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DOE OSTI2023

Rheology guiding the design and printability of aqueous colloidal composites for additive manufacturing

Vat photopolymerization (VP) and direct ink write (DIW) additive manufacturing (AM) provide complex geometries with precise spatial control employing a vast array of photo-reactive polymeric systems. Although VP is recognized for superior resolution and surface finish, DIW provides versatility for higher viscosity systems. However, each AM platform presents specific rheological requirements that are essential for successful 3D printing. First, viscosity requirements constrain VP polymeric materials to viscosities below 10 Pa s. Thus, this requirement presents a challenging paradox that must be overcome to attain the physical performance of high molecular weight polymers while maintaining suitable viscosities for VP polymeric materials. Second, the necessary rheological complexity that is required for DIW pastes requires additional rheological measurements to ensure desirable thixotropic behavior. Further, this manuscript describes the importance of rheological measurements when designing polymeric latexes for AM. Latexes effectively decouple the dependency of viscosity on molecular weight, thus enabling high molecular weight polymers with low viscosities. Photo-crosslinking of water-soluble monomers and telechelic oligomeric diacrylates in the presence of the latex enables the fabrication of a scaffold, which is restricted to the continuous aqueous phase and effectively surrounds the latex nanoparticles enabling the printing of otherwise inaccessible high molecular weight polymers. Rheological testing, including both steady and oscillatory shear experiments, provides insights into system properties and provides predictability for successful printing. This perspective article aims to provide an understanding of both chemical functionality (photo- and thermal-reactivity) and rheological response and their importance for the successful design and evaluation of VP and DIW processable latex formulations.

36 MATERIALS SCIENCE↗

DOE OSTI2023

One-Component, Vinyl-Functional Fluorosilicone Pastes for Direct Ink Write Additive Manufacturing

In this study, one-component (“all-in-one”) fluorosilicone pastes specifically designed for application in direct ink write (DIW) additive manufacturing (AM) have been developed. They consist of vinyl-functional, methyltrifluoropropyl-containing (MeTfpS) terpolysiloxane base polymer(s), hydrosilyl-functional cross-linker(s), UV-sensitive hydrosilylation catalyst, thixotropic additive, and trimethylsilylated fumed silica filler, with other additives that are optional. Their base polymers were completely amorphous and linear, with molecular weights (M n ) ranging from approximately 7000 to 50,000 and specifically targeted for the viscosity range ideal for DIW. These polymers were prepared by anionic ring-opening polymerizations (A-ROP) of appropriate mixtures of cyclic siloxanes initiated by oligomeric, difunctional tetramethylammonium silanolate in the presence of a difunctional tetramethyldivinyldisiloxane end-blocker and molecular weight regulator. Two series of these terpolymers were prepared: one containing 5 mol % MeTfpS repeat units but varying degrees of polymerization (DP) from 80 to 600 and the other containing 5, 10, and 15 mol % MeTfpS at a constant DP of 600. All polymers contained mostly random distributions of MeTfpS repeat units. The pastes were prepared by speed-mixing, ready for use in DIW, they exhibited ideal thixotropy, long-term (over a year) rheological stability, and can be quickly (within seconds) and permanently cured by room-temperature, UV-activated hydrosilylation in air. The resulting elastomers showed base polymer molecular weight- and composition-dependent stress-induced crystallization (SIC) at higher molecular weights and lower trifluoropropyl contents and tunable Shore A hardness (30A–40A) typical for soft polysiloxane rubbers. Preliminary DIW tests indicated excellent printability with high precision of printed shapes and clearly present these one-component pastes as prime candidates for simple and practical printing of silicone elastomer objects.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Drift-ordered fluid vorticity equation with energy consistency

Although drift-ordered fluid models are widely applied in tokamak edge turbulence simulations, the models used are acknowledged not to conserve energy or even electrical charge. The present paper aims to remove many of the existing pitfalls in drift-fluid models, however, with the objective of finding a solution simple enough to be implemented in numerical applications. Our main result is an improved version of the drift-Braginskii equations involving a generalized vorticity function. In the new drift-Braginskii system, the quasi-neutrality condition translates into a transport equation for a generalized vorticity, expressed in conservation form, and related to the total mass-weighted circulation. It is found that kinetic energy conservation can be achieved if the polarization flow is defined recursively. In conclusion, the resulting model conserves the kinetic energy associated with E×B and diamagnetic flows and retains the associated perpendicular kinetic energy flux.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Imaging systematics and clustering of DESI main targets

