Session Y34: Connecting Real and Reciprocal Space in Soft Matter Research

Design and characterization of single ion conducting elastomeric networks

Ionoelastomers, or low glass-transition (Tg) polyelectrolyte networks made by polymerization and crosslinking of ionic liquid monomers, are promising materials in a variety of contexts including low-voltage electroadhesives and electromechanical transducers. We have recently studied several classes of networks containing different polymer backbones, pendent groups, and crosslinkers, and sought to understand how chemical and structural parameters of networks influence their performance using both real and reciprocal space measurements. In one example we have introduced high-mobility silicone backbones that lower Tg and improve conductivity of the materials. Remarkably, heterojunctions of polyanionic and polycationic networks show strongly temperature-dependent rectification, providing insight into the critical role of interfacial polymer dynamics in the operation of these devices. In another example, we have introduced a modular route to prepare polyanionic networks that relies on Sulfur (VI) Fluoride exchange (SuFEx) 'click' chemistry, enabling a systematic study of how ion conductivity depends on the length of short perfluorinated pendent groups. A non-monotonic dependence is identified due to the interplay between ion dissociation and aggregation. Finally, we have studied a variety of different crosslinkers as a route to tune conductivity, stiffness, and toughness of the materials.   

Time-resolved Structural Insights of Supramolecular Assembly Process via SANS

Fundamentally understanding the supramolecular assembly process remains an experimental challenge due to the rapid kinetics involved and the complex nature of various simultaneous non-covalent interactions. In this study, we aimed to capture the different stages of the gelation process, starting from nucleation, using a slow-evolving supramolecular gel derived from a urea-based gelator. We monitored the self-assembly process in real time using time-resolved small-angle neutron scattering (TR-SANS) at various scales, complemented by NMR and rheological data. The scattering data revealed the early formation of a hollow columnar structure, with gelator monomers radially arranged along the long axis of the fiber, and solvent in the core. The sonication led to the uniform growth of fibers and fiber entanglement, whereas the absence of this stimulus promoted extensive bundle formation at a later stage, particularly at the microscopic level, resulting in a mechanically robust gel system. This comprehensive understanding of the supramolecular gel assembly process and its responsiveness to stimuli opens up opportunities for fine-tuning these growth processes. This knowledge has potential applications in industries such as cosmetics, 3D printing ink development, and the paint industry.   

Simulated soft X-ray scattering bridges real-space and reciprocal-space characterization of multi-phase organic materials

While resonant soft X-ray scattering is increasingly used to measure compositional fluctuations in multi-phase organic materials, some more recent approaches exploit the X-ray polarization to simultaneously probe orientational ordering in the constituent bonds. However, to simultaneously measure orientational and compositional fluctuations with precision, a complementary real-space characterization method becomes necessary. Here, we show how three real-space imaging techniques – Photo-induced atomic force microscopy (PiFM), conventional AFM, and TEM – can be used as the basis for models from which we can simulate soft X-ray scattering using a "virtual instrument", the NIST RSoXS Simulation Suite (NRSS). We show that this combined experimental and computational analysis method leads to sub-nm sensitivity to molecular orientation gradients at the interfaces between domains in co-vapor deposited films, which cannot be found from the X-ray scattering alone. Conversely, the X-ray scattering reveals a secondary compositional length-scale in the materials that is invisible to the real-space microscopies. The combination of the transmission reciprocal-space technique with the real-space microscopy provides a means to investigate the bulk structure of the film, as opposed to simply the surface layer. This approach fuses real-space and reciprocal-space characterization to enhance the information content of both synergistically – that is, the resulting measurement is greater than the sum of its parts.   

A Model-free Method for Profiling Size Dispersity in Soft Condensed Matter Using Small-angle Scattering

In this work, we introduce a model-free approach for determining the size polydispersity of soft condensed matter through small-angle scattering techniques. We outline a strategy that utilizes the method of central moment expansion to extract the mean and fluctuation of particle size and skewness of the size distribution function (SDF) from spectral analysis in a bias-free manner. With the polydispersity being moderate, we can reconstruct the SDF using the maximum entropy principle. We demonstrate the validity of our analytical approach by numerically benchmarking a model study over a wide range of size non-uniformity. Our results show that this method is effective for quantifying the size distribution of general soft matter systems.   

