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At least 631 records · Page 35

Crystallization kinetics and nanoparticle ordering in semicrystalline polymer nanocomposites

There has been considerable interest in the nucleation and crystallization of polymers in the presence of nanoparticles (NPs, or nanofillers in general, NFs). Most of the extensive work in this area has focused on anisotropic, non-Brownian NFs (e.g., clay sheets, carbon nanotubes) whose spatial dispersion state in these nanocomposites is controlled by the process by which they are formed. Hence, NF spatial dispersion is generally limited and often remains poorly characterized. Thermodynamic handles that can be used to control NF dispersion state in the polymer melt include (a) favorable interactions between the polymer chains and the bare NP surfaces, or (b) the density and length of the chains, with the same chemistry as the matrix, grafted to the NP surface. These relatively large NFs merely act as stationary objects that affect the kinetics of nucleation by providing heterogeneous sites, and the crystallization rate by confining the polymer in the melt state. The dispersion state of the NFs can dramatically affect the nucleation and crystallization of the matrix, but in most cases reported, the NFs increase nucleation efficiency relative to the neat polymer. At higher NF loadings, the effect of polymer confinement by the NFs dominates, leading to a decrease in crystal growth rates. This review describes the most important lessons learned from these commonly studied systems and then extends to polymer composite systems containing small, mobile spherical NPs (typically smaller than 100 nm in size). The role of NP mobility, which provides for dynamic confinement of the polymer melt, on the kinetics of polymer crystallization (nucleation, growth, and overall crystallization) and how this behavior is mostly consistent with the case of immobile NF is a second important focus of this review. In addition to the role of NFs on crystallization kinetics, recently reported nanoparticle ordering phenomena such as the effect of matrix crystallization on the organization of small spherical NPs within the amorphous regions of the semicrystalline morphology are discussed. In conclusion, such phenomena are clearly not observed for large NFs and hence provide a point of departure from past works in this area.

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Effects of Superparamagnetic Iron Nanoparticles on Electrocatalysts for the Reduction of Oxygen

It is of great research interest to understand the nanostructures contributing to the activity observed in the reduction of oxygen by non-platinum group metal (PGM) electrocatalysts in acidic media. Iron- and nitrogen-containing carbon networks are often the most studied structures, among which single-atom iron-coordinated nitrogen (FeNx) moieties have often been proposed to be the structures leading to the high activity in these non-PGM electrocatalysts. Iron nanoparticles embedded within a carbon support are also formed under certain conditions as a result of the synthetic processes in making non-PGM electrocatalysts. In this study, we present a study to understand the oxygen reduction reaction (ORR) activity of prepared iron- and nitrogen-containing non-PGM electrocatalysts obtained through the pyrolysis of metal–organic framework (MOF) precursors. We studied the structure–property relationship among nanostructures made from the MOF precursor ZIF-8 under different pyrolysis conditions. Density functional theory calculations were used to explain the effect of structural moieties on the ORR activity. Our results suggest that iron-coordinated C–N structures and iron nanoparticles act synergistically to catalyze the ORR.

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Microwave Synthesis of Spinel MgFe 2 O 4 Nanoparticles and the Effect of Annealing on Photocatalysis

Through microwave heating in ethanol and with subsequent annealing, crystalline MgFe 2 O 4 nanoparticles are produced rapidly and in high yields >99%. Under varied annealing temperatures, the degree of Mg and Fe site inversion changes the optical, electronic, and composition of the nanoparticles. Here, a small particle size of ~10 nm is achievable with the aid of an ammonium salt mineralizer that caps the particles during nucleation and growth. Particles with the lowest inversion parameter and limited sintering upon annealing (at 600 ºC) exhibit the greatest production of hydroxyl radicals under visible light illumination. As such, these particles also facilitate the degradation of methylene blue dye faster than those particles annealed at higher temperature and show a rate constant of 0.061 h –1 for degrading 10 ppm methylene blue with 10 mg catalyst.