ABSTRACT We evaluate the impact of imaging systematics on the clustering of luminous red galaxies (LRG), emission-line galaxies (ELG), and quasars (QSO) targeted for the upcoming Dark Energy Spectroscopic Instrument (DESI) survey. Using Data Release 7 of the DECam Legacy Survey, we study the effects of astrophysical foregrounds, stellar contamination, differences between north galactic cap and south galactic cap measurements, and variations in imaging depth, stellar density, galactic extinction, seeing, airmass, sky brightness, and exposure time before presenting survey masks and weights to mitigate these effects. With our sanitized samples in hand, we conduct a preliminary analysis of the clustering amplitude and evolution of the DESI main targets. From measurements of the angular correlation functions, we determine power law fits $r_0 = 7.78 \pm 0.26\, h^{-1}$Mpc, γ = 1.98 ± 0.02 for LRGs and $r_0 = 5.45 \pm 0.1\, h^{-1}$Mpc, γ = 1.54 ± 0.01 for ELGs. Additionally, from the angular power spectra, we measure the linear biases and model the scale-dependent biases in the weakly non-linear regime. Both sets of clustering measurements show good agreement with survey requirements for LRGs and ELGs, attesting that these samples will enable DESI to achieve precise cosmological constraints. We also present clustering as a function of magnitude, use cross-correlations with external spectroscopy to infer dN/dz and measure clustering as a function of luminosity, and probe higher order clustering statistics through counts-in-cells moments.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Fused filament fabrication of polymer composites for extreme environments

Vast improvements have been made to the capabilities of advanced manufacturing (AM), yet there are still limitations on which materials can effectively be used in the technology. To this end, parts created using AM would benefit from the ability to be developed from feedstock materials incorporating additional functionality. A common three-dimensional (3D) printing polymer, acrylonitrile butadiene styrene, was combined with bismuth and polyvinylidene fluoride via a solvent treatment to fabricate multifunctional composite materials for AM. Composites of varying weight percent loadings were extruded into filaments, which were subsequently 3D printed into blocks via fused filament fabrication. Investigating the material properties demonstrated that in addition to the printed blocks successfully performing as radiation shields, the chemical, thermal, and mechanical properties are suitable for AM. Thus, this work demonstrates that it is possible to enhance AM components with augmented capabilities while not significantly altering the material properties which make AM possible.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Advanced sorbents for modular oxygen production for REMS gasifiers

