SEARCH · Engineering Papers

Results for “NANOPARTICLES”

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.

SourceResearch connectionFrom yearThrough year

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

At least 271 records · Page 15

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Shell-mediated control of surface chemistry of highly stoichiometric magnetite nanoparticles

Magnetite (Fe 3 O 4 ) nanoparticles are one of the most studied nanomaterials for different nanotechnological and biomedical applications. However, Fe 3 O 4 nanomaterials gradually oxidize to maghemite (γ-Fe 2 O 3 ) under conventional environmental conditions leading to changes in their functional properties that determine their performance in many applications. Here we propose a novel strategy to control the surface chemistry of monodisperse 12 nm magnetite nanoparticles by means of a 3 nm-thick Zn-ferrite epitaxial coating in core/shell nanostructures. We have carried out a combined Mössbauer spectroscopy, dc magnetometry, X-ray photoelectron spectroscopy and spatially resolved electron energy loss spectroscopy study on iron oxide and Fe 3 O 4 /Zn 0.6 Fe 2.4 O 4 core/shell nanoparticles aged under ambient conditions for 6 months. Our results reveal that while the aged iron oxide nanoparticles consist of a mixture of γ-Fe 2 O 3 and Fe 3 O 4 , the Zn-ferrite-coating preserves a highly stoichiometric Fe 3 O 4 core. Therefore, the aged core/shell nanoparticles present a sharp Verwey transition, an increased saturation magnetization and the possibility of tuning the effective anisotropy through exchange-coupling at the core/shell interface. The inhibition of the oxidation of the Fe 3 O 4 cores can be accounted for in terms of the chemical nature of the shell layer and an epitaxial crystal symmetry matching between the core and the shell.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2022

Active learning of polarizable nanoparticle phase diagrams for the guided design of triggerable self-assembling superlattices

Polarizable nanoparticles are of interest in materials science because of their rich and complex phase behavior that can be used to engineer nanostructured materials with long-range crystalline order. To understand and rationally navigate the design space of polarizable nanoparticles for self-assembling highly ordered superlattices, we developed a coarse-grained computational model to describe the nanoparticle-nanoparticle interactions in implicit solvent and employ the computationally efficient image method to model many-body polarization interactions. We conducted high-throughput virtual screening over a five-dimensional particle design space spanned by temperature, particle size, particle charge, particle dielectric, and solvent dielectric using enhanced sampling molecular dynamics calculations within an active learning framework to efficiently map out the regions of thermodynamic stability of the self-assembled aggregates. We validate our predictions in comparisons against small angle x-ray scattering measurements of gold nanoparticles surface functionalized with metal chalcogenide ligands. Lastly, we use our validated phase maps to computationally design switchable nanostructured materials capable of triggered assembly and disassembly as a function of temperature and solvent dielectric with potential applications as sensors, smart windows, optoelectronic devices, and in medical diagnostics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Flow synthesis of iron oxide nanoparticles: using multiple precursor additions to improve size control

Controlling the size of iron oxide nanoparticles while maintaining uniformity in flow-based synthesis systems has been a great challenge in nanoparticle synthesis. Using an extended LaMer mechanism, we improve both the size and shape uniformity as compared to a conventional flow synthesis. The key to this approach is the injection of additional Fe precursor during the flow reaction, providing extra precursor during the growth process leading to a size focusing step. This approach has also allowed for the systematic variation of the nanoparticle size produced in the flow reaction. This work represent a new synthetic concept for improving the size uniformity of iron oxide nanoparticles in flow-based synthesis and has potential for application across a wide range of nanoparticle systems.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Atomic-scale in situ observation of electron beam and heat induced crystallization of Ge nanoparticles and transformation of Ag@Ge core-shell nanocrystals

