Nanometer Length Scale Research Articles

Time-resolved Brownian tomography of single nanocrystals in liquid during oxidative etching

Colloidal nanocrystals inherently undergo structural changes during chemical reactions. The robust structure-property relationships, originating from their nanoscale dimensions, underscore the significance of comprehending the dynamic structural behavior of nanocrystals in reactive chemical media. Moreover, the complexity and heterogeneity inherent in their atomic structures require tracking of structural transitions in individual nanocrystals at three-dimensional (3D) atomic resolution. In this study, we introduce the method of time-resolved Brownian tomography to investigate the temporal evolution of the 3D atomic structures of individual nanocrystals in solution. The methodology is applied to examine the atomic-level structural transformations of Pt nanocrystals during oxidative etching. The time-resolved 3D atomic maps reveal the structural evolution of dissolving Pt nanocrystals, transitioning from a crystalline to a disordered structure. Our study demonstrates the emergence of a phase at the nanometer length scale that has received less attention in bulk thermodynamics.

Super-resolution imaging of proteins inside live mammalian cells with mLIVE-PAINT.

Super-resolution microscopy has revolutionized biological imaging, enabling the visualization of structures at the nanometer length scale. Its application in live cells, however, has remained challenging. To address this, we adapted LIVE-PAINT, an approach we established in yeast, for application in live mammalian cells. Using the 101A/101B coiled-coil peptide pair as a peptide-based targeting system, we successfully demonstrate the super-resolution imaging of two distinct proteins in mammalian cells, one localized in the nucleus, and the second in the cytoplasm. This study highlights the versatility of LIVE-PAINT, suggesting its potential for live-cell super-resolution imaging across a range of protein targets in mammalian cells. We name the mammalian cell version of our original method mLIVE-PAINT.

Investigating spin-orbit interaction of light in micro- and nanophotonic systems using polarization Mueller matrix

Coupling between the spin and orbital angular momentum of light has led to a variety of exotic spin–orbit photonic effects, establishing a paradigm of nanophotonic meta-devices to control and manipulate light at the nanometer length scale. For the advancement of spin–orbit photonic technologies, quantitative characterization and unambiguous physical interpretation of the various spin–orbit interaction (SOI) effects exhibited in complex nanophotonic systems are of utmost importance. This Perspective addresses some of these outstanding challenges in the domain of spin–orbit photonics, focusing on the simultaneous manifestation of multiple SOI effects in hybridized nanophotonic systems and the role of polarization-based techniques in their characterization and quantification. After introducing the fundamentals and physical origins of SOI effects, we present a polarization Mueller matrix technique as a powerful tool to probe, decouple, and independently quantify these effects in a unified experimental framework, demonstrated with hybridized waveguided plasmonic crystals. We further discuss the potential of structured light and tailored polarization to enable unconventional SOI phenomena in simple nanostructured metamaterials and metasurfaces. These advancements not only enhance our fundamental understanding of SOI phenomena across micro- and nanoscale optical systems but also pave the way for the development of multifunctional and tunable spin–orbit photonic devices.

Nonlinear Elasticity of Amorphous Silicon and Silica from Density Functional Theory.

Density functional theory calculations and a finite deformation method are used to calculate second- and, most notably, third-order elastic constants of amorphous silicon and amorphous silicon dioxide, as represented by model structures generated via melt-quench force-field molecular dynamics simulations. Linear and nonlinear elastic constants are used to deduce macroscopic elastic moduli, such as the bulk and shear moduli, their pressure derivatives, and the elastic Grüneisen parameter. Our calculations show that the elastic properties of amorphous silicon reach the isotropic elastic limit within the nanometer length scale, attaining characteristics, both linear and nonlinear, comparable to those of crystalline silicon. In contrast, the nonlinear elastic properties of silica retain an anisotropic character over the nanometer length scales, yielding nonetheless the expected pressure-induced softening of the elastic moduli. This atypical elastic behavior is correlated to the occurrence of long-wavelength acoustic modes with negative Grüneisen parameters.

