Macroscale Structures Research Articles

Regulation of protonation in aramid nanofiber films for improved mechanical properties

Abstract Aramid nanofibers (ANFs) are a strong and heat resistant nanomaterial that can be isolated from commercial poly(p-phenylene terephthalamide) (PPTA) fibers. These nanofibers allow bottom-up self-assembly to form macroscale structures in various form factors through a simple reprotonation process. However, the mechanical properties of these reassembled ANF structures often fall short of those of PPTA fibers, mainly due to insufficient packing, non-uniform microstructures, and low crystallinity. In this study, we present ANF films with improved mechanical properties prepared by a repeated spin-coating technique combined with a reprotonation process using deionized water and formic acid at concentrations ranging from 20 to 60 wt.%. Extensive analyses were performed on the resulting ANF films to evaluate their surface and cross-sectional morphologies, chemical bonds and compositions, thermal stabilities, and mechanical properties. The fabricated ANF films exhibited remarkable performance, with an elastic modulus of 6.7 GPa, tensile strength of 680 MPa, and toughness of 7.7 MJ m−³, while maintaining the inherent thermal stability of PPTA fibers. These properties significantly exceed those of previously reported ANF films, broadening the potential applications for ANF films in various fields.

Joint Observation of a Series of Magnetic Holes by Tianwen-1 and MAVEN on Mars

Magnetic holes (MHs) are transient magnetic structures responsible for energy conversion in space plasma. Using single-spacecraft measurements from Mars Atmosphere and Volatile EvolutioN (MAVEN), the existence of MHs on Mars has been confirmed. However, due to the limitations of single-spacecraft observations, significant uncertainty also arises on the identification of the spatial scale and 3D geometry of MHs. In this study, we report a series of MHs successively detected by Tianwen-1 near the high-latitude magnetopause and by the MAVEN spacecraft near the subsolar magnetopause. The large separation between Tianwen-1 and MAVEN (∼4 R M) suggests these MHs are macroscale structures extending along the axial direction. Additionally, we observe whistler waves generated by electron perpendicular anisotropy in one of the macroscale MHs. This study is the first joint observation of Martian MHs, shedding light on the research of transient magnetic structures on Mars.

Evidence for convergence of distributed cortical processing in band-like functional zones in human entorhinal cortex.

The wide array of cognitive functions associated with the hippocampus is supported through interactions with the cerebral cortex. However, most of the direct cortical input to the hippocampus originates in the entorhinal cortex, forming the hippocampal-entorhinal system. In humans, the role of the entorhinal cortex in mediating hippocampal-cortical interactions remains unknown. In this study, we used precision neuroimaging to examine the distributed cortical anatomy associated with the human hippocampal-entorhinal system. Consistent with animal anatomy, our results associate different subregions of the entorhinal cortex with different parts of the hippocampus long axis. Furthermore, we find that the entorhinal cortex comprises three band-like zones that are associated with functionally distinct cortical networks. Importantly, the entorhinal cortex bands traverse the proposed human homologs of rodent lateral and medial entorhinal cortices. Finally, we show that the entorhinal cortex is a major convergence area of distributed cortical processing and that the topography of cortical networks associated with the anterior medial temporal lobe mirrors the macroscale structure of high-order cortical processing.

Understanding following patterns among high-performance athletes

Abstract Professional sport enhances social interaction among athletes through meetings such as training groups, large-scale competitions and sponsored events. Among these, the Olympic Games represent the largest international competition, having a unique global impact. This event is assumed to promote international cooperation through the values of sport, becoming an opportunity for creating global connections between athletes. Here we want to understand the properties of the following activity, an indicator of the information flows, between highly successful Olympic athletes both at microscopic and macroscopic scales. To do so, we built a database including the Twitter usernames of Olympic medallists in Tokyo 2020, using it for creating the follower-followee network, a directed network representing who followed whom in this social network. From our database of 1052 athletes, we found 7326 connections among 964 athletes. The most popular athletes, those with highest number of followers, were Kevin Durant (basketball, USA), Allyson Felix (athletics, USA), Teddy Riner (judo, France), Alex Morgan (football, USA) and Simone Biles (gymnastics, USA). The macro-scale network structure displayed organized patterns related to microscopic—node—properties such as sex, country and sport, evidencing assortative connectivity patterns. These assortative patterns highlighted the need for a model quantifying the features’ importance in the followees choice, which we introduced through a gravity approach modelling the number of connections between homogeneous groups. Our research remarks the importance of datasets built from public exposure of professional athletes, serving as a proxy to investigate interesting aspects of many complex socio-cultural systems at different scales.

