The NSRCs hold joint workshops to share research and user projects that are ongoing at the five centers. These exchanges of information have provided the staff at the NSRCs with the opportunity to learn about topics/thrusts in nanoscience at the other nanocenters and to develop an understanding of the different areas of expertise among the staff members. They have also facilitated discussions towards possible future areas of collaboration between the centers and provided basic information so that potential NSRC users can be directed toward the optimal center and staff to meet their research needs.
BNL - Upton, NY 18-May-2020 – 20-May-2020
CINT - 2019 CINT Annual Meeting
Santa Fe, NM 23-Sep-2019 – 24-Sep-2019
CNMS - 2020 User Meeting
(Virtual) Oak Ridge, TN 10-Aug-2020 – 12-Aug-2020
Lemont, IL 20-Apr-2020 – 24-Apr-2020
The Foundry - 2020 User Meeting
(Virtual) Berkeley, CA 20-Aug-2020 – 21-Aug-2020
This work demonstrates that dynamic holographic optical tweezers are capable of manipulating single micrometer-scale anisotropic particles in a microfluidic environment with the precision and stability required for X-ray Bragg diffraction experiments. Optical trapping is a known noncontact sample manipulation technique to study the structure and dynamics of nano- and mesoscale objects without inducing undesired changes in structure. Combining optical trapping with hard X-ray microscopy techniques, such as coherent diffraction imaging and crystallography, provides a nonperturbing environment where electronic and structural dynamics of an individual particle in solution can be followed in situ. Our work demonstrates that dynamic holographic optical tweezers are capable of manipulating single micrometer-scale anisotropic particles in a microfluidic environment with the precision and stability required for X-ray Bragg diffraction experiments—thus functioning as an “optical goniometer.”
Scientists from MIT and CFN researchers have developed a new, scalable approach to creating highly-uniform single-crystal nanodiamonds. The team combined self-assembled nanopatterning with plasma etching for precise synthesis of large quantities of 30 nm diameter nanodiamonds. Photoluminescence measurements demonstrated single-photon emission from single nitrogen vacancy centers located within the nanodiamonds.
Solid-state defects are a leading material system candidate for quantum communication and sensing. Single optical defects in nanodiamonds have potential as a sensor platform with unparalleled sensitivity.
A CFN user project investigated Mn- and N- doped catalysts (Mn-N-C) for the oxygen reduction reaction in fuel cells using combined computational and experimental methods. Calculations showed that the Mn-N-C catalyst has the potential to achieve a performance near that of a Pt catalyst (60 mV lower in terms of half-wave potential). Experiments showed the new catalyst has superior stability over 10,000 cycles compared to an Fe catalyst, degrading 75% percent less even after undergoing twice as many potential cycles.
Polymer electrolyte membrane fuel cells have the potential to reduce energy use, pollutant emissions, and dependence on fossil fuels. Efficient, stable catalysts that are free from platinum group metals are key for widespread fuel-cell adoption.
CFN scientists and a team of collaborators have devised a new approach for building nanomaterials for use as nanomedicines. The team developed a class of biocompatible molecular coatings and used them to stabilize wireframed DNA origami cages. The coatings give the structure multifunctionality and environmental stability. In this work, the researchers showed that the designed nanomaterials are capable of carrying an anticancer drug and delivering medicines with a controllable release.
Although DNA nanotechnology provides a toolkit for creating programmable nanostructures with potential for biomedical applications, a challenge is the limited structural integrity of these materials in complex biological fluids. The molecular coatings developed in this work solve this challenge, paving the way for this approach to be used in drug delivery, bioimaging, and cellular targeting.
A collaborative team from CFN, NSLS-II, and Stony Brook University created a machine-learning algorithm based on a convolutional neural network that accelerates the process of imaging materials with coherent X-rays. This imaging method, called X-ray ‘ptychography,’ is a powerful, high-resolution technique that typically requires long experimental and computational time. The machine-learning algorithm accelerates ptychographic imaging by around 90% based on simulations compared to conventional methods.
The speed provided by this new, machine learning-based method makes possible the use of X-ray ptychography for high-resolution studies of beam sensitive materials, and to image in-situ dynamics of nanomaterials in different environments.
