Chiwoo Park

Segmentation, Inference and Classification of Partially Overlapping Nanoparticles

This paper presents a method that enables automated morphology analysis of partially overlapping nanoparticles in electron micrographs. In the undertaking of morphology analysis, three tasks appear necessary: separate individual particles from an agglomerate of overlapping nano-objects; infer the particles missing contours; and ultimately, classify the particles by shape based on their complete contours. Our specific method adopts a two-stage approach: the first stage executes the task of particle separation, and the second stage conducts simultaneously the tasks of contour inference and shape classification. For the first stage, a modified ultimate erosion process is developed for decomposing a mixture of particles into markers, and then, an edge-to-marker association method is proposed to identify the set of evidences that eventually delineate individual objects. We also provided theoretical justification regarding the separation capability of the first stage. In the second stage, the set of evidences become inputs to a Gaussian mixture model on B-splines, the solution of which leads to the joint learning of the missing contour and the particle shape. Using twelve real electron micrographs of overlapping nanoparticles, we compare the proposed method with seven state-of-the-art methods. The results show the superiority of the proposed method in terms of particle recognition rate.

Probing the Degradation Mechanisms in Electrolyte Solutions for Li-Ion Batteries by in Situ Transmission Electron Microscopy

Development of novel electrolytes with increased electrochemical stability is critical for the next generation battery technologies. In situ electrochemical fluid cells provide the ability to rapidly and directly characterize electrode/electrolyte interfacial reactions under conditions directly relevant to the operation of practical batteries. In this paper, we have studied the breakdown of a range of inorganic/salt complexes relevant to state-of-the-art Li-ion battery systems by in situ (scanning) transmission electron microscopy ((S)TEM). In these experiments, the electron beam itself caused the localized electrochemical reaction that allowed us to observe electrolyte breakdown in real-time. The results of the in situ (S)TEM experiments matches with previous stability tests performed during battery operation and the breakdown products and mechanisms are also consistent with known mechanisms. This analysis indicates that in situ liquid stage (S)TEM observations could be used to directly test new electrolyte designs and identify a smaller library of candidate solutions deserving of more detailed characterization. A systematic study of electrolyte degradation is also a necessary first step for any future controlled in operando liquid (S)TEM experiments intent on visualizing working batteries at the nanoscale.

Observing the Growth of Metal–Organic Frameworks by in Situ Liquid Cell Transmission Electron Microscopy

Liquid cell transmission electron microscopy (LCTEM) can provide direct observations of solution-phase nanoscale materials, and holds great promise as a tool for monitoring dynamic self-assembled nanomaterials. Control over particle behavior within the liquid cell, and under electron beam irradiation, is of paramount importance for this technique to contribute to our understanding of chemistry and materials science at the nanoscale. However, this type of control has not been demonstrated for complex, organic macromolecular materials, which form the basis for all biological systems and all of polymer science, and encompass important classes of advanced porous materials. Here we show that by controlling the liquid cell membrane surface chemistry and electron beam conditions, the dynamics and growth of metal-organic frameworks (MOFs) can be observed. Our results demonstrate that hybrid organic/inorganic beam-sensitive materials can be analyzed with LCTEM and, at least in the case of ZIF-8 dynamics, the results correlate with observations from bulk growth or other standard synthetic conditions. Furthermore, we show that LCTEM can be used to better understand how changes to synthetic conditions result in changes to particle size. We anticipate that direct, nanoscale imaging by LCTEM of MOF nucleation and growth mechanisms may provide insight into controlled MOF crystal morphology, domain composition, and processes influencing defect formation.

Minimum Cost Multi-Way Data Association for Optimizing Multitarget Tracking of Interacting Objects

This paper presents a general formulation for a minimum cost data association problem which associates data features via one-to-one, m-to-one and one-to-n links with minimum total cost of the links. A motivating example is a problem of tracking multiple interacting nanoparticles imaged on video frames, where particles can aggregate into one particle or a particle can be split into multiple particles. Many existing multitarget tracking methods are capable of tracking non-interacting targets or tracking interacting targets of restricted degrees of interactions. The proposed formulation solves a multitarget tracking problem for general degrees of inter-object interactions. The formulation is in the form of a binary integer programming problem. We propose a polynomial time solution approach that can obtain a good relaxation solution of the binary integer programming, so the approach can be applied for multitarget tracking problems of a moderate size (for hundreds of targets over tens of time frames). The resulting solution is always integral and obtains a better duality gap than the simple linear relaxation solution of the corresponding problem. The proposed method was validated through applications to simulated multitarget tracking problems and a real multitarget tracking problem.

A Computable Plug-In Estimator of Minimum Volume Sets for Novelty Detection

A minimum volume set of a probability density is a region of minimum size among the regions covering a given probability mass of the density. Effective methods for finding the minimum volume sets are very useful for detecting failures or anomalies in commercial and security applications---a problem known as novelty detection. One theoretical approach of estimating the minimum volume set is to use a density level set where a kernel density estimator is plugged into the optimization problem that yields the appropriate level. Such a plug-in estimator is not of practical use because solving the corresponding minimization problem is usually intractable. A modified plug-in estimator was proposed by Hyndman in 1996 to overcome the computation difficulty of the theoretical approach but is not well studied in the literature. In this paper, we provide theoretical support to this estimator by showing its asymptotic consistency. We also show that this estimator is very competitive to other existing novelty detection methods through an extensive empirical study.