Effect of Solvent and Cholesterol on Liposome Production by the Reverse-Phase Evaporation (RPE) Method.

Liposomal drug delivery is a promising approach for delivering therapeutics effectively. While most liposomes are designed to be nanometer-sized for efficient cellular uptake, micron-sized liposomes are gaining interest due to their larger drug-loading capacity. When combined with macroscale structures, such as implants and hydrogels, they offer prolonged therapeutic delivery. This study investigates how solvents affect the production of micron-sized liposomes, with or without cholesterol, using the reverse-phase evaporation (RPE) method. Although the RPE method is established for producing micron-sized liposomes, the influence of solvents on liposome size and uniformity is not well understood. The study explores whether controlling the size of inverse micelles, an intermediate product, through the use of different organic solvents affects the final liposome size. Three solvents─diethyl ether, methanol, and acetone─were tested for their effect on the formation of inverse micelles and liposomes and their sizes. Results showed that without cholesterol, diethyl ether produced uniform inverse micelles, leading to mostly nanosized liposomes. Methanol and acetone resulted in phase separation, preventing uniform liposome formation, although some micron-sized liposomes appeared. The acetone sample yielded mostly oil droplets. The results showed that forming inverse micelles lead to nanosized liposomes. With cholesterol, phase separation was dominant in methanol, but micron-sized liposomes still formed. Across all cases, cholesterol reduced the liposome size. The findings reveal that inverse micelles are not always reliable predictors of the final liposome size and that the RPE method is highly sensitive to solvent polarity and lipid-solvent interactions. Overall, the findings of this study provide valuable insights into how the choice of solvent and lipid composition can influence the production of liposomes via the RPE method. These insights are critical for optimizing liposome production and influencing future designs of liposomal drug delivery systems.

Macroscale connectome topographical structure reveals the biomechanisms of brain dysfunction in Alzheimer's disease.

The intricate spatial configurations of brain networks offer essential insights into understanding the specific patterns of brain abnormalities and the underlying biological mechanisms associated with Alzheimer's disease (AD), normal aging, and other neurodegenerative disorders. This study investigated alterations in the topographical structure of the brain related to aging and neurodegenerative diseases by analyzing brain gradients derived from structural MRI data across multiple cohorts (n = 7323). The analysis identified distinct gradient patterns in AD, aging, and other neurodegenerative conditions. Gene enrichment analysis indicated that inorganic ion transmembrane transport was the most significant term in normal aging, while chemical synaptic transmission is a common enrichment term across various neurodegenerative diseases. Moreover, the findings show that each disorder exhibits unique dysfunctional neurophysiological characteristics. These insights are pivotal for elucidating the distinct biological mechanisms underlying AD, thereby enhancing our understanding of its unique clinical phenotypes in contrast to normal aging and other neurodegenerative disorders.

“Cocktail-like” double-layered polyimide/alumina composite aerogels via side-direction freezing method as designable high-temperature thermal insulations

The extremely low thermal conductivity, low density, and high-temperature stability of polyimide (PI) aerogels are promising candidates for thermal insulation applications. In this work, the layered PI/Al2O3 composite aerogels (PACAs) were developed, inspired by the biomimetic idea of a “cocktail” layered structure to enhance the service temperature of PI. By exploiting the density difference between the water-soluble polyimide acid (PAA) solution and Al2O3 slurry, we successfully fabricated PACAs with a macro-scale layered structure through side-direction freezing and thermal imidization processes. The PACAs exhibit significant anisotropic structural characteristics, with pore channels presenting a honeycomb structure in the Z-direction, effectively suppressing heat transfer along this direction. Both PI and Al2O3 aerogels demonstrate anisotropic properties in different directions, with the PI aerogel achieving excellent thermal insulation with a thermal conductivity of 0.0197 W/(m·K) along the Z-direction. By adjusting the relative thickness of the PI and Al2O3 layers, optimal thermal insulation performances were achieved at temperatures of 300 °C, 600 °C, and 900 °C. These results showcase the innovative advantages of the layered structure in enhancing thermal insulation while leveraging the low thermal conductivity of PI and the high-temperature tolerance of Al2O3.

