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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

In Situ Ultra-Small- and Small-Angle X-ray Scattering Study of ZnO Nanoparticle Formation and Growth through Chemical Bath Deposition in the Presence of Polyvinylpyrrolidone

ZnO inverse opals combine the outstanding properties of the semiconductor ZnO with the high surface area of the open-porous framework, making them valuable photonic and catalysis support materials. One route to produce inverse opals is to mineralize the voids of close-packed polymer nanoparticle templates by chemical bath deposition (CBD) using a ZnO precursor solution, followed by template removal. To ensure synthesis control, the formation and growth of ZnO nanoparticles in a precursor solution containing the organic additive polyvinylpyrrolidone (PVP) was investigated by in situ ultra-small- and small-angle X-ray scattering (USAXS/SAXS). Before that, we studied the precursor solution by in-house SAXS at T = 25 °C, revealing the presence of a PVP network with semiflexible chain behavior. Heating the precursor solution to 58 °C or 63 °C initiates the formation of small ZnO nanoparticles that cluster together, as shown by complementary transmission electron microscopy images (TEM) taken after synthesis. The underlying kinetics of this process could be deciphered by quantitatively analyzing the USAXS/SAXS data considering the scattering contributions of particles, clusters, and the PVP network. A nearly quantitative description of both the nucleation and growth period could be achieved using the two-step Finke–Watzky model with slow, continuous nucleation followed by autocatalytic growth.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High‐Speed Slot‐Die Coating with Donor‐Priority Rapid Aggregation Kinetics for Improved Morphology and Efficiency in Ecofriendly Organic Solar Cells

Abstract Solution‐processable organic solar cells (OSCs) represent a promising renewable photovoltaic technology with significant potential for eco‐compatible production. While high power conversion efficiencies (PCEs) have been achieved in OSCs, scaling this technology for high‐throughput manufacturing remains challenging. Key reason lies in the lack of efficient control strategies for the complex and long‐duration morphology evolution during high‐speed coating process with ecofriendly solvents. Here, a donor‐priority rapid aggregation process (DP‐RAP) scheme is proposed to solve this issue by adjusting the aggregation kinetics of donor and acceptor components. DP‐RAP enables blends with a nanoscale fiber network structure and favorable crystallinity, which contributes to balanced carrier transport and reduced recombination losses. As a result, the PCE is improved from 14.3% (reference) to 17.4% (DP‐RAP) for ultra‐high speed coated PM6:BTP‐eC9 devices in atmosphere, which is one of the highest values for non‐halogenated solvent‐processed solar cells at coating speeds of 500 mm s −1 . Moreover, the DP‐RAP based devices remain a stable PCE of approximately 17.4% across a broad range of coating speeds (20–500 mm s −1 ), illustrating its tolerance to the varied manufacturing conditions. This work highlights a promising avenue for the high‐speed, ecofriendly production of efficient OSCs, pushing the boundaries of practical manufacturing in renewable energy technologies.

Chemistry↗
DOE OSTI2020

Biased localization of actin binding proteins by actin filament conformation

The assembly of actin filaments into distinct cytoskeletal structures plays a critical role in cell physiology, but how proteins localize differentially to these structures within a shared cytoplasm remains unclear. Here, we show that the actin-binding domains of accessory proteins can be sensitive to filament conformational changes. Using a combination of live cell imaging and in vitro single molecule binding measurements, we show that tandem calponin homology domains (CH1–CH2) can be mutated to preferentially bind actin networks at the front or rear of motile cells. We demonstrate that the binding kinetics of CH1–CH2 domain mutants varies as actin filament conformation is altered by perturbations that include stabilizing drugs and other binding proteins. These findings suggest that conformational changes of actin filaments in cells could help to direct accessory binding proteins to different actin cytoskeletal structures through a biophysical feedback loop.

