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Reference | Presentation | Center for Nanoscale Materials

2020 Seminars Archive

Current CNM seminars

Date Title
November 23, 2020

"Microfluidics for ultra-high-throughput Chemistry and Biology", Andrew deMello, Department of Chemistry & Applied Biosciences, ETH Zurich. Host: Elena Shevchenko/ Xiao-Min Lin

The past 25 years have seen considerable progress in the development of microfabricated systems for use in the chemical and biological sciences. Interest in microfluidic technology. At a basic level, microfluidic activities have been stimulated by the fact that physical processes can be more easily controlled when instrumental dimensions are reduced to the micron scale. The relevance of such technology is significant and characterized by a range of features that accompany system miniaturization.

My lecture will discuss how the spontaneous formation of droplets in microfluidic systems can be exploited to perform a variety of complex analytical processes and why the marriage of such systems with optical spectroscopies provides a direct route to high-throughput and high-information content experimentation.

Droplet-based microfluidic systems allow the generation and manipulation of discrete droplets contained within an immiscible continuous phase. Significantly, they allow for the production of monodisperse droplets at rates in excess of tens of KHz and independent control of each droplet in terms of size, position and chemical makeup. I will provide examples of how droplet-based microfluidic systems can be used to perform a range of experiments including nanomaterial synthesis, cell- based assays and DNA amplification. Finally, the handling and processing of fL-nL volume fluids represents a critical challenge for molecular detection, and still defines one of the key limitations in the use of a microfluidic system in a given application. To this end, I will also describe recent studies focused on the development of novel label- free detection methods and imaging flow cytometry platforms.

October 16, 2020

"Coherent X-rays to Probe Structural Heterogeneities in Matter", Dina Sheyfer, Materials Science Division, Argonne National Laboratory. Host: Martin Holt

Progress in advanced functional materials across a broad spectrum of applications relies on understanding interactions and heterogeneity at nanometer-to-micrometer length scales. The nanoscale heterogeneities can be harnessed to alter the structural, magnetic, electronic, and electrochemical behavior; they govern the physical processes and macroscopic properties in matter. In particular, nanostructured complexes' interactions in liquids govern the phase separation phenomena, and local crystalline defects in catalysts affect the electro-catalytic activity. Despite great effort and success in discovering and characterizing macroscopic states of matter, progress in understanding the fundamental mechanisms behind these phenomena is often hampered by the challenges of probing structural dynamics of matter at the nanoscale. Recent developments in coherent x-ray sources and methods significantly advance the possibilities of nanoscale measurements, offering superb spatial and temporal resolution (ultrasmall and ultrafast). In this seminar, I will present my current research activities in applying frontier coherent X-rays methods - Bragg Coherent Diffractive Imaging and X-ray Photon Correlation Spectroscopy - to probe structure and dynamics of local heterogeneities in soft and hard condensed matter. I will discuss current and future integration and development of coherent methods with diffraction-limited focused beams in the context of the strategic plan of the Center for Nanoscale Materials and near-term of the Advanced Photon Source Upgrade.

October 15, 2020

"Exploration of unbiased and fingerprint based biased potential energy surface of nano materials", Deb Sankar De, University of Basel. Host: Maria Chan

The potential energy landscape (PES) is a fundamental property in computational modeling for a compound. The detailed knowledge of the PES topology allows the prediction of the equilibrium conformations, thermodynamic and dynamic properties of multiatomic systems, e.g. molecules, clusters, or bulk. The first part of the presentation will be on unbiased structure prediction at the tight binding density functional theory (DFTB) level to explore different stable configurations of 4H-SiC in different environments. SiC is a wide band gap semiconductor suitable for high power and frequency electronic devices. However, experimental results suggest that during the formation of SiO2 layer on the surface of SiC substrate by thermal oxidation the SiO2/4H-SiC interfaces are dominated by π bonded carbon clusters. During oxidation, we have observed carbon 5 or 6 ring clusters as well as carbon silicon chains which consist of carbon rings and carbon silicon rings, appeared at the interface of SiO2/4H-SiC. These findings provide novel insight into the structural and electronic properties of the realistic SiO2/SiC interface.

The second part will be on exploration of biased PES to understand complex reaction pathways. Determining the pathway of a reaction/transformation is of great importance in chemistry, physics and materials sciences. However, due to the indistinguishability of atoms, finding complex reaction and transformation pathways, containing a large number of intermediate states, is difficult within the existing methods at the density functional theory level. We have resolved this issue by introducing a bias that is invariant under atomic index permutations and that can target a single well defined configuration as the final configuration of a chemical reaction or physical transformation. In this way we can overcome the index mapping problem. The forces arising from the bias, by construction, do not depend on the indexing of the atoms. We have thus reduced the combinatorial atomic indexing problem, that has an exponential scaling, to a global minimization problem on a biased PES involving an indexing invariant penalty function. The later problem can be solved easily in practice. The penalty function we propose is universal and can be applied to any reaction or transformation. We expect that this method will give atomistic insight into complex reaction pathways i.e. in catalysis as well as complex phase and shape transformations.

