Multiple Length Scales Research Articles

Highly Efficient Nonfullerene Organic Solar Cells: Morphology Control and Characterizations

Nonfullerene acceptors (NFAs) are currently a major research focus in the development of organic solar cells (OSCs) because of their readily tunable optical and electronic properties, enabling bulk heterojunction (BHJ) NFA‐based OSCs to achieve photovoltaic efficiencies exceeding 19%. Significant efforts have been made to yield the optimal nanoscale morphology, enabling the achievement of highly efficient NFA‐based OSCs. This review discusses the structural characteristics of NFAs and their relationship with morphology. Subsequently, the correlation between the morphology and photovoltaic parameters is introduced, which provides a fundamental basis for morphology modulation. This review then points out the major challenges of morphological characterization of NFA‐based blend films while using some conventional real‐space techniques due to low phase contrast and summarizes recently emerging characterization techniques capable of characterizing high‐contrast phase morphologies at multiple length scales. Finally, strategies for targeted morphological optimization through materials design, processing solvents, posttreatment, and ternary strategies are presented, although it is challenging to obtain an ideal morphology (e.g., appropriate phase separation and ideal donor/acceptor molecular interactions) due to the anisotropic structural characteristics of NFAs in the as‐cast films. This review is expected to provide guidance to continuously advancing the success of NFA‐based OSCs from the morphology perspective in the future.

Single-chain simulation of Ising density functional theory for weak polyelectrolytes.

Conventional theories of weak polyelectrolytes are either computationally prohibitive to account for the multidimensional inhomogeneity of polymer ionization in a liquid environment or oversimplistic in describing the coupling effects of ion-explicit electrostatic interactions and long-range intrachain correlations. To bridge this gap, we implement the Ising density functional theory (iDFT) for ionizable polymer systems using the single-chain-in-mean-field algorithm. The single-chain-in-iDFT (sc-iDFT) shows significant improvements over conventional mean-field methods in describing segment-level dissociation equilibrium, specific ion effects, and long-range intrachain correlations. With an explicit consideration of the fluctuations of polymer configurations and the position-dependent ionization of individual polymer segments, sc-iDFT provides a faithful description of the structure and thermodynamic properties of inhomogeneous weak polyelectrolyte systems across multiple length scales.

Multiscale modeling of liquid jet breakup in crossflow using an Eulerian/Lagrangian approach

Liquid atomization is a very complex issue, involving multiple length and time scales over several orders of magnitude. To better understand the atomization characteristics of the main injection in a lean premix prevaporize (LPP) combustor, a volume of fluid (VOF)–particle conversion algorithm Lagrangian particle tracking (LPT) coupled approach was proposed to simultaneously reproduce the primary and secondary breakup processes. A VOF model with an adaptive mesh refinement strategy was used to resolve the liquid disintegration on a large scale. The small liquid structures qualified as droplets were transformed into discrete particles based on particle conversion criteria. Next, these particles were tracked using the LPT method to simulate the secondary breakup process. The proposed coupled method used in the Eulerian/Lagrangian framework was validated against liquid jet in crossflow experimental data. The numerical results achieved good agreement with the experimental data. Finally, the proposed method was used to predict the atomization characteristics of the main injection in an LPP combustor under various aerodynamic conditions. Qualitative and quantitative information about liquid deformation and spray characteristics were obtained, which varied depending on the aerodynamic parameters.