A collaborative team of users, led by University of Wisconsin-Madison, worked closely with CFN staff to show that ultrathin films of samarium nickel oxide can mask the thermal radiation emitted from sources. The cloaking mechanism is due to this quantum material undergoing a unique, gradual insulator-to-metal phase transition across the temperature range of 100 °C and 140 °C — the temperature range of interest.
This study shows that quantum materials may be used to manage thermal radiation — important for applications such as infrared camouflage, privacy shielding, and for heat transfer control.
Alzheimer’s disease takes an enormous human toll. Current estimates suggest that one in ten persons age 65 or older are living with it, which accounts for an estimated 60–80% of dementia victims. This major brain disorder gradually destroys memory and cognitive functions. Scientists at the Center for Nanoscale Materials and Advanced Photon Sources, together with Argonne National Laboratory's Biosciences Division and Korea (KIST/KAIST), have developed nanotechnology that can trap and clear the brain peptides that contribute to this disease. The nanotechnology developed in this project could prevent the abnormal assembly of β-amyloid (Aβ) peptides in the brain, a major hallmark of Alzheimer's disease. Effective depletion of these peptides could significantly delay Alzheimer’s progression. This technology, based on mesoporous silica nanostructures, may thus represent an attractive therapeutic agent for the clinical treatment of Alzheimer’s disease in the future.
Nanoparticles of lithium metal formed on the surface of a solid state lithium ion electrolyte by an atomic force microscope. The particle size and height can be controlled by using carefully chosen voltage amplitude and sweep rates. The particles can be as small as 50 nanometers in diameter and a few nanometers high, and can potentially be used in lithium nanobatteries. A. Kumar, T.M. Arruda, A. Tselev, I.N. Ivanov, J.S. Lawton, T.A. Zawodzinski, O. Butyaev, X. Zayats, S. Jesse, and S.V. Kalinin, “Nanometer-scale mapping of irreversible electrochemical nucleation processes on solid Li-ion electrolytes.” Scientific Reports 3, 1621 (2013)
A team of scientists from CFN, Peking University, and Soochow University designed and characterized a new fuel cell catalyst — a platinum-lead core/shell structure, shaped as a nanoplate. The catalyst shape and chemical composition dramatically enhances the oxygen evolution reaction — important for fuel cell performance — while providing stability during operation.
Femtosecond laser etching is fast enough that the material vaporizes material rather than melting or burning it. Consequently, cuts made with this laser are cleaner than other nanosecond lasers that may leave ragged edges or cause material scarring. The image demonstrates this capability by etching the CINT logo onto a human hair. This etching enables highly accurate study of microfluidic processes in a variety of materials. At Los Alamos National Lab, rock samples are etched with the femtosecond laser to create a microfluidic device specific to the rock of interest and liquids within these devices are tested under conditions of extreme heat and pressure. Accordingly, earth scientists are able to study the movement of fluids through rock similar to deep earth conditions. It would be very difficult to study these exact conditions in the natural system.
Foundry industry users developed a multinozzle emitter array (MEA), a silicon chip that can dramatically shorten the time it takes to identify proteins, peptides, and other molecular components within small volumes of biological samples. This patented technology is now being commercialized by Newomics Inc., to further develop the product and build a platform for personalized health care. Some of the early work on multinozzle emitters was done at the Molecular Foundry.
Newomics’ product, which is based on the core technology developed at Berkeley Lab, is designed to work with mass spectrometers, a machine commonly used by research scientists, the pharmaceutical industry, and increasingly in clinical labs, to measure the structure and concentration of molecules. Once molecular parts are isolated, scientists can begin to understand how they work together as a system, a field known as systems biology, which holds great promise for better medicines and diagnostics as well as a host of other applications.
The dominant method for analyzing biological molecules such as peptides and proteins in a complex mixture is electrospray ionization mass spectrometry (ESI-MS), a technique in which molecular samples are delivered to the machine as an ionized mist, propelled by an electric current. But there is a bottleneck at the front end of ESI-MS, making it slow and expensive. Each sample has to be loaded, lined up, and sprayed one at a time.
Instead of a single capillary, Newomics’ M3 emitter has eight or more nozzles working together to split a single large flow into smaller flows. For the MEA, up to 96 M3 emitters are packaged on a single chip. The development of these technologies involved a blend of microfluidic, microelectronic, and electrochemical innovations.
