Macroscopic Level Research Articles

Molecular-scale dissipative chemistry drives the formation of nanoscale assemblies and their macroscale transport.

Fuelled chemical systems have considerable functional potential that remains largely unexplored. Here we report an approach to transient amide bond formation and use it to harness chemical energy and convert it to mechanical motion by integrating dissipative self-assembly and the Marangoni effect in a source-sink system. Droplets are formed through dissipative self-assembly following the reaction of octylamine with 2,3-dimethylmaleic anhydride. The resulting amides are hydrolytically labile, making the droplets transient, which enables them to act as a source of octylamine. A sink for octylamine was created by placing a drop of oleic acid at the air-water interface. This source-sink system sets up a gradient in surface tension, which gives rise to a macroscopic Marangoni flow that can transport the droplets in solution with tunable speed. Carbodiimides can fuel this motion by converting diacid waste back to anhydride. This study shows how fuelling at the molecular level can, via assembly at the supramolecular level, lead to liquid flow at the macroscopic level.

Mean-field analysis of synaptic alterations underlying deficient cortical gamma oscillations in schizophrenia.

Deficient gamma oscillations in the prefrontal cortex (PFC) of individuals with schizophrenia (SZ) are proposed to arise from alterations in the excitatory drive to fast-spiking interneurons (E I) and in the inhibitory drive from these interneurons to excitatory neurons (I E). Consistent with this idea, prior postmortem studies showed lower levels of molecular and structural markers for the strength of E I and I E synapses and also greater variability in E I synaptic strength in PFC of SZ. Moreover, simulating these alterations in a network of quadratic integrate-and-fire (QIF) neurons revealed a synergistic effect of their interactions on reducing gamma power. In this study, we aimed to investigate the dynamical nature of this synergistic interaction at macroscopic level by deriving a mean-field description of the QIF model network that consists of all-to-all connected excitatory neurons and fast-spiking interneurons. Through a series of numerical simulations and bifurcation analyses, findings from our mean-field model showed that the macroscopic dynamics of gamma oscillations are synergistically disrupted by the interactions among lower strength of E I and I E synapses and greater variability in E I synaptic strength. Furthermore, the two-dimensional bifurcation analyses showed that this synergistic interaction is primarily driven by the shift in Hopf bifurcation due to lower E I synaptic strength. Together, these simulations predict the nature of dynamical mechanisms by which multiple synaptic alterations interact to robustly reduce PFC gamma power in SZ, and highlight the utility of mean-field model to study macroscopic neural dynamics and their alterations in the illness.

Tailored Interaction between Cellulose Nanowhiskers and Core-Shell Particles Determines the Optical and Mechanical Properties in Hybrid Films.

Hybrid materials of core-shell particles and cellulose nanowhiskers (CNWs) were synthesized to produce opal films with increasing tensile strength. After the incorporation of CNWs into the processed particle films, differences in the mechanical and optical properties were noticeable, which stemmed from the adhesion forces between the cellulose and the particles' shell material. Two different particle compositions were compared, using polystyrene as cores, and either poly(ethyl acrylate) (PEA) or a copolymer of ethyl acrylate and 3 wt % of 2-hydroxyethyl methacrylate (HEMA) as the shell material. Stronger interactions between the particles containing HEMA and the CNWs were displayed via atomic force microscopy particle manipulation experiments, where higher forces were required to deliberately move P(EA-co-HEMA) particles on a CNW substrate compared to PEA particles. The stronger interaction behaviors increased the disorder of the particles within the opal films toward photonic glasses with angle-independent structural colors. Also, the increase in tensile strength from <1 MPa at a cellulose content of 0 wt % to 6 MPa at the optimal CNW content of 15 wt % was more pronounced compared to the particle-cellulose mixture with only PEA in the particle shell. Thus, the presence of the hydroxy groups in the particles' shell material on the molecular level significantly influenced the optical and mechanical properties on the macroscopic level.

Fiber anatomy and histological characteristics of the innervation of the triangular fibrocartilage complex.

To evaluate the precise origin of sensory nerves through gross anatomical study of the TFCC, synthesized alongside imaging and histological techniques. Six cadaveric forearm specimens were obtained to map the course and branches of the ulnar nerve through macrodissection. Immunohistochemical staining targeting PGP 9.5 and type IV collagen was performed on frozen TFCC sections to visualize nerve fibers microscopically. Computed tomography, magnetic resonance imaging, and arthrography findings were also reviewed. At the macroscopic level, the articular branches supplying the TFCC originated predominantly from the dorsal branch of the ulnar nerve. Microscopic analysis revealed positive PGP 9.5 expression and discernible neural marker expression, signifying fine nerve fiber ingrowth within the TFCC. Imaging modalities aided the diagnosis of TFCC lesions. The dorsal cutaneous branch of the ulnar nerve, medial cutaneous nerve of the forearm, and volar sensory branch of the ulnar nerve emerged as the principal nerves innervating the TFCC. This study provides anatomical evidence that the TFCC receives innervation from branches of the ulnar nerve and contains sensory nerve fibers. These findings enhance understanding of potential neuropathic pain mechanisms in TFCC injuries and offer insights to guide surgical interventions. Further investigations are warranted to elucidate the clinical implications.