Crystallization of amorphous materials by thermal annealing has been investigated for numerous applications in the fields of nanotechnology, such as thin-film transistors and thermoelectric devices. The phase transition and shape evolution of amorphous germanium (Ge) and Ag@Ge core–shell nanoparticles with average diameters of 10 and 12 nm, respectively, were investigated by high-energy electron beam irradiation and in situ heating within a transmission electron microscope. The transition of a single Ge amorphous nanoparticle to the crystalline diamond cubic structure at the atomic scale was clearly demonstrated. Depending on the heating temperature, a hollow Ge structure can be maintained or transformed into a solid Ge nanocrystal through a diffusive process during the amorphous to crystalline phase transition. Selected area diffraction patterns were obtained to confirm the crystallization process. In addition, the thermal stability of Ag@Ge core–shell nanoparticles with an average core of 7.4 and a 2.1 nm Ge shell was studied by applying the same beam conditions and temperatures. The results show that at a moderate temperature (e.g., 385 °C), the amorphous Ge shell can completely crystallize while maintaining the well-defined core–shell structure, while at a high temperature (e.g., 545 °C), the high thermal energy enables a freely diffusive process of both Ag and Ge atoms on the carbon support film and leads to transformation into a phase segregated Ag–Ge Janus nanoparticle with a clear interface between the Ag and Ge domains. In conclusion, this study provides a protocol as well as insight into the thermal stability and strain relief mechanism of complex nanostructures at the single nanoparticle level with atomic resolution.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Morphological descriptors of nanoparticles: The link between atomistic structures and x-ray absorption spectra

Understanding and quantifying the morphology of nanoparticles are essential for linking their atomic structure to diverse applications and verifying theoretical models. While experimental information on the structure of nanoparticles in the size range below ∼5 nm can be extracted from x-ray absorption spectroscopy using a small number of descriptors—most commonly coordination numbers—developing an understanding of morphology descriptors from experimental data remains a challenge. Here, in this study, we introduce NanoGene, a genetic algorithm-based method for generating structurally diverse nanoparticle models guided by user-defined descriptors. We establish correlations among structural, size-related, and morphological descriptors and demonstrate how experimentally accessible parameters, such as coordination numbers, can be leveraged to infer otherwise inaccessible ones, such as the generalized coordination number or particle oblateness. Principal component and clustering analyses reveal the relative importance of descriptors, with the number of atoms emerging as a key discriminant of the nanoparticle structure. By providing both the methodology and an extensive dataset of nanoparticle geometries, this work offers a practical foundation for descriptor-based analysis and interpretation of experimental observations, bridging the gap between local atomic coordinates and global morphological characterization.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2021

Anomalous nanoparticle surface diffusion in LCTEM is revealed by deep learning-assisted analysis

Significance Liquid cell transmission electron microscopy (LCTEM) is an emerging technique, which enables nanoscale visualization and tracking of single nanoparticles near interfaces with unprecedented spatial resolution. Here, we studied the diffusion of nanoparticles in LCTEM experiments using techniques powered by deep neural networks and statistical tests. We observed two underlying regimes of diffusive behavior which are governed by the interaction of the electron beam, the nanoparticle, the nearby substrate, and the liquid environment. This understanding forms the foundation to use LCTEM for single-nanoparticle tracking for a broad range of nanoparticles, interfaces, and liquids.

Jamali, Vida↗

DOE OSTI2025

Quantifying dispersity in size and shape of nanoparticles from small-angle scattering data using machine learning based CREASE

Here, we use machine learning (ML) enhanced computational reverse engineering analysis of scattering experiments (CREASE) to interpret small-angle X-ray scattering (SAXS) data obtained from a system of nanoparticles without a priori knowledge of their exact shapes (e.g. spheres or ellipsoids), sizes (0.5–50 nm) and distributions. The SAXS measurements yielded three categories of scattering profiles exhibiting 'strong', 'weak' and 'no' features. Diminishing features (e.g. broadening or disappearing peaks) in scattering profiles have always been attributed to the presence of significant dispersity in the system. Such featureless SAXS data are not suitable for traditional analysis using analytical models. If one were to fit a relevant analytical model (e.g. the lmfit analytical model for polydisperse spheres) to these 'weak' and 'no' SAXS profiles from our nanoparticle systems, one would obtain non-unique interpretations of the data. Relying on electron microscopy to identify the distributions of nanoparticle shapes and sizes is also unfeasible, especially in high-throughput synthesis and characterization loops. In such situations, to identify the distributions of particle sizes and shapes that could be present in the sample, one must rely on methods like ML-CREASE to interpret the data quickly and output all relevant interpretations about the structure present in the system. The ML-CREASE optimization loop takes the experimental scattering profile as input and outputs multiple candidate solutions whose computed scattering profiles match the SAXS profile input. The ML-CREASE method outputs distributions of relevant structural features, such as the volume fraction of the nanoparticles in the system and the mean and standard deviation of the particle size and aspect ratio, assuming a type of distribution (e.g. normal, log-normal) for size and aspect ratio. We find that, for the SAXS profiles analyzed here, accounting for the shape dispersity along with size dispersity of the nanoparticles using ML-CREASE improved the match between the computed scattering profiles and input experimental profiles.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Facet-dependent dispersion and aggregation of aqueous hematite nanoparticles

