Search NASA⌕ Search

Engineering topics

Luijten, Erik

Publications and source records attributed to Luijten, Erik.

11 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Influence of Pore Length on Hydrogenolysis of Polyethylene within a Mesoporous Support Architecture

Due to the plastic waste crisis, selective chemical upcycling of polyolefins into value-added products is a topic of intense interest, demanding polymer deconstruction processes that afford control over the product chain lengths. Recently, a catalytic architecture was synthesized in which a polyolefin melt infiltrates a porous support, and its chains are cleaved by a metal nanoparticle catalyst at the bottom of the pores, yielding a narrow distribution of alkane products. Although the influence of various parameters of these catalytic materials, including the effects of the nanoparticle size and pore diameter on product chain length, has been examined before, here, we investigate the role of the pore length in the cleavage process through the first study that combines catalytic hydrogenolysis and coarse-grained modeling to gain insights not available by experiment alone. We show that the pore length can permit control over the average product length with qualitative agreement between experiment and simulation. In conclusion, we go beyond this observation to uncover the dynamic phenomenon responsible for the pore-length dependence of the cleavage products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Treasuring trash: Pt/SrTiO 3 catalysts process plastic waste into high-value materials

Single-use plastics are ubiquitous throughout modern society because they have properties that make them desirable in a wide variety of applications, including low cost of production, high thermal and chemical stability, and tunable mechanical properties. Several of the recycling methods currently used to process single-use plastic waste, such as mechanical recycling and pyrolysis, do not selectively process plastic materials into uniform products, as would be advantageous for repurposing these materials in pursuit of a circular economy. Catalytic hydrogenolysis, whereby C–C bonds in polymers are broken over a supported catalyst in the presence of H 2 , is a promising approach to converting plastics into value-added products that can be sold commercially without further purification. Here, in this account, we present some recent advances in polyolefin hydrogenolysis, with a focus on Pt nanoparticles on SrTiO 3 nanocuboid supports (Pt/STO). Current developments in this field, general challenges, and future directions are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Two Mesoporous Domains Are Better Than One for Catalytic Deconstruction of Polyolefins

Catalytic hydrogenolysis of polyolefins into valuable liquid, oil, or wax-like hydrocarbon chains for second-life applications is typically accompanied by the hydrogen-wasting co-formation of low value volatiles, notably methane, that increase greenhouse gas emissions. Catalytic sites confined at the bottom of mesoporous wells, under conditions in which the pore exerts the greatest influence over the mechanism, are capable of producing less gases than unconfined sites. A new architecture was designed to emphasize this pore effect, with the active platinum nanoparticles embedded between linear, hexagonal mesoporous silica and gyroidal cubic MCM-48 silica (mSiO 2 /Pt/MCM-48). This catalyst deconstructs polyolefins selectively into ~C 20 –C 40 paraffins and cleaves C–C bonds at a rate (TOF = 4.2 ± 0.3 s –1 ) exceeding that of materials lacking these combined features while generating negligible volatile side products including methane. The time-independent product distribution is consistent with a processive mechanism for polymer deconstruction. In contrast to time- and polymer length-dependent products obtained from non-porous catalysts, mSiO 2 /Pt/MCM-48 yields a C 28 -centered Gaussian distribution of waxy hydrocarbons from polyolefins of varying molecular weight, composition, and physical properties, including low-density polyethylene, isotactic polypropylene, ultrahigh-molecular-weight polyethylene, and mixtures of multiple, post-industrial polyolefins. Here, coarse-grained simulation reveals that the porous-core architecture enables the paraffins to diffuse away from the active platinum site, preventing secondary reactions that produce gases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Phase separation and ripening in a viscoelastic gel

The process of phase separation in elastic solids and viscous fluids is of fundamental importance to the stability and function of soft materials. We explore the dynamics of phase separation and domain growth in a viscoelastic material such as a polymer gel. Using analytical theory and Monte Carlo simulations, we report a domain growth regime in which the domain size increases algebraically with a ripening exponent α that depends on the viscoelastic properties of the material. For a prototypical Maxwell material, we obtain α = 1 , which is markedly different from the well-known Ostwald ripening process with α = 1 / 3 . We generalize our theory to systems with arbitrary power-law relaxation behavior and discuss our findings in the context of the long-term stability of materials as well as recent experimental results on phase separation in cross-linked networks and cytoskeleton.