By clearing up the bottleneck and increasing throughput, Newomics’ emitter could dramatically reduce the cost of testing each sample. And by improving the sensitivity, it will also be possible to detect very low concentrations of molecules. For example, they showed they could analyze many different modified forms of proteins such as glycated albumin and apolipoproteins, in addition to the conventional glucose and HbA1c in diabetes monitoring, using a single drop of blood. Such tests have the potential to enable better long-term monitoring and disease management of diabetes.
The team of scientists developed and implemented a ‘physics-aware’ algorithm to correct for missing information in experimental X-ray scattering datasets. Because the algorithm relies on well-understood physics of X-ray scattering, the ‘healing’ operation provides robust and physically-rigorous results and outperforms all other conventional image interpolation methods.
Experimental X-ray scattering images always contain missing data and artifacts, which complicate further analysis, especially rapid, automated analysis. This healing operation is an essential pre-processing step for machine-learning interpretation of scientific data.
X-ray scattering is a powerful way to measure the structure of materials at the molecular- and nano-scale. Scattering images contain features, such as peaks and rings, which encode structural information. As with most scientific data, collected X-ray scattering images are inevitably ‘incomplete,’ with missing data being due to limits of the measurement, or experimental considerations. These missing data render automated data analysis of the datasets much more difficult. In this work, the team developed an image healing algorithm designed for X-ray scattering/diffraction datasets. Because the algorithm is ‘physics-aware’ (incorporating known properties of an X-ray scattering measurement), it outperforms all other image healing methods when applied to X-ray scattering data. The healed images can then be easily fed into existing data analysis pipelines. Importantly, the image healing is also a crucial pre-processing step for input to machine-learning methods — which would otherwise tend to focus on the high-intensity — but ultimately irrelevant — image defects.
Researchers have created a new catalyst that brings them one step closer to artificial photosynthesis — a system that would use renewable energy to convert carbon dioxide (CO2) into stored chemical energy.
As in plants, their system consists of two linked chemical reactions: one that splits water (H2O) into protons and oxygen gas, and another that converts CO2 into carbon monoxide (CO). The CO can then be converted into hydrocarbon fuels through an established industrial process. The system would allow both the capture of carbon emissions and the storage of energy from solar or wind power.
Foundry scientists Yufeng Liang and David Prendergast performed theoretical modeling work used to interpret X-ray spectroscopy measurements made in the study, published Nov. 20 in Nature Chemistry. This work was done in support of a project originally proposed by a team from the University of Toronto.
Last year, the team developed catalysts for such reactions. But while one of their catalysts worked under neutral conditions, the other required high pH levels in order to be most active. That meant that when the two were combined, the overall process was not as efficient as it could be: energy was lost when moving charged particles between the two parts of the system.
The team has now overcome this problem by developing a new catalyst for the first reaction – the one that splits water into protons and oxygen gas. Unlike the previous catalyst, this one works at neutral pH, and under those conditions it performs better than any other catalyst previously reported.
The new catalyst is made of nickel, iron, cobalt and phosphorus, all elements that are low-cost and pose few safety hazards. It can be synthesized at room temperature using relatively inexpensive equipment, and the team showed that it remained stable as long as they tested it, a total of 100 hours.
The team employed X-ray experiments at the Canadian Light Source and Berkeley Lab’s Advanced Light Source (ALS) to reveal the working principle behind this new catalyst, mainly focusing on the nickel chemistry during the reaction itself. The Theory Facility at Berkeley Lab’s Molecular Foundry specializes in the interpretation of such X-ray results, connecting chemical intuition to the atomic and electronic structure models of working materials.
What is the scientific achievement?
We have fabricated highly-porous, highly-uniform silicon nitride membranes by replicating features from self-assembled block copolymer films. With porosities over 30% and thickness <100 nm, the membranes are designed for high throughput. Pore sizes are controllably tuned to molecular scales, for selective gas permeation. Capillary condensation within nanoscale pores enhances selectivity beyond that expected from molecule size differences.
Why does this achievement matter?
Membranes underlie integral separation processes in energy production, water purification, medicine, environmental cleanup, and chemical processing. These highly-uniform, highly-porous inorganic membranes may provide durability for high temperature operation in extreme environments.