October 14, 2020

"Build a Synchrotron in An Electron Microscope for Nanoscience Research", Feng Wang, Energy & Photon Sciences Directorate, Brookhaven National Laboratory

Designing materials of desired functionality requires characterizing their structure and properties at the relevant spatial and temporal scales and, ideally, under the real operating conditions. Electron microscopy, as presented by scanning/transmission electron microscopy (S/TEM)-electron energy-loss spectroscopy (EELS), is arguably the most cherished characterization technique as it allows not only ​"seeing" individual atoms, but also identifying their chemical and bonding states, and tracking how they dynamically evolve in an operating device. Due to the advancement in aberration correction, monochromation and direct electron detection, the imaging/spectroscopy capability of a modern transmission electron microscope (TEM) has been greatly enhanced, making it a powerful machine that may rival a 3rd-generation synchrotron for nanoscience research. The recent development, particularly in in situ, operando and ultrafast techniques, opens a fundamentally new window for unveiling the transport properties of electrodes and solid-electrolyte interfaces (SEI) that dictate the real-world performance of batteries and other electrochemical devices.

In this talk, I will present our recent efforts in developing S/TEM-EELS techniques specialized to characterizing radiation-sensitive battery materials with high spatial/temporal resolution and chemical sensitivity, and to tracking electronic/ionic transport locally within individual particles and across SEI in batteries. I will further discuss the new opportunities for correlative electron/X-ray spectro-imaging at multiple length scales in multiple dimensions (i.e., 4D of the real space and time, plus the energy and momentum dimensions), by pushing the limit of the start-of-the-art instrumentation/sample environment and coupling with machine learning/data analytics. I will conclude by presenting my vision of shaping the TEM facility, like the one at CNM, into a world-leading user facility, featured by ​"A Synchrotron in A Microscope" that enables cutting-edge nanoscience research.

October 13, 2020

"Building Better Batteries: From Mechanistic Insights to Practical Advancements", Shanshan Yao, Interdisciplinary Science Department, Brookhaven National Laboratory. Host: Yuzi Liu

Ever-growing global electric energy storage demand arising from the booming markets of portable electronics, electric vehicles (EVs) and unmanned aerial vehicles motivates the development of safe, sustainable, and cost-effective energy storage systems with high energy/power densities and long cyclic life. Apart from the conventional lithium ion batteries (LIBs), sodium ion batteries (SIBs) and lithium sulfur batteries (LSBs) exhibit great potentials as alternative candidates for next-generation batteries due to the much cheaper precursor materials, environmental benignity and electrochemical performance comparable to LIBs. In order to realize their successful applications to power EVs and smart grids in the near future, it is essential to develop energy storage materials with abundant resources, rationally designed functional and structural features and excellent structural stabilities. This presentation focuses mainly on promising energy storage nanomaterials e.g., red P, Sb2S3, and fast-charging lithium titanate, in an effort to mitigate the critical challenges known to rechargeable batteries and accelerate their practical commercialization. Adopting the cutting-edge experimental techniques, such as in situ TEM/SAED and synchrotron XRD/XAS studies with theoretical calculations, the underlying relationship between the microstructural features and electrochemical properties are well established.

October 9, 2020

"Data Driven Models for Microscopic Image Analysis", Sikha Okkath Krishnanunni, Amrita Vishwa Vidyapeetham University. Host: Maria Chan

With rapid evolution of microscopy techniques and the overwhelming amount of available visual data, there is a demand for automated image processing and analysis solutions in biological studies. Computerized microscopic image analysis plays an important role, since it allows to process huge volume of image data by extracting detailed information which is not captured by human eye. Due to the large amount of visual data, data-driven methodologies started overruling classical image processing algorithms. This talk primarily focuses on various data-driven methodologies for object detection (Region of Interest) and segmentation which is a crucial step in microscopic image analysis. Two major data-drivel models are considered for the study: Dynamic Mode Decomposition (DMD) and Deep-Learning Models.

Dynamic Mode Decomposition (DMD) is an emerging tool originally introduced in the fluid mechanics community for analyzing the behavior of nonlinear systems in an equation free manner. The key idea is to utilize the analytical power of Dynamic Mode Decomposition which combines two of the prominent data analysis tools, namely Principle Component Analysis and Fourier analysis, to get the insight of an image and use that information to generate an initial saliency map. Saliency map highlights the part of an image that grabs human attention in the first sight in terms of various low-level image features such as color, intensity etc. The study explores the application of color-based Dynamic Mode Decomposition based saliency model for the detection and segmentation of blasts from microscopic cancer images. The Dynamic Mode Decomposition based saliency model was further extended for the extraction of salient edge map, which represents the boundary of prominent [most salient] object in the image. Intensity based DMD model was explored for the detection of region of interest from monochrome microscopic images such as TEM (Transmission Electron Microscopic images) or STEM (Scanning Transmission Electron Microscopic images).

The second part of the study explores deep-learning models, which tries to capture the inherent structures in an image from a labelled dataset for various applications. Deep-learning model known as Mask RCNN(MRCNN) is explored for the detection and segmentation of blasts from microscopic cancer images. The Mask-RCNN is an instance segmentation algorithm that identifies pixel wise boundary of objects belonging to a particular class and segments them into different instances. Application of MRCNN for the detection of object of interest from STEM image is also explored. Application of deep learning models for the automatic classification of microscopy images is also included in the study. A multi input classifier using deep features is proposed for the classification SEM (Scanning Electron Microscopy) images.