Macroscale vertical power-law distribution of bacteria in dark oceans can emerge from microscale bacteria-particle interactions

Microbes in the dark oceans are a key determinant of remineralization of sinking carbon particles. However, most marine ecosystem models overlook how microbes aggregate on particles and the microscale interactions between particle-associated microbes, making it difficult to obtain mechanistic insights on their vertical power-law decay pattern. Here, we present a spatial population model where the attachment and detachment processes of bacterial cells depend on local density of particle-associated bacteria. We show that the power-law relationship can emerge when the non-random aggregated distribution of bacteria is considered without any depth-specific environmental parameters. Furthermore, the comparison between model behavior and empirical patterns in the Pacific and Southern Ocean indicated that temperature-dependent hydrolysis rate and nutrient-dependent sinking rate of particles are key parameters to explain the regional variations of the power-law exponent. The mechanistic approach developed here provides a pathway to link micro-scale interactions between individuals to macro-scale food chain structures and carbon cycle

Structure-function coupling in macroscale human brain networks.

Precisely how the anatomical structure of the brain gives rise to a repertoire of complex functions remains incompletely understood. A promising manifestation of this mapping from structure to function is the dependency of the functional activity of a brain region on the underlying white matter architecture. Here, we review the literature examining the macroscale coupling between structural and functional connectivity, and we establish how this structure-function coupling (SFC) can provide more information about the underlying workings of the brain than either feature alone. We begin by defining SFC and describing the computational methods used to quantify it. We then review empirical studies that examine the heterogeneous expression of SFC across different brain regions, among individuals, in the context of the cognitive task being performed, and over time, as well as its role in fostering flexible cognition. Last, we investigate how the coupling between structure and function is affected in neurological and psychiatric conditions, and we report how aberrant SFC is associated with disease duration and disease-specific cognitive impairment. By elucidating how the dynamic relationship between the structure and function of the brain is altered in the presence of neurological and psychiatric conditions, we aim to not only further our understanding of their aetiology but also establish SFC as a new and sensitive marker of disease symptomatology and cognitive performance. Overall, this Review collates the current knowledge regarding the regional interdependency between the macroscale structure and function of the human brain in both neurotypical and neuroatypical individuals.

Involution symmetry quantification using recurrences.

Symmetries are ubiquitous in science, aiding theoretical comprehension by discerning patterns in mathematical models and natural phenomena. This work introduces a method for assessing the extent of symmetry within a time series. We explore both microscopic and macroscopic features extracted from a recurrence plot. By analyzing the statistics of small recurrence matrices, our approach delves into microscale dynamics, facilitating the identification of symmetric time series segments through diagonal macroscale structures on a recurrence plot. We validate our approach by successfully quantifying involution symmetries for three-dimensional dynamical models, specifically, order-2 rotational symmetry in the Lorenz '63 model, and inversion symmetry in the Chua circuit. Our quantifier also detects symmetry breaking in the modified Lorenz model for El Niño phenomenon. The method can be applied in a versatile manner, not only to three-dimensional trajectories but also to univariate time series. Symmetry quantification in time series is promising for enhancing dynamical system modeling and profiling.

High-harmonic spin-orbit angular momentum generation in crystalline solids preserving multiscale dynamical symmetry.

Symmetries essentially provide conservation rules in nonlinear light-matter interactions and facilitate control and understanding of photon conversion processes or electron dynamics. Since anisotropic solids have rich symmetries, they are strong candidates for controlling both optical micro- and macroscale structures, namely, spin angular momentum (circular polarization) and orbital angular momentum (spiral wavefront), respectively. Here, we show structured high-harmonic generation linked to the anisotropic symmetry of a solid. By strategically preserving a dynamical symmetry arising from the spin-orbit interaction of light, we generate multiple orbital angular momentum states in high-order harmonics. The experimental results exhibit the total angular momentum conservation rule of light even in the extreme nonlinear region, which is evidence that the mechanism originates from a dynamical symmetry. Our study provides a deeper understanding of multiscale nonlinear optical phenomena and a general guideline for using electronic structures to control structured light, such as through Floquet engineering.

