Degradation via sintering is an ongoing challenge that impedes the broad commercial success of supported metallic nanoparticle catalysts. To mitigate degradation via informed catalyst design and process operations, here we aim to disambiguate the underlying mechanisms of sintering by combining theory and experiment in a quantitative framework. While mechanistic sintering models exist, they only model a single sintering pathway, even though multiple sintering mechanisms can occur simultaneously or dominate at different stages of the process. Data-driven machine learning models have emerged as a means to represent complex processes through data regression. However, machine learning models have very large data needs and lack mechanistic insights due to their black-box encoding. To develop an interpretive model of catalyst degradation via sintering, we constructed a hybrid model combining mechanistic "physics-based" models and data-driven methods to obtain both reliable predictions and mechanistic insights regarding experimentally observed sintering phenomena. Focusing on nanoparticle sintering in the Rh-TiO2 catalyst for the reverse water-gas shift (RWGS) reaction, the hybrid model couples a mechanistic term for Ostwald ripening with energy values calculated via density functional theory (DFT) with a parametric, data-driven discrepancy function term for unmodeled mechanisms. The hybrid model is trained using Bayesian inference with data collected from small-angle X-ray scattering (SAXS) in situ experiments wherein average nanoparticle diameter versus time was measured at three relevant operating temperatures. The calibrated hybrid model results show that an Ostwald ripening-only model parameterized with fixed DFT energies does not fully capture the time and temperature dependence of the SAXS-observed sintering kinetics, and that an additional functional contribution, or DFT energy calibration, is required to reconcile simulation and experiment. Analysis of the hybrid-model error confirms that the hybrid model outperforms both the purely mechanistic and purely data-driven alternatives in terms of expected predictive accuracy for time-evolving average particle sizes. Furthermore, the results support the hypothesis that the Ostwald ripening mechanism is less important for explaining the sintering phenomena as operating temperature increases under an assumed fixed DFT parameterization. This could be explained in one of two ways: either latent, unmodeled sintering mechanisms dominate at higher temperatures, or the DFT uncertainty increases with temperature. The proposed modeling approach directly links theory to experiments and simulations via a statistical hybrid modeling framework and can be extended to other catalytic systems to improve predictive models and mechanistic understanding.

Abstract Machine learning (ML) and artificial intelligence (AI) are changing how catalysis science is performed, with the prospect of accelerating the discovery of catalysis knowledge. Despite the substantial efforts and resources invested in the development and application of AI/ML approaches, their performance still exhibits clear limitations due to the lack of usable data. Although decades of work have produced an abundance of catalysis data, much of it was generated without sufficient understanding of AI/ML or community-wide consensus on terminology and reporting standards. As a result, not all of this data is suitable for training models. It is therefore essential to define what constitutes AI-ready data and understand the efforts required to obtain it, in order to prepare for the era of AI-driven catalysis. In this perspective, we define the essential properties of AI-ready data that enable the construction of robust catalysis AI/ML models. Then, we discuss current practices in computational and experimental catalysis through the lens of data readiness for AI/ML applications. Lastly, we discuss community-level efforts needed to establish large-scale datasets ready for AI-driven catalysis research.

Nanosized cerium oxide (CeO2) has been extensively used as the oxygen storage component in automotive emission control systems. However, the possible influence of atomically dispersed Ce in these catalysts has not been recognized. Here, we demonstrate the controllable transformation of ceria nanoparticles into isolated cerium cations on γ-Al2O3 via reductive atom trapping in 10% H2 at 800 °C, achieving over half-monolayer coverage. Dispersed Ce1 ions anchored by surface penta- and octa-coordinated Al sites exhibit outstanding thermal stability in air up to 500 °C, enabling further loading of active metals with well-defined catalyst structures. With this strategy, supported single-atom Rh1 surrounded by dispersed Ce1 is confirmed to exhibit much superior performance to Rh1 on bare Al2O3 or nanocrystalline CeO2 in catalyzing NO reduction by CO, exhibiting a striking one-order-of-magnitude increase in activity. Dispersed Ce1 exhibits greatly enhanced oxygen transfer capability compared to ceria nanoparticles and introduces a modified reaction mechanism that involves an adjacent Rh1-Ce1 motif, resulting in a greatly decreased activation barrier (from 192 to 96 kJ/mol). The reactivity enhancements are also seen with Ce1-promoted Pt nanoparticles for oxidation of CO and hydrocarbons.