Coupling diffusion and finite deformation in phase transformation materials

We present a multiscale theoretical framework to investigate the interplay between diffusion and finite lattice deformation in phase transformation materials. In this framework, we use the Cauchy–Born Rule and the Principle of Virtual Power to derive a thermodynamically consistent theory coupling the diffusion of a guest species (Cahn–Hilliard type) with the finite deformation of host lattices (nonlinear gradient elasticity). We adapt this theory to intercalation materials – specifically Li1−2Mn2O4 – to investigate the delicate interplay between Li-diffusion and the cubic-to-tetragonal deformation of lattices. Our computations reveal fundamental insights into the microstructural evolution pathways under dynamic discharge conditions, and provide quantitative insights into the nucleation and growth of twinned microstructures during intercalation. Additionally, our results identify regions of stress concentrations (e.g., at phase boundaries, particle surfaces) that arise from lattice misfit and accumulate in the electrode with repeated cycling. These findings suggest a potential mechanism for structural decay in Li2Mn2O4. More generally, we establish a theoretical framework that can be used to investigate microstructural evolution pathways, across multiple length scales, in first-order phase transformation materials.

Multi-scale measurement of stiffness in the developing ferret brain

Cortical folding is an important process during brain development, and aberrant folding is linked to disorders such as autism and schizophrenia. Changes in cell numbers, size, and morphology have been proposed to exert forces that control the folding process, but these changes may also influence the mechanical properties of developing brain tissue. Currently, the changes in tissue stiffness during brain folding are unknown. Here, we report stiffness in the developing ferret brain across multiple length scales, emphasizing changes in folding cortical tissue. Using rheometry to measure the bulk properties of brain tissue, we found that overall brain stiffness increases with age over the period of cortical folding. Using atomic force microscopy to target the cortical plate, we found that the occipital cortex increases in stiffness as well as stiffness heterogeneity over the course of development and folding. These findings can help to elucidate the mechanics of the cortical folding process by clarifying the concurrent evolution of tissue properties.

Localized engineering of grain boundary morphology by electro-nano-pulsing processing

We report a novel electro-nano-pulsing (ENP) processing method to achieve localized engineering of grain boundary (GB) morphology in polycrystalline metallic materials. ENP is extraordinarily capable of generating intense nanopulse electric current with a current density greater than a few to several hundreds of 1010A/m2 and a pulse duration on the order of a few 100ns. Such a level of current density is ∼3–5 magnitudes higher than that is usually achieved during the Spark Plasma Sintering process. Using the Nichrome-80 superalloy as a model material, we observed a variety of GB roughening phenomena at multiple length scales, resulting in the generation of diverse forms of atomistic facets, nanoscale serrations, and nanoscale step-like GB morphologies after the ENP processing. We think that the excessive GB heat localization and electron wind force or stress are the main factors contributing to the GB morphological changes during the ENP processing. The ENP processing provides a new unique grain boundary engineering strategy to manipulate the GBs with the changes localized at the GB region, without altering its adjacent grains.

Intermediate Polymer Relaxation Explains the Anomalous Rheology of Nanocomposites with Ultrasmall Attractive POSS Nanoparticles.

The rheological properties of entangled polymers loaded with very small, strongly attractive polyhedral oligomeric silsesquioxane (POSS) fillers differ from that of nanocomposites with larger fillers by (1) the shorter breadth of the entanglement plateau and (2) the relatively unchanged terminal viscosity with increasing POSS loading. Although such anomalous rheological properties can rewrite the property-processing map of materials (e.g., high glass transition temperature and low viscosity), their mechanism remains unclear. In this study, we report that polymer relaxations on intermediate time scales between α and entire-chain relaxation, so-called "slower processes", are responsible for this unusual rheological behavior of poly(2-vinylpyridine)/octa(aminophenyl)silsesquioxane (P2VP/OAPS) nanocomposites. To uncover the effects of entanglements on the nanocomposite dynamics, rheometry is used for variable matrix molecular weights. Results show a systematic change in the rheological response, which is independent of the molecular weight, and in turn, the presence of entanglements. This supports a physical interpretation that a slower process dominates the rheological response of the material at intermediate frequencies on length scales larger than the segment length or the OAPS diameter, while the underlying physical time scales associated with the entanglement relaxation remain unchanged. Such insights are anticipated to assist the future rational design of other highly attractive and ultrasmall nanoparticles that enable a fine-tuned rheological response of nanocomposites across multiple length scales.