Benchmarking forcefields for molecular dynamics simulations of polyamide-based reverse-osmosis membranes

Molecular dynamics simulations offer unique insights about solvent and solute transport through the active layer of reverse-osmosis membranes at sub-nanometer to nanometer length scales. Over the last two decades, several cross-linked polyamide membranes, formed by trimesoyl chloride and m-phenylenediamine, have been simulated with different forcefields and water models. However, a clear rationale for choosing a forcefield-water combination is missing. In this work, the accuracy of eleven forcefield-water combinations is evaluated by a direct comparison of properties against experimentally synthesized polyamide membranes with similar chemical compositions. Overall, six forcefields and three water models are compared. The polyamide membranes are simulated in equilibrium in both dry and hydrated states, as well as in a non-equilibrium reverse osmosis. The best-performing forcefields predicted the experimental pure water permeability of 3D printed polyamide membranes within a 95 % confidence interval. Finally, the best forcefield was used to elucidate membrane properties for a range of desalination conditions. At experimentally relevant pressure pure-water permeability was validated and dense interconnected free volume regions (pores) were observed in the membrane that connected feed- and permeate-side. The desalination studies demonstrated increased salt partitioning with feed-side pressure, however, at very high pressures partitioning decreased due to membrane compaction.

Skyrmion dynamics in attractive and repulsive local magnetic fields

The study of the behavior of magnetic skyrmions in local magnetic fields’ nanometric length-scale has gained increasing interest in recent years due to the theoretical proposal of magnetic skyrmion–superconducting vortex pairs as potential hosts for topologically protected bound states, which hold high promise for applications in quantum computing. From a magnetic interaction point-of-view, the key interest lies in understanding the skyrmion dynamics triggered by the magnetic energy landscape generated by the superconducting vortex. Here, we present a micromagnetic study of the dynamics of nanometric skyrmions inside a Gaussian magnetic field profile, which is used as a simplified version of the vortex magnetic flux. On the one hand, our calculations show that local non-linear magnetic fields can be very effective in controlling the dynamics of magnetic skyrmions; in particular, they offer the appealing possibility to manipulate skyrmions in a two dimensional space. On the other hand, they also show that the dynamics of a skyrmion in a local magnetic field can be manipulated via a uniform external magnetic field without any change in the magnetic field gradient. An analytical expression for the skyrmion velocity is given, and the corresponding microscopic dynamics are confirmed by the micromagnetic simulations. This work is expected to motivate more theoretical and experimental studies of the behavior of magnetic skyrmions in proximity to superconducting vortices.

Probing inhomogeneous cuprate superconductivity by terahertz Josephson echo spectroscopy

Inhomogeneities crucially influence the properties of quantum materials, yet methods that can measure them remain limited and can access only a fraction of relevant observables. For example, local probes such as scanning tunnelling microscopy have documented that the electronic properties of cuprate superconductors are inhomogeneous over nanometre length scales. However, complementary techniques that can resolve higher-order correlations are needed to elucidate the nature of these inhomogeneities. Furthermore, local tunnelling probes are often effective only far below the critical temperature. Here we develop a two-dimensional terahertz spectroscopy method to measure Josephson plasmon echoes from an interlayer superconducting tunnelling resonance in a near-optimally doped cuprate. The technique allows us to study the multidimensional optical response of the interlayer Josephson coupling in the material and disentangle intrinsic lifetime broadening from extrinsic inhomogeneous broadening for interlayer superconducting tunnelling. We find that inhomogeneous broadening persists up to a substantial fraction of the critical temperature, above which this is overcome by the thermally increased lifetime broadening.

Orbital magnetism through inverse Faraday effect in metal clusters.

In view of the recent increased interest in light-induced manipulation of magnetism in nanometric length scales this work presents metal clusters as promising elementary units for generating all-optical ultrafast magnetization. We perform a theoretical study of the opto-magnetic properties of metal clusters through ab-initio real-time (RT) simulations in real-space using time-dependent density functional theory (TDDFT). Through ab-initio calculations of plasmon excitation with circularly polarized laser pulse in atomically precise clusters of simple and noble metals, we discuss the generation of orbital magnetic moments due to the transfer of angular momentum from light field through optical absorption at resonance energies. Notably, in the near-field analysis we observe self-sustained circular motion of the induced electron density corroborating the presence of nanometric current loops which give rise to orbital magnetic moments due to the inverse Faraday effect (IFE) in the clusters. The results provide valuable insights into the quantum many-body effects that influence the IFE-mediated light-induced orbital magnetism in metal clusters depending on its geometry and chemical composition. At the same time, they explicitly demonstrate the possibility for harnessing magnetization in metal clusters, offering potential applications in the field of all-optical manipulation of magnetism.