42 ENGINEERING↗
DOE OSTI2021

A model for the fragmentation kinetics of crumpled thin sheets

As a confined thin sheet crumples, it spontaneously segments into flat facets delimited by a network of ridges. Despite the apparent disorder of this process, statistical properties of crumpled sheets exhibit striking reproducibility. Experiments have shown that the total crease length accrues logarithmically when repeatedly compacting and unfolding a sheet of paper. Here, we offer insight to this unexpected result by exploring the correspondence between crumpling and fragmentation processes. We identify a physical model for the evolution of facet area and ridge length distributions of crumpled sheets, and propose a mechanism for re-fragmentation driven by geometric frustration. This mechanism establishes a feedback loop in which the facet size distribution informs the subsequent rate of fragmentation under repeated confinement, thereby producing a new size distribution. We then demonstrate the capacity of this model to reproduce the characteristic logarithmic scaling of total crease length, thereby supplying a missing physical basis for the observed phenomenon.

42 ENGINEERING↗
DOE OSTI2024

Materials genome innovation for computational software (magics) center

Functional layered material (LM) architectures will dominate nanomaterials science in this century. We have developed theory, modeling, simulation, and software and data tools that enhance understanding and AI guide synthesis, enable characterization of complex structures, and improve capabilities in the predictive design and growth of LMs. Research at the Center has focused on: Computational synthesis and characterization: AI guided synthesis and experimental synthesis of stacked LMs with tailored properties via optimized chemical vapor deposition (CVD) growth and liquid-phase exfoliation; study defects, edges, grain boundaries, wrinkling of atomic layers and their effects on chemical, mechanical, electrical, and optical properties. Far-from-equilibrium processes: Joint experimental and simulation based probe of electronic processes with NAQMD and ultrafast X-ray free-electron laser (XFEL) and ultrafast electron diffraction (UED) facilities at Stanford. Experimentally validate NAQMD by ultrafast electron diffraction and X-ray spectroscopy studies of structural and excited state dynamics, shape fluctuations, and phonon dynamics. Scalable software: Simulation engines for desktop-to-exascale platforms using low-overhead, linear-scaling QMD algorithms; divide-conquer-recombine NAQMD with electronic excitations; extended-Lagrangian reactive molecular dynamics (RMD), machine learning (ML) based neural-network quantum molecular dynamics (NNQMD), and super-state accelerated molecular dynamics (AMD) and kinetic Monte Carlo codes; thermal and electrical transport software; and design 3D architectures of LMs with desired functionality using scalable software. Distribution of software and data, and training: Software and simulation-experimental data generated within the Center are distributed to the materials science community via Berkeley Materials Project (MP) framework. We have also organized three workshops for software distribution and training at USC (Nov. 2017, Mar. 2018) and Gaithersburg, MD (Nov. 2018) to train researchers, with the last one in focused on underrepresented groups, in collaboration with Howard University which is one of the largest HBCUs. The Center supported a total of 46 personnel and 6 undergraduate students. These include 14 faculty, 11 postdoctoral research associates, 20 graduate research assistants, and mentored 6 undergraduate students. This resulted in the publications of 63 research papers that include 46 publications on Reactive and Quantum Dynamics Simulations, 13 publications on Machine Learning for Quantum Materials, and 4 publications on Quantum Computing.

2D Materials↗
DOE OSTI2021

A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure

In this work, cascading transmission line failures are studied through a dynamical model of the power system operating under fixed conditions. The power grid is modeled as a stochastic dynamical system where first-principles electromechanical dynamics are excited by small Gaussian disturbances in demand and generation around a specified operating point. In this context, a single line failure is interpreted in a large deviation context as a first escape event across a surface in phase space defined by line security constraints. The resulting system of stochastic differential equations admits a transverse decomposition of the drift, which leads to considerable simplification in evaluating the quasipotential (rate function) and, consequently, computation of exit rates. Tractable expressions for the rate of transmission line failure in a restricted network are derived from large deviation theory arguments and validated against numerical simulations. Extensions to realistic settings are considered, and individual line failure models are aggregated into a Markov model of cascading failure inspired by chemical kinetics. Cascades are generated by traversing a graph composed of weighted edges representing transitions to degraded network topologies. Numerical results indicate that the Markov model can produce cascades with qualitative power-law properties similar to those observed in empirical cascades.