Under a DOE funded effort, Thermosolv LLC has been developing a sorbent-based oxygen production technology for gasification and oxy-combustion applications. The sorbents utilize oxygen-storage properties of certain perovskites to (1) selectively adsorb oxygen at moderate temperature from compressed air and (2) release the adsorbed oxygen into a vacuum or a sweep gas such as CO2 and/or steam. Through cyclic operations of multiple sorbent beds such a process can be made continuous. Pressure drop across the sorbent bed and other similar design considerations dictate that the sorbent be in the form of a high surface-area-to-volume pellet and yet still possess sufficient crush strength and integrity. Process efficiency is determined by the sorption and desorption kinetics and sorbent capacity. Typical process involves cycle times in the order of 100 seconds, a time too short to fully utilize the full volume of the sorbent and the sorption capacity of the sorbent pellets. As a part of this project, Thermosolv LLC undertook the development of oxygen sorbents as a high-surface area supported sorbent on a light-weight inert support to (1) reduce the cost, (2) increase the productivity, and (3) reduce the overall weight of the reactor. Using a previously developed perovskite (LSCF 1991) with its well-characterized performance, composite sorbent pellets each consisting of an inert core coated by a thin layer of the functional perovskite material were produced. Several low-density, inexpensive inert solids supports in the 1/8”-1/4” size were selected to keep the pressure drop across the sorbent bed in the acceptable range. A number of commercial technologies including spray coating, dip coating, incipient impregnation of porous substrate followed by thermal annealing at various temperatures and duration were employed and tested to produce robust composite supported sorbent pellets. A sense of optimum coating thickness was developed by testing sorbent pellets of various increasing diameter pellets. LSCF-1991extrudates ranging in size from 1/32” to 3/16” were tested in a TGA and in a fixed-bed pressure swing test set-up for sorption/desorption cycles of interest. The data show that for the operational conditions of interest the coating thickness for a supported sorbent pellet needs to be approximately 1/32” (about 0.8 mm). Subsequent work thereby concentrated on developing composite pellets of various substrates, shapes and sizes with coating layer of about this size range. Candidate support materials were chosen based on cost, inertness, mechanical strength, thermal expansion and chemical stability with sorbent material, and in a size range to give an acceptable pressure drop in fixed-bad reactor configurations. Coating application methods used for application of the sorbent onto the support included precipitation, spray coating and dip coating from sorbent slurries and sol gels. For all coated supports where we could successfully apply a uniform coating of desired thickness with an acceptable handling performance in terms of exfoliation, TGA-based cyclic sorption/desorption testing was performed to determine cycling capacity of oxygen. Among nearly twenty different support materials tested, best adhesion performance was obtained from stainless saddle supports. In the bench-scale fixed-bad tests stainless steel saddle support composite pellets with a coating thickness in the 0.6 mm or so range, the composite sorbent pellet performance approached up to 95% of that of the 2 mm parent material pellets. In a parallel approach to reducing the cost of the sorbent, alternate sorbent formulations replacing/reducing the amount of cobalt in the LSCF family were also investigated. Successful formulations that could match the performance of LSCF 1991 were identified based on TGA cyclic tests as LSCF 1919 and LSF 1910. In the range of operational envelop of cycle times and other relevant process operating conditions, the reduced Co formulations showed comparable performance in the bench-scale fixed-bad cyclic operations. As a part of this project, we also attempted substitution of La and Sr with Ca and Ba, and Co with Mn, Ni and Cu with little success, but the overall project goal of reducing sorbent cost in terms of raw material and manufacturing expenses was successful.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2026

Toxicity of Common Fluoropolymers and PFAS on C. Elegans and an Innovative Strategy to Promote in Situ Remediation - 26128

Per- and polyfluoroalkyl substances (PFAS) are a broad class of synthetic chemicals used in a variety of modern technologies and consumer products. PFAS have an alkyl backbone with all or most of the hydrogen (H) atoms replaced with fluorine (F) atoms. PFAS generally fall into one of two categories, they can be non-polymeric with relatively low molecular weights, or long-chain polymers. PFAS are a diverse group of organic compounds that can be solids, liquids, dispersions, or gases. Their mobility and toxicity are influenced by their chain lengths and functional groups. Non-polymeric PFAS are common building blocks for the manufacturing of fluoropolymers. Non-polymeric PFAS are known to be mobile in the environment and some are considered contaminants of emerging concern. Polymeric PFAS, or fluoropolymers, are typically thought to be of low concern, however few systematic investigations into their toxicity have been completed. PFAS, including fluoropolymers and microplastics, have been found in pristine environments and animals located far from site of origin including the arctic and deep ocean.

Jacobs, Stephanie [Savannah River National Laborat↗

DOE OSTI2026

Assessing Structural, Thermal, and Functional Characteristics of Marigold Flower Protein as a Sustainable Food Ingredient

The demand for sustainable and alternative protein sources has been on the rise, driving interest in the valorization of underutilized plants. This study evaluated Calendula officinalis (marigold), a common floral waste, as a sustainable alternative protein source for the food industry. The primary objective of this study was to investigate the physicochemical properties of protein fractions from Calendula officinalis flower to evaluate their potential as a novel protein ingredient. Extraction of the Calendula officinalis flower yielded 92.17% of the crude protein. A sequential extraction of albumin, globulin, glutelin, and prolamin from marigold flower revealed albumin as the dominant fraction (65.47%) and exhibited the highest protein functionality, including water-holding capacity (2.37 g/g), oil-holding capacity (2.49 g/g), and emulsifying capacity (65.22 mL/g). Compared with other protein fractions, glutelin showed a relatively high emulsifying and foaming capacity (EC: 59.13 mL/g; FC: 16.23%). Differential scanning calorimetry revealed high thermal stability for albumin (T p = 105.28 °C) and glutelin (T p = 97.6 °C). Sodium Dodecyl Sulfate–Polyacrylamide Gel Electrophoresis (SDS-PAGE) and Liquid Chromatography–Mass Spectrometry (LC-MS) confirmed the presence of abundant low-molecular-weight polypeptides (<37 kDa), which enhanced emulsification, while scanning electron microscopy revealed porous structures aligned with hydration properties. Antioxidant activity was higher in albumin and glutelin, linked to surface hydrophobicity. LC-MS/MS identified 33 short-chain proteins, including oxidoreductase proteins and lipid-transfer proteins. Findings highlight marigold flower proteins as a sustainable, functional ingredient for a diverse range of food applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Shape‐Dependent Multi‐Weight Magnetic Artificial Synapses for Neuromorphic Computing