Nanoparticle aggregates in solution controls surface reactivity and function. Complete dispersion often requires additive sorbents to impart a net repulsive interaction between particles. Facet engineering of nanocrystals offers an alternative approach to produce monodisperse suspensions simply based on facet-specific interaction with solvent molecules. Here, we measure the dispersion/aggregation of three morphologies of hematite (α-Fe 2 O 3 ) nanoparticles in varied aqueous solutions using ex situ electron microscopy and in situ small-angle x-ray scattering. We demonstrate a unique tendency of (104) hematite nanoparticles to maintain a monodisperse state across a wide range of solution conditions not observed with (001)- and (116)-dominated particles. Density functional theory calculations reveal an inert, densely hydrogen-bonded first water layer on the (104) facet that favors interparticle dispersion. Results validate the notion that nanoparticle dispersions can be controlled through morphology for specific solvents, which may help in the development of various nanoparticle applications that rely on their interfacial area to be highly accessible in stable suspensions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Imaging elliptically polarized infrared near-fields on nanoparticles by strong-field dissociation of functional surface groups

Abstract We investigate the strong-field ion emission from the surface of isolated silica nanoparticles aerosolized from an alcoholic solution, and demonstrate the applicability of the recently reported near-field imaging at 720 nm [Rupp et al., Nat. Comm., 10(1):4655, 2019] to longer wavelength (2 $$\mu $$ μ m) and polarizations with arbitrary ellipticity. Based on the experimental observations, we discuss the validity of a previously introduced semi-classical model, which is based on near-field driven charge generation by a Monte-Carlo approach and classical propagation. We furthermore clarify the role of the solvent in the surface composition of the nanoparticles in the interaction region. We find that upon injection of the nanoparticles into the vacuum, the alcoholic solvent evaporates on millisecond time scales, and that the generated ions originate predominantly from covalent bonds with the silica surface rather than from physisorbed solvent molecules. These findings have important implications for the development of future theoretical models of the strong-field ion emission from silica nanoparticles, and the application of near-field imaging and reaction dynamics of functional groups on isolated nanoparticles. Graphical abstract

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

All-optical nanoscopic spatial control of molecular reaction yields on nanoparticles

Molecular adsorbate reactions on nanoparticles play a fundamental role in areas such as nano-photocatalysis, atmospheric, and astrochemistry. They can be induced, enhanced, and controlled by field localization and enhancement on the nanoparticle surface. In particular, the ability to perform highly controlled near-field-mediated reactions is key to deepening our understanding of surface photoactivity on nanosystems. Here, using reaction nanoscopy, we experimentally demonstrate all-optical nanoscopic control of surface reaction yields by tailoring the near fields on nanoparticles with waveform-controlled linear and bicircular two-color laser pulses, respectively. We observe site-selective proton emission from the dissociative ionization of adsorbate molecules on Si O 2 nanoparticles as a function of the polarization and relative phase of the two-color pulses. The angularly resolved close-to-uniform mapping between the surface reaction yields and the measured ion momentum enables the observation and spatial control of molecular reactions on the nanoparticle surface with nanoscopic resolution. The experimental results are modeled and reproduced qualitatively by classical trajectory Monte Carlo simulations. Our work paves the way toward reliable all-optical control of photocatalytic chemical reactions on nanoscale surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Nanoparticle self-assemblies with modern complexity