Study of oxygen transport in glassy polymers on a nanometer length scale utilizing the kinetic Monte Carlo simulations.

Diffusion-controlled deactivation of excited phenanthrene and oxidation of triplet aryl-nitrene by molecular oxygen were used to determine the energetics of oxygen jump rates in the set of glassy polymers: poly(methyl methacrylate), poly(n-butyl methacrylate), polycarbonate, polystyrene, and polysulfone. To interpret experimental results, a simple model based on the transition state theory of diffusion jump has been used. The kinetic Monte Carlo simulations of phenanthrene deactivation and nitrene oxidation were carried out in a cubic lattice that modeled a polymer matrix. The bonds of the lattice were assigned to be activation barriers for the diffusion jumps of oxygen molecules from one site of the lattice to another. The standard deviation, σ‡, and spatial correlation length, rc, of the free energy of diffusion jump have been determined. It is shown that the spatial correlation of oxygen jump rates on a nanometer scale and the entropic nature of the dynamic heterogeneity are common features of all the studied polymers.

Light-Induced Redox Dynamics in Plasmonic Nanostructures and Photosensitizer Transition Metal Compounds

Solar energy harvesting is a nuanced scientific field that involves precise control across energy, time, and nanometer length scales. The understanding of how to harness sunlight to promote chemical reactions is vital for the advancement of renewable energy applications and a sustainable future. Limited knowledge of the precise chemical mechanisms underlying solar energy harvesting restricts widespread technological use and the development of photovoltaic devices that utilize light-harvesting biodegradable materials. My research focuses on developing a fundamental understanding of visible light-activated excited state chemistry that is of great importance in elucidating the mechanisms, structures, and design features for catalysis. In this talk, we will examine excited-state chemical structure dynamics in nanomaterials that use light-matter interactions to drive interfacial or intramolecular charge transfer. Steady-state and time-resolved optical/X-ray spectroscopy allow for the observation of these dynamics across an expansive timescale—from the seconds timescale of oxygen-evolving reactions to the ultrafast timescales of electronic and vibrational motion. Novel light-matter interactions in plasmonic nanostructures, biomimetic metal oxides, and molecular transition metal complexes will be discussed. The talk will conclude with future opportunities for expanding the scope of nanoscale photocatalysis and for designing enhanced localized control over excited-state chemistry.[1] E. A. Sprague-Klein*, X. He, M. W. Mara, B. J. Reinhart, S. Lee, L. M. Utschig, K. L. Mulfort, L. X. Chen*, D. M. Tiede*. “Photo-Electrochemical Effect in the Water Oxidation Catalyst Cobalt-Phosphate (CoPi),” ACS Energy Letters, 2022, 7, 9, 3129–3138. [2] N. L. Warren, U. Yunusa, A. B. Singhal, E. A. Sprague-Klein*. "Facilitating Excited-State Plasmonics and Photochemical Reaction Dynamics," Chemical Physics Reviews, (Accepted December 2023).[3] U. Yunusa, N. Warren, D. Schauer, P. Srivastava, E. Sprague-Klein*. "Plasmon resonance dynamics and enhancement effects in Tris(bipyridine)ruthenium(II) gold nanosphere oligomers," Nanoscale, (Under Review).

Considerations for ultrafast photomagnetism in manganese(III)-based single-molecule magnets

Manipulation of magnetic materials is a cornerstone of digital data storage technologies. Recently, it has been shown that femtosecond laser pulses are capable of switching the magnetization in a material between two stable configurations faster than ever before. One state-of-the-art method is to use laser pulses to control the magnetic anisotropy by photoexciting crystal-field transitions. The photoinduced change in anisotropy applies a torque to the magnetic moment, which reorientates it in a different direction. So far, research has focused solely on condensed matter materials. However, there is a huge variety of molecule-based magnetic materials that have been and continue to be developed. In particular, single-molecule magnets (SMMs) provide a highly tunable platform and have the added advantage of operating on nanometer length scales. This review discusses recent research in the area of ultrafast magnetism in SMMs, with a focus on manganese(III)-based transition metal complexes. Experimental data are reviewed, showing that control of the strength of the photoinduced anisotropy, the lifetime of excited states, and the dephasing times are possible and can be used to develop some design criteria for the best optically controllable SMMs.