Poly-(δ-valerolactone) (PVL) and poly-(l-lactic acid) (PLLA) are bioderivable, compostable, and chemically recyclable plastics with synergistic properties for addressing plastic waste accumulation in receiving environments. Though recyclable-by-design polymers often fall short of competing with incumbent materials, blending affords a means to leverage individual component strengths toward ideal tunable properties. Polymer blends are often immiscible, but a range of methodologies are available to promote mixing. Here, we report on the compatibilization of 9 immiscible PVL and PLLA blends with three different compatibilization agents: thermoplastic starch, synthesized PVL-co-PLLA statistical copolymers (SCPs), and synthesized PVL-b-PLLA block-type copolymers. Resulting degrees of compatibilization are observed through scanning electron microscopy, corroborated by thermal and mechanical analyses monitoring performance as a function of microdomain size. Small-angle and wide-angle X-ray scattering experiments are conducted to observe the influence of compatibilizers on individual crystalline phases to further elucidate material behavior. Molecular dynamics simulations provide key insights into the interfacial interactions between homopolymers and compatibilizers. Finally, a suite of end-of-life avenues is established by biodegradation in industrial composting conditions, chemical recycling by deconstruction to hydroxymethyl esters, and direct chemical depolymerization to lactone precursors in mixed feed. Overall, we highlight several promising blends and the counterintuitive SCP compatibilization phenomenon toward high-performance, sustainable materials.

Efficient catalytic deconstruction of plastics requires facile solvent and catalyst access to polymer substrates to minimize mass transfer effects. Autoxidation using Co-(II) acetate, Mn-(II) acetate, and a radical carrier in acetic acid is a promising strategy to deconstruct mixed plastic waste, yet the role of polymer morphology in governing solvent and catalyst accessibility remains poorly understood. Here, in situ simultaneous small- and wide-angle X-ray scattering (SAXS/WAXS), complemented by X-ray fluorescence (XRF) imaging and high-pressure differential scanning calorimetry (DSC), were used to elucidate interactions between acetic acid, a Co/Mn catalyst solution, and semicrystalline polyethylene (PE) and amorphous polystyrene (PS) from room temperature to 160 °C. In PE, acetic acid and catalyst access were confined to amorphous regions and cryomilled particle interfaces at room temperature, while crystalline lamellae remained intact after soaking for up to 34 h. Increasing temperature enabled solvent uptake into PE, followed by solvent-assisted softening above 100 °C, and a modest melting-point depression that removed lamellar transport barriers upon melting. Conversely for PS, acetic acid penetrated the glassy polymer without inducing chain mobility until the glass transition was reached, above which the observed structural changes were consistent with enhanced segmental mobility which enabled bulk penetration. These results suggest that polymer morphology and thermally activated physical transitions arising from diffusion and polymer-solvent interactions can influence whether autoxidation of plastics is transport-limited or kinetically controlled, providing a framework for aligning reaction conditions with reaction outcomes.

Porosity defects remain a significant challenge in the laser powder bed fusion (LPBF) process, adversely affecting the mechanical properties and reliability of additively manufactured components. This study investigates the real-time formation and trajectory of gas bubbles during LPBF of Al6061 alloy using advanced in-situ X-ray characterization and machine learning. The unsupervised Gaussian mixture model and particle tracking algorithm developed are able to precisely track and quantify the properties of gas bubbles and keyhole pores. Our analysis identified five distinct types of gas bubble formation and movement patterns, emphasizing the diverse origins and behaviors of these defects. It enables precise quantification of trajectories, velocities, and morphological changes of gas bubbles, offering a granular view of the subsurface dynamics within the melt pool. Additionally, we explored keyhole-induced pore dynamics, revealing the critical role of keyhole oscillation and collapse for the formation of both large and small gas pores. It defines four different regions of gas bubble movement within the melt pool, providing a clearer understanding of how local fluid dynamics affect pore behavior. The results underscore the importance of integrating in-situ experimental observation and automated machine learning to develop a more robust predictive model for defect formation in LPBF.