Abstract In neuromorphic computing, artificial synapses provide a multi‐weight (MW) conductance state that is set based on inputs from neurons, analogous to the brain. Herein, artificial synapses based on magnetic materials that use a magnetic tunnel junction (MTJ) and a magnetic domain wall (DW) are explored. By fabricating lithographic notches in a DW track underneath a single MTJ, 3–5 stable resistance states that can be repeatably controlled electrically using spin‐orbit torque are achieved. The effect of geometry on the synapse behavior is explored, showing that a trapezoidal device has asymmetric weight updates with high controllability, while a rectangular device has higher stochasticity, but with stable resistance levels. The device data is input into neuromorphic computing simulators to show the usefulness of application‐specific synaptic functions. Implementing an artificial neural network (NN) applied to streamed Fashion‐MNIST data, the trapezoidal magnetic synapse can be used as a metaplastic function for efficient online learning. Implementing a convolutional NN for CIFAR‐100 image recognition, the rectangular magnetic synapse achieves near‐ideal inference accuracy, due to the stability of its resistance levels. This work shows MW magnetic synapses are a feasible technology for neuromorphic computing and provides design guidelines for emerging artificial synapse technologies.

Leonard, Thomas↗

DOE OSTI2024

A phase-field diffraction model for thermo-hydro-mechanical propagating fractures

This paper introduces a novel diffraction based thermo-hydraulic–mechanical (THM) model for fracture propagation using a phase-field fracture (PFF) approach. The key innovation of the THM-PFF model lies in its integrated treatment of four solution variables—displacements, phase-field, pressure, and temperature—each governed by a combination of conservation of momentum (mechanics problem), a variational inequality (constrained minimization problem), mass conservation (pressure problem), and energy conservation (temperature problem). This leads to a new formulation of a coupled variational inequality system. A major advancement is the development of an extended fixed-stress algorithm, where displacements, phase-field, pressures, and temperatures are solved in a staggered sequence. An important aspect of this work is the global coupling of pressures and temperatures across the domain using diffraction systems, with diffraction coefficients defined by material parameters weighted by the diffusive phase-field variable. To ensure robust local mass conservation, we employ enriched Galerkin finite elements (EG) for both pressure and temperature diffraction equations. By enriching the continuous Galerkin basis functions with discontinuous piecewise constants, EG accurately represents solution and parameter discontinuities while preserving local mass and energy conservation—crucial aspects for THM problems and realistic behavior. Moreover, the use of a predictor–corrector local mesh adaptivity scheme is employed, allowing the model to handle small phase-field length-scale parameters while maintaining high numerical accuracy and reasonable computational cost. Furthermore, these new model and algorithmic developments represent significant advances in the field and have been substantiated through rigorous numerical tests.

