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Automated image segmentation for accelerated nanoparticle characterization
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A Bayesian approach to nanoparticle characterization
We introduce and numerically validate a Bayesian method for plasmonic nanometrology. Applicable to any system described by a scattering cross section, our approach quantifies uncertainty automatically and enables model comparison through Bayes factors.
Image processing pipeline for AI-driven nanoparticle megalibrary characterization
Recent innovations have made it possible to produce megalibraries, millions of structurally and compositionally distinct nanoparticles on a chip. These megalibraries yield vast volumes of data that are impossible to analyze manually, necessitating the development of automated tools. In previous work, we created a binary classification machine learning model to select quality nanoparticle images for downstream analysis. In this work, we show that adding a custom image processing step before training can produce significantly higher-performing models in a fraction of the time and make them more robust to different image noise levels and microscope acquisition settings. The image processing pipeline proposed here effectively cleans raw nanoparticle images, enhances key features, and allows us to use much lower resolution images and simpler neural network model architectures. These features result in higher performance and significant cost savings. Experiments demonstrate superior performance relative to baseline, including an 18.2% improvement in recall and a 13.1% increase in accuracy. Given the high cost of downstream analysis, it is critical to minimize false positives, and our best-performing model reaches a precision of 95.9% and a weighted F-score of 95.1% on an unseen test set. Additionally, model training time is reduced from hours to less than a minute. We also show that, using this custom image processing pipeline, model performance is significantly improved at lower pixel resolutions compared to downsizing alone. We expect that adopting this pipeline for AI-driven automated nanoparticle characterization will allow researchers to rapidly and accurately analyze much greater volumes of data, thereby accelerating materials discovery.
Nanoparticle Filtration in a RTM Processed Epoxy/Carbon Fiber Composite
Several epoxy matrix composite panels were fabricated by resin transfer molding (RTM) E862/W resin onto a triaxially braided carbon fiber pre-form. Nanoparticles including carbon nanofiber, synthetic clay, and functionalized graphite were dispersed in the E862 matrix, and the extent of particle filtration during processing was characterized. Nanoparticle dispersion in the resin flashing on both the inlet and outlet edges of the panel was compared by TEM. Variation in physical properties such as Tg and moisture absorption throughout the panel were also characterized. All nanoparticle filled panels showed a decrease in Tg along the resin flow path across the panel, indicating nanoparticle filtration, however there was little change in moisture absorption. This works illustrates the need to obtain good nano-particle dispersion in the matrix resin to prevent particle agglomeration and hence particle filtration in the resultant polymer matrix composites (PMC).
Development of Bismuth and Platinum Bi-Metallic Nanoparticles to Enhance Melt Wire Temperature Resolution
Advanced manufacturing (AM) based on direct-write (DW) technologies has emerged as the predominant enabler for the fabrication of active and passive sensors to be deployed in harsh operating environments seen in a nuclear reactor. Recently, Idaho National Laboratory and Boise State University have recently established capabilities to incorporate advanced manufacturing (AM) methods to accelerate, modernize and enhance functionality of nuclear sensors and instrumentation to achieve the goal of enhancing the safety and efficiency of nuclear reactors. A significant thrust of this work includes the development of nuclear relevant feedstock materials compatible with a variety of direct-write processes for the development, fabrication and testing of AM sensors for peak temperature detection and neutron flux monitoring. For this report, bismuth and bismuth/platinum (BiPt) bi-metallic nanoparticles were synthesized using wet chemical approaches to develop bi-metallic feedstock materials to enhance the sensitivity of melt wires for peak temperature detection. Nanoparticle characterization was performed with Transmission Electron Microscopy (TEM) for nanoparticle composition and size, Transmission X-Ray Diffraction Fluorescence (TXRF) for nanoparticle composition, and Differential Scanning Calorimetry to elucidate the melting point of BiPt bi-metallic nanoparticles. Preliminary results indicate bi-metallic BiPt nanoparticles as a viable pathway for fabricating high resolution AM melt wires.
Guide to Making XPS Measurements on Nanoparticles
This guide briefly summarizes issues and considerations important for the use of X-ray Photoelectron Spectroscopy (XPS) for characterizing nanoparticles, which are important in many areas of science and technology. Because the surfaces play a major role in determining nanoparticle behaviors, XPS is an increasingly useful tool for understanding their properties, including addressing variations and non-reproducibility issues associated with these materials. The unusual physical and chemical behaviors of these particles must be considered in preparing and characterizing these materials. This guide is one of a series intended to highlight best practices in the use of XPS.