October 8, 2020

"In Situ Single Particle Studies of High Capacity Anode Materials for Lithium-Ion Batteries", Xinwei Zhou, Department of Mechanical Engineering, Indiana University-Purdue University Indianapolis. Host: Yuzi Liu

Group IV elements (Si, Ge, Sn) have been considered promising anode materials for lithium-ion batteries because their high theoretical specific capacities. However, these elements have large volume change during lithiation and delithiation, which causes pulverization of active material, leading to fast capacity fading. To address this issue, it is necessary to understand the microstructure evolution and electrochemical performance of these elements during cycling. In the past few years, we developed different in situ single particle methods in focused ion beam - scanning electron microscopy (FIB-SEM) and transmission X-ray microscopy (TXM) systems. In this report, I will talk about three projects based on different methods. The first two projects are FIB-SEM studies of Sn and Ge anodes. The third project is TXM study of Ge/GeSe electrodes. I will also talk about my current work on solid state electrolytes and future research plan.

October 5, 2020

"Manipulation of nanoscale interactions for energy transduction and conversion", Elena A. Rozhkova, Nanoscience and Technology Division, Argonne National Laboratory. Host: Gary Wiederrecht

The light-matter interaction has been making a major impact since the ignition and evolution of life on Earth. It is the cornerstone of modern life-changing technologies such as sustainable energy, sensing, imaging, and cutting-edge quantum information science. The study of this phenomenon on fundamental and applied levels unites physics, chemistry, life sciences, materials science, and engineering.

In our work, we use a powerful combination of chemical synthesis, fabrication, and synthetic biology to develop hybrid hierarchical structures from atoms and molecules, resulting in new functions that go far beyond the individual starting components.

This talk will include recent examples of engineered nano-hybrid architectures based on light-driven natural proton pump fused with inorganic nanostructures for applications in H2 production, CO2 reduction to value-added chemicals, and cell-like ATP synthesis.

I will also show an example of impactful user work on magnetic particles-enabled magneto-mechanical energy conversion to ionic signaling, which opened up a new field of research for future healthcare technologies.

Finally, I will provide some ideas about future opportunities within the unique synergistic expertise, tools, synthetic capabilities, and collaborative experiences available in the nPBS group.

October 2, 2020

"In Situ X-ray Studies on Semiconductor Nanostructures: Diffraction, Imaging, and Beyond", Tao Zhou, Nanoscience and Technology Division, Argonne National Laboratory. Host: Martin Holt

Synchrotron X-ray is a powerful tool for the non-destructive characterization of nanoscale materials. In this talk, I will be covering my experience on three different beamlines, centered on the in situ studies of semiconductor nano-structures. I will start by describing our work on the in situ growth of Ge/Si thin film and nanowires, using grazing incidence x-ray diffraction. I'll then showcase our operando study on Si electrodes for Li-ion battery and time resolved study on surface acoustic wave devices, using full field diffraction imaging. For the future directions, I shall talk about our ongoing work on the development of a unified method for coherent diffraction imaging (Bragg Ptychography), enabled by AI related tools. I will also mention how such tools can be applied for phase retrieval in electron microscopy.

September 30, 2020

"Coupled Lattice and Carrier Dynamics in Technologically Relevant Semiconductors", Richard Schaller, Nanoscience and Technology Division, Argonne National Laboratory, Host: Gary Wiederrecht

Colloidal semiconductor nanomaterials such as 0D quantum dots and 2D nanoplatelets can be produced on large scale with precise control over ensemble optical properties. Quantum confinement in such systems offers size-tunable energy gap, strong photoluminescence, and, in some cases desirable properties such as optical gain and lasing. Discretized energy levels, distinct electronic structure relative to the bulk composition, and surface features of these systems dominate physics within. Roles of electron-phonon coupling, thermal energy deposition, and dissipation in these nanoscopic structures can impact properties and engender limits on material performance and stability with appreciable voids in understanding. Research I've performed initially concentrated on carrier dynamics in these structures and has evolved to target comprehensive understanding of carrier-phonon and phonon-phonon couplings. Methods implemented to probe these questions span transient absorption, ultrafast photoluminescence, time-resolved x-ray diffraction, and transient Raman. Future directions of importance for discerning carrier and lattice dynamics and address longstanding gaps in fundamental understanding of these and related structures, as will other thoughts regarding opportunities.