Membrane-Mediated Cooperative Interactions of CD47 and SIRPα.

The specific binding of the ubiquitous 'marker of self' protein CD47 to the SIRPα protein anchored in the macrophage plasma membrane results in the inhibition of the engulfment of 'self' cells by macrophages and thus constitutes a key checkpoint of our innate immune system. Consequently, the CD47-SIRPα protein complex has been recognized as a potential therapeutic target in cancer and inflammation. Here, we introduce a lattice-based mesoscale model for the biomimetic system studied recently in fluorescence microscopy experiments where GFP-tagged CD47 proteins on giant plasma membrane vesicles bind to SIRPα proteins immobilized on a surface. Computer simulations of the lattice-based mesoscale model allow us to study the biomimetic system on multiple length scales, ranging from single nanometers to several micrometers and simultaneously keep track of single CD47-SIRPα binding and unbinding events. Our simulations not only reproduce data from the fluorescence microscopy experiments but also are consistent with results of several other experiments, which validates our numerical approach. In addition, our simulations yield quantitative predictions on the magnitude and range of effective, membrane-mediated attraction between CD47-SIRPα complexes. Such detailed information on CD47-SIRPα interactions cannot be obtained currently from experiments alone. Our simulation results thus extend the present understanding of cooperative effects in CD47-SIRPα interactions and may have an influence on the advancement of new cancer treatments.

Delivery of probiotics and enzymes in self-assemblies of lipids and biopolymers based on colloidal principles

Food is a complex soft matter, because various components, such as proteins, lipids, and carbohydrates, are self-assembled via non-covalent, colloidal interactions and form hierarchical structures at multiple length scales. Soft matter scientists have shown an increasing interest in understanding the general principles governing the food structure formation. During the last several decades, an increasing number of studies have shown that the maintenance of healthy gastrointestinal tract and its microbiome is essential for human health and wellbeing. The realization of the importance of the gastrointestinal microbiome has led to the development of probiotics, which are defined as living bacteria that confer a health benefit on the host. Probiotic bacteria and enzymes can be delivered to the intestinal system by formulating appropriate carriers and including these into food ingested by humans. Despite this simple statement, it involves many challenges in the field of soft matter science. This review aims to highlight how the key concepts in soft matter science can be used to design, characterize, and evaluate self-assembled formulations of probiotics and enzymes based on lipids and biopolymers. The topics covered in this review includes the emulsification of oil-water mixtures, the self-assembly of lipids and polymers at interfaces, the electrostatics and viscoelasticity of interfaces, and the wetting/adhesion of colloidal particles.

Visualization of Self-Assembly and Hydration of a β-Hairpin through Integrated Small and Wide-Angle Neutron Scattering.

Fundamental understanding of the structure and assembly of nanoscale building blocks is crucial for the development of novel biomaterials with defined architectures and function. However, accessing self-consistent structural information across multiple length scales is challenging. This limits opportunities to exploit atomic scale interactions to achieve emergent macroscale properties. In this work we present an integrative small- and wide-angle neutron scattering approach coupled with computational modeling to reveal the multiscale structure of hierarchically self-assembled β hairpins in aqueous solution across 4 orders of magnitude in length scale from 0.1 Å to 300 nm. Our results demonstrate the power of this self-consistent cross-length scale approach and allows us to model both the large-scale self-assembly and small-scale hairpin hydration of the model β hairpin CLN025. Using this combination of techniques, we map the hydrophobic/hydrophilic character of this model self-assembled biomolecular surface with atomic resolution. These results have important implications for the multiscale investigation of aqueous peptides and proteins, for the prediction of ligand binding and molecular associations for drug design, and for understanding the self-assembly of peptides and proteins for functional biomaterials.

