Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data competition”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 397 records · Page 22

Adsorption Thermodynamics for Process Simulation

Adsorption has rapidly evolved in recent decades and is an established separation technology extensively practiced in gas separation industries and others. However, rigorous thermodynamic modeling of multicomponent adsorption equilibrium remains elusive, and industrial practitioners rely heavily on expensive and time-consuming trial-and-error pilot studies to develop adsorption units. Here, this article highlights the need for rigorous adsorption thermodynamic models and the limitations and deficiencies of existing models such as the extended Langmuir isotherm, dual-process Langmuir isotherm, and adsorbed solution theory. It further presents a series of recent advances in the generalization of the classical Langmuir isotherm of single-component adsorption by deriving an activity coefficient model to account for the adsorbed phase adsorbate–adsorbent interactions, substituting adsorbed phase adsorbate and vacant site concentrations with activities, and extending to multicomponent competitive adsorption equilibrium, both monolayer and multilayer. Requiring a minimum set of physically meaningful model parameters, the generalized Langmuir isotherm for monolayer adsorption and the generalized Brunauer–Emmett–Teller isotherm for multilayer adsorption address various thermodynamic modeling challenges including adsorbent surface heterogeneity, isosteric enthalpies of adsorption, BET surface areas, adsorbed phase nonideality, adsorption azeotrope formation, and multilayer adsorption. Also discussed is the importance of quality adsorption data that cover sufficient temperature, pressure, and composition ranges for reliable determination of the model parameters to support adsorption process simulation, design, and optimization.

09 BIOMASS FUELS↗

The Thermal and Kinematic Sunyaev–Zeldovich Effect in Galaxy Clusters and Filaments Using Multifrequency Temperature Maps of the Cosmic Microwave Background: A399–A401 Cluster Pair Case Study

We present a multifrequency and multi-instrument methodology to study the physical properties of galaxy clusters and cosmic filaments using cosmic microwave background observations. Our approach enables simultaneous measurement of both the thermal (tSZ) and kinematic Sunyaev–Zeldovich (kSZ) effects, incorporates relativistic corrections, and models astrophysical foregrounds such as thermal dust emission. We do this by jointly fitting a single physical model across multiple maps from multiple instruments at different frequencies, rather than fitting a model to a single Compton-y map. We demonstrate the success of this method by fitting the A399–A401 galaxy cluster pair and filament system using archival data from the Planck satellite and new, targeted deep data from the Atacama Cosmology Telescope, covering 11 different frequencies over 14 maps from 30 GHz to 545 GHz. Our tSZ results are consistent with previous work using Compton-y maps. We measure the line-of-sight peculiar velocities of the cluster–filament system using the kSZ effect and find statistical uncertainties on individual cluster peculiar velocities of ≲600 km s −1 , which are competitive with current state-of-the-art measurements. Additionally, we measure the optical depth of the filament component with a signal-to-noise of 8.5σ and reveal hints of its morphology. This modular approach is well-suited for application to future instruments across a wide range of millimeter and submillimeter wavebands.

Gill, Ajay S. [National Research Council of Canada↗

Generalization of thermodynamic Langmuir isotherm for mixed‐gas adsorption equilibria

Abstract This work presents a comprehensive thermodynamic model for both pure component isotherms and mixed‐gas adsorption equilibria. A generalization of thermodynamic Langmuir isotherm, the proposed model assumes competitive adsorption of multiple adsorbates on adsorbent surface for mixed‐gas adsorption equilibria, and it applies an area‐based adsorption nonrandom two‐liquid activity coefficient model in the activity coefficient calculations for the adsorbate phase. The resulting generalized Langmuir isotherm properly captures both surface loading dependence and adsorbate phase composition dependence for mixed‐gas adsorption equilibria. The model is validated with accurate representations of gas adsorption equilibrium data for varieties of unary, binary, and ternary gas systems. The model results are further compared with those calculated from extended Langmuir isotherm and Ideal Adsorbed Solution Theory.