September 28, 2020

"Microstructural Investigation of Ti-6A1-4V Using Phase Field Modeling and Image-Driven Machine Learning", Arun Baskaran, Materials Science and Engineering, Rensselaer Polytechnic Institute. Host Maria Chan

A key factor that has accelerated the material design process in recent times has been the integration of computational methods and data science initiatives with experimental techniques. Research efforts focused on phase field modeling and image-driven machine learning, in collaboration with an experimental research team, has enabled an improved understanding of microstructure and microstructural phenomena in Ti-6 wt%Al-4 wt%V (Ti64). Ti64 is a dual-phase alloy at room temperature, with the stable phases being α (HCP) and β (BCC). A multi-phase field model is implemented to simulate the microstructural evolution in Ti64 under the conditions of continuous cooling and applied stress. The growth rate of α lamellar-tip under different cooling conditions was recorded using in-situ SEM and has been incorporated into the phase field model to enable quantitative simulations of continuous cooling. In addition, incorporating an explicit nucleation scheme using the Order-Parameter-Only (OPO) method and an analytically consistent nucleation rate has enabled us to model the Ti64 morphologies that is expected for different processing routes. The goal of augmenting the human expert's inference of material microstructure images has been enabled by a research effort that focuses on image-driven machine learning methods. In the first project as part of this effort, contextual segmentation of morphological features was performed on a dataset of Ti64 micrographs by implementing a task pipeline. The pipeline consisted of a convolutional neural network for high-level classification of input images. Subsequently, label-specific feature segmentation was performed using histogram of gradients and marker-based watershed algorithms. In the second project within this focus area, a generative adversarial network was trained using a progressive growing technique to produce a dataset of synthetic microstructures. The synthetic and real images were converted to their representation in Fourier space, and the spatial frequencies exhibited by the images were analyzed to get a deeper insight into the robustness of the generative model. Finally, work in progress is reported towards an ongoing review of the adoption of computer vision techniques to fulfill and augment the requirements of material scientists.

September 25, 2020

"Dynamics of Ultrafast Melting of Grains in Polycrystalline Gold Thin Films", Tadesse A. Assefa, Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory, Host: Martin Holt

Abstract: Functional materials are often amorphous or polycrystalline with a large density of defects and grain boundaries, which affects their property. Understanding the structure of materials with complementary non-destructive X-ray techniques becomes essential to design better and more efficient materials. This talk will describe the results of an ultrafast single-shot melting experiment carried out at the PAL-XFEL, Pohang Accelerator Laboratory, Korea, combined with synchrotron-based X-ray nanodiffraction to examine the domain formed. Polycrystalline thin films of gold were irradiated with femtosecond optical pulses in the fluence range sufficient to melt the film. Shortly after the irradiation, a single-shot X-ray diffraction pattern was captured on a large area detector. Heterogeneous two-phase melting was observed in the form of a split Au (111) diffraction powder ring. The colder, high-Q, peak showed oscillations as a function of pump-probe delay time, consistent with acoustic waves generated in the film. The new peak on the low-Q side was attributed to a compressed, hotter region of gold that absorbs the latent heat during the melting and increases with the melt-front moving in time. To understand the grain size effects of melting, we further characterize individual grains from fully and partially laser melted regions of the same sample at Hard X-ray Nanoprobe (HXN), NSLS-II, using X-ray nanodiffraction (nanoXRD) technique. Our preliminary result shows that the grain sizes in the laser melted region have increased compared with the average grain sizes of the thin film estimated using the Scherrer formula. Also, the individual grains in the laser melted region are less strained compared to the grains from the unmelted region. Further thermal annealing results in larger grains at the expense of the small ones due to surface tension effects like an Ostwald ripening process.

September 23, 2020

"Nano-imaging with X-ray ptychography: method development and application", Yudong Yao, X-ray Science Division, Argonne National Laboratory. Host: Martin Holt

Ptychography has become a very popular and successful imaging technique that is able to view the internal structure of samples at a high spatial resolution that is not limited by the quality of the focusing optics. X-ray ptychography has been demonstrated to obtain quantitative insight of samples at sub-10 nm resolution, in material science, biology, and electronics. Combined with the scanning x-ray fluorescence microscopy, which shares the same scanning mechanism as the ptychography technique, this correlative method can provide both elemental and structural information of specimens at high spatial resolution. In addition, by acquiring ptychographic projections for a set of rotation angles, ptychography also has valuable applications in 3D X-ray imaging. As a combination of coherent diffraction imaging and scanning microscopy techniques, ptychography allows imaging for extended sample region with a customizable field-of-view (FOV). But this comes at a cost of the increasing data acquisition time and data volume, resulting in a trade-off between the large FOV and high spatial resolution in ptychographic imaging. The broadband illumination ptychography method and multi-beam ptychography method were developed to achieve high-throughput, high-resolution imaging. This talk will introduce the X-ray ptychography technique and its applications, as well as the development of high-throughput ptychography methods.

September 21, 2020

"Dynamic Structural Imaging Through Nanoscale Bragg Diffraction Microscopy", Martin Holt, Argonne National Laboratory

The unique imaging power of nano-focused synchrotron hard x-rays can be harnessed to provide non-destructive methods for 3D visualization of crystallographic phase and strain in solid-state materials. This gives access to understanding extremely subtle lattice perturbations (10^-5 dc/c) near optically active defects or interfaces within fabricated heterostructures that can be located potentially microns away from surfaces without sectioning the sample. The use of time-resolved coherent synchrotron illumination synchronized to external stimuli can further augment this approach to understand excitation-driven energy flow and dynamic structure-function relationships across broad classes of classical and quantum materials for energy. Current work and future directions such as the integration of these approaches with in-situ and time-resolved electron microscopy capabilities at the CNM for true multimodal / multi-platform microscopy, as well as new capabilities enabled by data-driven algorithmic methods and the near-term completion of diffraction limited storage rings such as the Advanced Photon Source Upgrade (APSU) will be explored in the context of recent results.