Sintering in seconds, elucidated by millisecond in situ diffraction

Materials, when sintered at high temperatures, undergo structural changes on multiple, hierarchical length scales but getting realtime information on these changes is difficult. To address this challenge, we developed a custom-built sample environment that allows us to investigate the structural evolution of materials during sintering using high-energy two-dimensional synchrotron X-ray diffraction (2D-XRD). Changes in the structure of SrFe12O19 ceramic magnet at multiple length scales were tracked in situ and modelled with millisecond time-resolution. In addition, we also demonstrated the ability to perform quantitative texture analysis from individual 2D-XRD images with a time resolution of 4 ms each. Owing to the high brightness X-ray source and advanced X-ray detectors, the evolution of crystallographic texture could be followed during sintering. This in situ approach can aid understanding of the synthesis–structure–property relationships in sintered materials, enabling the development of improved functional materials.

Multichannel measurements of C. elegans largest Lyapunov exponents using optical diffraction.

Dynamic diffraction (DOD) is a form of microscopy that allows the dynamic tracking of changing shapes in a 1D time series. DOD can capture the locomotion of a nematode while swimming freely in a 3D space, allowing the locomotion of the worm to more closely mimic natural behavior than in some other laboratory environments. More importantly, we are able to see markers of chaos as DOD covers dynamics on multiple length scales. This work introduces a multichannel method to measure the dynamic complexity of microscopic organisms. We show that parameters associated with chaos, such as the largest Lyapunov exponent (LLE), the mean frequency, mutual information (MI), and the embedding dimension, are independent of the specific point sampled in the diffraction pattern, thus demonstrating experimentally the consistency of our dynamic parameters sampled at various locations (channels) in the associated optical far-field pattern.

Consequences and Control of Multiscale Order/Disorder in Chiral Magnetic Textures.

Transition metal intercalated transition metal dichalcogenides (TMDs) are promising platforms for next-generation spintronic devices based on their wide range of electronic and magnetic phases, which can be tuned by varying the host lattice or intercalant's identity, stoichiometry, or spatial order. Some of these compounds host a chiral magnetic phase in which the helical winding of magnetic moments propagates along a high-symmetry crystalline axis. Previous studies have demonstrated that variation in intercalant concentrations can have a dramatic effect on the formation of chiral domains and ensemble magnetic properties. However, a systematic and comprehensive study of how atomic-scale order and disorder impact these chiral magnetic textures is so far lacking. Here, we leverage a combination of imaging modes in the (scanning) transmission electron microscope (S/TEM) to directly probe (dis)order across multiple length scales and show how subtle changes in the atomic lattice can tune the mesoscale spin textures and bulk magnetic response in Cr1/3NbS2, with direct implications for the fundamental understanding and technological implementation of such compounds.

Revealing the predictive power of neural operators for strain evolution in digital composites

The demand for high-performance materials, along with advanced synthesis technologies such as additive manufacturing and 3D printing, has spurred the development of hierarchical composites with superior properties. However, computational modelling of such composites using physics-based solvers, while enabling the discovery of optimal microstructures, have prohibitively high computational cost hindering their practical application. To this extent, we show that Neural Operators (NOs) can be used to learn and predict the strain evolution in 2D digital composites. Specifically, we consider three architectures, namely, Fourier NO (FNO), Wavelet NO (WNO), and Multi-wavelet NO (MWT). We demonstrate that by providing a few initial strain frames as input, NOs can accurately predict multiple future time steps in an extremely data-efficient fashion, especially WNO. Further, once trained, NOs forecast the strain trajectories for completely unseen boundary conditions. Among NOs, only FNO offers super-resolution capabilities for estimating strains at multiple length scales, which can provide higher material and pixel-wise resolution. We also show that NOs can generalize to arbitrary geometries with finer domain resolution without the need for additional training. Based on all the results presented, we note that the FNO exhibits the best performance among the NOs, while also giving minimum inference time that is almost three orders magnitude lower than the conventional finite element solutions. Thus, FNOs can be used as a surrogate for accelerated simulation of the strain evolution in complex microstructures toward designing the next composite materials.