A sustainable plastics future will require high recycling rates and the use of biogenic feedstocks, which together are catalyzing interest in replacing fossil fuel-derived, noncircular polyolefin packaging materials with bio-based, chemically recyclable polyesters. Here we present a catalytic methanolysis process capable of depolymerizing both fossil fuel- and bio-based polyesters, including polyethylene terephthalate (PET), polylactic acid, polybutylene adipate terephthalate and polybutylene succinate in one reactor under mild conditions with high monomer yields. We scaled this process to 1 kg and integrated separations engineering using activated carbon, crystallization, extraction and distillation to remove contaminants and recover individual monomers from depolymerized mixed polyesters with high yield and purity. PET synthesized from monomers isolated from postconsumer materials showed comparable mechanical and thermal properties to PET from commercial monomers. Techno-economic analysis and life cycle assessment show that this process is economically viable and exhibits lower environmental impacts than primary production of respective polymers. Net-zero bioplastics are possible when combined with high recycling rates. This study presents a mixed polyester recycling process integrated with monomer separation and purification for both fossil- and bio-based plastics. Techno-economic and life cycle analyses confirm its environmental and commercial advantages, advancing the path toward circular, low-emission polyester plastics.

Bottom-up assembly of nanocrystals (NCs) into ordered arrays, or superlattices (SLs), is a promising route to design materials with new functionalities, but the degree of control over assembly into functional structures remains challenging. Using electrostatics, rather than density, to tune the interactions between semiconductor NCs, we watch self-assembly proceeding through a metastable liquid phase. We systematically investigate the phase behavior as a function of quench conditions in situ and in real time using small angle X-ray scattering (SAXS). Through quantitative fitting to colloid, liquid, and SL models, we extract the time evolution of each phase and the system phase diagram, which we find to be consistent with short-range attractive interactions. Using the phase diagram's predictive power, we establish control of the self-assembly rate over three orders of magnitude, and identify one- and two-step self-assembly regimes, with only the latter implicating the metastable liquid as an intermediate. Importantly, the presence of the metastable liquid increases SL formation rates relative to the equivalent one-step pathway, and SL order counterintuitively increases with the rate, revealing a highly desirable and generalizable kinetic strategy to promote and enhance ordered assembly.

Titanium dioxide (TiO2) is one of the most extensively studied oxides as an active catalyst or catalyst support, particularly in energy and environmental applications, but the atomistic mechanisms governing its dynamic response to reactive environments and their correlation to reactivity remain largely elusive. Using in situ environmental transmission electron microscopy (ETEM), synchrotron X-ray diffraction (XRD), ambient-pressure X-ray photoelectron spectroscopy (AP-XPS), temperature-programmed reduction (TPR), reactivity measurements, and theoretical modeling, we reveal the dynamic interplay between oxygen loss and replenishment of anatase TiO2 under varying reactive conditions. Under H2 exposure, anatase TiO2 undergoes surface reduction via lattice oxygen loss, forming Ti3O5. In contrast, CO2 exposure induces oxygen replenishment, reversing stoichiometry. In mixed H2/CO2 environments, the reverse water-gas shift (RWGS) reaction proceeds selectively on stepped and high-indexed TiO2 surfaces, whereas the thermodynamically stable TiO2(101) surface remains inactive and intact. Critically, H2 pretreatment generates oxygen vacancies on TiO2(101), transforming it into an active Ti3O5 or defect-rich surface that catalyzes RWGS. By correlating surface structure, defect dynamics, and gas-phase interactions, this work deciphers the competition between H2-driven reduction and CO2-driven oxidation pathways at the atomic scale. These insights establish defect engineering as a strategic lever to activate inert TiO2 facets, advancing the design of adaptive catalysts for sustainable fuel synthesis technologies.