July 16, 2020

"Structural dynamics enabled functionality in quantum nanoscale materials", Vladimir Stoica, Pennsylvania State University. Host: Jianguo Wen

Ultrafast science is focused on enabling the control and visualization of systems that have been driven out of equilibrium. By following the time-resolved response, we can separate competing or cooperating orders connected to functional properties of quantum materials and use this knowledge to tackle their fundamental understanding. A paradigm shift in this field is not only moving forward the dynamical characterization strategies with nanoscale resolution, but also the discovery of new states of matter by design. In this talk, I will focus on a few forefront examples from my recent work on light-induced manipulation of complex order in quantum materials that present emergent dynamics in their structural, electronic, and magnetic degrees of freedom. The key is to harness probes that can visualize specific types of order directly and connect those findings with theory. This talk with cover examples of polar (structure), electronic, magnetic, and topological order. I will highlight an example of manipulation of complex polar topology in superlattices that hosts novel excitations when driven by an ultrafast electric field via a THz pulse or can host new ordered phases with optical excitation, that do not exist on the equilibrium phase diagram. The second example will explore the selective probing of structural, electronic and magnetic order optically generated metastable metal-insulator transition in a manganite oxide, which is elucidated by sorting out the cooperative magnetic, charge, orbital and structural ordering evolution with resonant and non-resonant X-ray scattering techniques. Finally, I will show work using ultrafast electron diffraction that is combined with optical spectroscopy to provide clues on the possible phonon dynamics implications on the anomalous quantum Hall effect observed in MnBi2Te4 topological insulators. With relation to future opportunities with ultrafast electron microscopy, I will discuss how moving from a scattering (reciprocal space) probe to real space can decisively illuminate related phenomena observations made with nanoscale resolution. Such combined multi-modal approaches are promising for advancing the coherent lattice control of quantum materials, which is important for understanding the transduction and decoherence that are essential components underpinning quantum information systems.

July 16, 2020

"Achieving Practical Applications of Quantum Computers", Matthew J. Otten, Nanoscience and Technology Division, Argonne National Laboratory. Host: Subramanian Sankaranarayanan

Recently, the Google quantum computing team claimed to have achieved quantum supremacy, carrying out a well-defined task on a quantum computer that would be intractable on classical computers. The task they performed this on, however, does not have clear practical applicability. Demonstrating practical applications of quantum computers is still an open problem. I will discuss my recent efforts to achieve practical applications of quantum computers, involving development of new quantum algorithms tailored to near-term devices, methods for understanding and mitigating inevitable errors, and the use of classical simulations and modeling to improve hardware. Advancements in each of these directions will be necessary to achieve practical applications of quantum computers.

July 15, 2020

"Probing Material Dynamics with Time-Resolved TEM", Thomas Gage, Nanoscience and Technology Division, Argonne National Laboratory. Host: Jianguo Wen

Time-resolved TEM is a powerful tool which can aid in the understanding of a vast array of material science questions by enabling visualization of localized dynamic processes. One such example presented here involves the amorphous to crystalline phase transformation. Yttrium iron garnet (YIG) has emerged as an important material in several scientific fields due to its unique magnetic and optical properties. Thin film integration of this complex oxide with common substrate materials has proven challenging due to lattice and thermal expansion mismatches resulting in poor crystallization and limiting its usefulness in practical applications. Here, we discover a novel annealing method for better YIG crystallization on non-garnet substrates and report the existence of a metastable phase which emerges during the annealing process. This research was enabled by a unique in situ laser annealing method which can be extended to other materials for high-throughput annealing studies. Also included in this talk will be a discussion of the new UEM tool at Argonne National Laboratory and its possible future research directions.

July 10, 2020

"Atomistic insights into Quantum materials: Defects and their functional properties", Shaobo Cheng, Department of Condensed Matter Physics and Materials Science, Brookhaven National Laboratory. Host: J.G. Wen

As main components of correlated quantum materials, Mott insulators and multiferroic materials hold promise for use in the next generation of electronic devices. However, the emergent physics within these systems remain poorly understood. In this talk, I will present our works on various quantum materials, including VO2 Mott insulator and multiferroic hexagonal systems, and show how charge, spin and lattice interact with each other. Taking advantage of the state-of-the-art transmission electron microscopy, we have systematically studied the atomic mechanisms triggering anomalous behaviors. The effects of oxygen vacancies, partial edge dislocations, and interfacial atomic reconstructions will be presented. The structural-property relationship has been successfully established in our studies. Our findings demonstrate the structural flexibility in transition metal oxides, and open the door to new tunable multifunctional applications.

March 11, 2020

"Active Thermochemical Tables: Graph Theory for Thermochemistry", David Bross, Chemical Sciences and Engineering Division, Argonne National Laboratory. Host: Pierre Darancet

Traditional sequential thermochemistry relies on assuming that the enthalpies of formation of all but one species in a reaction are known, and uses the selected determination of the reaction enthalpy to derive the unknown enthalpy of formation. While this produces an apparently straightforward provenance for the derived enthalpy, the latter implicitly depends on the assumed auxiliary enthalpies. Subsequent sequential steps keep compounding the tree of these implicit dependencies, producing a final tabulation that is riddled with hidden progenitor-progeny dependencies. Contrary to this, Active Thermochemical Tables (ATcT) constructs a Thermochemical Network (TN), a bipartite graph with all of the determined reactions, statistically analyzes the TN, and finally solves the TN for all species simultaneously.