Nanoscale Spatial and Transient Mapping of Charge Carriers for Operationally Stable MA-Free Perovskite Solar Cells

Perovskite solar cells (PSCs) have shown great potential to yield high power conversion efficiency surpassing 26.1% for single junction and 33.7% in tandem PSC/Si device yet retaining such performance under continuous operation has remained elusive. Understanding the nanoscopic photochemical changes that drive instabilities in perovskite semiconductors is critical for mitigating device degradation. We develop a multimodal technique to reveal the photodynamic disorder in methylammonium (MA)-free wide-bandgap perovskites leading to the poor radiative efficiency and meager device performance at high temperatures.

Transient Photo-response atomic force microscopy (TP-AFM) is used to spatially map the surface topography and transient map for photodynamics such as recombination lifetime (t_r), transport time (t_t) and diffusion length (L_D) in wide-bandgap PSCs operative at high temperatures. We visualize the changes in the heat-induced structural changes associated with the carrier trapping through grain interiors and grain boundaries that affect the device longevity.  Importantly, we discover that both performance losses and intrinsic degradation processes can be mitigated by modulating the composition of active layer absorber with the fine tuning of its chemical properties. This multimodal workflow to correlate the nanoscale landscape of photodynamics and topography evaluation will be applicable to other analogous perovskite compositions for which a local depiction of performance and operational stability has yet to be recognized.   

Dynamics of Complex Fluids Formed by Ionizable Polymers: From Reciprocal Space to Real Space and Back

The dynamics of complex fluids, particularly of soft matter formed by polymers that consist of segments with inherently different characteristics,  span a broad range of coupled times  and length scale motion from ns to macroscopic times, across length scale that varies from 0.1nm to macroscopic.  This coupling presents a challenge to elucidating the dynamic processes that underlie the properties of these soft systems, driving our journey from reciprocal to real space and back. Using neutron techniques, including  elastic  and quasi elastic scattering , coupled with large scale atomistic and coarse grains molecular dynamics simulations  we were able to attain insight into a long-standing puzzle of how very limited number of ionizable groups tethered to a  polymer backbone  arrest  their macroscopic motion in different complex fluids.   Results for the dynamics of polystyrene  ionizable polymers based complex fluids, including melts, solutions, and micellar phases will be presented, as the complexity of the polymers is increased.    Reciprocal space measurements of SANS, QENS and NSE for polystyrene sulfonate will be first introduces followed  by measurements of complex fluids formed by   co- polymers  that contain a polystyrene sulfonate block.  Moving to real space, results of large-scale molecular dynamics simulations of the same systems will be presented. In conclusion, we will showcase the dynamics derived from the computed complex fluids and illustrate their integration with the reciprocal findings obtained through neutron scattering techniques.   

Mapping Reciprocal Space to Real Space: A Semi-Automated Tool for Advanced Defect Analysis in 3D

Recent advances in self-assembly and microscopy allow for 3D imaging of complex nanostructures arising in both soft and hard condensed matter systems. At the same time, advances in computing allow for sophisticated simulation models that aid in the understanding and exploration of these systems. However, both experimental and computational analysis of complex crystals at the atomic, molecular and colloidal scales face a common challenge: the lack of generalizable tools for structure, strain, and dislocation analysis. Generalizable methods such as Fourier filtering often rely on manual inspection of structural data, making them impractical for systematic, large-scale studies. Although domain specific tools for automated analysis of 2D electron diffraction patterns exist, these tools are not generalizable to arbitrary 2D and 3D datasets. Here, we present a structure agonistic software tool for semi-automated analysis of 2D and 3D particle data. Our algorithm is robust to noise originating from liquid, polycrystalline or otherwise disordered regions, and can be used to analyze strain in atomic, molecular and colloidal crystals of varying complexity and from disparate data collection sources. This method holds promise for advancing structural studies across a variety of domains by providing researchers with a powerful, generalizable tool for understanding and exploring complex crystal structures.   