The electrochemical CO2 reduction reaction (CO2RR) holds enormous potential as a carbon-neutral route to the sustainable production of fuels and platform chemicals. The durability for long-term operation is currently inadequate for commercialization, however, and the underlying deactivation process remains elusive. A fundamental understanding of the degradation mechanism of electrocatalysts, which can dictate the overall device performance, is needed. In this work, we report the structural dynamics and degradation pathway of Cu oxide nanoparticles (CuOx NPs) during the CO2RR by using in situ small-angle X-ray scattering (SAXS) and X-ray absorption spectroscopy (XAS). The in situ SAXS reveals a reduction in the size of NPs when subjected to a potential at which no reaction products are detected. At potentials where the CO2RR starts to occur, CuOx NPs are agglomerated through a particle migration and coalescence process in the early stage of the reaction, followed by Ostwald ripening (OR) as the dominant degradation mechanism for the remainder of the reaction. As the applied potential becomes more negative, the OR process becomes more dominant, and for the most negative applied potential, OR dominates for the entire reaction time. The morphological changes are linked to a gradual decrease in the formation rate for multicarbon products (C2H4 and ethanol). Other reaction parameters, including reaction intermediates and local high pH, induce changes in the agglomeration process and final morphology of the CuOx NPs electrode, supported by post-mortem ex situ microscopic analysis. The in situ XAS analysis suggests that the CuOx NPs reduced into the metallic state before the structural transformation was observed. The introduction of high surface area carbon supports with ionomer coating mitigates the degree of structural transformation and detachment of the CuOx NPs electrode. These findings show the dynamic nature of Cu nanocatalysts during the CO2RR and can serve as a rational guideline toward a stable catalyst system under electrochemical conditions.

This paper explores using Additive Manufacturing (AM), specifically Electron Beam Powder Bed Fusion (EB-PBF), to enhance the efficiency of designing vacuum electronic devices (VEDs). While highlighting achievements in copper VED production, we address challenges in the certification process due to the presence of defects. We discuss in-situ techniques using a Total Electron Emissions (TEE) sensor array and machine learning during EB-PBF for real-time VED qualification, aiming to overcome the limitations of post-fabrication evaluation methods.

Self-assembly of colloidal nanocrystals (NCs) into superlattices (SLs) is an appealing strategy to design hierarchically organized materials with promising functionalities. Mechanistic studies are still needed to uncover the design principles for SL self-assembly, but such studies have been difficult to perform due to the fast time and short length scales of NC systems. To address this challenge, we developed an apparatus to directly measure the evolving phases in situ and in real time of an electrostatically stabilized Au NC solution before, during, and after it is quenched to form SLs using small-angle X-ray scattering. By developing a quantitative model, we fit the time-dependent scattering patterns to obtain the phase diagram of the system and the kinetics of the colloidal and SL phases as a function of varying quench conditions. The extracted phase diagram is consistent with particles whose interactions are short in range relative to their diameter. We find the degree of SL order is primarily determined by fast (subsecond) initial nucleation and growth kinetics, while coarsening at later times depends nonmonotonically on the driving force for self-assembly. We validate these results by direct comparison with simulations and use them to suggest dynamic design principles to optimize the crystallinity within a finite time window. The combination of this measurement methodology, quantitative analysis, and simulation should be generalizable to elucidate and better control the microscopic self-assembly pathways of a wide range of bottom-up assembled systems and architectures.

Operational stability underpins the successful application of organic mixed ionic-electronic conductors (OMIECs) in a wide range of fields, including biosensing, neuromorphic computing, and wearable electronics. In this work, both the operation and stability of a p-type OMIEC material of various molecular weights are investigated. Electrochemical transistor measurements reveal that device operation is very stable for at least 300 charging/discharging cycles independent of molecular weight, provided the charge density is kept below the threshold where strong charge-charge interactions become likely. When electrochemically charged to higher charge densities, an increase in device hysteresis and a decrease in conductivity due to a drop in the hole mobility arising from long-range microstructural disruptions are observed. By employing operando X-ray scattering techniques, two regimes of polaron-induced structural changes are found: 1) polaron-induced structural ordering at low carrier densities, and 2) irreversible structural disordering that disrupts charge transport at high carrier densities, where charge-charge interactions are significant. These operando measurements also reveal that the transfer curve hysteresis at high carrier densities is accompanied by an analogous structural hysteresis, providing a microstructural basis for such instabilities. This work provides a mechanistic understanding of the structural dynamics and material instabilities of OMIEC materials during device operation.

Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data collection strategies (SwitchBAX, InfoBAX, and MeanBAX), bypassing the time-consuming and difficult process of task-specific acquisition function design. Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We demonstrate this approach on datasets for TiO2 nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches. Overall, our framework provides a practical solution for navigating the complexities of materials design, and helps lay groundwork for the accelerated development of advanced materials.