In addition to providing enthalpies of formation that are widely recognized as the most accurate benchmarks available today, this introduces numerous features and tools that are not available in sequential thermochemistry. This approach maintains the covariances between each species, and thus enables any reaction enthalpy to be conveniently queried from our website, ATcT​.anl​.gov, producing the correct uncertainty. As an example, for the bond dissociation of water the query returns an uncertainty of ±0.001 kJ/mol, while normal error propagation ignoring the covariances produces an uncertainty nearly 37 times larger. There are numerous metrics that can be constructed from the TN, including a provenance analysis by variance decomposition and a projection matrix analysis for reaction influences. Finally, analyzing the network as a graph enables the distance between all species to be conveniently computed, which has a median and a mean of ~3 in the latest released version of the TN (version 1.122g). This distance metric shows the high degree of connectivity ('small world') within the TN and indeed demonstrates the necessity of using a TN approach rather than traditional sequential thermochemistry. These features and recent developments from ATcT will be discussed.

March 10, 2020

"Advanced rheology measurements for anion exchange membranes and shear thickening slurries", Matthew Liberatore, Department of Chemical Engineering, University of Toledo. Host: Xiao-Min Lin

Rheology measures non-Newtonian behavior of polymer, colloid, and other interesting systems. More advanced measurements monitor changes in rheology while simultaneously probing or manipulating the material. Two distinct projects will be discussed.

Anion exchange membranes (AEM) facilitate ion transport in polymer electrolyte membrane fuel cells. An AEM must have a high ionic conductivity, low fuel crossover, and be chemically and mechanically stable over the lifetime of a fuel cell. Mechanical breakdown due to humidity cycling is a common limitation. A custom-built humidity delivery system was developed for a rheometer to test films at a range of temperatures (30-100°C) and relative humidity conditions (0-95% RH). The humidity oven can be used with many rheometer accessories, including dynamic mechanical analysis. Using a series of block, random, and crosslinked polymers a relationship between mechanical stress and water stress has been proposed to predict durability of anion exchange membranes in a working electrochemical device.

Chemical mechanical polishing (CMP) slurries exhibit shear thickening at very high shear rates (>10,000 s-1). During a high shear polishing process, it was hypothesized that individual fumed silica particles (~100 nm) collide with one another to form large agglomerates (>500 nm) that cause the slurry to shear thicken. These agglomerates tend to dig into the material surface triggering defects such as scratches or gouges during polishing (costing the semiconductor industry billions of dollars in lost production annually). Overall, the project aims to understand thickening at high shear, link rheology with particle structure, and alter slurries to eliminate thickening.

March 4, 2020

"Autonomous Molecular Design for Electrolyte Materials", Logan Ward, Data Sciences & Learning Division, Argonne National Laboratory. Host: Pierre Darancet

The challenges faced in developing new electrolyte materials are common with many other design problems. Materials with the desired properties are rare, computations needed to assess candidate materials are slow and the experiments too expensive to evaluate any significant fraction of the search space. Presented with these challenges, we envision integrating several classes of artificial intelligence together to perform simulations autonomously on Exascale supercomputing resources. In this talk, we highlight the diverse range of issues and recent progress towards bringing this vision into reality. We will focus on suite of machine learning models developed at Argonne to predict a variety of properties of electrolytes and our progress towards coupling these algorithms with quantum chemistry calculations.

March 3, 2020

"Publishing for impact and my role within a society publisher​"Jeremy P. Allen, Chemical Science, Royal Society of Chemistry. Host: Elena Shevchenko

The Royal Society of Chemistry is one of the leading international chemical societies and it plays an important role within the community – helping to support those working in and studying the chemical sciences with a wide range of activities. During this presentation I will introduce the work that the Royal Society of Chemistry does, my role within the organization and the route I took from PhD student to being the Deputy Editor for Chemical Science. I will also highlight the role publisher's play in the community and the benefits they can afford for supporting the dissemination of an author's research output. Finally, I will discuss the peer review and publication process, including how to choose a journal, getting your paper noticed and what to do after publication.

March 2, 2020

"Electronic Chemical Sensors for Healthcare a Io T Applications", Sufi Zafar, IBM Thomas J. Watson Research Center. Host: Gary Wiederrecht

Chronic diseases are a major healthcare challenge, resulting in soaring cost and reduced economic productivity. Mobile sensors (e.g. handheld, wearables, implant) that can measure analytes from biofluids have the potential to provide cost-effective and enhanced treatments for chronic diseases. Since mobile sensors need to function reliably with minimal human intervention over extended periods of time, the foremost challenge is ensuring data accuracy. To address this important issue, we have proposed two main innovations. The bipolar junction transistor (BJT) device is proposed and demonstrated as a significantly superior transducer in comparison to the current state of the art field effect transistors (FET) transducer. The BJT based sensors are shown to be well suited for mobile sensing applications with inherently simpler calibration, enhanced sensitivity and low power requirements. Another source of data inaccuracy is the failure of reference electrodes due to clogging during prolonged incubation in biofluids. We have demonstrated an innovative reference electrode that not only remains unclogged when inserted in a dense solid matric (e.g. tissue, soil, food) but is easier to miniaturize and integrate on a silicon chip. Hence, these two enhancements address important challenges in mobile healthcare sensing and are also applicable to IoT applications such as in-situ soil nutrient measurements and food safety monitoring.