Thanks to decades of tireless efforts, nanoparticle assemblies have reached at an extremely high level of controllability, sophistication, and complexity, with new insights provided by integration with graph theory, cutting-edge characterization, and machine learning (ML)-based computation and modeling, as well as with ever-diversifying applications in energy, catalysis, biomedicine, optics, electronics, magnetics, organic biosynthesis, and quantum technology. Nanoparticle assemblies can be crystalline, known as superlattices or supracrystals. Their assembly entails a transition from disorder—dispersed nanoparticles—to order, which can be achieved through classical nucleation pathways or nonclassical pathways via prenucleation precursors or particle aggregation. Further, the periodic lattices allow facile manipulations of electrons, phonons, photons, and even spins, leading to advanced device components and metamaterials. Meanwhile, aperiodic assemblies out of nanoparticles, such as gels, networks, and amorphous solids, also start to attract attentions. Despite the loss of periodicity, symmetry-lowering or symmetry-breaking three-dimensional (3D) structures emerge with unique properties, such as chiroptical activity, topological mechanical strength, and quantum entanglement. Real-space imaging such as electron microscopy and X-ray based tomography methods are utilized to characterize these complex structures, while mathematical tools such as graph theories are in need to describe such complex structures. This issue aims to provide a timely review of the efforts in this greatly broadened materials design space including experiment, simulation, theory, and applications. Nine top experts (and their teams) from four countries deliver six review papers, summarizing fundamental mechanistic understandings of nanoparticle assemblies, highlighted with the developments of state-of-the-art in situ characterization tools and ML-assisted reverse engineering, and newly emergent applications of nanoarchitectures.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Rapid growth of anthropogenic organic nanoparticles greatly alters cloud lifecycle in the Amazon rainforest

Atmospheric nanoparticles can significantly influence Earth’s climate if they grow to sizes large enough to nucleate cloud droplets. While the contribution of extremely low volatility vapors to nanoparticle growth has been discussed extensively, the role of semivolatile organics has been largely overlooked. Here we examine the growth and impacts of air pollution nanoparticles from an isolated metropolis amidst the Amazon rainforest. Although extremely low volatility organics are necessary for the initial growth of nanoparticles, model analysis suggests that dynamic gas-particle partitioning of semivolatile oxidation products of anthropogenic and natural hydrocarbons is predominantly responsible for the observed rapid growth to 50 nm. Furthermore, cloud-resolving simulations demonstrate that the grown particles appreciably modify shallow cloud droplet size distribution, suppress precipitation, and enhance the transition to deep clouds. With condensable semivolatile organics typically formed in greater proportion, similar nanoparticle growth and impacts on clouds could likely occur in heavily urbanized forested regions globally.

Zaveri, Rahul A↗

DOE OSTI2020

DATASET: Rapid growth of anthropogenic organic nanoparticles greatly alters cloud lifecycle in the Amazon rainforest

Zaveri, Rahul A↗

DOE OSTI2025

Signal Differentiation of Moving Magnetic Nanoparticles for Enhanced Biodetection and Diagnostics

Magnetic nanoparticles are extensively utilized as markers/signal labelling in various biomedical applications. Detecting and distinguishing magnetic signals from similarly sized moving magnetic nanoparticles in microfluidic systems is crucial yet challenging for biosensing. In this study, we have developed an original method to detect and differentiate magnetic signals from moving superparamagnetic (SPM) and ferrimagnetic (FM) nanoparticles of comparable sizes. Our approach utilizes a highly sensitive magnetic-coil-based sensor that harnesses the combined effects of giant magnetoimpedance (GMI) and an LC-resonance circuit, offering performance superior to that of conventional GMI sensors. Iron oxide nanoparticles, which have similar particle sizes but differing coercivities (zero for SPM and non-zero for FM) or similar zero coercivities but differing particle sizes, flow through the magnetic coil at controlled velocities. Their distinct effects are analyzed through changes in the complex impedance of the sensing system. Our findings provide a unique pathway for utilizing SPM and FM nanoparticles as innovative magnetic markers to identify specific biological entities, thereby expanding their potential applications.