What is the scientific achievement?
CFN users from Rutgers University worked with CFN staff to perform high-resolution, 3D imaging of metallic nanostructures by scanning transmission electron microscopy (STEM). The measured 3D structure of these ‘nanostars’ was used as input for finite element simulations of the material physical and optical properties, in remarkable agreement with experimental measurements.
Why does this achievement matter?
Nanomaterials can have enhanced optical properties stemming from plasmonic effects — giving them promise for advanced sensors and diagnostic applications. This study represents the first time that information from STEM tomography has been used to predict nanomaterial physical and optical properties.
At the Center for Integrated Nanotechnology (CINT), researchers discovered an efficient way to make combined solar panels and light-emitting devices. Rather than using blocks of hybrid perovskite materials, they layered several thin sheets on top of each other. In this new layered pattern, they discovered important “layer-edge states.” In these states, energy is highly conserved. When excited by light or other sources, the material produces energy that doesn’t instantly dissipate and can be used to charge batteries or do other work. That is, it creates long-lived, free charge carriers that can be harvested and manipulated.
Quasicrystals made of Penrose tilings are fascinating structural arrangements with small repeating units but without any overall pattern periodicity. They are mesmerizing, because the human eye seeks to find patterns that do not quite exist. In this work, the researchers observed that quasicrystals made of nanomagnets form magnetic states having both an ordered, rigid ‘skeleton’ spanning the entire network, and smaller domains with configurations that are switchable without energy cost.
Bistable magnetic elements can naturally represent bits of stored digital information, and interactions between elements can be used to perform logical operations. In magnetic quasicrystals, different groups of nanomagnets can play each role.
Atom-level deactivation processes in industrial zeolite catalysts are revealed in atom probe tomography (APT), which yields the first direct observations of chemical distributions. J. E. Schmidt, R. Oord, W. Guo, J. D. Poplawsky, B. M. Weckhuysen.
Nature Communications 8, 1666 (2017). DOI: 10.1038/s41467-017-01765-0
Nanotechnology researchers studying small bundles of carbon nanotubes have discovered an optical signature showing excitons bound to a single nanotube are accompanied by excitons tunneling across closely interacting nanotubes. That quantum tunneling action could impact energy distribution in carbon nanotube networks, with implications for light-emitting films and light harvesting applications. In the study, a collaborative research team from Los Alamos National Laboratory, the Center for Integrated Nanotechnologies and the National Institute of Standards and Technology showed that Raman spectroscopy (a form of light scattering) can provide more extensive characterization of intertube excitons. The team used chemical separations to isolate a sample of a single type of carbon nanotube structure. The nanotubes in these samples were then bundled to force interactions between individual nanotubes.
Over the past decade, researchers have been working to create nanoscale materials and devices using DNA as construction materials through a process called “DNA origami.”
Now, for the first time, a team of researchers from Lawrence Berkeley National Laboratory (Berkeley Lab) and Ohio State University have generated 3-D images from 129 individual molecules of flexible DNA origami particles. Their work provides the first experimental verification of the theoretical model of DNA origami.
The research team focused on DNA structures modeled after a basic mechanism called a “Bennett linkage,” which is a 3-D structure consisting of a chain of four rods connected by hinges. This creates a skewed quadrilateral shape in which the hinges are not parallel or in-line. Using Bennett linkages as building blocks, it’s possible to create expandable, useful structures, like supports for tents that can be rapidly assembled.
The researchers relied on a technique developed at the Molecular Foundry to image the individual molecules that make up these structures. The method, called individual-particle electron tomography (IPET), takes pictures of a target molecule from multiple viewing angles, and then combines these pictures to create one 3-D, whole-molecule rendering, similar to how a medical computed tomography (CT) scan works.
Researchers captured 129 3-D images, with a resolution of 6 to 14 nanometers, that enabled them to tease out information about the dynamics and flexibility of DNA origami structures.
An international team of Foundry staff and users have created a method to direct the self-assembly of MOFs and nanocrystals into new types of 2D structures. This is the first time that researchers have been able to guide the self-assembly of organized 2D structures using MOFs and nanocrystals. This development will enable the design of new functional materials for catalysts, energy storage, and more.
What is the scientific achievement?