Hamid, Usman↗

Chemical insights into the multi-regime low-temperature oxidation of di-n-propyl ether: Jet-stirred reactor experiments and kinetic modeling

To further understand the combustion characteristics and the reaction pathways of acyclic ethers, in this work the oxidation of di-n-propyl ether (DPE) was investigated in a jet-stirred reactor (JSR) combined with a photoionization molecular-beam mass spectrometer. The experiments were carried out at near-atmospheric pressure (700 Torr) and over a temperature range of 425–850 K. Based on the experimental data and previous studies on ether oxidation, a new kinetic model was constructed and used to interpret the oxidation chemistry of DPE. In DPE oxidation, a high reactivity at low temperatures and two negative temperature coefficient (NTC) zones were observed. These behaviors are explained in this work by taking advantage of the obtained species information and the modeling analyses: the two NTC zones are caused by the competition of chain branching and termination reactions of the fuel itself and specific oxidation intermediates, respectively. Furthermore, the general requirements to have double-NTC behavior are discussed. A variety of crucial fuel-specific C 6 species, such as ketohydroperoxides and diones, were detected in the species pool of DPE oxidation. Their formation pathways are illuminated based on rate-of-production (ROP) analyses. Propanal was identified as the most abundant small molecule intermediate, and its related reactions have an important impact on the oxidation process of DPE. Both acetic acid and propionic acid were detected in high concentrations. A new formation pathway of propionic acid is proposed and incorporated into the kinetic model to achieve a more accurate prediction for propionic acid mole fractions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Metastable antiphase boundary ordering in CaFe 2 O 4

CaFe 2 O 4 is an $\textit{S}$ = 5/2 antiferromagnet exhibiting two magnetic orders that shows regions of coexistence at some temperatures. Using a Green’s function formalism, we model neutron scattering data of the spin wave excitations in this material, elucidating the microscopic spin Hamiltonian. In doing so, we suggest that the low-temperature A phase order (↑↑↓↓) finds its origins in the freezing of antiphase boundaries created by thermal fluctuations in a parent B phase order (↑↓↑↓). In this work, the low-temperature magnetic order observed in CaFe 2 O 4 is thus the result of a competition between the exchange coupling along $\textit{c}$, which favors the B phase, and the single-ion anisotropy, which stabilizes thermally generated antiphase boundaries, leading to static metastable A phase order at low temperatures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Hard–Soft Core–Shell Architecture Formation from Cubic Cobalt Ferrite Nanoparticles

Cubic bi-magnetic hard–soft core–shell nanoarchitectures were prepared starting from cobalt ferrite nanoparticles, prevalently with cubic shape, as seeds to grow a manganese ferrite shell. The combined use of direct (nanoscale chemical mapping via STEM-EDX) and indirect (DC magnetometry) tools was adopted to verify the formation of the heterostructures at the nanoscale and bulk level, respectively. The results showed the obtainment of core–shell NPs (CoFe 2 O 4 @MnFe 2 O 4 ) with a thin shell (heterogenous nucleation). In addition, manganese ferrite was found to homogeneously nucleate to form a secondary nanoparticle population (homogenous nucleation). This study shed light on the competitive formation mechanism of homogenous and heterogenous nucleation, suggesting the existence of a critical size, beyond which, phase separation occurs and seeds are no longer available in the reaction medium for heterogenous nucleation. These findings may allow one to tailor the synthesis process in order to achieve better control of the materials’ features affecting the magnetic behaviour, and consequently, the performances as heat mediators or components for data storage devices.

36 MATERIALS SCIENCE↗

Closed Loop Pumped Storage Hydropower Resource Assessment of the United States

The data includes a geospatial and spreadsheet representation of a resource analysis for closed loop pumped storage systems across the Continental United States, Alaska, Hawaii, and Puerto Rico. The data includes energy storage potential, water volume, distance from source to storage, hydraulic head, dollars per kilowatt of storage, and transmission spurline cost for each pumped storage hydropower (PHS) reservoir. Each reservoir represented in this dataset is represented on potential 10 hour storage duration PSH system comprised of two reservoirs. Units of measure are laid out in the dataset. Pumped storage hydropower (PSH) represents the bulk of the United States' current energy storage capacity: 23 gigawatts (GW) of the 24 GW national total (Denholm et al. 2021). This capacity was largely built between 1960 and 1990. PSH is a mature and proven method of energy storage with competitive round-trip efficiency and long life spans. These qualities make PSH a very attractive potential solution to energy storage needs, particularly for longer-duration storage (8 hours or more); such storage will be crucial to bridge gaps in electricity production as variable wind and solar production continue to comprise an ever-larger portion of the United States' energy portfolio. This study seeks to better understand the technical potential for PSH development in the United States by developing a national-scale resource assessment for closed-loop PSH. For more information, please refer to the Closed Loop Pumped Storage Hydropower Resource Assessment for the United States linked in the resources.