Self-discharge and chemically induced mechanical effects degrade calendar and cycle life in intercalation-based electrochromic and electrochemical energy storage devices. In rechargeable lithium-ion batteries, self-discharge in cathodes causes voltage and capacity loss over time. The prevailing self-discharge model centers on the diffusion of lithium ions from the electrolyte into the cathode. We demonstrate an alternative pathway, where hydrogenation of layered transition metal oxide cathodes induces self-discharge through hydrogen transfer from carbonate solvents to delithiated oxides. In self-discharged cathodes, we further observe opposing proton and lithium ion concentration gradients, which contribute to chemical and structural heterogeneities within delithiated cathodes, accelerating degradation. Hydrogenation occurring in delithiated cathodes may affect the chemo-mechanical coupling of layered cathodes as well as the calendar life of lithium-ion batteries.

Various nondestructive diagnostic techniques have been proposed for in situ process monitoring of laser powder bed fusion (LPBF), including melt pool pyrometry, whole-layer optical imaging, acoustic emission, atomic emission spectroscopy, high speed melt pool imaging, and thermionic emission. Correlations between these in situ monitoring signals and defect formation have been demonstrated with acoustic signals having been shown to predict pore formation with especially high confidence in recent machine learning studies. In this work, time-resolved acoustic data are collected in both the conduction and keyhole welding regimes of LPBF-processed Ti-6Al-4V alloy. A non-dimensionalized Strouhal number analysis, used in whistle aeroacoustics, is applied to demonstrate that the acoustic signals recorded in the keyhole regimes can be directly associated with the vapor depression morphology. This mechanistic understanding developed from whistle aeroacoustics shows that acoustic monitoring during the LPBF process can provide a direct probe into the vapor depression dynamics and defect occurrence, especially in the keyhole regimes relevant to printing and defect formation.

A limited understanding regarding the underlying dynamics and mechanisms of material removal during continuous wave laser drilling has presented significant challenges in achieving precision and process control. To address this, we employed high-fidelity, in situ synchrotron x-ray imaging to reveal previously unknown material behaviors during continuous wave laser drilling with power modulation. Our findings highlight that high-aspect ratio drill holes are achieved when the laser modulation frequency falls within the range of 8–12 kHz, provided that the laser average power and modulation amplitude levels meet the specified limits. Under these conditions, we identified a material removal mechanism driven by incremental accumulation of recoil pressure that gradually pushes material upward from deep within the substrate to the surface. This mechanism manifested as a low-frequency fluctuation in the vapor depression depth, resulting in periodic instances of material ejection. Furthermore, our study underscores that rapid expansion of the melt pool and the widening of the drill hole opening can impede effective material removal by redirecting energy from material ejection to increasing the melt pool size. This investigation contributes essential insights into the subsurface dynamics involved in the drilling of high-aspect ratio holes, furthering our fundamental understanding of this intricate process.

For polymer recycling research, consistent polymer substrates sourced from widely available vendors are useful to enable direct comparisons between studies. Additionally, when reporting new recycling approaches, it is essential to characterize polymer chemical composition, physical properties, structure, and the presence of additives. Here we characterized 59 polymers from common commercial vendors across 20 different polymer classes, representing >95% of global plastic production by mass. Structural characterization was conducted with gel permeation chromatography, Fourier-transform infrared spectroscopy, and small and wide-angle X-ray scattering, and bulk characterization included CHNS measurements and elemental analysis by inductively coupled plasma mass spectrometry (ICP-MS). Thermal properties were measured using differential scanning calorimetry (DSC) and thermal gravimetric analysis. Nearly all plastics studied contained inorganic and organic additives, including halogens, sulfur-containing compounds, and antioxidants, which were investigated by either ICP-MS, accelerated solvent extraction followed by gas chromatography-mass spectrometry (GC-MS), pyrolysis GC-MS and high-resolution GC-MS. In general, the polymers vary from what they were reported to be, with 5 polymers exhibiting molar mass distributions different from that provided by vendors, 6 polymers exhibiting bimodal molecular mass distributions, and 10 polymers displaying unexpected thermal properties measured by DSC including multiple glass transitions and unusual exotherms. Finally, we also investigated changes in properties pre- and post-cryomilling, a common preprocessing technique in recycling studies. Here we found that 16 polymers had changes in either the average molecular mass, dispersity, or percent crystallinity after cryomilling. Taken together, this study further highlights the need to conduct thorough characterization on polymer substates while also providing a baseline analytical characterization for widely available research plastics. We have further made all data available through an online database.