Diffraction systems↗

DOE OSTI2025

Fast-RF-Shimming: Accelerate RF shimming in 7T MRI using deep learning

Ultrahigh field (UHF) Magnetic Resonance Imaging (MRI) offers an elevated signal-to-noise ratio (SNR), enabling exceptionally high spatial resolution that benefits both clinical diagnostics and advanced research. However, the jump to higher fields introduces complications, particularly transmit radiofrequency (RF) field ($B^{+}_{1}$) inhomogeneities, manifesting as uneven flip angles and image intensity irregularities. These artifacts can degrade image quality and impede broader clinical adoption. Traditional RF shimming methods, such as Magnitude Least Squares (MLS) optimization, effectively mitigate $B^{+}_{1}$ inhomogeneity, but remain time-consuming. Recent machine learning approaches, including RF Shim Prediction by Iteratively Projected Ridge Regression and other deep learning architectures, suggest alternative pathways. Although these approaches show promise, challenges such as extensive training periods, limited network complexity, and practical data requirements persist. In this paper, we introduce a holistic learning-based framework called Fast-RF-Shimming, which achieves a 5000 × speed-up compared to the traditional MLS method. In the initial phase, we employ random-initialized Adaptive Moment Estimation (Adam) to derive the desired reference shimming weights from multi-channel $B^{+}_{1}$ fields. Next, we train a Residual Network (ResNet) to map $B^{+}_{1}$ fields directly to the ultimate RF shimming outputs, incorporating the confidence parameter into its loss function. Finally, we design Non-uniformity Field Detector (NFD), an optional post-processing step, to ensure the extreme non-uniform outcomes are identified. Comparative evaluations with standard MLS optimization underscore notable gains in both processing speed and predictive accuracy, which indicates that our technique shows a promising solution for addressing persistent inhomogeneity challenges.

Deep learning↗

DOE OSTI2025

Understanding Trigger Linkage Dynamics in Energetic Materials Using Mixed Picramide Nitrate Ester Explosives

The ability to predict the handling sensitivity of new organic energetic materials has been a longstanding goal. We report the synthesis and characterization of six new nitropicramide energetic materials with mixed functional groups that mimic known explosives such as nitroglycerin, erythritol tetranitrate (ETN), and pentaerythritol tetranitrate (PETN). The molecules have been studied theoretically using quantum molecular dynamics (QMD) simulations and density functional theory (DFT) calculations to identify the weakest bond in the reactants - the trigger-linkages - which control handling sensitivity, and to quantify their specific enthalpies of explosion. In good accord with the drop weight impact sensitivity data, our calculations predict that the sensitivities of the molecules are very similar owing to the small variations of the energy output and rates of trigger linkage rupture. In addition, both the QMD and DFT calculations point to the nitropicramide N–NO 2 bonds as the trigger linkages rather than the more typical O–NO 2 bonds. We propose that the switch of the trigger linkage from the nitrate esters to the nitramine groups arises from the strongly electron withdrawing character of the adjacent trinitrobenzene groups.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Morphological traits for allometric scaling of the European Sea Bass Dicentrarchus labrax (Linnaeus, 1758) from Southern Portugal population

Abstract The present study aimed to determine the allometric scaling among a selection of morphological traits in European sea bass ( Dicentrarchus labrax ) to estimate fish body weight. A set of morphological traits (fish body weight, length, height, and width) were directly measured in 146 fish of a recirculating aquaculture system, with body weights ranging from 17.11 to 652.21 g. In addition, a collection of digital imagery of each anesthetized fish from the side and top views were used to estimate other traits (indirect measures). Multiple regression analysis and regression coefficients were calculated using all possible combinations of biometric data (predictors) to estimate fish body weight, applying different numerical fitting models (linear, log‐linear, quadratic, exponential). The results showed that the best combination of traits for estimating fish body weight were fish body width, length and height, collected from direct measure ( R 2 = 0.995), for a log‐linear model fitting, which revealed more accurate determinations than the most commonly used length–weight relationship. Nevertheless, other combinations of morphological traits and fitting models were also found to be suitable in successfully predict fish body weight, with variability ranging between 92.5% and 98.5%. For indirect measures, the best predictor was a combination of traits from top view (width, eye distance and area without fins) fitted with a log‐linear function. These results comprise a relevant baseline in supporting the high potential of noninvasive methods to accurately follow the growth of European sea bass juveniles, recurring to imagery analysis of anesthetized fish. It has major potential applications in feeding consumption trials and fish growth models, as it allows for continuously following up fish growth under different experimental conditions without therein distress derived from manipulation.

