Publications

Colloidal nanocrystal/DNA conjugates hold the promise of becoming powerful probes for biological diagnostics as well as versatile building blocks for nanotechnology. To fully realize this potential, it is important to precisely control the number of oligonucleotides bound to the nanocrystal. Here we demonstrate electrophoretic isolation of 5 and 10 nm gold nanocrystals bearing discrete numbers of single-stranded DNA (1−5). The potential use of these discrete conjugates in the fabrication of novel nanostructures is discussed.

We apply a scientific machine learning framework to aid the prediction and understanding of nanomaterial formation processes via a joint spectral-kinetic model. We apply this framework to study nucleation and growth of 2D perovskite nanosheets. Colloidal nanomaterials have size-dependent optical properties, and can be observed in situ, 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 via a layer-by-layer chemical exfoliation process from lead bromide nanocrystals or via 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 in-situ 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 the understanding of complex chemical systems.

Lanthanide-doped nanocrystals are of particular interest for the research community not only due to their ability to shape light by downshifting, quantum cutting, and upconversion but also because novel optical properties can be found by the precise engineering of core-shell nanocrystals. Because of the large surface area-to-volume ratio of nanocrystals, the luminescence is typically suppressed by surface quenching. Here, we demonstrate a mechanism that exploits surface quenching processes to improve the luminescence of our core-shell lanthanide-doped nanocrystals. By carefully tuning the shell thickness of inert β-NaLuF₄ around β-NaYF₄ nanocrystals doped with Yb³⁺ and Er³⁺, we unravel the relationship between quantum yield and shell thickness, and quantify surface quenching rates for the relevant Er³⁺ and Yb³⁺ energy levels. This enhanced understanding of the system's dynamics allowed us to design nanocrystals with a surface quenching-assisted mechanism for bright NIR to NIR downshifting with a distinctive efficiency peak for an optimized shell thickness.

Hole transfer between a CdSe/CdS core/shell semiconductor nanorod and a surface-ligated alkyl ferrocene is investigated by a combination of ab initio quantum chemistry calculations and electrochemical and time-resolved photoluminescence measurements. The calculated driving force for hole transfer corresponds well with electrochemical measurements of nanorods partially ligated by 6-ferrocenylhexanethiolate. The calculations and the experiments suggest that single step hole transfer from the valence band to ferrocene is in the Marcus inverted region. Additionally, time-resolved photoluminescence data suggest that two-step hole transfer to ferrocene mediated by a deep trap state is unlikely. However, the calculations also suggest that shallow surface states of the CdS shell could play a significant role in mediating hole transfer as long as their energies are close enough to the nanorod highest occupied molecular orbital energy. Regardless of the detailed mechanism of hole transfer, our results suggest that holes may be extracted more efficiently from well-passivated nanocrystals by reducing the energetic driving force for hole transfer, thus minimizing energetic losses.

Significance Currently, the effect of branching of nanoparticles tens of nanometers in size on the mechanical properties of structural composites is not well understood due to the limited availability of branched nanoscale fillers. We report that branched nanofillers have the potential for optimization of nanocomposite Young’s modulus over their linear counterparts. Lattice spring model simulations reveal that the mechanism for this improvement involves the ability of branched nanoparticles to optimize the ratio of both filler and stiff interfacial bonds aligned with the tensile axis, as opposed to linear nanoparticles, which optimize only the filler bond orientation when parallel to the stretching axis. We believe this information could inform the design of nanocomposites with optimized mechanical properties for a variety of structural applications.

We investigate the early stages of cesium lead bromide perovskite formation through absorption spectroscopy of stopped-flow reactions, high-throughput mapping, and direct synthesis and titration of potential precursor species. Calorimetric and spectroscopic measurements of lead bromide complex titrations combined with theoretical calculations suggest that bromide complexes with higher coordination numbers than previously considered for nonpolar systems can better explain observed behaviors. Synthesis mapping of binary lead halides reveals multiple lead bromide species with absorption peaks higher than 300 nm, including a previously observed species with a peak at 313 nm and two species with peaks at 345 and 370 nm that also appear as reaction intermediates during the formation of lead bromide perovskites. Based on theoretical calculations of excitonic energies that match within 50 meV, we give a preliminary assignment of these species as two-dimensional magic-sized clusters with side lengths of 2, 3, and 4 unit cells. Kinetic measurements of the conversion of benzoyl bromide precursor are connected to stopped-flow measurements of product formation and demonstrate that the formation of complexes and magic-sized clusters (i.e., nucleation) is controlled by precursor decomposition, whereas the growth rate of 2D and 3D perovskites is significantly slower.