Translating nanoparticle dosimetry from conventional in vitro systems to occupational inhalation exposures
As encouraged by Toxicity Testing in the 21st Century, researchers increasingly apply high-throughput in vitro approaches to identify and characterize nanoparticle hazards, including conventional aqueous cell culture systems to assess respiratory hazards. Translating nanoparticle dose from conventional toxicity testing systems to relevant human exposures remains a major challenge for assessing occupational risk of nanoparticle exposures. Here, we explored existing computational tools and data available to translate nanoparticle dose metrics from cellular test systems to inhalation exposures of silver nanoparticles in humans. We used the Multiple-Path Particle Dosimetry (MPPD) Model to predict deposition of humans exposed to 20 and 110 nm silver nanoparticles at 0.9 µg/m3 over an 8 hr period, the proposed a National Institute of Occupational Safety and Health (NIOSH) recommended exposure limit (REL). MPPD predicts 1.88 and 0.98 µg of silver deposited in an 8 hr period for 20 and 110 nm nanoparticles, respectively, with 20 nm particles displaying nearly 11-fold higher total surface area deposited. Peak nanoparticle concentrations occurred more proximal in the pulmonary tract compared to mass deposition patterns (generation 5 vs. generations 20-21, respectively) due to regional differences in lung lining fluid volumes. Assuming 0.4% nanoparticle dissolution by mass as previously measured, we predict peak concentrations of silver ions in cells of 0.24 and 0.17 µg/mL for 20 and 110 nm particles, respectively. Both predicted concentrations are below the measured toxic threshold of 1.7 µg/mL of silver ions in cells from in vitro assessments. Assuming 4% dissolution by mass, we predict 10-fold higher silver concentrations in tissues, peaking at 2.4 and 1.7 µg/mL, for 20 and 110 nm nanoparticles respectively, exceeding the observed in vitro toxic threshold and highlighting the importance and sensitivity of dissolution rates. Overall, this approach offers a framework for extrapolating nanotoxicity results from in vitro cell culture systems to human exposures. Aligning appropriate dose metrics from in vitro and in vivo hazard characterizations and human pulmonary doses from occupational exposures are critical components for successful nanoparticle risk assessment and worker protection and provide guidance for designing future in vitro studies aimed at relevant human exposures.
XPCS Microrheology and Rheology of Sterically Stabilized Nanoparticle Dispersions in Aprotic Solvents
In this study, X-ray photon correlation spectroscopy (XPCS) microrheology and conventional bulk rheology were performed on silica nanoparticle dispersions associated with battery electrolyte applications to probe the properties of these specific complex materials and to explore the utility of XPCS microrheology in characterizing nanoparticle dispersions. Sterically stabilized shear-thickening electrolytes were synthesized by grafting poly(methyl methacrylate) chains onto silica nanoparticles. Coated silica dispersions containing 5–30 wt % nanoparticles dispersed in propylene carbonate were studied. In general, both XPCS microrheology and conventional rheology showed that coated silica dispersions were more viscous at higher concentrations, as expected. The complex viscosity of coated silica dispersions showed shear-thinning behavior over the frequency range probed by XPCS measurements. However, measurements using conventional mechanical rheometry yielded a shear viscosity with weak shear-thickening behavior for dispersions with the highest concentration of 30% particles. Our results indicate that there is a critical concentration needed for shear-thickening behavior, as well as appropriate particle size and surface polymer chain length, for this class of nanoparticle-based electrolytes. The results of this study can provide insights for comparing XPCS microrheology and bulk rheology for related complex fluids and whether XPCS microrheology can capture expected macroscopic rheological properties by probing small-scale particle dynamics.
In Situ Measurement of Adhesion for Multimetallic Nanoparticles
The adhesion of nanoparticles to their supports is key to their performance and stability. However, scientific advances in this area have been hampered by the difficulty of experimentally probing adhesion. To date, only a single technique has been developed that can directly measure nanoparticle adhesion, and this technique is inherently limited to monometallic systems. We present a versatile technique for the direct measurement of adhesion for bimetallic nanoparticle systems. This technique combines the spatial resolution of transmission electron microscopy with the force resolution of an atomic force microscope to probe individual, well-characterized nanoparticles. A first study of supported bimetallic nanoparticles provides new insights into the complex impact of alloying on nanoparticle adhesion, explained by charge transfer between constituent metals. The new experimental technique is readily extensible to study other multimetallic nanoparticle systems, including the effects of particle size, shape, and orientation, thus enabling advances in our understanding of nanoparticle physics.