Another important healthcare area is early diagnostics. The ability to detect biomolecules at ultra-low concentrations requires that the critical dimension of the biosensor is comparable to that of the biomolecule. Since FET is the only device that can be miniaturized to nanometer scale without losing device performance, silicon nanowire FET sensors are investigated for early disease diagnostic applications. A combined approach of nanofabrication, device simulation, and material studies is applied to demonstrate nanowire (30 nm width) FET sensors that not only have the lowest (~3%) reported sensor-to-sensor variations but also have significantly enhanced sensing characteristics, thus enabling early stage cancer and other disease diagnostics.

February 24, 2020

"Nanofabrication and Devices Group Science, Prospects, and Vision", David Czaplewski, Center for Nanoscale Materials, Argonne National Laboratory. Host: Gary Wiederrecht

In this talk, I will present work being performed in the Nanofabrication and Devices Group at the Center for Nanoscale Materials in the Nanoscience and Technology division at Argonne National Laboratory. I will present current work in several areas including mechanical resonators, metasurfaces, carbon allotropes (including friction and quantum applications), and quantum systems. I will interlace the presentation with a vision for future work in these topical areas that includes collaborations among the groups within the center, with our user base and with external scientists. I will also present on the capabilities of the conventional labs and the cleanroom, including the integration of the future capabilities of the Argonne Cleanroom. I will talk about how to utilize the capabilities of the nanofabrication facility and conventional labs to drive the vision of the group to perform cutting edge science going forward. Lastly, I'll include discussion about how the NFD group can benefit from and contribute to key future strategic programs at ANL, including the CMB and QIS programs.

February 19, 2020

"Towards developing energy efficient systems based on novel nanocarbon materials", Anirudha Sumant, Center for Nanoscale Materials, Argonne National Laboratory.

Developing energy efficient systems across the length scale with little or no burden on the environment has been a focus of research efforts across the globe and it spans a wide spectrum of research areas whether it is related to micro/nano devices or meso/macroscale systems. In this context, I'll discuss my research efforts utilizing novel carbon materials such as diamond and graphene in the fabrication of energy efficient micro and nanosystems with new functionalities, by manipulating materials properties during the synthesis process. Further, I'll discuss how manipulating the interaction of materials at nanoscale and understanding the atomistic-scale dynamical process at surface and interfaces can have a profound impact on macroscale. In this case, I'll mention few specific examples where friction and wear between two sliding surfaces at macroscale could be reduced to near zero (superlubricity) utilizing a combination of nanomaterials such as graphene and nanodiamond at nanoscale under certain conditions. This discovery presents a paradigm shift in the understanding of the frictional behavior of 2D materials-based solid lubricants alone and in combination with nanoparticles and offers a direct pathway for designing energy efficient frictionless tribological systems, which are oil-free and hence environment friendly.

February 19, 2020

AI Driven Materials Manufacturing at Argonne, Noah Paulson, Applied Materials Division, Argonne National Laboratory, Host: Pierre Darancet

Argonne National Laboratory is well known as a center of fundamental materials expertise and exceptional user facilities. A complementary expertise is that of materials manufacturing, where researchers aim to bring new materials capabilities to the market. The Materials Engineering Research Facility at Argonne is a locus of that effort and houses diverse manufacturing capabilities. Many of these processes are highly instrumented, resulting in a wealth of information that is currently under-utilized. There is an opportunity to leverage this near real-time feedback to accelerate process optimization and result in better material outcomes. In this work, I present suitable AI techniques to tackle this problem and demonstrate their application to two Argonne manufacturing capabilities, atomic layer deposition and flame spray pyrolysis.

February 13, 2020

High-throughput calculations of X-ray absorption spectra and machine learning-assisted chemical environment identification, Yiming Chen, Nanoscience and Technology Division, Argonne National Laboratory, Host: Maria Chan

X-ray absorption spectroscopy (XAS) is a robust characterization technique to probe local environments, oxidation states and electronic states. Conventional interpretation of XAS data requires a set of experimental fingerprints but the paucity of such data poses challenges to efficient and effective analysis of XAS. Computational spectroscopy is a widely adopted alternative to experimental reference spectra. With decent maturity in spectroscopy mythologies and computing power, computational packages are able to deliver spectra that are comparable to experimental ones with reasonable cost. This talk will introduce the world's largest existing collection of computed XAS spectra to date, XASdb, that contains more than 540,000 K-edge and 140,000 L-edge XANES for over 40,000 unique materials. This large-scale database also lays the foundation for data-hungry machine learning applications, which will be covered in two projects. The first project is the development and implementation of an Ensemble-Learned Spectra IdEntification (ELSIE) algorithm that leverages ensemble learning techniques to identify similar XANES spectra from XASdb. This spectral matching algorithm allows any user to compare multiple X-ray absorption spectra and find matches within the XASdb for an uploaded spectrum. The second project is to apply machine learning models to the prediction of chemical environment from XAS spectra. Random forest models stand out in this multi-label classification task and exhibit decent prediction accuracy. These findings indicate that the combination of the XASdb with these machine learning techniques will be an invaluable resource to the materials research community by greatly enhancing the efficiency at which experimental XAS spectra can be analyzed.