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Enhancing Nanoparticle Detection in Interferometric Scattering (iSCAT) Microscopy Using a Mask R-CNN

Interferometric scattering microscopy (iSCAT) is a label-free optical microscopy technique that enables imaging of individual nano-objects such as nanoparticles, viruses, and proteins. Essential to this technique is the suppression of background scattering and identification of signals from nano-objects. In the presence of substrates with high roughness, scattering heterogeneities in the background, when coupled with tiny stage movements, cause features in the background to be manifested in background-suppressed iSCAT images. Traditional computer vision algorithms detect these background features as particles, limiting the accuracy of object detection in iSCAT experiments. Here, in this paper, we present a pathway to improve particle detection in such situations using supervised machine learning via a mask region-based convolutional neural network (mask R-CNN). Using a model iSCAT experiment of 19.2 nm gold nanoparticles adsorbing to a rough layer-by-layer polyelectrolyte film, we develop a method to generate labeled datasets using experimental background images and simulated particle signals and train the mask R-CNN using limited computational resources via transfer learning. We then compare the performance of the mask R-CNN trained with and without inclusion of experimental backgrounds in the dataset against that of a traditional computer vision object detection algorithm, Haar-like feature detection, by analyzing data from the model experiment. Results demonstrate that including representative backgrounds in training datasets improved the mask R-CNN in differentiating between background and particle signals and elevated performance by markedly reducing false positives. The methodology for creating a labeled dataset with representative experimental backgrounds and simulated signals facilitates the application of machine learning in iSCAT experiments with strong background scattering and thus provides a useful workflow for future researchers to improve their image processing capabilities.

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Adapted Cell Design for the Operando X‑Ray Absorption Study of a Structurally Evolving Cu Nanoparticle Ensemble during the CO2 Electroconversion to Multicarbon Products

An improved understanding of the materials that will sustain the future of energy production, storage, and delivery calls for better characterization tools. Operando characterization methods have thus become essential for investigating electrocatalytic materials. Without their resulting insights, the study of highly performing catalysts post-mortem cannot viably facilitate the further development of functional catalysts. Herein, we present an operando electrochemical cell designed for hard X-ray absorption spectroscopy (XAS) and specifically adapted to the study of an electrocatalytically active Cu nanoparticle ensemble. So far, this nanocatalyst has proven to pose quite a challenge to characterize due to its unique structural dynamics. Adopting a design comparable to the H-cell employed for all activity testing, we report the satisfactory translation of the active site formation into an XAS-compatible cell. The simultaneous collection of CO2-derived products during XAS characterization enabled the operando characterization of this CO2-reducing active structure. We report a Cu–Cu coordination number of the first scattering path higher than suggested in our previous studies, highlighting the importance of monitoring metastable nanoelectrocatalysts in operando. This study illustrates important caveats for the electrocatalysis community when considering the application of operando XAS. Our results highlight that the sample size, homogeneity, and stability determine how to interpret the measured signal. Considering these parameters carefully, the operando EXAFS results confirm the exceptional undercoordinated character of the Cu nanoparticle ensemble during CO2 reduction to C2+ products.

Louisia, Sheena↗

Directional Gateway to Metal Oxidation: 3D Chemical Mapping Unfolds Oxygen Diffusional Pathways in Rhodium Nanoparticles

The interaction of oxygen with a reactive metal is ubiquitous, yet the precise atomic-level mechanisms and pathways leading to the formation of a surface oxide are not well-understood. We report oxygen atom distributions inside Rh single nanoparticles using atom probe microscopy (APM) and demonstrate that mainly facets of the ?022¯? crystallographic directions act as oxygen-permeable gateways. The highly anisotropic spatial distribution of incorporated oxygen atoms is in agreement with video-field emission analyses according to which {113} facets of the ?022¯? zones act as portals for subsurface diffusion. In addition to providing a more fundamental understanding of the precursor states to metal corrosion, in particular for the case of nanosized metal particles, our studies are also relevant for heterogeneous catalysis where catalytic activity and selectivity conform to reaction-induced structural changes of metal nanoparticles.