Hwang, Kee Young↗

DOE OSTI2024

Optimization and Multimachine Learning Algorithms to Predict Nanometal Surface Area Transfer Parameters for Gold and Silver Nanoparticles

Interactions between gold metallic nanoparticles and molecular dyes have been well described by the nanometal surface energy transfer (NSET) mechanism. However, the expansion and testing of this model for nanoparticles of different metal composition is needed to develop a greater variety of nanosensors for medical and commercial applications. In this study, the NSET formula was slightly modified in the size-dependent dampening constant and skin depth terms to allow for modeling of different metals as well as testing the quenching effects created by variously sized gold, silver, copper, and platinum nanoparticles. Overall, the metal nanoparticles followed more closely the NSET prediction than for Förster resonance energy transfer, though scattering effects began to occur at 20 nm in the nanoparticle diameter. To further improve the NSET theoretical equation, an attempt was made to set a best-fit line of the NSET theoretical equation curve onto the Au and Ag data points. An exhaustive grid search optimizer was applied in the ranges for two variables, 0.1≤C≤2.0 and 0≤α≤4, representing the metal dampening constant and the orientation of donor to the metal surface, respectively. Three different grid searches, starting from coarse (entire range) to finer (narrower range), resulted in more than one million total calculations with values C=2.0 and α=0.0736. The results improved the calculation, but further analysis needed to be conducted in order to find any additional missing physics. With that motivation, two artificial intelligence/machine learning (AI/ML) algorithms, multilayer perception and least absolute shrinkage and selection operator regression, gave a correlation coefficient, R2, greater than 0.97, indicating that the small dataset was not overfitting and was method-independent. This analysis indicates that an investigation is warranted to focus on deeper physics informed machine learning for the NSET equations.

Demers, Steven M. E. (ORCID:0000000192213246)↗

DOE OSTI2024

Rapid Prototyping for Nanoparticle-Based Photonic Crystal Fiber Sensors

The advent of nanotechnology has motivated a revolution in the development of miniaturized sensors. Such sensors can be used for radiation detection, temperature sensing, radio-frequency sensing, strain sensing, and more. At the nanoscale, integrating the materials of interest into sensing platforms can be a common issue. One promising platform is photonic crystal fibers, which can draw in optically sensitive nanoparticles or have its optical properties changed by specialized nanomaterials. However, testing these sensors at scale is limited by the the need for specialized equipment to integrate these photonic crystal fibers into optical fiber systems. Having a method to enable rapid prototyping of new nanoparticle-based sensors in photonic crystal fibers would open up the field to a wider range of laboratories that could not have initially studied these materials in such a way before. This manuscript discusses the improved processes for cleaving, drawing, and rapidly integrating nanoparticle-based photonic crystal fibers into optical system setups. The method proposed in this manuscript achieved the following innovations: cleaving at a quality needed for nanoparticle integration could be done more reliably (≈100% acceptable cleaving yield versus ≈50% conventionally), nanoparticles could be drawn at scale through photonic crystal fibers in a safe manner (a method to draw multiple photonic crystal fibers at scale versus one fiber at a time), and the new photonic crystal fiber mount was able to be finely adjusted when increasing the optical coupling before inserting it into an optical system (before, expensive fusion splicing was the only other method).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2006

Carbon Materials Metal/Metal Oxide Nanoparticle Composite and Battery Anode Composed of the Same

A method of forming a composite material for use as an anode for a lithium-ion battery is disclosed. The steps include selecting a carbon material as a constituent part of the composite, chemically treating the selected carbon material to receive nanoparticles, incorporating nanoparticles into the chemically treated carbon material and removing surface nanoparticles from an outside surface of the carbon material with incorporated nanoparticles. A material making up the nanoparticles alloys with lithium.

Hung, Ching-Cheh↗

DOE OSTI2020

Copper nanoparticle structures for reduction of carbon dioxide to multicarbon products

This disclosure provides systems, methods, and apparatus related to copper nanoparticle structures for reduction of carbon dioxide to multicarbon products. In one aspect, a method includes providing a plurality of copper nanoparticles. The plurality of copper nanoparticles are deposited on a support. The plurality of copper nanoparticles are transformed to a plurality of copper structures during an operation in which carbon dioxide is reduced. The plurality of copper nanoparticles on the support are used as a working electrode in an electrochemical cell during the operation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