Kinetic Monte Carlo, rare events, Cascading Failur↗
DOE OSTI2021

Consequences of adsorbate-adsorbate interactions for apparent kinetics of surface catalytic reactions

Lateral adsorbate interactions at catalyst surfaces are known to influence adsorption energies and reaction rates. Lattice-based kinetic Monte Carlo (kMC) simulations are able to capture these influences, but such models are typically parameterized for a specific reaction network and catalyst surface. Here we report kMC simulations to probe the influence of lateral interactions on simulated rates, rate orders, apparent activation energies, and Sabatier plots. We construct a simple, two-step reaction network involving a single adsorbate and rate- limiting diatomic dissociation, employ a generic repulsive lateral interaction model consistent with known adsorbate-adsorbate interactions on metal surfaces, and a rate model consistent with known Brønsted-Evans-Polyani relationships for diatomic dissocations. Furthermore, we juxtapose reaction kinetics over a wide range of reaction conditions and catalyst binding energies, as a function of interaction strength. We find that at a given zero-coverage binding energy and external conditions, adsorbate coverage decreases monotonically with increasing interaction strength, but absolute rates can vary linearly or nonlinearly. Interactions flatten the Sabatier volcano and shift the maximum towards stronger binding. Influences on apparent rate orders and activation energies are modest and are sensitive to interaction-induced adsorbate ordering. Model predictions are sensitive to lattice size effects at high coverages. The results, modelled for a simple reaction system, highlight the generic consequences of lateral interactions and guidance for identifying their signatures in observed kinetics.

kinetic Monte Carlo↗
DOE OSTI2022

Machine learning and atomic layer deposition: Predicting saturation times from reactor growth profiles using artificial neural networks

In this work, we explore the application of deep neural networks to the optimization of atomic layer deposition (ALD) processes. In particular, we focus on a one-shot optimization problem, where we try to predict the optimal dose time that leads to saturation everywhere in the reactor based on thickness values measured at different points of an ALD reactor after a single trial growth. In order to tackle this problem, we introduce a dataset designed to train neural networks to predict saturation times based on these inputs for a cross-flow ALD reactor. Here, we then explore the predictive ability of artificial neural networks of different depths and sizes using a separate testing dataset to evaluate their accuracies. The results obtained show that networks trained using stochastic gradient descent methods can accurately predict saturation times without requiring any additional information on the surface kinetics. This provides a viable approach to minimize the number of experiments required to optimize new ALD processes in a known reactor, and it highlights the way machine learning can be leveraged for thin film growth and manufacturing. While the datasets and training procedure depend on the reactor geometry, the trained neural networks provide a general surrogate model connecting thickness values and trial dose times with optimal saturation times that can be reused for different ALD processes within the same reactor.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A General Materials Data Science Framework for Quantitative 2D Analysis of Particle Growth from Image Sequences

Abstract Phase transformations are a challenging problem in materials science, which lead to changes in properties and may impact performance of material systems in various applications. We introduce a general framework for the analysis of particle growth kinetics by utilizing concepts from machine learning and graph theory. As a model system, we use image sequences of atomic force microscopy showing the crystallization of an amorphous fluoroelastomer film. To identify crystalline particles in an amorphous matrix and track the temporal evolution of the particle dispersion, we have developed quantitative methods of 2D analysis. 700 image sequences were analyzed using a neural network architecture, achieving 0.97 pixel-wise classification accuracy as a measure of the correctly classified pixels. The growth kinetics of isolated and impinged particles were tracked throughout time using these image sequences. The relationship between image sequences and spatiotemporal graph representations was explored to identify the proximity of crystallites from each other. The framework enables the analysis of all image sequences without the requirement of sampling for specific particles or timesteps for various materials systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Kinetic Monte Carlo Framework for Coupled Degradation and Dehydration of Anion Exchange Membranes