Lambeets, Sten V M.↗

Origin of Mechanical Enhancement in Polymer Nanoparticle (NP) Composites with Ultrahigh NP Loading

Polymer nanoparticle composites (PNCs) with ultrahigh loading of nanoparticles (NPs) (>50%) have been shown to exhibit markedly improved strength, stiffness, and toughness simultaneously compared to the neat systems of their components. Recent experimental studies on the effect of polymer fill fraction in these highly loaded PNCs reveal that even at low polymer fill fractions, hardness and modulus increase significantly. In this work, we aim to understand the origin of these performance enhancements by examining the dynamics of both polymer and NPs under tensile deformation. We perform molecular dynamics simulations of coarse-grained, glassy polymer in random-close-packed NP packings with a varying polymer fill fraction. We characterize the mechanical properties of the PNC systems, compare the NP rearrangement behavior, and study the polymer segmental and chain-level dynamics during deformation below the polymer glass transition. Finally, our simulation results confirm the experimentally observed increase in modulus at low polymer fill fractions, and we provide evidence that the source of mechanical enhancement is the polymer bridging effect.

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Uneven Strain Distribution Induces Consecutive Dislocation Slipping, Plane Gliding, and Subsequent Detwinning of Penta-Twinned Nanoparticles

Twin structures possess distinct physical and chemical properties by virtue of their specific twin configuration. However, twinning and detwinning processes are not fully understood on the atomic scale. Integrating in situ high resolution transmission electron microscopy and molecular dynamic simulations, here, we find tensile strain in the asymmetrical 5-fold twins of Au nanoparticles leads to twin boundary migration through dislocation sliding (slipping of an atomic layer) along twin boundaries and dislocation reactions at the 5-fold axis under an electron beam. Migration of one or two layers of twin planes is governed by energy barriers, but overall, the total energy, including surface, lattice strain, and twin boundary energy, is relaxed after consecutive twin boundary migration, leading to a detwinning process. In addition, surface rearrangement of 5-fold twinned nanoparticles can aid in the detwinning process.

36 MATERIALS SCIENCE↗

Solvent Isotherms and Structural Transitions in Nanoparticle Superlattice Assembly

Here, we introduce a Molecular Theory for Compressible Fluids (MOLT-CF) that enables us to compute free energies and other thermodynamic functions for nanoparticle superlattices with any solvent content, including the dry limit. Quantitative agreement is observed between MOLT-CF and united-atom molecular dynamics simulations performed to assess the reliability and precision of the theory. Among other predictions, MOLT-CF shows that the amount of solvent within the superlattice decreases approximately linearly with its vapor pressure and that in the late stages of drying, solvent-filled voids form at lattice interstitials. Applied to single-component superlattices, MOLT-CF predicts fcc-to-bcc Bain transitions for decreasing vapor pressure and for increasing ligand length, both in agreement with experimental results. We explore the stability of other single-component phases and show that the C14 Frank-Kasper phase, which has been reported in experiments, is not a global free-energy minimum. Implications for precise assembly and prediction of multicomponent nanoparticle systems are discussed.

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Trihydrogen Formation on Gold Nanoparticles in Strong Laser Fields

The tri-hydrogen cation (H+ 3 ) plays a central role in proton-transfer chemistry, astrochemical pathways, and hydrogen plasma environments, acting as a key indicator of ultrafast proton rearrangement. Although H+ 3 formation is studied extensively in the gas phase, its surface-mediated generation and its sensitiv- ity to nanoparticle morphology remain largely unexplored. Gold nanoparticles (AuNPs), which can localize surface charge and sustain strong electric fields, offer an ideal platform to probe such nonequilibrium reaction pathways. Us- ing reaction nanoscopy, we spatially map H+ 3 production on AuNPs exposed to intense femtosecond laser fields. By comparing spherical and faceted nanopar- ticles, we demonstrate how morphology modulates charge density and governs reaction efficiency. We find that sharp features on faceted particles concen- trate charge more effectively, promoting molecular fragmentation and enabling proton rearrangement and migration that enhance H+ 3 yields. This work opens new directions for exploiting strong-field interactions at metal interfaces to drive nanoscale reactivity and photocatalysis.