Science & Technology - Other Topics↗
DOE OSTI2023

Mechanistic Insights into Processive Polyethylene Hydrogenolysis through In Situ NMR

Chemical polymer upcycling by processive catalysts is a promising plastic waste remediation strategy, with the capability of producing selective, high-value products from waste plastics with minimal energy input. We previously designed a novel processive catalyst with a mesoporous SiO 2 shell/Pt nanoparticle/SiO 2 core architecture (mSiO 2 /Pt/SiO 2 ) that deconstructs polyolefins within narrow pores. Here, in this paper, we elucidate the mechanism of processive polyolefin hydrogenolysis using in situ magic-angle spinning (MAS) nuclear magnetic resonance (NMR) spectroscopy and coarse-grained molecular dynamics simulations. We observe that most polyethylene–Pt interactions do not lead to C–C bond cleavage but rather to the release of the polymer via a dehydrogenation–rehydrogenation cycle. The porous architecture increases the likelihood that a released polymer is later cleaved and enables the catalyst to perform multiple successive cleavages to the same polymer chain. Both experiment and simulation show that the extent of processivity is strongly correlated with the length of the pores, with longer pores leading to a higher processivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Unravelling crystal growth of nanoparticles

Crystal growth from nanoscale constituents is a ubiquitous phenomenon in biology, geology and materials science. Numerous studies have focused on understanding the onset of nucleation and on producing high-quality crystals by empirically sampling constituents with different attributes and varying the growth conditions. However, the kinetics of post-nucleation growth processes, an important determinant of crystal morphology and properties, have remained underexplored due to experimental challenges associated with real-space imaging at the nanoscale. Here we report the imaging of the crystal growth of nanoparticles of different shapes using liquid-phase transmission electron microscopy, resolving both lateral and perpendicular growth of crystal layers by tracking individual nanoparticles. We observe that these nanoscale systems exhibit layer-by-layer growth, typical of atomic crystallization, as well as rough growth prevalent in colloidal systems. Surprisingly, the lateral and perpendicular growth modes can be independently controlled, resulting in two mixed crystallization modes that, until now, have received only scant attention. Combining analytical considerations with molecular dynamics and kinetic Monte Carlo simulations, we develop a comprehensive framework for our observations, which are fundamentally determined by the size and shape of the building blocks. Furthermore, these insights unify the understanding of crystal growth across four orders of magnitude in particle size and suggest novel pathways to crystal engineering.

Imaging techniques↗
DOE OSTI2022

Seeking regularity from irregularity: unveiling the synthesis–nanomorphology relationships of heterogeneous nanomaterials using unsupervised machine learning

Nanoscale morphology of functional materials determines their chemical and physical properties. However, despite increasing use of transmission electron microscopy (TEM) to directly image nanomorphology, it remains challenging to quantify the information embedded in TEM data sets, and to use nanomorphology to link synthesis and processing conditions to properties. We develop an automated, descriptor-free analysis workflow for TEM data that utilizes convolutional neural networks and unsupervised learning to quantify and classify nanomorphology, and thereby reveal synthesis–nanomorphology relationships in three different systems. While TEM records nanomorphology readily in two-dimensional (2D) images or three-dimensional (3D) tomograms, we advance the analysis of these images by identifying and applying a universal shape fingerprint function to characterize nanomorphology. After dimensionality reduction through principal component analysis, this function then serves as the input for morphology grouping through unsupervised learning. We demonstrate the wide applicability of our workflow to both 2D and 3D TEM data sets, and to both inorganic and organic nanomaterials, including tetrahedral gold nanoparticles mixed with irregularly shaped impurities, hybrid polymer-patched gold nanoprisms, and polyamide membranes with irregular and heterogeneous 3D crumple structures. In each of these systems, unsupervised nanomorphology grouping identifies both the diversity and the similarity of the nanomaterial across different synthesis conditions, revealing how synthetic parameters guide nanomorphology development. Our work opens possibilities for enhancing synthesis of nanomaterials through artificial intelligence and for understanding and controlling complex nanomorphology, both for 2D systems and in the far less explored case of 3D structures, such as those with embedded voids or hidden interfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Hybrid Nanocrystals of Small Molecules and Chemically Disordered Polymers