Array↗

Co-Based superalloy morphology evolution: A phase field study based on experimental thermodynamic and kinetic data

Cobalt-based superalloys with two phase $\gamma$/$\gamma '$ microstructures offer great promise as candidates for next-generation high-temperature alloys for applications, such as turbine blades. It is essential to understand the thermodynamic and kinetic factors that influence the microstructural evolution of these alloys in order to optimize the alloy compositions and processing steps with a goal to improve their coarsening, creep and rafting behavior. We are using a continuum phase field approach to study the diffusion process and to predict the equilibrium shapes of Co-Al-W $\gamma '$ precipitates. In order to obtain quantitatively predictive capabilities, we extract chemical free energies for the $\gamma$/$\gamma '$ phases based on CALculation of PHAse Diagrams (CALPHAD) thermodynamic data and diffusion mobilities for Co alloys based on CALPHAD kinetic data. We also use experimental or first-principles data for other quantities, such as misfit strain and interface information, for the parameterization of our model. A particular focus of our study is to understand how different energy balances, misfit strain and kinetics affect the coarsening and rafting behavior of $\gamma '$ precipitates, and the sensitivity of the final precipitate shape to materials parameters. Here, we find that the equilibrium shape of the precipitate results from a delicate competition between chemical, interfacial, and elastic energies, and it is very sensitive to changes in model parameters. Here, we examine how modeling input parameters affect the equilibrium shape of precipitates and relate these parameters to experimentally available values.

36 MATERIALS SCIENCE↗

Assessment of Cloud-Based Applications Enabling a Scalable Risk-Informed Predictive Maintenance Strategy Across the Nuclear Fleet

The current light water reactor fleet uses time-based or failure-based maintenance strategies to achieve high-capacity factors. But to make nuclear more competitive in the energy market, these reactors could utilize emerging technologies in terms of artificial intelligence (AI) and cloud computing to enable a cost-effective, predictive maintenance strategy. This report examines the feasibility of cloud computing for the nuclear industry’s needs in terms of the cloud’s computing capabilities, feasibility, and regulatory concerns. The technical viability of cloud computing was analyzed using one year worth of data from a boiling water reactor’s safety relief valve. Models were hosted on a local desktop, Idaho National Laboratory’s high-performance computer, and Microsoft Azure. Data was loaded, processed, and two types of models were trained in an A/B fashion. Based on the speed at which these actions were completed, it was used to determined that cloud computing has adequate computing resources. Additionally, the computing power can scale with the demanded load. To enable cloud computing in the existing fleet, additional sensors, networks, and other requirements must be implemented to ensure a smooth transition from current maintenance strategies. However, there is a benefit as the plant no longer needs manage their own servers, software, cybersecurity, and IT support staff. Many of these features can be offloaded on to the cloud provider. A comprehensive analysis was completed that showed the current annual cost of operating is more expensive than using cloud computing resources. Lastly, the regulatory framework does not explicitly address AI or autonomous control. Currently, the NRC and other regulatory bodies are evaluating providing guidance to address gaps rather than new regulations to address the use of AI and ML. But since many of the AI applications are focused on non-safety related applications, such as balance-of-plant components, they will likely have little or no regulatory restrictions or necessary approvals. Demonstrating how AI can improve maintenance and operation of these non-safety related systems seems like the likely path forward for implementing AI and cloud computing resources inside nuclear power plants (NPPs).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Integrated Risk-Informed Condition Based Maintenance Capability and Automated Platform: Technical Report 1

Due to continuing global energy market trends, driven heavily by the abundant preserves of natural gas, there is an immediate need to reduce costs associated with operation and maintenance (O&M) for the current domestic nuclear power industry and for future reactor developments. This is to ensure that nuclear power generation remains an economically competitive and viable option in the energy market. O&M costs include labor-intensive preventive maintenance (PM) programs, which involve manually-performed inspection, calibration, testing, and maintenance of plant assets at periodic frequency and time-based replacement of assets, irrespective of their condition. This has resulted in an expensive, labor-centric business model to achieve high capacity factors. Fortunately, there are technologies (advanced sensors, data analytics, and risk assessment methodologies) that can enable the transition from a labor-centric business model to a technology-centric business model. The technology-centric business model will result in a significant reduction of PM activities, laying the foundation for real-time condition assessment of plant assets, reducing overall labor and part costs. To enable this transition, PKMJ Technical Services LLC is partnering with the U.S. Department of Energy’s Idaho National Laboratory (operated by the Battelle Energy Alliance, LLC) and the Public Services Enterprise Group (PSEG) Nuclear, LLC in the Integrated Risk-Informed Condition-Based Maintenance Capability and Automated Platform Project. In this report, the configuration of a digital cloud platform using Microsoft Azure is discussed, data from the PSEG Salem Nuclear Generating Station Units 1 & 2 are imported into a digital cloud platform, and the data is used for an evaluation of several key areas: cost impact analysis, risk-informed model development, and preventive maintenance strategy optimization. First, the cost impact analysis reviews which plant assets are potential good candidates for condition-based monitoring. Next, INL utilized the data in their local environment to develop the risk-informed model; which provides estimates of failure rates and probability of failures of assets based upon their past performance. The developed model is performed on assets selected from the cost impact analysis. Lastly, engineers assess the preventive maintenance strategy for the selected assets at PSEG against maintenance strategies in the nuclear industry for similar assets to potentially identify acceptable justification for the extension of current maintenance frequencies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Continual learning in the presence of repetition