Confusion-Driven Machine Learning of Structural Phases of a Flexible, Magnetic Stockmayer Polymer
We use a semisupervised, neural-network-based machine learning technique, the confusion method, to investigate structural transitions in magnetic polymers, which we model as chains of magnetic colloidal nanoparticles characterized by dipole–dipole and Lennard-Jones interactions. As input for the neural network, we use the particle positions and magnetic dipole moments of equilibrium polymer configurations, which we generate via replica-exchange Wang–Landau simulations. We demonstrate that by measuring the classification accuracy of neural networks, we can effectively identify transition points between multiple structural phases without any prior knowledge of their existence or location. We corroborate our findings by investigating relevant conventional order parameters. Our study furthermore examines previously unexplored low-temperature regions of the phase diagram, where we find new structural transitions between highly ordered helicoidal polymer configurations.
Multimodal X-ray nano-spectromicroscopy analysis of chemically heterogeneous systems
Abstract Understanding the nanoscale chemical speciation of heterogeneous systems in their native environment is critical for several disciplines such as life and environmental sciences, biogeochemistry, and materials science. Synchrotron-based X-ray spectromicroscopy tools are widely used to understand the chemistry and morphology of complex material systems owing to their high penetration depth and sensitivity. The multidimensional (4D+) structure of spectromicroscopy data poses visualization and data-reduction challenges. This paper reports the strategies for the visualization and analysis of spectromicroscopy data. We created a new graphical user interface and data analysis platform named XMIDAS (X-ray multimodal image data analysis software) to visualize spectromicroscopy data from both image and spectrum representations. The interactive data analysis toolkit combined conventional analysis methods with well-established machine learning classification algorithms (e.g. nonnegative matrix factorization) for data reduction. The data visualization and analysis methodologies were then defined and optimized using a model particle aggregate with known chemical composition. Nanoprobe-based X-ray fluorescence (nano-XRF) and X-ray absorption near edge structure (nano-XANES) spectromicroscopy techniques were used to probe elemental and chemical state information of the aggregate sample. We illustrated the complete chemical speciation methodology of the model particle by using XMIDAS. Next, we demonstrated the application of this approach in detecting and characterizing nanoparticles associated with alveolar macrophages. Our multimodal approach combining nano-XRF, nano-XANES, and differential phase-contrast imaging efficiently visualizes the chemistry of localized nanostructure with the morphology. We believe that the optimized data-reduction strategies and tool development will facilitate the analysis of complex biological and environmental samples using X-ray spectromicroscopy techniques.
Advanced characterization of surface-modified nanoparticles and nanofilled antibacterial dental adhesive resins
Nanotechnology can improve the performance of dental polymers. The objective of this study was to modify the surfaces of nanoparticles with silanes and proteins, characterize nanoparticles’ agglomeration levels and interfaces between nanoparticles and the polymeric matrix. Undoped (n-TiO 2 ), nitrogen-doped (N_TiO 2 ) and nitrogen-fluorine co-doped titanium dioxide nanoparticles (NF_TiO 2 ) were synthesized and subjected to surface modification procedures in preparation for Small-Angle X-Ray Scattering (SAXS) and Small-Angle Neutron Scattering (SANS) characterizations. Experimental adhesives were manually synthesized by incorporating 20% (v/v) of n-TiO 2 , N_TiO 2 or NF_TiO 2 (as-synthesized or surface-modified) into OptiBond Solo Plus (OPTB). Specimens (n = 15/group; d = 6.0 mm, t = 0.5 mm) of OPTB and experimental adhesives were characterized using Time-of-Flight Secondary Ion Mass Spectroscopy (ToF-SIMS), 2-D ToF-SIMS chemical imaging and SANS. SAXS results indicated that surface-modified nanoparticles displayed higher scattering intensities in a particle-size dependent manner. ToF-SIMS results demonstrated that nanoparticles’ incorporation did not adversely impact the parental polymer. 2-D ToF-SIMS chemical imaging demonstrated the distribution of Ti + and confirmed nitrogen-doping levels. SANS results confirmed nanoparticles’ functionalization and revealed the interfaces between nanoparticles and the polymer matrix. Metaloxide nanoparticles were successfully fabricated, incorporated and covalently functionalized in a commercial dental adhesive resin, thereby supporting the utilization of nanotechnology in dentistry.