Semi-analytical and experimental heat input study of additively manufactured Zr-based bulk metallic glass: Insights into nano- and global-scale relaxation and crystallization

Laser powder bed fusion (LPBF) of Zr-based bulk metallic glasses (BMGs) has recently attracted attention due to its capacity for microstructure control, the potential for custom-tailored properties, and its versatile applicability across various industries. It is crucial to strike a balance between relative density, relaxation, and crystallinity tailored to a specific application when 3D printing amorphous components. However, prior findings have not revealed an exclusive study concerning low fractions of crystallinity (<5 vol%) and atomic-scale effects during the LPBF process, e.g. the relaxation degree of the amorphous structure. This study employs a systematic experimental approach, complemented by semi-analytical modeling, to comprehensively recognize the nano- and macro-scale amorphous structure and the associated mechanical behavior. Differential scanning calorimetry (DSC) along with flexural stress-strain measurements disclose that the highest relaxation and crystallization enthalpies, which are obtained for samples printed with lower heat input, are not in correlation with the desired mechanical properties. On the contrary, samples printed with a heat input of ΔH=44 reveal the maximum density range (up to 99.97 %) and showcase laboratory-XRD amorphous structure. This results in the highest flexural strength (up to 2080 MPa) and elastic deformation range (up to 2.6 %), along with the lowest Young’s modulus span (60–70 GPa). In this case, the crystallinity (0.02–0.2 vol%) associated with the desired mechanical properties is calculated from the modeling results. The outcomes indicate increased heating/ cooling rates corresponding to the amplified heat input, with cooling rates reaching up to 5×104 °C/s and heating rates up to 6×105 °C/s. However, underlying layers are subjected to reduced heating and cooling rates. This observation not only validates the intricate thermal dynamics in LPBF but also elucidates the crystalline formation in heat-affected zones (HAZs) with associated lower heating and cooling rates.

Droplet asymmetry bouncing on structured surfaces: A simulation based on SPH method

The impact of a liquid droplet onto a macro-structured super-hydrophobic surface, results in an asymmetric spreading-retraction process and a significant decrease in contact time. The process involves large deformation of droplets and evolution of surface morphology, so it is difficult to efficiently simulate using traditional grid-based methods. In this study, a numerical model is established using the mesh-free smoothed particle hydrodynamics (SPH) method to simulate the interaction of liquid droplets with solid surfaces exhibiting complex macro-textures. Leveraging the Lagrange nature of SPH, liquid droplets can be model using free surface boundary condition. The weakly compressible SPH formulation is employed to discretize the governing equations of liquid. Surface tension is captured by introducing an additional negative pressure term, inducing attractive forces among fluid particles. Kernel functions are carefully chosen to mitigate stress instability resulting from droplet spreading and retraction. The model is then applied to simulate the dynamic processes of droplets impact on flat and non-flat super-hydrophobic surfaces. The bouncing dynamics of droplets are investigated under varying conditions, including Weber number, size, and morphology of macro-scale surface structures. Our simulation results for contact time, maximum diameter, and droplet morphology align closely with experimental observations. The contact time of the droplet exhibits a dependency on its size and surface geometry, with a transition towards planar geometry diminishing asymmetric spreading effects. The findings highlight the significance of Weber number and cylinder diameter in determining contact time, providing insights for the design of optimized surface structures for specific droplet impact parameters. In addition, the use of a free-surface condition for modeling proved computationally efficient for 3D simulations. The model effectively reproduces nonlinear behaviors such as droplet spreading, splashing, and bouncing, highlighting the significant advantage of SPH in simulating such problems.