Kinetic Monte Carlo (kMC) simulations, augmented with temporal-acceleration schemes, can efficiently handle stiff reaction-transport networks when fast processes rapidly relax to quasi-equilibrium on a fixed lattice. However, in glassy anion-exchange membranes (AEM), rare and irreversible chemical degradation events continuously reshape the nanoscale morphology, and the associated hydration and transport degrees of freedom remain far from a well-defined local equilibrium. This combination of evolving state space and nonequilibrated fast dynamics lies outside the scope of existing kMC acceleration frameworks. Here, to address this challenge, we introduce an auxiliary-particle kinetic Monte Carlo (AP-kMC) scheme. In AP-kMC, short-lived mobile particles spawned at degradation sites execute hop, water-elimination, and decay moves, enforcing rapid local relaxation of the hydration structure while preserving the stochastic rules of kMC. Parameterized with molecular-dynamics morphologies and experimental solution degradation kinetics, AP-kMC reproduces the evolution of ion-exchange capacity, water uptake, and conductivity, and reveals a feedback loop in which poorly hydrated sites degrade first and each degradation event induces further local dehydration. The resulting thinning and fragmentation of water channels cause loss of hydrophilic percolation and abrupt conductivity collapse well before complete charge loss. AP-kMC thus reframes AEM durability as a coupled degradation–drying–percolation problem and provides a transferable strategy to simulate reactive, out-of-equilibrium polymer electrolytes where local solvation controls reactivity.

organic↗
DOE OSTI2022

A numerical approach for detecting switch-like bistability in mass action chemical reaction networks with conservation laws

Theoretical analysis of signaling pathways can provide a substantial amount of insight into their function. One particular area of research considers signaling pathways capable of assuming two or more stable states given the same amount of signaling ligand. This phenomenon of bistability can give rise to switch-like behavior, a mechanism that governs cellular decision making. Investigation of whether or not a signaling pathway can confer bistability and switch-like behavior, without knowledge of specific kinetic rate constant values, is a mathematically challenging problem. Recently a technique based on optimization has been introduced, which is capable of finding example parameter values that confer switch-like behavior for a given pathway. Although this approach has made it possible to analyze moderately sized pathways, it is limited to reaction networks that presume a uniterminal structure. It is this limited structure we address by developing a general technique that applies to any mass action reaction network with conservation laws. In this paper we developed a generalized method for detecting switch-like bistable behavior in any mass action reaction network with conservation laws. The method involves (1) construction of a constrained optimization problem using the determinant of the Jacobian of the underlying rate equations, (2) minimization of the objective function to search for conditions resulting in a zero eigenvalue, (3) computation of a confidence level that describes if the global minimum has been found and (4) evaluation of optimization values, using either numerical continuation or directly simulating the ODE system, to verify that a bistability region exists. The generalized method has been tested on three motifs known to be capable of bistability. We have developed a variation of an optimization-based method for the discovery of bistability, which is not limited to uniterminal chemical reaction networks. Successful completion of the method provides an S-shaped bifurcation diagram, which indicates that the network acts as a bistable switch for the given optimization parameters.

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

Multioutput Convolutional Neural Network for Improved Parameter Extraction in Time-Resolved Electrostatic Force Microscopy Data

Time-resolved scanning probe microscopy methods, like time-resolved electrostatic force microscopy (trEFM), enable imaging of dynamic processes ranging from ion motion in batteries to electronic dynamics in microstructured thin film semiconductors for solar cells. Reconstructing the underlying physical dynamics from these techniques can be challenging due to the interplay of cantilever physics with the actual transient kinetics of interest in the resulting signal. Previously, quantitative trEFM used empirical calibration of the cantilever or feed-forward neural networks trained on simulated data to extract the physical dynamics of interest. Both these approaches are limited by interpreting the underlying signal as a single exponential function, which serves as an approximation but does not adequately reflect many realistic systems. Here, we present a multi-branched, multi-output convolutional neural network (CNN) that uses the trEFM signal in addition to the physical cantilever parameters as input. The trained CNN accurately extracts parameters describing both single-exponential and bi-exponential underlying functions, and more accurately reconstructs real experimental data in the presence of noise. This article demonstrates an application of physics-informed machine learning to complex signal processing tasks, enabling more efficient and accurate analysis of trEFM.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Generative Physics-Informed Neural Network Solving Multi-Scale and Multi-Phase Plasma Chemical Flow Field