Continual learning (CL) provides a framework for training models in ever-evolving environments. Although re-occurrence of previously seen objects or tasks is common in real-world problems, the concept of repetition in the data stream is not often considered in standard benchmarks for CL. Unlike with the rehearsal mechanism in buffer-based strategies, where sample repetition is controlled by the strategy, repetition in the data stream naturally stems from the environment. This report provides a summary of the CLVision challenge at CVPR 2023, which focused on the topic of repetition in class-incremental learning. The report initially outlines the challenge objective and then describes three solutions proposed by finalist teams that aim to effectively exploit the repetition in the stream to learn continually. The experimental results from the challenge highlight the effectiveness of ensemble-based solutions that employ multiple versions of similar modules, each trained on different but overlapping subsets of classes. This report underscores the transformative potential of taking a different perspective in CL by employing repetition in the data stream to foster innovative strategy design.

Class-incremental learning↗

Why is the winner the best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi- center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), im- age preprocessing (97%), data curation (79%), and post- processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

Eisenmann, Matthias↗

The GREEN ‘omics of Nutrient Feedbacks to Soil Warming

The GREEN ‘omics of Nutrient Feedbacks in Soil project advanced the DOE Biological and Environmental Research (BER) mission by developing and applying isotope-enabled ’omics tools to understand how soil microbes regulate carbon and nutrient cycling. Guided by the Growth Rate, growth Efficiency, and stoichiometry of Essential Nutrients (GREEN ’omics) framework, the project aimed to build a predictive, systems-level understanding of microbial traits that control ecosystem biogeochemistry. In a collaboration among Northern Arizona University (lead), West Virginia University, Lawrence Livermore National Laboratory, and Pacific Northwest National Laboratory, we combined quantitative stable isotope probing (qSIP), Chip-SIP, NanoSIMS, and genome-resolved metagenomics across long-term experiments in Arctic, boreal, temperate, and tropical ecosystems. The project produced three key outcomes: 1) We showed that community-weighted temperature sensitivities of bacterial growth (Q10) can predict ecosystem-scale soil respiration responses across diverse soils. 2) We provided the first in situ evidence for density-dependent population dynamics in soil bacteria and demonstrated that nutrient additions intensify competition, concentrating carbon use into fewer taxa. 3) We improved and extended isotope-enabled ’omics methods by quantifying qSIP measurement error to guide experimental design and coupling SIP with genome-resolved metagenomics to reveal cross-kingdom interactions among bacteria, fungi, and viruses. Together, these results show that a small number of microbial traits and taxa exert disproportionate control over soil carbon and nutrient cycling, providing critical data and methods to improve representation of microbial processes in Earth system models.

54 ENVIRONMENTAL SCIENCES↗

Intelligent Experiments through Real-Time AI: Fast Data Processing and Autonomous Detector Control for High-Energy Nuclear Experiments