Dagar, Ritika [SLAC National Accelerator Laborator↗

SiO 2 Is Wasted Space in Single-Nanometer-Scale Silicon Nanoparticle-Based Composite Anodes for Li-Ion Electrochemical Energy Storage

The electrode processing conditions of silicon-based composite anodes play a pivotal role in the resulting interfacial chemical speciation and, thus, the electrochemical cycling behavior of the electrode. Systematically investigating how small chemical changes to the surface of the silicon nanoparticle (NP) affect larger, electrode-level properties is a strategy that will inform design principles to maximize electrode energy density and extend electrode lifetime. Here, we incorporate silicon nanoparticles (NPs) with an average diameter of 5.5 nm synthesized from the gas phase through a nonthermal plasma method into composite anode half-cell coin cells. We perform chemistry to functionalize the native hydride-terminated silicon NP surface with N-methylpyrrolidone (NMP) to improve slurry properties and reduce the silicon reactivity prior to composite electrode fabrication. We construct composite electrodes from these silicon NPs and observe an 86% capacity retention over 100 cycles at a rate of C/5, with an initial silicon specific capacity of 2600 mAh/g. We expose the same Si NPs to water that completely oxidizes these small-diameter NPs to SiO 2 and find that the SiO 2 NPs in the same electrode configuration exhibit no obvious lithium alloying capacity in the electrochemical potential range of lithium silicide alloy formation. As this result stands in contrast to existing literature, we provide a discussion on the origin of the discrepancies.

25 ENERGY STORAGE↗

Graphene Quantum Dot Composites Embedded with CoZn Alloy Nanoparticles for Enhanced Oxygen Reduction and Evolution Reactions

Developing efficient and durable electrocatalysts for oxygen reduction (ORR) and oxygen evolution reactions (OER) is crucial for energy conversion technologies. In this study, core–shell graphene quantum dot (GQD) composites embedded with Co, Zn, and CoZn alloy nanoparticles were synthesized using a solid-state microwave synthesis method and systematically investigated for their electrochemical performance. The structural and compositional analyses confirm that the integration of transition metal nanoparticles enhances the electronic properties of GQDs, providing abundant active sites and facilitating charge transfer. Electrochemical characterization reveals that GQD-CoZn-3 (GQD:Co:Zn = 10.5:0.5:0.5) exhibits superior ORR and OER catalytic activities, achieving the highest current densities of 205 A g–1 and 102 A g–1 at 100 mV s–1, respectively. These performance values surpass those of pristine GQDs and single-metal (Co or Zn) embedded counterparts, emphasizing the strong synergistic effect between the CoZn alloy and the functionalized carbon framework. Impedance measurements further reveal a substantial reduction in charge-transfer resistance, while chronoamperometry tests demonstrate excellent catalytic stability and CO tolerance, highlighting the robustness of the core–shell GQD structure. These findings provide valuable insights into the design of advanced carbon-based electrocatalysts for energy applications.