AI-Powered 3D Image Reconstruction for Analyzing Lithium Ion Battery Microstructures at the Nanoscale

A clean energy future will rely heavily on lithium-ion batteries to shift away from carbon-based energy sources. These devices are closed off from the outside world and operate under highly non-ambient conditions (e.g., liquid electrolytes, air-free environments). Their performance is largely driven by the 3D microstructures of the internal components and their evolutions. For example, breached layers in batteries due to lithium dendrite growth can lead to catastrophic failures and safety concerns. As such, imaging the internal microstructures and processes is important for observing and measuring device construction details and understanding how defects are distributed and change performance during operation. In particular, 3D imaging allows for analysis of features and parameters that can only be fully understood in 3D, like porosity and tortuosity, and can also provide realistic microstructural input for 3D modelling and simulation approaches.Within this scope, X-ray microscopy provides a unique method to image electrochemical devices because of the non-destructive nature of the technique. Modern x-ray microscopes enable 3D imaging even at the nanoscale, bringing capabilities once reserved for the synchrotron into the laboratory. Advances in tomographic image reconstruction have allowed researchers to increasingly maximize the impact of x-ray microscopy data through higher image quality and enabling faster data acquisition schemes. Recently, artificial intelligence (AI) has been incorporated into these algorithms, dramatically increasing the performance and capability of the technique. Here, we apply multiple novel reconstruction technologies that leverage AI to dramatically improve the performance of laboratory-based nanoscale x-ray microscopes, showing examples of improvements in imaging lithium-ion battery electrode materials.We show how AI-powered tomographic reconstruction can reveal new levels of detail in lithium-ion battery materials and enable up to 10 times faster data collection across the micrometer and nanometer length-scales. These capabilities shift the paradigm in volume analysis for electrochemical devices and give researchers unprecedented insight into the detailed microstructures that drive device performance, enabling new levels of understanding and helping to power the drive to a clean energy future.

Artificial Graphene Nanoribbons with Tailored Topological States

Low-dimensional materials functioning at the nanoscale are a critical component for a variety of current and future technologies. From the optimization of light harvesting solar technologies to novel electronic and magnetic device architectures, key physical phenomena are occurring at the nanometer and atomic length-scales and predominately at interfaces. In this presentation, I will discuss low-dimensional material research occurring in the Quantum and Energy Materials (QEM) group at the Center for Nanoscale Materials. Specifically, the synthesis of artificial graphene nanoribbons by positioning carbon monoxide molecules on a copper surface to confine its surface state electrons into artificial atoms positioned to emulate the low-energy electronic structure of graphene derivatives. We demonstrate that the dimensionality of artificial graphene can be reduced to one dimension with proper “edge” passivation, with the emergence of an effectively-gapped one-dimensional nanoribbon structure. Remarkably, these one-dimensional structures show evidence of topological effects analogous to graphene nanoribbons. Guided by first-principles calculations, we spatially explore robust, zero-dimensional topological states by altering the topological invariants of quasi-one-dimensional artificial graphene nanostructures. The robustness and flexibility of our platform allows us to toggle the topological invariants between trivial and non-trivial on the same nanostructure. Our atomic synthesis gives access to nanoribbon geometries beyond the current reach of synthetic chemistry, and thus provides an ideal platform for the design and study of novel topological and quantum states of matter. Figure 1

Uncertainty relations in thermodynamics of irreversible processes on a mesoscopic scale

Studies of mesoscopic structures have become a leading and rapidly evolving research field ranging from physics, chemistry, and mineralogy to life sciences. The increasing miniaturization of devices with length scales of a few nanometers is leading to radical changes in the realization of new materials and in shedding light on our understanding of the fundamental laws of nature that govern the dynamics of systems at the mesoscopic scale. We investigate thermodynamic processes in small systems in Onsager’s region based on recent experimental results and previous theoretical research. We show that fundamental quantities such as the total entropy production, the thermodynamic variables conjugate to the thermodynamic forces, and the Glansdorff–Prigogine’s dissipative variable may be discretized at the mesoscopic scale. We establish the canonical com- mutation rules (CCRs) valid at the mesoscopic scale. The numerical value of the discretization constant is estimated experimentally. The ultraviolet divergence problem is solved by applying the correspondence principle with Einstein–Prigogine’s fluctuations theory in the limit of macroscopic systems. Examples of quantization of thermodynamic systems out of the Onsager region are currently being finalized.