The aim of this project is to develop software and hardware for fast real-time data processing and autonomous detector control and calibration for the sPHENIX and the future EIC experiments. Below summarizes Georgia Tech team efforts in the past year: 1. We developed a real-time clustering algorithm and FPGA-based pipeline architecture for processing fired pixel data from ALPIDE sensors in sPHENIX experiments. Our Columnar Clustering Co-Design introduces a hardware-aware, stream-friendly approach that segments pixel data by column pairs using a Column Pair Clustering (CPC) strategy, followed by Cluster Stitching to merge adjacent subclusters. Implemented in Vitis HLS, the pipeline comprises five stages—read-in, subclustering, stitching, analysis, and write-out—connected by tagged HLS streams with custom end-of-event signaling for robust synchronization. We designed a pipelined dataflow model optimized for throughput, low latency, and minimal buffering, enabling scalable clustering across events of arbitrary size. Our system maintains spatial precision via center-of-mass and shape key extraction and efficiently handles edge cases such as fragmented or nested clusters. Compared against DBSCAN in both software and hardware, our approach demonstrates competitive performance under FPGA constraints. 2. We also conducted a comprehensive algorithm-to-hardware co-design of connected component analysis tailored for sPHENIX experiments, focusing on real-time, low-latency processing using FPGAs and High-Level Synthesis (HLS). Starting from a Python-based particle tracking pipeline, the team translated the core logic—graph traversal via DFS and Union-Find—into an HLS-compatible C++ model, replacing dynamic memory and recursion with static arrays and pipelined control flow. The final design includes a fully streamed and dataflow-compatible Union-Find kernel optimized across five iterations, incorporating loop pipelining, array partitioning, AXI/FIFO interface tuning, and function flattening. Experimental results show up to 14.8× speedup over the CPU baseline, reducing per-graph latency to 1.58 μs and demonstrating strong resource efficiency with only ~7k LUTs and zero BRAM usage. The design maintains functional correctness against the Python reference using a Python-based C-simulation framework and Mean Squared Error metrics. This work validates the potential of HLS-driven FPGA designs for edge-level HEP data acquisition, laying a scalable foundation for future integration with real-time detector pipelines and multi-graph processing systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Spatial evidence of cryptic methane cycling and methylotrophic metabolisms along a land–ocean transect in salt marsh sediment

Methylotrophic methanogenesis in the sulfate-rich zone of coastal and marine sediments couples with anaerobic oxidation of methane (AOM), forming the cryptic methane cycle. This study provides evidence of cryptic methane cycling in the sulfate-rich zone across a land–ocean transect of four stations–two brackish, one marine, and one hypersaline–within the Carpinteria Salt Marsh Reserve (CSMR), southern California, USA. Samples from the top 20 cm of sediment from the transect were analyzed through geochemical and molecular (16S rRNA) techniques, in-vitro methanogenesis incubations, and radiotracer incubations utilizing 35 S-SO 4 , 14 C-mono-methylamine, and 14 C-CH 4 . Sediment methane concentrations were consistently low (3 to 28 µM) at all stations, except for the marine station, where methane increased with depth reaching 665 µM. Methanogenesis from mono-methylamine was detected throughout the sediment at all stations with estimated CH 4 production rates in the sub-nanomolar to nanomolar range per cm 3 sediment and day. 16S rRNA analysis identified methanogenic archaea (Methanosarcinaceae, Methanomassiliicoccales, and Methanonatronarchaeacea) capable of producing methane from methylamines in sediment where methylotrophic methanogenesis was found to be active. Metabolomic analysis of porewater showed mono-methylamine was mostly undetectable (<3 µM) or present in trace amounts (<10 µM) suggesting rapid metabolic turnover. In-vitro methanogenesis incubations of natural sediment showed no linear methane buildup, suggesting a process limiting methane emissions. AOM activity, measured with 14 C-CH 4 , overlapped with methanogenesis from mono-methylamine activity at all stations, with rates ranging from 0.03 to 19.4 nmol cm −3 d −1 . Geochemical porewater analysis showed the CSMR sediments are rich in sulfate and iron. Porewater sulfate concentrations (9–91 mM) were non-limiting across the transect, supporting sulfate reduction activity (1.5–2,506 nmol cm −3 d −1 ). Porewater sulfide and iron (II) profiles indicated that the sediment transitioned from a predominantly iron-reducing environment at the two brackish stations to a predominantly sulfate-reducing environment at the marine and hypersaline stations, which coincided with the presence of phyla (Desulfobacterota) involved in these processes. AOM activity overlapped with sulfate reduction and porewater iron (II) concentrations suggesting that AOM is likely coupled to sulfate and possibly iron reduction at all stations. However, 16S rRNA analysis identified anaerobic methanotrophs (ANME-2) only at the marine and hypersaline stations while putative methanogens were found in sediment across all stations. In one sediment horizon at the marine station, methanogen families (Methanosarcinaceae, Methanosaetaceae, Methanomassiliicoccales, and Methanoregulaceae) and ANME 2a,2b, and 2c groups were found together. Collectively, our data suggest that at the brackish stations methanogens alone may be involved in cryptic methane cycling, while at the marine and hypersaline stations both groups may be involved in the process. Differences in rate constants from incubations with 14 C-labeled methane and mono-methylamine suggest a non-methanogenic process oxidizing mono-methylamine to inorganic carbon, likely mediated by sulfate-reducing bacteria. Understanding the potential competition of sulfate reducers with methanogens for mono-methylamine needs further investigation as it might be another important process responsible for low methane emissions in salt marshes.