Panda, Pradeep Kumar↗

Self-Assembled Magnetic Nanoparticle Layers: Structural Control for Reconfigurable Magnetism and Functional Applications

We advance soft X-ray vector ptychographic tomography to map the 3D magnetization field in self-assembled superparamagnetic nanoparticles at a liquid–liquid interface, revealing how layered structures influence magnetic ordering. We observe that monolayers with low coordination numbers exhibit weak magnetic order, with magnetic vortices disrupting spin alignment. In contrast, bilayers and trilayers with higher coordination numbers display long-range magnetic order with strong spin correlations across larger distances and a suppression of magnetic vortices. We further quantify the average distance for vortex–antivortex pairs as 26.0 ± 2.0 nm, while vortex–vortex and antivortex–antivortex pairs exhibit larger separations, averaging 44.9 ± 5.2 and 54.1 ± 7.4 nm, respectively. These experimental results are supported by micromagnetic Monte Carlo simulations. Our findings illustrate how layered structures enhance magnetic order and spin correlation in superparamagnetic nanoparticle assemblies, providing a promising approach for tuning magnetic properties in applications such as data storage, microrobotics, and biomedicine.

Lu, Xingyuan↗

Density Functional Tight Binding Insights into Plasmonic Silver–Platinum Nanoparticles and Alloys for Enhanced Photocatalysis

Developing accurate and efficient Slater-Koster (SK) tight-binding parameter sets is essential for quantum plasmonic studies of alloyed metal nanoparticles, as conventional time dependent density functional theory (TD-DFT) calculations are computationally prohibitive for larger clusters. In this work, we develop and validate density functional tight binding (DFTB) parameter sets for both ground state (GS-SK) and excited state (ES-SK) calculations to study the structural, electronic, and optical properties of silver (Ag), platinum (Pt), and Ag–Pt nanoalloys. Our investigation of the ground state properties demonstrates that the GS-SK parameters enable DFTB to closely reproduce the electronic structures of platinum clusters with diverse sizes and geometries – showing qualitative agreement with DFT for density of states (DOS) profiles and energy levels. The ES-SK parameters accurately describe excited-state properties compared to TD-DFT reference calculations, including the broad, featureless absorption profiles of Pt that are dominated by interband transitions. Using the ES-SK parameters within a real-time TD-DFTB framework, we compute size-dependent optical absorption spectra of Ag, Pt and Ag-Pt nanocubes containing up to 1099 atoms (size ∼4.18 nm). A detailed study of Ag–Pt and Pt-Ag core–shell nanoparticles shows quenching of the Ag plasmon resonance even at monolayer coverage for Ag-Pt, but not for Pt-Ag. We also show how to define submonolayer Ag-core Pt-shell cubic structures that have similar optical properties to those generated experimentally for much larger particles, which offers potential for describing plasmon-enhanced photocatalysis. Collectively, the GS-SK and ES-SK parameter sets provide an accurate, computationally efficient approach for modeling the complex optical and electronic behavior of noble–transition metal nanostructures and their alloys.

SPR↗

Pt Nanoparticles on Atomic-Metal-Rich Carbon for Heavy-Duty Fuel Cell Catalysts: Durability Enhancement and Degradation Behavior in Membrane Electrode Assemblies

Proton exchange membrane fuel cells (PEMFCs) are a promising zero-emission power source for heavy-duty vehicles (HDVs). However, long-term durability of up to 25,000 h is challenging because current carbon support, catalyst, membrane, and ionomer developed for traditional light-duty vehicles cannot meet the stringent requirement. Therefore, understanding catalyst degradation mechanisms under the HDV condition is crucial for rationally designing highly active and durable platinum group metal (PGM) catalysts for high-performance membrane electrode assemblies (MEAs). Herein, we report a PGM catalyst consisting of platinum nanoparticles with a high content (40 wt %) on atomic-metal-site (e.g., MnN 4 )-rich carbon support. MEAs with the Pt (40 wt %)/Mn–N–C cathode catalyst achieved significantly enhanced performance and durability, generating 1.41 A cm –2 at 0.7 V under HDV conditions (0.25 mgPt cm –2 and 250 kPa abs pressure) and retaining 1.20 A cm –2 after an extended and accelerated stress test up to 150,000 voltage cycles. Electron microscopy studies indicate that most fine Pt nanoparticles are retained on or/and in the carbon support covered with the ionomer throughout the catalyst layer at the end of life. During the long-term stability test, the observed electrochemical active surface area reduction and performance loss primarily result from Pt depletion in the catalyst layer due to Pt dissolution and redeposition at the interface of the cathode and membrane. Importantly, the first-principle density functional theory calculations further reveal a support entrapment effect of the Mn–N–C, in which the MnN 4 site can specifically adsorb the Pt atom and further retard the Pt dissolution and migration, therefore enhancing long-term MEA durability.