Low-temperature plasmas (LTPs) are non-equilibrium systems with near-room-temperature gas and highly energetic electrons. This makes them ideal for delicate applications in biomedicine and semiconductor manufacturing, enabling processes like wound healing, sterilization, etching, and plasma-enhanced chemical vapor deposition without thermal damage. However, LTPs involve complex chemistries, with hundreds of species and thousands of reactions, complicating their diagnosis, prediction, and control. Conventional diagnostics, such as Fourier-transform infrared spectroscopy (FTIR), laser-induced fluorescence (LIF), and optical emission spectroscopy (OES), offer limited species detection, while mass spectrometry (MS) struggles with low-sensitivity species. Additionally, LTP simulations face multi-scale challenges, as macroscopic fluid dynamics and microscopic particle collisions operate on vastly different timescales. To address these issues, we developed an artificial intelligence (AI) based diagnostic system: a generative physics-informed neural network (PINN-Gen) that can predict spatially resolved species concentrations and temperatures in LTPs by integrating experimental data from planar LIF with microscopic plasma chemical kinetics and macroscopic fluid mechanics, including plasma-liquid interactions at the interface between two phases. PINN-Gen solves no equations but checks the errors of physical laws by substituting the output from neural network, and the comparison with the experimental results. Thus, it naturally avoids the multi-scale difficulty of numerical simulations and predicts the results of conventionally unsolvable multi-scale and multi-phase problems. The real-time prediction will be robust due to the physical information used in the training of such a neural network, and only very limited input of condition required due to its generative feature.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Resolving the Evolution of Atomic Layer-Deposited Thin-Film Growth by Continuous In Situ X-Ray Absorption Spectroscopy

In situ synchrotron X-ray absorption near-edge structure characterization of thin-film titania growth by atomic layer deposition (ALD) over ZnO nanowires reveals persistent low-coordinated Ti motifs leading to a new picture of ALD growth. Through the design of growth and measurement cycles, Ti K-edge spectral data are continuously recorded so as to characterize the film evolution as a function of ALD cycle number and the surface changes within the time scale of the ALD cycle. A unified set of analysis tools is developed to interpret the time-series of spectral data. A prenucleation stage of growth, a transition region, and then a steady-state growth stage are observed with distinguishable features. Multivariate curve resolution analysis, that is physically constrained, demonstrates two specific spectral components with associated, time-dependent concentrations. The bulk-film component tracks the stages of growth. The surface and interface components, present throughout the stages of growth, reveal a significant coverage of relatively isolated or loosely networked tetrahedrally coordinated Ti atomic motifs. Lastly, spectral signatures for the intra-cycle growth kinetics are reconstructed at a time resolution of ~1 s and demonstrate that the transient Ti motifs on the growing surface stabilize within a few seconds of the Ti precursor pulse.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Kinetically versus thermodynamically controlled factors governing elementary pathways of GaP(111) surface oxidation

GaP and related III-V semiconductors have attracted interest as high-efficiency photoelectrochemical electrodes for solar water splitting. However, their efficacy is linked to the presence, identity, and integrity of native surface oxides, which are structurally and chemically complex and evolve during operation. Using ambient pressure X-ray photoelectron spectroscopy (APXPS) coupled with ab initio simulations, we track key chemical motifs expressed during evolution of GaP(111) surface oxides, their associated reaction kinetics, and their correlations with electronic properties. We identify two distinct thermal regimes corresponding to kinetically and thermodynamically controlled oxidation. Below 600 K, exposure to O2 generates kinetically facile Ga-O-Ga configurations, whereas higher temperatures cause activated oxygen to insert into Ga–P bonds as part of a thermodynamically driven transformation into a complex, heterogeneous 3D network of surface POx (1≤x ≤ 4) groups and Ga2O3 species, the latter of which eventually dominates upon depletion of surface phosphorus. Here our study highlights the critical competition between kinetic and thermodynamic factors during GaP oxidation, yielding insights for fabricating stable III-P-based photoelectrodes with precisely engineered surface properties.

08 HYDROGEN↗
DOE OSTI2021

Hydrodechlorination of 1,2-Dichloroethane on Platinum Catalysts: Insights from Reaction Kinetics Experiments, Density Functional Theory, and Microkinetic Modeling