Alignment Controlled Aramid Nanofiber‐Assembled Films

AbstractAramid nanofibers (ANFs) are a strong and heat‐resistant nanomaterial that can be isolated from commercial para‐aramid fibers, which allow a bottom‐up self‐assembly to form ordered macroscale structures like ANF films. However, the anisotropic nature of high aspect ratio ANFs is not fully exploited when fabricating ANF films for the optimal mechanical properties. In this research, direct ink writing (DIW) is applied to produce ANF‐assembled films with arbitrary shapes, and the shear‐induced alignment of ANFs can follow the printing path direction. Therefore, controlled alignment of ANFs following the computer‐programmed printing pattern is achieved by DIW, which provides a path for the application of topology and nanofiber alignment optimization in nanofiber‐assembled films. In addition, the resulting DIW ANF films exhibit outstanding Young's modulus of 8.39 GPa, tensile strength of 198 MPa, and tensile toughness of 19.4 MJ m−3 in the alignment direction, together with a wide working temperature range up to 440 °C without losing 50% of its room temperature storage modulus. Moreover, the demonstrated self‐joining ability, rollability, and lamination processability of the DIW ANF films expand their potential applications toward high‐temperature ultrathin tubes, substrates for flexible printed circuit boards, and three‐dimensional all‐ANF lightweight structural parts in extreme environments.

Enhanced interfacial bonding strength of hot-pressed titanium Alloy/CFRTP joint via laser ablation and interfacial adhesive

Joining titanium alloy with carbon fiber reinforced thermoplastic (CFRTP) is one of the hot subjects in the automotive and aerospace industries. To improve the joint strength of TC4/CFRTP PA6, this study combined the surface treatment of TC4 alloy, including laser ablation, Silane Coupling Agent (SCA) treatment, and interfacial PA6 adhesive. The results showed that the pretreatment and the laser texturing induced the micro-scale porous and macro-scale groove grid structure, respectively. The superhydrophilicity of the laser texturing as well as amino polar groups induced by SCA treatment promoted the adhesion of the thermoplastic PA6 melt into the microstructure of the TC4 alloy surface. The mechanical interlocking and hydrogen bonding between TC4 alloy and CFRTP PA6 significantly improved the shear strength of the TC4/CFRTP PA6 joint (17.5 MPa). Furthermore, with the incorporation of interfacial PA6 adhesive, the joint strength was further enhanced, and the damage of the TC4/CFRTP PA6 joint was transformed from interfacial to interlaminar CFRTP PA6.

Vat photopolymerization of dual-scale porosity alumina honeycombs using phase-separable camphene porogen dissolved in binary photopolymer blends

Dual-scale porosity ceramics comprised of macro-scale pore networks surrounded by porous frameworks manufactured by additive manufacturing (AM) can offer significantly enhanced functions compared to conventional porous ceramics. However, exploring new ways of more tightly controlling micro-scale porous structures for AM still remains challenging. Here, we propose a novel type of vat photopolymerization using camphene as a phase-separable porogen dissolved in binary photopolymer blends for manufacturing dual-scale porosity alumina honeycombs. More specifically, solid camphene with various contents (40 vol%, 50 vol%, and 60 vol%) could be completely dissolved in blends of triethylene glycol dimethacrylate (TEGDMA) and polyethylene glycol diacrylate (PEGDA) used as solvent and anti-solvent, respectively, at room temperature and then crystallize dendritically at ∼ 5 °C via phase separation. After which, thin layers of phase-separated alumina suspensions could be selectively photopolymerized by a digital light processing (DLP) engine to construct 3-dimensionally interconnected macro-scale pore channels. In addition, micro-scale pores could be created in alumina frameworks by removing camphene-rich crystals by freeze-drying. This new approach enabled manufacturing of well-defined macro-scale honeycomb structures with tailored micro-scale porosities. As the camphene content increased from 40 vol% to 60 vol%, micro-scale porosity increased from 39.1 ± 0.5 vol% to 57.5 ± 0.6 vol%, and thus the overall porosity increased from 58.5 ± 0.1 vol% to 68.8 ± 0.3 vol%, while the compressive strength decreased from 355.6 ± 67.1 MPa to 97.6 ± 10.2 MPa.