Organic crystals formed by small molecules can be highly functional but are often brittle or insoluble structures with limited possibilities for use or processing from a liquid phase. A possible solution is the nanoscale integration of polymers into organic crystals without sacrificing long-range order and therefore function. This enables the organic crystals to benefit from the advantageous mechanical and chemical properties of the polymeric component. We report here on a strategy in which small molecules co-crystallize with side chains of chemically disordered polymers to create hybrid nanostructures containing a highly ordered lattice. Synchrotron X-ray scattering, absorption spectroscopy, and coarse-grained molecular dynamics simulations reveal that the polymer backbones form an “exo-crystalline” layer of disordered chains that wrap around the nanostructures becoming a handle for interesting properties. The morphology of this “hybrid bonding polymer” nanostructure is dictated by the competition between the polymers’ entropy and the enthalpy of the lattice allowing for control over the aspect ratio of the nanocrystal by changing the degree of polymer integration. Further, we observed that nanostructures with an exo-crystalline layer of polymer exhibit enhanced fracture strength, self-healing capacity, and dispersion in water, which benefits their use as light-harvesting assemblies in photocatalysis. Guided by computation, future work could also explore these hybrid nanostructures as building blocks for functional materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Hydrodynamically Controlled Self‐Organization in Mixtures of Active and Passive Colloids

Abstract Active particles are known to exhibit collective behavior and induce structure in a variety of soft‐matter systems. However, many naturally occurring complex fluids are mixtures of active and passive components. The authors examine how activity induces organization in such multi‐component systems. Mixtures of passive colloids and colloidal micromotors are investigated and it is observed that even a small fraction of active particles induces reorganization of the passive components in an intriguing series of phenomena. Experimental observations are combined with large‐scale simulations that explicitly resolve the near‐ and far‐field effects of the hydrodynamic flow and simultaneously accurately treat the fluid–colloid interfaces. It is demonstrated that neither conventional molecular dynamics simulations nor the reduction of hydrodynamic effects to phoretic attractions can explain the observed phenomena, which originate from the flow field that is generated by the active colloids and subsequently modified by the aggregating passive units. These findings not only offer insight into the organization of biological or synthetic active–passive mixtures, but also open avenues to controlling the behavior of passive building blocks by means of small amounts of active particles.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Formation and surface melting of nanoparticle superlattices in a solution

The wisdom in the saying of “There are no two snowflakes alike” lies in the importance of history or kinetic pathways in the phase transitions of solids. Likewise, “artificial solids,” namely superlattices consisting of functional nanoparticles, have lattice size, surface morphology, crystallinity, symmetry, and structural reconfiguration (for example, transition into a disordered state) highly dependent on the kinetic pathways as the nanoparticles interact with each other in solution [1]. Great progresses have been made in understanding the formation pathways of superlattices using liquid-phase transmission electron microscopy (TEM) [2-4]. For example, by tracking single nanoparticle’s trajectories, especially aided by U-net neural network-based machine learning, previous studies mapped the fundamental nanoparticle interactions at nanometer resolution [5]. Nonclassical, two-step nucleation pathway has also been elucidated in the system of nanoprisms, by optimizing protocols such as loading nanoparticle suspensions over the supersaturation threshold and minimizing particle‒substrate interaction [2]. Surface morphologies or exposed facets of superlattices have been shown to follow the principles of Wulff construction rule, where the facet-dependent surface energy can be measured based on the capillary wave theory [4]. However, the reverse process of crystallization of superlattices, the conversion from crystalline to disordered state, has been much less explored. On one hand, the melting of nanoparticle superlattices can provide a preferred pathway to induce structural reorganization or shuffling of building blocks for them to transform into different types of crystal structures. On the other hand, understanding nanoscale superlattice melting and comparing such behaviors with the prevailing surface melting theories developed for atomic/molecular solids can provide a potent way to engineer phase transitions of supra- and hierarchical structures constructed from nanoscale entities (e.g., DNA-coated nanoparticles, proteins), for their applications in reprogrammable and switchable materials with multifunctional properties [6, 7]. The experimental challenges to observe melting of superlattices are twofold. Practically it is difficult to load the initial superlattice form, in an intact manner, into the highly confined liquid-phase TEM chamber for in-situ observation. Here, the triggering of melting also needs meticulous manipulation of nanoparticle concentration, interparticle interaction, and solution environment.

Kim, Ahyoung↗