Atomically Resolved Characterization of Optically Driven Ligand Reconfiguration on Nanoparticle Catalyst Surfaces
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Grain‐Boundary‐Rich Noble Metal Nanoparticle Assemblies: Synthesis, Characterization, and Reactivity
Abstract Here, a comprehensive study on the synthesis, characterization, and reactivity of grain‐boundary (GB)‐rich noble metal nanoparticle (NP) assemblies is presented. A facile and scalable synthesis of Pt, Pd, Au, Ag, and Rh NP assemblies is developed, in which NPs are predominantly connected via Σ3 (111) twin GBs, forming a network. Driven by water electrolysis, the random collisions and oriented attachment of colloidal NPs in solution lead to the formation of Σ3 (111) twin boundaries and some highly mismatched GBs. This synthetic method also provides convenient control over the GB density without altering the crystallite size or GB type by varying the NP collision frequency. The structural characterization reveals the presence of localized tensile strain at the GB sites. The ultrahigh activity of GB‐rich Pt NP assembly toward catalytic hydrogen oxidation in air is demonstrated, enabling room‐temperature catalytic hydrogen sensing for the first time. Finally, density functional theory calculations reveal that the strained Σ3(111) twin boundary facilitates oxygen dissociation, drastically enhancing the hydrogen oxidation rate via the dissociative pathway. This reported large‐scale synthesis of the Σ3 (111) twin GB‐rich structures enables the development of a broad range of high‐performance GB‐rich catalysts.
In-operando surface-sensitive probing of electrochemical reactions on nanoparticle electrocatalysts: Spectroscopic characterization of reaction intermediates and elementary steps of oxygen reduction reaction on Pt
In this paper we present an in-operando spectroscopic study of the reaction intermediates involved in ORR on electrified Pt nanoparticle electrocatalysts. To accomplish this, we have synthesized Ag-Pt core–shell nanoparticles, that contain a thin shell of Pt (~1 nm) uniformly deposited on a larger Ag core (~60 nm). We show that these nanostructures are ideal platforms for in-operando Plasmon Enhanced Raman Spectroscopy (PERS) of ORR on Pt. We combined these PERS studies with DFT calculations to assign the measured vibrational spectra to relevant surface intermediates as a function of electrocatalyst potential. Overall, these vibrational assignments were further verified and validated by performing PERS measurements using de-oxygenated and heavy oxygen (O 2 -18) saturated electrolyte. These studies allowed us to identity the presence of critical reaction intermediates (OH, O 2 , OOH, and H 2 O) as the function of operating voltage on the Pt surface and derive voltage-dependent elementary step mechanisms of the reaction.
End-to-End Automated Segmentation Framework for Four-Dimensional Scanning Transmission Electron Microscopy Data
Four-dimensional scanning transmission electron microscopy (4D-STEM) is powerful for rapidly characterizing arrays of nanoparticles produced via high-throughput synthesis. However, such 4D-STEM datasets typically contain thousands of nanoparticles, each characterized by thousands of diffraction patterns spatially distributed across the nanoparticle, necessitating efficient and comprehensive analysis. We propose an end-to-end segmentation framework to automatically segment each nanoparticle into regions with distinct composition/orientation of crystal grains, using only the 4D-STEM data. Bragg disk information is extracted in a physics-informed manner from the diffraction patterns at each spatial location and combined with the real space coordinates to form feature vectors. These feature vectors are then used as inputs to a Gaussian mixture model (GMM) to segment the nanoparticle into distinct regions. We also develop two visualization tools based on the GMM outputs to infer the interface transition and the degree of superposition. Our framework comprehensively integrates machine learning tools and physics knowledge, and provides a basis for substantially compressing enormous 4D-STEM datasets, e.g., by replacing the full 4D-STEM dataset for each nanoparticle with only a single set of Bragg disk features for each distinct crystal grain identified in the nanoparticle. In this article, we demonstrate the power of our framework by presenting results for real, complex datasets.