Colloidal superlattices are fascinating materials made of ordered nanocrystals, yet they are rarely called “atomically precise.” That is unsurprising, given how challenging it is to quantify the degree of structural order in these materials. However, once that order crosses a certain threshold, constructive interference of X-rays diffracted by the nanocrystals dominates the diffraction pattern, offering a wealth of structural information. By treating nanocrystals as scattering sources forming a self-probing interferometer, we developed a multilayer diffraction method that enabled the accurate determination of nanocrystal size, interparticle spacing, and their fluctuations for samples of self-assembled CsPbBr<sub>3</sub> and PbS nanomaterials. The average nanocrystal displacement of 0.32-1.4 Å in the studied superlattices provides a figure of merit for their structural perfection and approaches the atomic displacement parameters found in traditional crystals. The method requires a laboratory-grade diffractometer and an open-source fitting algorithm for data analysis, providing a competitive alternative to resource-intensive synchrotron experiments.

The synthesis quality of artificial inorganic nanocrystals is most often assessed by transmission electron microscopy (TEM) for which new high-throughput advances have dramatically increased both the quantity and information richness of metal nanoparticle (mNP) characterization. Existing automated data analysis algorithms of TEM mNP images generally adopt a supervised approach, requiring a significant effort in human preparation of labelled data that reduces objectivity, efficiency, and generalizability. We have developed an unsupervised algorithm AutoDetect-mNP for automated analysis of TEM images that objectively extracts morphological information of convex mNPs from TEM images based on their shape attributes, requiring little to no human input in the process. The performance of AutoDetect-mNP is tested on two datasets of bright field TEM images of Au nanoparticles with different shapes, demonstrating that the algorithm is quantitatively reliable, and can thus serve as a generalizable measure of the morphology distributions of any mNP synthesis. The AutoDetect-mNP algorithm will aid in future developments of high-throughput characterization of newly synthesized mNPs, and the future advent of time-resolved TEM studies that can investigate reaction mechanisms of mNP synthesis and reactivity.

Formation mechanisms of dendrite structures have been extensively explored theoretically, and many theoretical predictions have been validated for micro- or macroscale dendrites. However, it is challenging to determine whether classical dendrite growth theories are applicable at the nanoscale due to the lack of detailed information on the nanodendrite growth dynamics. Here, we study iron oxide nanodendrite formation using liquid cell transmission electron microscopy (TEM). We observe "seaweed"-like iron oxide nanodendrites growing predominantly in two dimensions on the membrane of a liquid cell. By tracking the trajectories of their morphology development with high spatial and temporal resolution, it is possible to explore the relationship between the tip curvature and growth rate, tip splitting mechanisms, and the effects of precursor diffusion and depletion on the morphology evolution. We show that the growth of iron oxide nanodendrites is remarkably consistent with the existing theoretical predictions on dendritic morphology evolution during growth, despite occurring at the nanoscale.

The electronic structure of cobalt nanocrystals suspended in liquid as a function of size has been investigated using in-situ x-ray absorption and emission spectroscopy. A sharp absorption peak associated with the ligand molecules is found that increases in intensity upon reducing the nanocrystal size. X-ray Raman features due to d-d and to charge-transfer excitations of ligand molecules are identified. The study reveals the local symmetry of the surface of {var_epsilon}-Co phase nanocrystals, which originates from a dynamic interaction between Co nanocrystals and surfactant + solvent molecules.

In the present work, we analyze the geometry and composition of the nanostructures obtained from the oxidation of iron nanoparticles. The initial oxidation of iron takes place by outward diffusion of cations through the growing oxide shell. This net material flow is balanced by an opposite flow of vacancies, which coalesce at the metal/oxide interface. Thus, the partial oxidation of colloidal iron nanoparticles leads to the formation of core−void−shell nanostructures. Furthermore, the complete oxidation of iron nanoparticles in the 3−8 nm size range leads to the formation of hollow iron oxide nanoparticles. We analyze the size and temperature range in which vacancy coalescence during oxidation of amine-stabilized iron nanoparticles takes place. Maghemite is the crystallographic structure obtained from the complete oxidation of iron nanoparticles under our synthetic conditions.