Anaerobic oxidation of methane↗

Precision measurement of the Z boson invisible width in pp collisions at s = 13 TeV

A precise measurement of the invisible width of the Z boson produced in proton-proton collisions at a center-of-mass energy of 13 TeV is presented using data recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 36.3 fb − 1 . The result is obtained from a simultaneous fit to kinematic distributions for two data samples of Z boson plus jets: one dominated by Z boson decays to invisible particles and the other by Z boson decays to muon and electron pairs. The invisible width is measured to be 523 ± 3 (stat) ± 16 (syst) MeV . This result is the first precise measurement of the invisible width of the Z boson at a hadron collider, and is the single most precise direct measurement to date, competitive with the combined result of the direct measurements from the LEP experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Ion correlations explain kinetic selectivity in diffusion-limited solid-state synthesis reactions

Establishing viable solid-state synthesis pathways for novel inorganic materials remains a major challenge in materials science. Previous pathway design methods using pairwise reaction approaches have navigated the thermodynamic landscape with first-principles data but lack kinetic information, limiting their effectiveness. This gap leads to suboptimal precursor selection and predictions, especially for reactions forming competing phases with similar formation energies, where ion diffusion is a critical influence. Here we demonstrate an inorganic synthesis framework by incorporating machine learning-derived transport properties through ‘liquid-like’ product layers into a thermodynamic cellular reaction model. In the Ba–Ti–O system, known for its competitive polymorphism, we obtain accurate predictions of phase formation with varying BaO:TiO2 ratios as a function of time and temperature. We find that diffusion–thermodynamics interplay governs phase compositions, with cross-ion transport coefficients critical for predicting diffusion-limited selectivity. This work bridges length scales and timescales by integrating solid-state reaction kinetics with first-principles thermodynamics and spatial reactivity.

Atomistic models↗

An Experimental and Theoretical Study of the High Temperature Reactions of the Four Butyl Radical Isomers

The high temperature gas phase chemistry of the four butyl radical isomers (n-butyl,sec-butyl, iso-butyl, andtert-butyl) was investigated in a combined experimental and theoretical study. Organic nitrites were used as convenient and clean sources of each of the butyl radical isomers. Rate coefficients for dissociation of each nitrite were obtained experimentally and are at, or close to, the high pressure limit. Low pressure experiments were performed in a diaphragmless shock tube with laser schlieren densitometry at post-shock pressures of 65, 130, and 260 Torr and post-shock temperatures of 700-1000 K. Additional experiments were conducted with iso-butyl radicals at 805 K and 8.7 bar to elucidate changes in mechanism at higher pressures. These experiments were performed in a miniature shock tube with synchrotron-based photoionization mass spectrometry. The mass spectra confirmed that scission of the O-NO bond is the primary channel by which the precursors dissociate, but they also provided evidence of a minor channel (<7.7%) through HNO loss and formation of an aldehyde. These high pressure experiments were also used to determine the disproportionation/recombination ratio for iso-butyl radicals as 0.3. Reanalysis of the lower-temperature literature and the present data yielded rate constants for the disproportionation reaction, iso-butyl + iso-butyl = iso-butene + iso-butane. A chemical kinetics model was developed for the reactions of the butyl isomers that included new paths for highly energized adducts. These adducts are formed by the addition of H, CH 3 or C 2 H 5 to the butyl radicals. Accompanying theoretical investigations show that chemically activated pathways are competitive with stabilization of the adduct by collision under the conditions of the laser schlieren experiments. These calculations also show that at 10 bar and T< 1000 K stabilization is the only important reaction, but at higher temperatures, even at 10 bar, chemically activated product channels should also be considered. Branching fractions and rate coefficients are presented for these reactions. Furthermore, this study also highlights the importance of the radical structure for determining branching ratios for disproportionation and recombination of alkyl radicals, and these were facilitated by theoretical calculations of recombination rate coefficients for the four butyl radical isomers. Overall, the results reveal previously unknown features of butyl radical chemistry under conditions that are relevant to a wide range of applications and reaction mechanisms are presented that incorporate pressure dependent rate coefficients for the key steps.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