Publications

In situ soft X-ray absorption spectroscopy (XAS) was employed to study the adsorption and dissociation of carbon monoxide molecules on cobalt nanoparticles with sizes ranging from 4 to 15 nm. The majority of CO molecules adsorb molecularly on the surface of the nanoparticles, but some undergo dissociative adsorption, leading to oxide species on the surface of the nanoparticles. We found that the tendency of CO to undergo dissociation depends critically on the size of the Co nanoparticles. Indeed, CO molecules dissociate much more efficiently on the larger nanoparticles (15 nm) than on the smaller particles (4 nm). We further observed a strong increase in the dissociation rate of adsorbed CO upon exposure to hydrogen, clearly demonstrating that the CO dissociation on cobalt nanoparticles is assisted by hydrogen. Our results suggest that the ability of cobalt nanoparticles to dissociate hydrogen is the main parameter determining the reactivity of cobalt nanoparticles in Fischer-Tropsch synthesis.

We apply a scientific machine learning (ML) framework to aid the prediction and understanding of nanomaterial formation processes via a joint spectral-kinetic model. We apply this framework to study the nucleation and growth of two-dimensional (2D) perovskite nanosheets. Colloidal nanomaterials have size-dependent optical properties and can be observed <i>in situ</i>, all of which make them a good model for understanding the complex processes of nucleation, growth, and phase transformation of 2D perovskites. Our results demonstrate that this model nanomaterial can form through two processes at the nanoscale: either <i>via</i> a layer-by-layer chemical exfoliation process from lead bromide nanocrystals or <i>via</i> direct nucleation from precursors. We utilize a phenomenological kinetic analysis to study the exfoliation process and scientific machine learning to study the direct nucleation and growth and discuss the circumstances under which it is more appropriate to use phenomenological or more complex machine learning models. Data for both analysis techniques are collected through <i>in situ</i> spectroscopy in a stopped flow chamber, incorporating over 500,000 spectra taken under more than 100 different conditions. More broadly, our research shows that the ability to utilize and integrate traditional kinetics and machine learning methods will greatly assist in the understanding of complex chemical systems.

Liquid phase electron microscopy (LPEM) is rapidly gaining importance for in situ studies of chemical processes. However, radiolysis due to interactions between the liquid medium and the electron beam results in the formation of highly reactive species that influence the studied processes. Our understanding of LPEM radiolysis is currently based on simulations that rely on data collected from measurements at low electron flux intensities, requiring extrapolation by several orders of magnitude to match the intensities utilized in LPEM. We demonstrate direct electrochemical measurements of radiolytic products during in situ LPEM, which allows us to directly assess the high flux accuracy of low-flux radiolysis models. Using a specially designed liquid cell for electrochemical detection, we quantify the primary expected stable radiolysis products H2 and H2O2 in a scanning electron microscope. We find H2 production is rapid and in reasonable agreement with predictions, but H2O2 levels are lower than expected from the low-flux extrapolated radiolysis models. This study demonstrates a new approach to experimentally validate simulations and indicates that the chemical environment may be far more reducing than predicted from current models.

In this paper, we demonstrate the synthesis of CdTe tetrapods, consisting of four nanorod arms extending, out at tetrahedral angles, including control of arm length and diameter. Applications of these new nanocrystals will exploit their optical, electronic and mechanical properties.

We synthesized and characterized a set of ultrasmall hexagonal-phase NaGdF₄: 20% Yb³⁺, 2% Er³⁺ upconversion nanoparticles with core diameters of 3.7 ± 0.5 nm. In order to assess passivation effects and the influence of possible core-shell intermixing and to identify optimum particle structures for combined imaging in the visible and near-infrared (vis-NIR: 410-850 nm) and short-wave infrared (SWIR: 1520 nm), NaYF₄ shells of varying thicknesses (monolayer to 10 nm) were introduced and the influence of this parameter on the upconversion and downshifting photoluminescence of these particles was studied at different excitation power densities. This included excitation power-dependent emission spectra, slope factors, quantum yields, and excited state decay kinetics. These measurements revealed enhancement factors of the upconversion quantum yield of >10 000 in the low power region and an excitation power density-independent quantum yield of the downshifted emission at 1520 nm between 0.1 and 14%. The optimized shell thickness for combined vis and SWIR imaging was identified as 5 nm. Moreover, lifetimes and quantum yields can be continuously tuned by shell thickness which can be exploited for lifetime multiplexing and encoding. The fact that we did not observe a saturation of the upconversion quantum yield or the excited state decay kinetics with increasing shell thickness is ascribed to a strong intermixing of the active core with the inert shell during the shelling procedure. This indicates the potential of spectroscopic tools to detect cation intermixing.

The surfactant-mediated shape evolution of titanium dioxide anatase nanocrystals in nonaqueous media was studied. The shape evolves from bullet and diamond structures to rods and branched rods. The modulation of surface energies of the different crystallographic faces through the use of a surface selective surfactant is the key parameter for the shape control.

The elastic stiffness of two polymer nanocomposite systems is investigated. The nanoscale fillers comprise cadmium selenide (CdSe, ∼4 nm) and cadmium selenide/cadmium sulfide (CdSe/CdS, ∼13 nm) quantum dots (QDs). The QDs are embedded within an electrospun structural block copolymer, poly(styrene-ethylene-butylene-styrene) (SEBS). Tensile testing shows a monotonic decrease in the tensile Young's modulus with increasing partially phase-separated QD concentration; this is to be compared to corresponding nanocomposites reinforced with nanorod (NR) and tetrapod (TP)-SEBS nanocomposites which show a monotonic increase with particle loading. While most studies to date emphasize the increase in Young's modulus in polymer nanocomposites at higher reinforcement loadings, few focus on the tunability of the modulus from reductions in stiffness. The present work reveals up to an ∼80% reduction in tensile Young's modulus with the addition of 5 vol % of QDs to electrospun SEBS. In this study, we sought mechanistic insight into this reduction in composite stiffness using a 2D lattice spring model. Simulation results reveal that the stiffness decrease with the addition of QD reinforcements is likely due to cavitation in the polymer in the vicinity of the QD aggregates arising from polymer debonding under tension. We anticipate that this study, performed with a commonly used structural rubber, may find use in designing polymer–matrix nanocomposite fibers with specific Young's moduli for applications requiring a tunable lower stiffness material.

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Abstract Scanning transmission electron microscopy is a common tool used to study the atomic structure of materials. It is an inherently multimodal tool allowing for the simultaneous acquisition of multiple information channels. Despite its versatility, however, experimental workflows currently rely heavily on experienced human operators and can only acquire data from small regions of a sample at a time. Here, we demonstrate a flexible pipeline-based system for high-throughput acquisition of atomic-resolution structural data using an all-piezo sample stage applied to large-scale imaging of nanoparticles and multimodal data acquisition. The system is available as part of the user program of the Molecular Foundry at Lawrence Berkeley National Laboratory.