Depth resolution in piezoresponse force microscopy

Piezoresponse force microscopy (PFM) is one of the most widespread methods for investigating and visualizing ferroelectric domain structures down to the nanometer length scale. PFM makes use of the direct coupling of the piezoelectric response to the crystal lattice, and hence, it is most often applied to spatially map the three-dimensional (3D) near-surface domain distribution of any polar or ferroic sample. Nonetheless, since most samples investigated by PFM are at least semiconducting or fully insulating, the electric ac field emerging from the conductive scanning force microscopy (SFM) tip penetrates the sample and, hence, may also couple to polar features that are deeply buried into the bulk of the sample under investigation. Thus, in the work presented here, we experimentally and theoretically explore the contrast and depth resolution capabilities of PFM, by analyzing the dependence of several key parameters. These key parameters include the depth of the buried feature, i.e., here a domain wall (DW), as well as PFM-relevant technical parameters such as the tip radius, the PFM drive voltage and frequency, and the signal-to-noise ratio. The theoretical predictions are experimentally verified using x-cut periodically poled lithium niobate single crystals that are specially prepared into wedge-shaped samples, in order to allow the buried feature, here the DW, to be “positioned” at any depth into the bulk. This inspection essentially contributes to the fundamental understanding in PFM contrast analysis and to the reconstruction of 3D domain structures down to a 1 μm-penetration depth into the sample.

Accessing self-diffusion on nanosecond time and nanometre length scales with minute kinetic resolution.

Neutron spectroscopy uniquely and non-destructively accesses diffusive dynamics in soft and biological matter, including for instance proteins in hydrated powders or in solution, and more generally dynamic properties of condensed matter on the molecular level. Given the limited neutron flux resulting in long counting times, it is important to optimize data acquisition for the specific question, in particular for time-resolved (kinetic) studies. The required acquisition time was recently significantly reduced by measurements of discrete energy transfers rather than quasi-continuous neutron scattering spectra on neutron backscattering spectrometers. Besides this reduction in acquisition times, smaller amounts of samples can be measured with better statistics, and most importantly, kinetically changing samples, such as aggregating or crystallizing samples, can be followed. However, given the small number of discrete energy transfers probed in this mode, established analysis frameworks for full spectra can break down. Presented here are new approaches to analyze measurements of diffusive dynamics recorded within fixed windows in energy transfer, and these are compared with the analysis of full spectra. The new approaches are tested by both modeled scattering functions and a comparative analysis of fixed energy window data and full spectra on well understood reference samples. This new approach can be employed successfully for kinetic studies of the dynamics focusing on the short-time apparent center-of-mass diffusion.

Differential effects of pectin-based dietary fibre type and gut microbiota composition on in vitro fermentation outcomes

Interactions between human gut microbiota and dietary fibres (DF) are influenced by the complexity and diversity of both individual microbiota and sources of DF. Based on 480 in vitro fermentations, a full factorial experiment was performed with six faecal inocula representing two enterotypes and three DF sources with nanometer, micrometer, and millimeter length-scales (apple pectin, apple cell walls and apple particles) at two concentrations. Increasing DF size reduced substrate disappearance and fermentation rates but not biomass growth. Concentrated DF enhanced butyrate production and lactate cross-feeding. Enterotype differentiated final microbial compositions but not biomass or fermentation metabolite profiles. Individual donor microbiota differences did not influence DF type or concentration effects but were manifested in the promotion of different functional microbes within each population with the capacity to degrade the DF substrates. Overall, consistent effects (independent of donor microbiota variation) of DF type and concentration on kinetics of substrate degradation, microbial biomass production, gas kinetics and metabolite profiles were found, which can form the basis for informed design of DF for desired rates/sites and consequences of gut fermentation. These results add further evidence to the concept that, despite variations between individuals, the human gut microbiota represents a community with conserved emergent properties.