Multi-directional neutron dark-field imaging with single absorption grating

Neutron dark-field imaging is a powerful technique for investigating the microstructural properties of materials through high-resolution full-field mapping of small-angle scattering. However, conventional neutron dark-field imaging utilizing Talbot–Lau interferometers is limited to probing only one scattering direction at a time. Here, we introduce a novel multi-directional neutron dark-field imaging approach that utilizes a single absorption grating with a two-dimensional pattern to simultaneously probe multiple scattering directions. The method is demonstrated to successfully resolve fiber orientations in a carbon compound material as well as the complex morphology of the transformed martensitic phase in additively manufactured stainless steel dogbone samples after mechanical deformation. The latter results reveal a preferential alignment of transformed domains parallel to the load direction, which is verified by EBSD. The measured real-space correlation functions are in good agreement with those extracted from the EBSD map. Our results demonstrate that multi-directional neutron dark-field imaging is overcoming significant limitations of conventional neutron dark-field imaging in assessing complex heterogeneous anisotropic microstructures and providing quantitative structural information on multiple length scales.

Using Thermomechanical Properties to Reassess Particles' Dispersion in Nanostructured Polymers: Size vs. Content.

Nanoparticle-filled polymers (i.e., nanocomposites) can exhibit characteristics unattainable by the unfilled polymer, making them attractive to engineer structural composites. However, the transition of particulate fillers from the micron to the nanoscale requires a comprehensive understanding of how particle downsizing influences molecular interactions and organization across multiple length scales, ranging from chemical bonding to microstructural evolution. This work outlines the advancements described in the literature that have become relevant and have shaped today's understanding of the processing-structure-property relationships in polymer nanocomposites. The main inorganic and organic particles that have been incorporated into polymers are examined first. The commonly practiced methods for nanoparticle incorporation are then highlighted. The development in mechanical properties-such as tensile strength, storage modulus and glass transition temperature-in the selected epoxy matrix nanocomposites described in the literature was specifically reviewed and discussed. The significant effect of particle content, dispersion, size, and mean free path on thermomechanical properties, commonly expressed as a function of weight percentage (wt.%) of added particles, was found to be better explained as a function of particle crowding (number of particles and distance among them). From this work, it was possible to conclude that the dramatic effect of particle size for the same tiny amount of very small and well-dispersed particles brings evidence that particle size and the particle weight content should be downscaled together.

Multimodal and multiscale characterization reveals how tendon structure and mechanical response are altered by reduced loading.

Tendons are collagen-based connective tissues where the composition, structure and mechanics respond and adapt to the local mechanical environment. Adaptation to prolonged inactivity can result in stiffer tendons that are more prone to injury. However, the complex relation between reduced loading, structure, and mechanical performance is still not fully understood. This study combines mechanical testing with high-resolution synchrotron X-ray imaging, scattering techniques and histology to elucidate how reduced loading affects the structural properties and mechanical response of rat Achilles tendons on multiple length scales. The results show that reduced in vivo loading leads to more crimped and less organized fibers and this structural inhomogeneity could be the reason for the altered mechanical response. Unloading also seems to change the fibril response, possibly by altering the strain partitioning between hierarchical levels, and to reduce cell density. This study elucidates the relation between in vivo loading, the Achilles tendon nano-, meso‑structure and mechanical response. The results provide fundamental insights into the mechanoregulatory mechanisms guiding the intricate biomechanics, tissue structural organization, and performance of complex collagen-based tissues. STATEMENT OF SIGNIFICANCE: Achilles tendon properties allow a dynamic interaction between muscles and tendon and influence force transmission during locomotion. Lack of physiological loading can have dramatic effects on tendon structure and mechanical properties. We have combined the use of cutting-edge high-resolution synchrotron techniques with mechanical testing to show how reduced loading affects the tendon on multiple hierarchical levels (from nanoscale up to whole organ) clarifying the relation between structural changes and mechanical performance. Our findings set the first step to address a significant healthcare challenge, such as the design of tailored rehabilitations that take into consideration structural changes after tendon immobilization.