January 29, 2020

Real-Time Coherent Diffraction Inversion Through Deep Learning, Henry Chan, Nanoscience and Technology Division, Argonne National Laboratory. Host: Pierre Darancet

Coherent X-ray diffraction imaging (CDI) is a powerful technique for operando materials characterization at the nanoscale. Uniquely, in this technique, the image resolution is not determined by the resolution of the optics used in the experiment but by the maximum angle through which the x-rays are scattered by the sample. The challenge lies in recovering the phases of those scattered x-rays, which are not measured by the detector, to retrieve the image of the sample from the data. The phase retrieval process can be computationally expensive, and the current methods has inherent deficiencies. In this talk, I will describe a deep convolutional neural network approach to this inversion problem and report the current status of the approach.

January 22, 2020

How Chemical Physics Can Help Us Understand Earth's Atmosphere, Rebecca Caravan, Chemical Sciences and Engineering Division, Argonne National Laboratory. Host: Pierre Darancet

The chemistry defining the composition of complex environments, such as Earth's atmosphere, is often treated as a ​"black box" - where stable starting and product species may be known, but the intermediate sequence of steps connecting the two are ill-defined. The ambiguity of the chemical processes involving short-lived reactive intermediate species that connect starting and product species leads to significant uncertainty in models and inhibits the development of predictive capabilities for broader applicability. Direct laboratory studies of these reactive intermediate species open up the ​"black box" to provide critical fundamental insights into reactions that may play critical roles in not-yet-understood phenomena, such as particulate matter formation from gas-phase starting species. I will discuss some recent work I led concerning the chemistry of Criegee intermediates – zwitterionic, reactive intermediates formed from the reaction of ozone with alkenes. Through broad collaboration, direct laboratory measurements, state-of-the-art theoretical work, in-situ environmental measurements and chemical models were brought together to investigate the role of a single sequence of chemical reactions in the formation of particulate matter in the Amazon region.

January 15, 2020

Quantum Physics of Single Electrons in Noble-Gas Quantum Liquids and Solids, Dafei Jin, Nanofabrication and Devices Group, Center for Nanoscale Materials, Host: Pierre Darancet

In this talk, I will present our recent theoretical studies of the nonequilibrium quantum dynamics of single electrons in quantum liquid and solid Helium, as well as the found new quantum electronic structures and optical behaviors at the interface of solid neon and liquid helium. I will also present our on-chip experimental design to use these trapped electrons as reconfigurable long-spin-coherence qubits and our preliminary experimental effort along this direction using our new optical dilution-fridge system.

January 9, 2020

"Revealing Charge Dynamics Across Time Scales", David Prendergast, Molecular Foundry, Lawrence Berkeley National Laboratory. Host: Ilke Arslan

A wide swath of energy-relevant processes involve the transfer of charge within materials systems, driven by fluctuating or static electric fields (light absorption, applied bias, etc.). The mobile species are electrons or ions and the processes themselves are intrinsically molecular or nanoscale events occurring within a much wider arena on time scales ranging from less than a femtosecond to microseconds or longer. These multiscale aspects require a suite of theoretical approaches to understand and predict charge dynamics in real-world contexts, some of which will be outlined in this presentation. With the advent of several operando spectroscopic techniques, some with molecular, nanoscale or interfacial sensitivity, there is an increasing demand for predictive theories that can make insightful interpretations of the data from such experiments. Ultimately, this marriage of measurement and simulation is mutually beneficial. We will show how it is driving theoretical advances to study photodissociation in small molecules and charge dynamics at biased interfaces.

January 8, 2020

"Accelerating Quantum Optics Experiment with Statistical Learning", Cristian Cortes, Nanoscience and Technology Division, Argonne National Laboratory, Host: Pierre Darancet.

Intensity interferometry is a hallmark technique in quantum optics that is routinely used to characterize light sources for applications in quantum computing, communications, metrology, and imaging. Non-idealities, such as imperfect experimental conditions, or intrinsically weak light sources, make the probability of detecting correlated photon events extremely low, rendering this technique time consuming in many circumstances. In this talk, I will show that it is possible to use statistical learning techniques, such as Bayesian maximum a posteriori estimation, to provide several order of magnitude speed-ups in parameter estimation using only a small number of detected photons. I will discuss the theoretical aspects of this approach, while presenting our results which validate the approach using real experimental data for: (i) thermal light under stationary conditions, as well as (ii) anti-bunched light emitted by a quantum dot driven by periodic laser pulses. The proposed methodology has a wide range of applicability and has the potential to impact the scientific discovery process across a multitude of domains.