Localized Ionic Reinforcement of Double Network Granular Hydrogels.

Nature produces soft materials with fascinating combinations of mechanical properties. For example, the mussel byssus embodies a combination of stiffness and toughness, a feature that is unmatched by synthetic hydrogels. Key to enabling these excellent mechanical properties are the well-defined structures of natural materials and their compositions controlled on lengths scales down to tens of nanometers. The composition of synthetic materials can be controlled on a micrometer length scale if processed into densely packed microgels. However, these microgels are typically soft. Microgels can be stiffened by enhancing interactions between particles, for example through the formation of covalent bonds between their surfaces or a second interpenetrating hydrogel network. Nonetheless, changes in the composition of these synthetic materials occur on a micrometer length scale. Here, 3D printable load-bearing granular hydrogels are introduced whose composition changes on the tens of nanometer length scale. The hydrogels are composed of jammed microgels encompassing tens of nm-sized ionically reinforced domains that increase the stiffness of double network granular hydrogels up to 18-fold. The printability of the ink and the local reinforcement of the resulting granular hydrogels are leveraged to 3D print a butterfly with composition and structural changes on a tens of nanometer length scale.

Flexible and rigid block copolymers from recyclable polyesters

The paper addresses a study on the thermoplastic elastomers (TPE) that can be synthesized using monomers retrieved from the chemically recycled semi-aromatic polyesters, such as terephthalic acid from low value stream recyclable polyethylene terephthalate (rPET). In contrast to PET, because of their physical and mechanical properties, TPEs are often used as an engineering polymer in automotive and consumer goods. The understanding of the crystallization and phase behavior of these materials is crucial to fine-tune the molecular structure and properties. We explore the interplay between the crystallization and phase separation in these materials by performing experiments on model thermoplastic elastomers consisting of polybutylene terephthalate (PBT) and polytetrahydrofuran (PTHF). A common denominator in the synthesis of PBT is terephthalic acid that can be obtained from Dimethyl Terephthalate (DMT) or recycling used PET. We show that by varying the compositions and block length of different blocks, in PBT-PTHF block copolymers, morphological variations from spherulites to dendrites can be induced, which could be captured using optical microscopy. By performing rheological and small angle X-ray scattering studies, we correlate these morphological variations at microscopic length scales, to the chain architecture at nanometer length scales. Our observations reveal that at the higher rigid PBT content, the process of phase separation strongly influences the crystallization kinetics that promotes the lamellar structure of the semi-crystalline polymer, observed at room temperature. On the other hand, in the presence of higher soft block content, the crystal growth occurs with minimal influence of the microphase separation. In broader generality, no morphological differences in the TPEs synthesized using the terephthalic acid from rPET are found. However, when the monomer from rPET is used, enhancement in crystallization rate occurs in the TPEs having similar molar mass and molecular configuration. We attribute the enhanced crystallization rate to the catalyst residues present in the terephthalic acid obtained fom rPET.

Optimal Molecular Dynamics System Size for Increased Precision and Efficiency for Epoxy Materials.

Molecular dynamics (MD) simulation is an important tool for predicting thermo-mechanical properties of polymer resins at the nanometer length scale, which is particularly important for efficient computationally driven design of advanced composite materials and structures. Because of the statistical nature of modeling amorphous materials on the nanometer length scale, multiple MD models (replicates) are typically built and simulated for statistical sampling of predicted properties. Larger replicates generally provide higher precision in the predictions but result in higher simulation times. Unfortunately, there is insufficient information in the literature to establish guidelines between MD model size and the resulting precision in predicted thermo-mechanical properties. The objective of this study was to determine the optimal MD model size of epoxy resin to balance efficiency and precision. The results show that an MD model size of 15,000 atoms provides for the fastest simulations without sacrificing precision in the prediction of mass density, elastic properties, strength, and thermal properties of epoxy. The results of this study are important for efficient computational process modeling and integrated computational materials engineering (ICME) for the design of next-generation composite materials for demanding applications.