Nanoscale Mapping in Organic and Perovskite Solar Cells: A Versatile Technique to Engineer the Photovoltaic Yields

Recently, organic solar cells (OSCs) and perovskite solar cells (PSCs) have emerged as solution-processable photovoltaic (PV) technologies with certified power conversion efficiencies (PCE) of about 18% and 25.7%, respectively, within a short time span. However, some challenges still exist in fully investigating and controlling the performance-limiting factors in the photoactive layers of OSCs and PSCs, such as surface conductivity, topography, and morphological distortions at the nanoscale. Although several efforts to investigate and address the above phenomena have been reported, the origin of the nanoscale defects and how they lead to performance losses have not yet been fully explained. This work aims to explain in-depth the nanoscale imaging and mapping within OSCs and PSCs and how remedies to the nanoscale defects can improve PV device performance. The similarities and differences in charge generation, charge separation, charge transport, charge collection, and charge recombination in these two technologies are discussed. These are linked back to the intrinsic material properties of organic and perovskite semiconductors, and how these factors impact photovoltaic device performance is elucidated. This nanoscale mapping technique can also be extended to test large-area active layer coatings fabricated through various deposition techniques, such as blade coating, slot-die coating, and doctor blading, to enhance the photovoltaic properties of scalable OSCs and PSCs.   

Aging of colloidal gels in microgravity

It is difficult to investigate the gelation process for long periods of time due to sedimentation under gravity. Sedimentation causes the delicate gel structures to collapse under their own weight, and we wish to understand what we'd see if this collapse does not occur. In this project, we look at microscope images of colloidal gels taken over 60 hours at the International Space Station by NASA. The gels use the depletion force, and the samples studied range from very sticky particles to barely sticky particles. We use particle tracking to study the structure and dynamics of the gels as they coarsen. We observe that with stronger attractive forces, particles form thick gel strands over time, whereas with weaker attractive forces, particles do not aggregate even at the longest timescales observed.   

Ground-truth information of mesostructure formation and local molecular arrangements in imidazolium-based ionic liquids for training deep neural network algorithm

Training data sets were generated from X-ray scattering data of ionic liquid (IL)/water mixtures as the basis for a deep neural network (DNN) computation architecture that can predict non-equilibrium, dynamical phenomena such as chemical reactions, self-assembly, and ionization. Specialized DNN-based architectures are computationally efficient and highly accurate alternatives to the quantum mechanical simulations, which are computationally expensive. However, despite their broad applicability in chemical and materials discovery, DNNs cannot describe non-equilibrium processes such as long-range charge transfer of electrons or finite effects of electron temperature. ILs are known to form various mesoscale ordered hierarchical structures on mixing with water, comparable to the phase behavior of lyotropic liquid crystals. The hierarchical self-assembly of ionic liquids is a complex phenomenon not easily predictable by computation. Thus, IL/water mixtures are an excellent platform to produce large data set for training a DNN algorithm. Model IL systems were formed by pairing a simple anion (chloride, nitrate, or thiocyanate) with a cationic linear hydrocarbon and mixing with varied fractions of water to form mesoscale ordered structures which were measured by X-ray scattering. Quantitative analysis was performed through crystallographic methods and calculation of the radial distribution functions, which will be discussed alongside potential implementation of the data to DNN training.

 

Research presented in this presentation was supported by the Laboratory Directed Research & Development (LDRD) Program of Los Alamos National Laboratory under project number 20210087DR. Part of this work was performed at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science. Los Alamos National Laboratory, an affirmative action equal opportunity employer, is managed by the Triad National Security, LLC for the U.S. DOE’s NNSA, under contract 89233218CNA000001.