CFN users from the University of Pennsylvania have developed a new method for fabricating 3D cell-sized machines from colloidal nanocrystal films. Manipulating the organic ligands that cap the nanocrystals in a thin film introduces controlled strain, which causes fabricated structures to “fold-up” into 3D configurations.
Why does this achievement matter?
This strategy may be used to fabricate 3D, cell-sized machines with unique integrated optical and magnetic properties derived from the nanocrystal constituents. Potential examples are helical structures for plasmonic metasurfaces and claw-shaped structures for capturing tumor cells and bacteria.
What is the scientific achievement?
A team of scientists from the CFN and the University of Buffalo developed and characterized a new Pt-alloy catalyst to promote the oxygen reduction reaction in fuel cells. This catalyst is formed by annealing Pt nanoparticles deposited onto graphene tubes co-doped with nitrogen, nickel, and cobalt. After annealing, the resulting Pt-alloy catalyst and N-doped graphene support enhanced catalytic activity and stability.
Why does this achievement matter?
Pt catalysts on carbon supports are used to promote the oxygen reduction reaction in commercial fuel cells, but these catalysts suffer from poor long-term stability. The Pt-alloy catalysts synthesized in this work have excellent activity and improved stability, and represent a new design strategy exploiting a unique hybrid configuration.
Using a single actuation signal, we generate a novel response—a frequency comb—in a micromechanical resonator and demonstrate the mechanism behind the behavior. Mode coupling can be nonresonant, where the frequencies of the different vibrational modes do not match and result in inefficiency or instability, or resonant, where the frequencies of the different modes satisfy proportional relationships and produce efficient energy transfer or improve stability. Both nonresonant and resonant typically require multiple external signals to create the multiple modes. In this work, using a micromechanical device, we generate both a flexural mode and a torsional mode with a single signal. The two modes have proportional frequencies and couple to generate a “frequency comb.” Further investigation traces the source of the novel behavior to a branching of the vibrational frequency into two stable paths—a bifurcation. A generic model describes the internal resonance using experimental data from the device. This completely mechanical model, which can be easily controlled, may be applied to certain biological systems and possibly as a way to emulate neuron interactions. Standard experimental measurements were used to determine the model parameters from the two vibrational modes, flexural and torsional, whose interactions are responsible for the unique frequency comb response. Capabilities from the Center for Nanoscale Materials include electrical equipment to measure device properties.
CFN staff in collaboration with the University of Illinois at Urbana-Champaign developed a polymerization model that evolves from a "soup" of different monomers into a complex system made from a limited number of polymer segments, which reduces the entropy in the system. Numerical results, supported by mathematical analysis, confirm the survival and extinction process that resembles natural selection, and drives the dramatic decrease of informational entropy.
The emergence of life from non-living matter is one of the greatest mysteries of fundamental science. The model could also be applied to make biopolymers and nanostructures.
A team of scientists from the Center for Mesoscale Transport Properties Energy Frontier Research Center working with CFN staff to study in real time how the size of zinc ferrite (ZnFe2O) nanoparticles determines their performance during battery operation. The team found that the improved performance observed when using smaller nanoparticles (6-9 nm) is because lithium is taken up by forming a solid-solution, a more efficient reaction pathway.
Understanding lithiation in materials as a function of particle size is helpful in designing batteries with improved performance and longevity. In-situ, dry cell transmission electron microscopy allows direct observation of structural changes in real time and with high spatial resolution.
CFN staff members found that increasing the film thickness of cylinder-forming block copolymers frustrates their self-assembly and results in highly defective nanoscale patterns. However, blending small homopolymers into the films alleviates the frustration, especially during early stages of pattern formation. In-situ grazing-incidence X-ray scattering and ex-situ electron microscopy reveal the ways that these homopolymers help produce more uniform and well-oriented patterns.
Improving the quality of nanopatterns produced via self-assembly in thicker polymer films creates new opportunities to engineer functional, large-area surface nanotextures and nanoporous membranes.
CFN Staff have discovered a new method to synthesize hollow metallic nanostructures with surface openings, which can carry and deliver cargos of guest nanoobjects. These nanowrappers have unique optical signatures originating from plasmonic effects and their complex nanoarchitecture. Advanced electron tomography provides 3D images at different stages of synthesis, which tracks their transition from Ag nanocubes with sharp corners to Au-Ag alloy nanowrappers with large cubic pores at all corners.