Catalytic hydrodechlorination is a promising strategy for treating industrial 1,2-dichloroethane wastes, for which Pt and Pt-based alloy catalysts are widely used. Here, we performed a detailed mechanistic study for 1,2-dichloroethane hydrodechlorination on Pt using a synergistic approach combining density functional theory (DFT) calculations, reaction kinetics experiments, and microkinetic modeling. Using planewave DFT calculations, we evaluated the reaction energy and activation energy barrier of each elementary step involved in the reaction network on Pt(111). The calculated energetics were then incorporated into a comprehensive mean-field microkinetic model accounting for a total of 65 elementary steps. The model-predicted reaction rates were compared with the results from our reaction kinetics experiments on SiO2-supported Pt catalysts. Our results indicated that the hydrodechlorination of 1,2-dichloroethane on Pt(111) starts with a H-removal step; then, it proceeds through a sequence of alternating dechlorination and dehydrogenation steps until vinylidene (CH 2 C*) is formed; finally, CH 2 C* is hydrogenated to the final product, ethane, sequentially via vinyl (CH 2 CH*), ethylene, and ethyl (CH 3 CH 2 *) intermediates. After model parameter adjustments, we achieved good agreement between our theoretical model and experimental results; the adjustments to the calculated parameters are consistent with the typically anticipated coverage effects. Furthermore, our study offers valuable mechanistic insights, which are useful for improving catalysts for this chemistry.

1,2-dichloroethane↗
DOE OSTI2020

Physics-guided machine learning approaches to predict the ideal stability properties of fusion plasmas

One of the biggest challenges to achieve the goal of producing fusion energy in tokamak devices is the necessity of avoiding disruptions of the plasma current due to instabilities. The disruption event characterization and forecasting (DECAF) framework has been developed in this purpose, integrating physics models of many causal events that can lead to a disruption. Two different machine learning approaches are proposed to improve the ideal magnetohydrodynamic (MHD) no-wall limit component of the kinetic stability model included in DECAF. First, a random forest regressor (RFR), was adopted to reproduce the DCON computed change in plasma potential energy without wall effects for a large database of equilibria from the national spherical torus experiment (NSTX). This tree-based method provides an analysis of the importance of each input feature, giving an insight into the underlying physics phenomena. Secondly, a fully-connected neural network has been trained on sets of calculations with the DCON code, to get an improved closed form equation of the no-wall β limit as a function of the relevant plasma parameters indicated by the RFR. The neural network has been guided by physics theory of ideal MHD in its extension outside the domain of the NSTX experimental data. The estimated value has been incorporated into the DECAF kinetic stability model and tested against a set of experimentally stable and unstable discharges. Moreover, the neural network results were used to simulate a real-time stability assessment using only quantities available in real-time. Finally, the portability of the model was investigated, showing encouraging results by testing the NSTX-trained algorithm on the mega ampere spherical tokamak (MAST).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

From Nonclassical to Classical: Crystallization Seeds Reshape Nucleation Mechanisms

Crystalline seeds are widely employed in crystallization to accelerate nucleation and control product polymorphs; yet, their impact on nucleation mechanisms remains poorly understood. While homogeneous nucleation of crystals from solution often proceeds through nonclassical pathways involving amorphous intermediates, it is unclear how seeds that promote heterogeneous nucleation reshape these mechanisms and govern polymorph selection. Here, in this study, we provide the first direct evidence that crystalline seeds can bypass the need for amorphous intermediates as nucleation sites, converting nonclassical nucleation mechanisms into classical, monomer-by-monomer crystallization pathways. Using molecular dynamics simulations of zeolite synthesis, we uncover a complex reaction network of competing nucleation processes mediated by intermediate interfacial polymorphs. The interplay between thermodynamic stability and kinetic favorability of these interfacial polymorphs dictates nucleation outcomes, creating a dynamic balance between the interfacial polymorph stability and crystallization rates. Furthermore, we show that the synthesis environment-whether monomers or aggregates serve as reactants-profoundly impacts these pathways. At moderate supersaturation, seeds eliminate amorphous intermediates and promote classical nucleation, whereas high supersaturation or aggregate-based reactants favor nonclassical pathways, even in the presence of seeds. These findings establish a general framework for understanding how seeds govern crystallization mechanisms, with broad implications for controlling nucleation kinetics, polymorph selection, and material properties. While focused on zeolites, this work reveals insights that may be applicable to biominerals, pharmaceuticals, functional materials, and catalysts, providing a basis for engineering crystallization pathways in diverse applications.

Chu-Jon, Carlos [Univ. of Utah, Salt Lake City, UT↗