Multiscale nonlinear analysis of 3D orthogonal woven C/SiC composites based on the CT reconstruction

AbstractDue to the effects of manufacturing process, there are lots of distributed voids in the interior area of three‐dimensional (3D) woven C/SiC composites, leading to the difficulties in predictions of damage evolution and ultimate strength. In this work, the mesoscale geometry of 3D woven C/SiC composites is reconstructed based on the high‐resolution CT scanning, considering the yarn path, yarn profile, and void content. The CT‐based reconstructed model agrees well with the experimental observations. Then, the constitutive laws of mesoscale constituents and macroscale structure are established and implemented numerically by the user‐defined material subroutine UMAT. Finally, the established constitutive laws are applied for the reconstructed model, and the mesoscale and macroscale nonlinear progressive damage behavior of 3D woven C/SiC composite structures are predicted using the hierarchical multiscale method, which are also validated by the experiments. The established CT‐based multiscale computational scheme would be a potential tool for performance evaluation and design of composites.

Information decomposition in complex systems via machine learning

One of the fundamental steps toward understanding a complex system is identifying variation at the scale of the system's components that is most relevant to behavior on a macroscopic scale. Mutual information provides a natural means of linking variation across scales of a system due to its independence of functional relationship between observables. However, characterizing the manner in which information is distributed across a set of observables is computationally challenging and generally infeasible beyond a handful of measurements. Here, we propose a practical and general methodology that uses machine learning to decompose the information contained in a set of measurements by jointly optimizing a lossy compression of each measurement. Guided by the distributed information bottleneck as a learning objective, the information decomposition identifies the variation in the measurements of the system state most relevant to specified macroscale behavior. We focus our analysis on two paradigmatic complex systems: a Boolean circuit and an amorphous material undergoing plastic deformation. In both examples, the large amount of entropy of the system state is decomposed, bit by bit, in terms of what is most related to macroscale behavior. The identification of meaningful variation in data, with the full generality brought by information theory, is made practical for studying the connection between micro- and macroscale structure in complex systems.

Performance analysis of electrical signal output of multi-state flexoelectric structures with parameter uncertainties through quasi-Monte Carlo method

Flexoelectric effect is a more universal electromechanical coupling effect than piezoelectric effect. Flexoelectric beams as the main structural component of flexoelectric power signal output have broad application prospects in the next generation of micro–nano electromechanical systems. However, the electrical signal output of flexoelectric structures in macro-scale is far less than the output of the piezoelectric signal. Therefore, it is urgent to explore the influence of the parameter uncertainties on the electrical signal output of the flexoelectric structures, in order to improve the electrical signal output of flexoelectric materials with excellent design performance. Based on the quasi-static theory, the output voltage model and the output charge model of flexoelectric structures as well as the effective piezoelectric coefficient model are constructed. Then the influences of the flexoelectric parameters on the output voltage and the output charge are researched as well as the influence of the effective piezoelectric coefficient. Finally, the influences of uncertain parameters under different electrical states (e.g. the electrical open circuit and short circuit states) on the output performance of flexoelectric signal are studied by the quasi-Monte Carlo method, in order to further provide a reference for the reliability analysis and optimization design of the flexoelectric structures.

A method for determining the locations and configurations of magnetic reconnection within three-dimensional turbulent plasmas

Context. Three-dimensional (3D) reconnection is an important mechanism for efficiently releasing energy during astrophysical eruptive events, which is difficult to be quantitatively analyzed especially within turbulent plasmas. Aims. In this paper, an efficient method for identifying locations and configurations of 3D reconnection from magnetohydrodynamical (MHD) data is developed. Methods. This method analyzes the local nonideal electric field and magnetic structure at an arbitrary position. As only performing algebraical manipulations on the discrete field data and avoiding computationally expensive operations such as field-line tracing and root-finding, this method naturally possesses high efficiency. To validate this method, we apply it to the 3D data from a high-resolution simulation of a Harris-sheet reconnection and a data-driven simulation of a coronal flux rope eruption. Results. It is shown that this method can precisely identify the local structures of discrete magnetic field. Through the information of nonideal electric field and the geometric attributes of magnetic field, the local structures of reconnection sites can be effectively and comprehensively determined. For fine turbulent processes, both qualitative pictures and quantitative statistical properties of small-scale reconnection structures can be obtained. For large-scale solar simulations, macro-scale magnetic structures such as flux ropes and eruption current sheets can also be recognized. Conclusions. We develop a powerful method to analyze multi-scale structures of 3D reconnection. It can be applied not only in MHD simulations but also in kinetic simulations, plasma experiments, and in situ observations.