Quantitative Systems Pharmacology & Machine Learning - A match made in heaven or hell?

As pharmaceutical development moves from early stage in vitro experimentation to later in vivo and subsequent clinical trials, data and knowledge are acquired across multiple time and length scales, from the subcellular to whole patient cohort scale. Realising the potential of this data for informing decision making in pharmaceutical development requires the individual and combined application of machine learning (ML) and mechanistic multiscale mathematical modelling approaches. Here we outline how these two approaches, both individually and in tandem, can be applied at different stages of the drug discovery and development pipeline to inform decision making compound development. The importance of discerning between knowledge and data is highlighted in informing the initial use of ML or mechanistic Quantitative Systems Pharmacology (QSP) models. We discuss the application of sensitivity and structural identifiability analyses of QSP models in informing future experimental studies, to which ML may be applied, as well as how ML approaches can be used to inform mechanistic model development. Relevant literature studies are highlighted and we close by discussing caveats regarding the application of each approach in an age of constant data acquisition. Significance Statement We consider when best to apply Machine Learning (ML) and mechanistic Quantitative Systems Pharmacology (QSP) approaches in the context of the drug discovery and development pipeline. We discuss the importance of prior knowledge and data available for the system of interest and how this informs the individual and combined application of ML and QSP approaches at each stage of the pipeline.

Microstructural insight into inhalation powder blends through correlative multi-scale X-ray computed tomography

Dry powder inhalers (DPI) are important for topical drug delivery to the lungs, but characterising the pre-aerosolised powder microstructure is a key initial step in understanding the post-aerosolised blend performance. In this work, we characterise the pre-aerosolised 3D microstructure of an inhalation blend using correlative multi-scale X-ray Computed Tomography (XCT), identifying lactose and drug-rich phases at multiple length scales on the same sample. The drug-rich phase distribution across the sample is shown to be homogeneous on a bulk scale but heterogeneous on a particulate scale, with individual clusters containing different amounts of drug-rich phase, and different parts of a carrier particle coated with different amounts of drug-rich phase. Simple scalings of the drug-rich phase thickness with carrier particle size are used to derive the drug-proportion to carrier particle size relationship. This work opens new doors to micro-structural assessment of inhalation powders that could be invaluable for bioequivalence assessment of dry powder inhalers.

Nanograded artificial nacre with efficient energy dissipation

The renowned mechanical performance of biological ceramics can be attributed to their hierarchical structures, wherein structural features at the nanoscale play a crucial role. However, nanoscale features, such as nanogradients, have rarely been incorporated in biomimetic ceramics because of the challenges in simultaneously controlling the material structure at multiple length scales. Here, we report the fabrication of artificial nacre with graphene oxide nanogradients in its aragonite platelets through a matrix-directed mineralization method. The gradients are formed via the spontaneous accumulation of graphene oxide nanosheets on the surface of the platelets during the mineralization process, which then induces a lateral residual stress field in the platelets. Nanoindentation tests and mercury intrusion porosimetry demonstrate that the material's energy dissipation is enhanced both intrinsically and extrinsically through the compressive stress near the platelet surface. The energy dissipation density reaches 0.159± 0.007 nJ/μm3, and the toughness amplification is superior to that of the most advanced ceramics. Numerical simulations also agree with the finding that the stress field notably contributes to the overall energy dissipation. This work demonstrates that the energy dissipation of biomimetic ceramics can be further increased by integrating design principles spanning multiple scales. This strategy can be readily extended to the combinations of other structural models for the design and fabrication of structural ceramics with customized and optimized performance.