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Molecular-scale Insights into Cooperativity Switching of x TAB Adsorption on Gold Nanoparticles

Quantifying adsorption behaviors is crucial for various applications such as catalysis, separation, and sensing, yet it is generally challenging to access in solution. Here, we report a combined experimental and computational study of the adsorption behaviors of alkyl-trimethylammonium bromides (xTAB), a class of ligands important for colloidal nanoparticle stabilization and shape control, with various alkyl chain lengths x on Au nanoparticles. We use density functional theory (DFT) to calculate xTAB binding energies on Au{111} and Au{110} surfaces with standing-up and lying-down configurations, which provides insights into the adsorption affinity and cooperativity differences of xTAB on these two facets. We demonstrate the key role of van der Waals interactions in determining the xTAB adsorption behavior. These computational results predict and explain the experimental discovery of xTAB’s adsorption behavior switch from stronger affinity, negative cooperativity to weaker affinity, positive cooperativity when the concentration of xTAB increases in solution. We also show that in the standing-up configuration, bilayer adsorption may occur on both facets, which can lead to different differential binding energies and consequently adsorption crossover between the two facets when the ligand concentration increases. Our combined experimental and computational approaches demonstrate a paradigm for gaining molecular-scale insights into adsorbate–surface interactions.

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Organosilica Nanoparticles

The dynamic field of nanotechnology is continually evolving, with organosilica nanoparticles (OSNPs) standing out as a significant and versatile class of materials. Combining the advantageous properties of organic and inorganic components, OSNPs offer unique capabilities that drive innovation across multiple scientific disciplines. Here, this primer is designed to be an accessible and informative guide for researchers embarking on their journey into the world of organosilica nanoparticles. Whether you want to incorporate OSNPs into your research or seek to understand the latest developments, this primer will provide the foundational knowledge and context needed to navigate this exciting and rapidly evolving field.

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Nanoparticle O–H Bond Dissociation Free Energies from Equilibrium Measurements of Cerium Oxide Colloids

A novel equilibrium strategy for measuring the hydrogen atom affinity of colloidal metal oxide nanoparticles is presented. Reactions between oleate-capped cerium oxide nanoparticle colloids (nanoceria) and organic proton-coupled electron transfer (PCET) reagents are used as a model system. Nanoceria redox changes, or hydrogen loadings, and overall reaction stoichiometries were followed by both 1 H NMR and X-ray absorption near-edge spectroscopies. Furthermore, these investigations revealed that, in many cases, reactions between nanoceria and PCET reagents reach equilibrium states with good mass balance. Each equilibrium state is a direct measure of the bond strength, or bond dissociation free energy (BDFE), between nanoceria and hydrogen. Further studies, including those with larger nanoceria, indicated that the relevant bond is a surface O–H. Thus, we have measured surface O–H BDFEs for nanoceria—the first experimental BDFEs for any nanoscale metal oxide. Remarkably, the measured CeO–H BDFEs span 13 kcal mol –1 (0.56 eV) with changes in the average redox state of the nanoceria colloid. Possible chemical models for this strong dependence are discussed. We propose that the tunability of ceria BDFEs may be important in explaining its effectiveness in catalysis. More generally, metal oxide BDFEs have been used as predictors of catalyst efficacy that, traditionally, have only been accessible by computational methods. These results provide important experimental benchmarks for metal oxide BDFEs and demonstrate that the concepts of molecular bond strength thermochemistry can be applied to nanoscale materials.

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