic Assemblies via Click Chemistry
The design, synthesis, and fabrication of functional nanomaterials with specific properties remain a long-standing goal for many scientific fields. The self-assembly of sequence-defined biomimetic synthetic polymers presents a fundamental strategy to explore the chemical space beyond biological systems to create advanced nanomaterials. Moreover, subsequent chemical modification of existing nanostructures is a unique approach for accessing increasingly complex nanostructures and introducing functionalities. Of these modifications, covalent conjugation chemistries, such as the click reactions, have been the cornerstone for chemists and materials scientists. Herein, we highlight some recent advances that have successfully employed click chemistries for the postmodification of assembled one-dimensional (1D) and two-dimensional (2D) nanostructures to achieve applications in molecular recognition, mineralization, and optoelectronics. Specifically, biomimetic nanomaterials assembled from sequence-defined macromolecules such as peptides and peptoids are described.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Exploring Ca–Ce–M–O (M = 3d Transition Metal) Oxide Perovskites for Solar Thermochemical Applications
Solar thermochemical (STC) processes hold promise as efficient ways to generate renewable fuels, fuel precursors, or chemical feedstocks using concentrated sunlight. Specifically, one actively researched approach is the two-step STC cycle, which uses a redox-active, off-stoichiometric, transition-metal oxide material to split water and/or CO 2 , generating H 2 and/or CO, respectively, or syngas (a combination of H 2 and CO). Identifying novel metal oxides that yield larger reduction extents (practically achievable off-stoichiometries) than the state-of-the-art CeO 2 is critical. Here, we explore the chemical space of Ca–Ce–M–O (M = 3d transition metal, except Cu and Zn) metal oxide perovskites, with Ca and/or Ce occupying the A site and M occupying the B site within an ABO 3 framework, as potential STC candidates. We use density functional theory (DFT)-based calculations and systematically evaluate the oxygen vacancy (VaO) formation energy (≈ enthalpy of reduction in an STC cycle), electronic properties, thermodynamic stability of CaMO 3 , CeMO 3 , and Ca 0.5 Ce 0.5 MO 3 perovskites, and the VaO formation energy within Ca 0.5 Ce 0.5 Ti 0.5 Mg 0.5 O 3 perovskite. We consider only Ca and/or Ce on the A site because of their similar size and the potential redox activity of Ce 4+ . If both Ce and M exhibit simultaneous reduction with Va O formation, the resulting perovskite could exhibit a larger entropy of reduction than a single cation reduction. The increased entropy produces increased reduction for fixed temperature, partial pressure of oxygen, and reduction enthalpy, and therefore increased STC efficiency. Importantly, we identify Ca 0.5 Ce 0.5 MnO 3 , Ca 0.5 Ce 0.5 FeO 3 , and Ca 0.5 Ce 0.5 VO 3 to be promising candidates based on their Va O formation energy and thermodynamic (meta)stability. Moreover, based on our calculated on-site magnetic moments, electron density of states, and electron density differences between pristine and defective structures, we find Ca 0.5 Ce 0.5 MnO 3 to exhibit simultaneous reduction of both Ce 4+ (A-site) and Mn 3+ (B-site), highlighting a particularly promising candidate for STC applications with a predicted higher entropy of reduction than CeO 2 . Lastly, we extract metrics that govern the trends in Va O formation energies, such as standard reduction potentials, and provide pointers for further experimental and theoretical studies, which will enable the design of improved materials for the STC cycle.
14 SOLAR ENERGY↗
Synthesis and Structural Study of Substituted Ternary Nitrides for Ammonia Production
Over the past few decades, inorganic nitride materials have grown in importance in part due to their potential as catalysts for the synthesis of NH 3 , a key ingredient in fertilizer and precursor to industrial chemicals. Of particular interest are the ternary (ABN) or higher-order nitrides with high metal-to-nitrogen ratios that show promise in enhancing NH 3 synthesis reaction rates and yields via heterogeneous catalysis or chemical looping. Although metal nitrides are predicted to be numerous, the stability of nitrogen triple bonds found in N 2 , especially in comparison to the metal–nitrogen bonds, has considerably hindered synthetic efforts to produce complex nitride compounds. In this study, we present an exhaustive down-selection process to identify ternary nitrides for a promising chemical looping NH 3 production mechanism. We also report on a facile and efficient two-step synthesis method that can produce well-characterized η-carbide Co 3 Mo 3 N/Fe 3 Mo 3 N or filled β-manganese Ni 2 Mo 3 N ternaries, as well as their associated quaternary, (Co,Fe) 3 Mo 3 N, (Fe,Ni) 2 Mo 3 N, and (Co,Ni) 2 Mo 3 N, solid solutions. To further explore the quaternary space, syntheses of (Co,Ni) 3 Mo 3 N (Ni ≤ 10 mol %) and Co 3 (Mo,W) 3 N (W ≤ 10 mol %) were also investigated. The structures of the nitrides were characterized via X-ray powder diffraction. The morphology and compositions were characterized with scanning electron microscopy. In conclusion, the multitude of chemically unique, but structurally related, nitrides suggests that properties such as nitrogen activity may be tunable, making the materials of great interest for NH 3 synthesis schemes.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
LAN: A Materials Notation for Two-Dimensional Layered Assemblies