Azevedo, Ana↗

DOE OSTI2025

Describing hadronization via histories and observables for Monte-Carlo event reweighting

We introduce a novel method for extracting a fragmentation model directly from experimental data without requiring an explicit parametric form, called Histories and Observables for Monte-Carlo Event Reweighting (HOMER), consisting of three steps: the training of a classifier between simulation and data, the inference of single fragmentation weights, and the calculation of the weight for the full hadronization chain. We illustrate the use of HOMER on a simplified hadronization problem, a q\bar{q} q q ‾ string fragmenting into pions, and extract a modified Lund string fragmentation function f(z) f ( z ) . We then demonstrate the use of HOMER on three types of experimental data: (i) binned distributions of high-level observables, (ii) unbinned event-by-event distributions of these observables, and (iii) full particle cloud information. After demonstrating that f(z) f ( z ) can be extracted from data (the inverse of hadronization), we also show that, at least in this limited setup, the fidelity of the extracted f(z) f ( z ) suffers only limited loss when moving from (i) to (ii) to (iii). Public code is available at https://gitlab.com/uchep/mlhad.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Velocity-Space Hybridization of Direct Simulation Monte Carlo and a Quasi-Particle Boltzmann Solver

This paper presents a new method for modeling rarefied gas flows based on hybridization of direct simulation Monte Carlo (DSMC) and discrete velocity method (DVM)-based quasi-particle representations of the velocity distribution function. It is aimed at improving the resolution of the tails of the distribution function (compared with DSMC) and computational efficiency (compared with DVM). Details of the method, such as the collision algorithm and the particle merging scheme, are discussed. The hybrid approach is applied to the study of noise in a Maxwellian distribution, computation of electron-impact ionization rate coefficient, as well as numerical simulation of a supersonic Couette flow. The hybrid-based solver is compared with pure DSMC and DVM approaches in terms of accuracy, computational speed, and memory use. It is shown that such a hybrid approach can provide a lower computational cost than a pure DVM approach, while being able to retain accuracy in modeling high-velocity tails of the distribution function. For problems where trace species have a significant impact on the flow physics, the proposed method is shown to be capable of providing better computational efficiency and accuracy compared with standard fixed-weight DSMC.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

GMLS-Nets: A Framework for Learning from Unstructured Data

Data fields sampled on irregularly spaced points arise in many applications in the sciences and engineering. For regular grids, Convolutional Neural Networks (CNNs) have been successfully used to gaining benefits from weight sharing and invariances. We generalize CNNs by introducing methods for data on unstructured point clouds based on Generalized Moving Least Squares (GMLS). GMLS is a non-parametric technique for estimating linear bounded functionals from scattered data, and has recently been used in the literature for solving partial differential equations. By parameterizing the GMLS estimator, we obtain learning methods for operators with unstructured stencils. In GMLS-Nets the necessary calculations are local, readily parallelizable, and the estimator is supported by a rigorous approximation theory. We show how the framework may be used for unstructured physical data sets to perform functional regression to identify associated differential operators and to regress quantities of interest. The results suggest the architectures to be an attractive foundation for data-driven model development in scientific machine learning applications

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Spatial coupling of gyrokinetic simulations, a generalized scheme based on first-principles

In this paper, we present a scheme that spatially couples two gyrokinetic codes using first-principles. Coupled equations are presented and a necessary and sufficient condition for ensuring accuracy is derived. This new scheme couples both the field and the particle distribution function. The coupling of the distribution function is only performed once every few time-steps, using a five-dimensional (5D) grid to communicate the distribution function between the two codes. This 5D grid interface enables the coupling of different types of codes and models, such as particle and continuum codes, or delta-f and total-f models. Transferring information from the 5D grid to the marker particle weights is achieved using a new resampling technique. Demonstration of the coupling scheme is shown using two XGC gyrokinetic simulations for both the core and edge. We also apply the coupling scheme to two continuum simulations for a one-dimensional advection–diffusion problem.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Effect of Polymer Chain Length on the Superlattice Assembly of Functionalized Gold Nanoparticles

We report on the assembly of gold nanoparticle (AuNPs) superlattices at the liquid/vapor interface and in the bulk of their suspensions. Interparticle distances in the assemblies are achieved on multiple length scales by varying chain lengths of surface grafted AuNPs by polyethylene glycol (PEG) with molecular weights in the range 2000–40,000 Da. Crystal structures and lattice constants in both 2D and 3D assemblies are determined by synchrotron-based surface-sensitive and small-angle X-ray scattering. Here, assuming knowledge of grafting density, we show that experimentally determined interparticle distances are adequately modeled by spherical brushes close to the θ point (Flory–Huggins parameter, x ≈ 1/2) for 2D superlattices at a liquid interface and a nonsolvent (χ = ∞) for the 3D dry superlattices.

36 MATERIALS SCIENCE↗