This research is a promising new strategy for synthesis of porous, 3D nanoarchitectures. Nanowrappers have biomedical potential as photothermal therapeutics, vehicles for photoinduced drug delivery, or agents for improved imaging contrast.
CINT scientists and collaborators were the first to grow an isotopically pure and highly uniform TMD material only six atoms thick. They compared this to an otherwise identical film of naturally abundant TMD, which has several different isotopes within the material. Along with characterizing the electronic band structure and vibrational spectra, the team found a surprisingly large effect in light emission, which is not predicted by current theoretical models.
By implanting silicon ions in diamond with extreme precision and then controlling the strain on the crystal structure, CINT scientists and collaborators showed that they could significantly increase the spin lifetimes of solid-state quantum bits. This is of fundamental importance to quantum mechanics and quantum computing.
CFN scientists and BNL collaborators from NSLS-II and the Computational Science Initiative deduced the structure of ultrathin titania (TiO2) coatings on ZnO nanowire photocatalysts by employing new data analytic approaches to X-ray absorption near edge structure (XANES) measurements. The ultrathin, amorphous TiO2 promotes efficient charge transfer during photocatalytic water splitting while also protecting the catalyst against photocorrosion.
This research implements a unique, data-driven approach to deciphering the structure of highly amorphous materials at the smallest dimensions from X-ray spectra, and lays the groundwork for understanding chemical and electronic properties.
CFN staff and collaborators studied the performance of magnetite (Fe3O4) as an inexpensive, nontoxic battery material. Batteries made using magnetite can have high capacities, but unfortunately their capacity fades with battery cycling. The team combined in-situ transmission electron microscopy and synchrotron X-ray absorption spectroscopy to understand the origins of this capacity —directly observing the accumulation of obstacles to electron transport in the magnetite material.
In-situ transmission electron microscopy allowed direct observations of electrode structural changes in real time. Understanding how kinetic barriers are linked to capacity fading in materials is important for their future practical implementation.
CFN Users from Columbia University working in collaboration with CFN staff found that intercalating oxygen at the interface between graphene and iridium can change the electronic states of this interface resulting in nearly-flat bands. The scientists used micro-spot angle-resolved photoemission spectroscopy (µARPES) measurements and density-functional-theory calculations to explain the mechanism by which ordered oxygen induces a nearly-flat band structure in graphene.
Controlling the electronic states of 2D interfaces will enable new opportunities to engineer band structures and electronic properties in graphene and other 2D materials.
CFN staff and users from ExxonMobil have developed a new approach to identifying heteroatoms, like nitrogen and sulfur, commonly found in aromatic hydrocarbon molecules. The team used non-contact atomic force microscopy (nc–AFM) measurements to determine the chemical structure of molecules that can be found in complex mixtures of crude oil.
NOx and SOx are two major pollutants that result from the combustion of fossil fuels. Straightforward and robust methods for identifying nitrogen- and sulfur-containing hydrocarbon molecules can improve methods to produce cleaner fuels from crude oil.
A collaborative team of DOE scientists developed a new artificial intelligence method for autonomous experimentation. Their versatile algorithm was integrated into an X-ray scattering instrument and deployed to study nanomaterials without need for human interaction.
Machine-guided scientific studies can liberate human scientists from micro-managing experiment details, allowing them to focus instead on understanding the scientific meaning of the results. The methods in this work demonstrate the ability of autonomous methods to achieve high-fidelity searches of experimental problems more efficiently than traditional approaches.
CFN and CNMS users from the University of Buffalo led a collaborative study of a new manganese catalyst that significantly enhances the important oxygen reduction reaction in fuel cells. The catalyst enables a large half-wave potential of 0.80 V and remains stable in acidic environments in which fuel cells operate. Mechanistic studies identify the 4-electron pathway responsible for the enhanced performance. The catalyst structure — atomically dispersed MnN4 embedded in graphitic carbon, was established by multimodal X-ray absorption spectroscopy and atomic resolution electron microscopy.
Discovery of catalysts that are free from platinum group metals is necessary for wider adoption of fuel cell technologies. The catalyst structure and reaction mechanism identified here provide clues for progress on this important goal.