Two-dimensional (2D) layered materials offer intriguing possibilities for novel physics and applications. Before any attempt at exploring the materials space in a systematic fashion, or combining insights from theory, computation, and experiment, a formal description of information about an assembly of arbitrary composition is required. Here, we introduce a domain-generic notation that is used to describe the space of 2D layered materials from monolayers to twisted assemblies of arbitrary composition, existent or not yet fabricated. The notation corresponds to a theoretical materials concept of stepwise assembly of layered structures using a sequence of rotation, vertical stacking, and other operations on individual 2D layers. Its scope is demonstrated with several example structures using common single-layer materials as building blocks. This work overall aims to contribute to the systematic codification, capture, and transfer of materials knowledge in the area of 2D layered materials.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Nonclassical Strong Metal–Support Interactions for Enhanced Catalysis
Strong metal–support interaction (SMSI), which encompasses reversible encapsulation and de-encapsulation and modulation of surface adsorption properties, imposes great impacts on the performance of heterogeneous catalysts. Recent development of SMSI has surpassed the prototypical encapsulated Pt-TiO 2 catalyst, affording a series of conceptually novel and practically advantageous catalytic systems. Here, in this work, we provide our perspective on recent progress in nonclassical SMSIs for enhanced catalysis. Unravelling the structural complexity of SMSI necessitates the combination of multiple characterization techniques at different scales. Synthesis strategies leveraging chemical, photonic, and mechanochemical driving forces further expand the definition and application scope of SMSI. Exquisite structure engineering permits elucidation of the interface, entropy, and size effect on the geometric and electronic characteristics. Materials innovation places the atomically thin two-dimensional materials at the forefront of interfacial active site control. A broader space is awaiting exploration, where exploitation of metal–support interactions brings compelling catalytic activity, selectivity, and stability.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Mechanistic Insights into CO 2 Electroreduction on Ni 2 P: Understanding Its Selectivity toward Multicarbon Products
Recently, nickel phosphides (Ni x P y ) have been reported to enable selective electrochemical formation of multicarbon products (C 3 and C 4 ) via the CO 2 reduction reaction (CO 2 RR); nevertheless, their activities remain low. In order to understand the roots of their high selectivity and low activity and to direct the design of more active Ni x P y -based CO 2 RR catalysts, we investigate the CO 2 RR mechanism on Ni 2 P using density functional theory (DFT) calculations. Iin this work, we reveal that the reaction proceeds through the formate pathway, followed by formaldehyde (H 2 CO*) formation and self-condensation. Moreover, we demonstrate that surface hydride transfer steps, along with surface-mediated C–C coupling, are essential in order to avoid C 1 product formation and boost selectivity toward multicarbon products. In addition, we find that the thermal surface hydride transfer from the surface to the physisorbed CO 2 is one of the key rate-limiting steps, and since it is not electroactive, it cannot be accelerated by applying an overpotential. Finally, our results also show that the hydrogen affinity of the surface and the dynamic surface reconstruction via H adsorption facilitate selective CO 2 reduction and C–C coupling on Ni 2 P. These findings provide an impetus for exploring materials design space to identify the physical principles that govern the thermodynamics of rate-limiting thermal steps in electrocatalytic processes.
36 MATERIALS SCIENCE↗
Unraveling Interdiffusion Phenomena and the Role of Nanoscale Diffusion Barriers in the Copper–Gold System
Diffusion is one of the most fundamental concepts in materials science, playing a pivotal role in materials synthesis, forming, and degradation. Of particular importance is solid state interdiffusion of metals which defines the usable parameter space for material combinations in the form of alloys. This parameter space can be explored on the macroscopic scale by using diffusion couples. However, this method reaches its limit when going to low temperatures, small scales, and when testing ultrathin diffusion barriers. Therefore, this work transfers the principle of the diffusion couples to small scales by using core–shell nanowires and in situ heating. This allows us to delve into the interdiffusion dynamics of copper and gold, revealing the interplay between diffusion and the disorder–order phase transition. Our in situ TEM experiments in combination with chemical mapping reveal the interdiffusion coefficients of Cu and Au at low temperatures and highlight the impact of ordering processes on the diffusion behavior. The formation of ordered domains within the solid-solution is examined using high-resolution imaging and nanodiffraction including strain mapping. In addition, we examine the effectiveness of ultrathin Al 2 O 3 barrier layers to control interdiffusion of the diffusion couple. Our findings indicate that a 5 nm thick layer serves as an efficient diffusion barrier. Furthermore, this research provides valuable insights into the interdiffusion behavior of Cu and Au on the nanoscale, offering potential applications in the development of miniaturized integrated circuits and nanodevices.
alloys↗