Colloidal Covalent Organic Frameworks

Covalent organic frameworks (COFs) are two- or three-dimensional (2D or 3D) polymer networks with designed topology and chemical functionality, permanent porosity, and high surface areas. These features are potentially useful for a broad range of applications, including catalysis, optoelectronics, and energy storage devices. But current COF syntheses offer poor control over the material’s morphology and final form, generally providing insoluble and unprocessable microcrystalline powder aggregates. COF polymerizations are often performed under conditions in which the monomers are only partially soluble in the reaction solvent, and this heterogeneity has hindered understanding of their polymerization or crystallization processes. Here we report homogeneous polymerization conditions for boronate ester-linked, 2D COFs that inhibit crystallite precipitation, resulting in stable colloidal suspensions of 2D COF nanoparticles. The hexagonal, layered structures of the colloids are confirmed by small-angle and wide-angle X-ray scattering, and kinetic characterization provides insight into the growth process. The colloid size is modulated by solvent conditions, and the technique is demonstrated for four 2D boronate ester-linked COFs. The diameter of individual COF nanoparticles in solution is monitored and quantified during COF growth and stabilization at elevated temperature using in situ variable-temperature liquid cell transmission electron microscopy imaging, a new characterization technique that complements conventional bulk scattering techniques. Solution casting of the colloids yields a free-standing transparent COF film with retained crystallinity and porosity, as well as preferential crystallite orientation. Collectively this structural control provides new opportunities for understanding COF formation and designing morphologies for device applications.

Understanding the Role of Solvation Forces on the Preferential Attachment of Nanoparticles in Liquid

Optimization of colloidal nanoparticle synthesis techniques requires an understanding of underlying particle growth mechanisms. Nonclassical growth mechanisms are particularly important as they affect nanoparticle size and shape distributions, which in turn influence functional properties. For example, preferential attachment of nanoparticles is known to lead to the formation of mesocrystals, although the formation mechanism is currently not well-understood. Here we employ in situ liquid cell scanning transmission electron microscopy and steered molecular dynamics (SMD) simulations to demonstrate that the experimentally observed preference for end-to-end attachment of silver nanorods is a result of weaker solvation forces occurring at rod ends. SMD reveals that when the side of a nanorod approaches another rod, perturbation in the surface-bound water at the nanorod surface creates significant energy barriers to attachment. Additionally, rod morphology (i.e., facet shape) effects can explain the majority of the side attachment effects that are observed experimentally.

Dynamic Data-Driven Fault Diagnosis of Wind Turbine Systems

In this multi-university collaborative research, we will develop a framework for the dynamic data-driven fault diagnosis of wind turbines which aims at making the wind energy a competitive alternative in the energy market. This new methodology is fundamentally different from the current practice whose performance is limited due to the non-dynamic and non-robust nature in the modeling approaches and in the data collection and processing strategies. The new methodology consists of robust data pre-processing modules, interrelated, multi-level models that describe different details of the system behaviors, and a dynamic strategy that allows for measurements to be adaptively taken according to specific physical conditions and the associated risk level. This paper summarizes the latest progresses in the research.

The Impact of Li Grain Size on Coulombic Efficiency in Li Batteries

One of the most promising means to increase the energy density of state-of-the-art lithium Li-ion batteries is to replace the graphite anode with a Li metal anode. While the direct use of Li metal may be highly advantageous, at present its practical application is limited by issues related to dendrite growth and low Coulombic efficiency, CE. Here operando electrochemical scanning transmission electron microscopy (STEM) is used to directly image the deposition/stripping of Li at the anode-electrolyte interface in a Li-based battery. A non-aqueous electrolyte containing small amounts of H2O as an additive results in remarkably different deposition/stripping properties as compared to the “dry” electrolyte when operated under identical electrochemical conditions. The electrolyte with the additive deposits more Li during the first cycle, with the grain sizes of the Li deposits being significantly larger and more variable. The stripping of the Li upon discharge is also more complete, i.e., there is a higher cycling CE. This suggests that larger grain sizes are indicative of better performance by leading to more uniform Li deposition and an overall decrease in the formation of Li dendrites and side reactions with electrolyte components, thus potentially paving the way for the direct use of Li metal in battery technologies.

Small Angle X-Ray Scattering Technique for the Particle Size Distribution of Nonporous Nanoparticles

Nanoparticles are small particles whose sizes are less than 100 nm. They have many industrial applications due to their unique properties. Their properties are often size-dependent; thus the accurate determination of nanoparticle sizes is important for quality assurance of nanoparticle production processes. A small angle X-ray scattering technique is a promising method used for characterizing nanoparticle sizes. It has distinctive advantages over other techniques such as electron microscope techniques. In this paper, we review the state-of-the-art methods for determining the sizes of nanoparticles with small angle X-ray experiments and discuss the advantages and limitations of the state-of-the-art methods.