Physical properties and hydration behaviors of paste casting brick containing coal gasification slag (CGS): Effect of curing condition and CGS dosage

In the process of developing coal chemical technologies, it is necessary to address environmental pollution and resource waste caused by the associated solid waste coal gasification slag (CGS). This study investigated the feasibility of using CGS as a substitute for fly ash (FA) in the preparation of paste casting bricks. The effects of curing conditions and CGS dosages on the physical properties of the specimens were investigated and related to the hydration behaviors through various microscopic techniques. The results showed that the occurrence of electrostatic flocculation and the enrichment of active substances increased the yield stress of the slurry as the CGS replacement rose, which manifested as decreased fluidity at the macroscopic level. The secondary hydration reaction was facilitated with the increased CGS replacement rate due to the desirable hydraulic activity of CGS. XRD, FTIR and TG results corroborated that the content of hydration products which contributed to strength development were enhanced with the addition of CGS under different curing conditions. XRD and SEM-EDS results indicated that the doping of elements such as Mg and Al under autoclaved condition led to a reduction in the grain size of tobermorite, and the micro-morphology tended to concentrate stresses under pressure. The results of this study promote the resourceful utilization of CGS in building materials and lay the foundation for further scale-up industrial application.

Disruptions in segregation mechanisms in fMRI-based brain functional network predict the major depressive disorder condition

This study investigates the functional brain network in major depressive disorder using network theory and a consensus network approach. At the macroscopic level, we found significant differences in connectivity measures such as node strength and clustering coefficient, with healthy controls exhibiting higher values. This is consistent with disruptions in functional brain network segregation in patients with major depressive disorder. Consensus network analysis revealed that the central executive and salience networks were predominant in healthy controls, whereas depressed patients showed greater overlap with the default mode network. No differences were found in network efficiency measures, indicating comparable brain network integration between healthy controls and major depressive disorder groups. Importantly, the clustering coefficient emerged as an effective diagnostic biomarker for depression, achieving high sensitivity (90%), specificity (92%), and overall precision (90%). Further analysis at the mesoscale level uncovered unique functional connections distinguishing healthy controls and major depressive disorder groups. Our findings underscore the utility of analyzing functional networks from the macroscale to the mesoscale, and provide insight into overcoming the challenges associated with intersubject variability and the multiple comparisons problem in network analysis.

Dynamic deformation and fracture of brass: Experiments and dislocation-based model

In this work, we perform a comprehensive study of the dynamic deformation and fracture of brass, including Taylor tests with classical and profiled cylinders and ball throwing experiments reaching the strain rates of about (0.1−1)/μs, as well as atomistic and continuum-level numerical modeling. Molecular dynamics (MD) simulations are used to construct the equation of state (EOS) of brass and to study its fracture characteristics at shear deformation under negative pressure. An original model of fracture under combined tensile-shear loading is formulated, which takes into account both the accumulation of empty volume in the process of lattice loosening due to the lattice defect production in the course of plastic deformation and further mechanical growth of voids controlled by the dislocation plasticity. This atomic-scale model is transmitted to the macroscopic experiment-scale level and embedded into 3D dislocation plasticity model to describe the dynamic deformation and fracture of brass using the numerical scheme of smoothed particle hydrodynamics (SPH). A part of experimental data is used to find the optimal parameters of the dislocation plasticity model by means of the Bayesian global optimization method accelerated with the help of artificial-neural-network (ANN)-based emulator of the 3D model. Another part of experimental data is used to fit the fracture model parameter. The remaining experimental data, which are not used in the parameterization, are applied to verify the parameterized model. The developed physical-based model provides correct and meaningful description of the dynamic deformation and fracture of brass, while the developed formalized approach to its parameterization opens a way to wider use of this type of models in the engineering applications, including studies on dynamic performance and high-speed processing technologies.

TAKE AN OLD DRUG AND STAY YOUNG? METFORMIN AS AN ANTI-AGING MEDICATION – A LITERATURE REVIEW.

AIMS: Metformin is the main drug used in the treatment of type 2 diabetes mellitus, and the most widely prescribed oral antihyperglycemic medication. Its mechanism of action is still not completely understood and research into new uses for the compound is still ongoing, with one possible novel application of the medication being to combat aging on both the cellular and the macroscopic level. The aim of the study is to evaluate the current state of knowledge on metformin’s potential as a lifespan-extending and anti-senescence drug, present evidence on the matter and assess possible lanes of further study into such effects. METHODS: The literature review was performed using Internet research paper databases (PubMed, Google Scholar, Medline), using articles available in English. RESULTS: Multiple papers suggest tentative support for the notion that metformin may contribute to slowing cellular senescence, as well as to extending the human lifespan. Theoretical knowledge exists that shows possible routes of action of metformin in this regard. CONCLUSIONS: There is ample evidence in the literature for metformin’s possible effects as an anti-aging drug. Numerous cellular pathways by which it could exert lifespan-extending effects have been elucidated, and in vivo studies on some non-human animals have shown a measurable decrease in their production of aging markers, as well as an increase in the lifespan. Studies on diabetic patients have also demonstrated a significant reduction in all-cause mortality in that particular group. However, there is a lack of data and a need for more research that would investigate metformin’s potential as a universal anti-senescence agent more directly. Two large-scale studies (MILES and TAME) in the area are currently underway.

Condensate remodeling reorganizes innate SS18 in synovial sarcomagenesis

SS18-SSX onco-fusion protein formed through aberrant chromosomal translocation t (X, 18; p11, q11), is the hallmark and plays a critical role in synovial sarcomagenesis. The recent works indicated that both the pathological SS18-SSX tumorigenic fusion and the corresponding intrinsic physiological SS18 protein can form condensates but appear to have disparate properties. The underlying regulatory mechanism and the consequent biological significance remain largely unknown. We show that the physical properties of oncogenic fusion protein SS18-SSX condensates within cells undergo alterations compared to the proto-oncogene protein SS18. By small-molecule screening and mutant assay, we identified the recognition of H2AK119ub histone modification could account for the distinctive properties of SS18-SSX1 condensates. Notably, we show that SS18-SSX1 condensates have impact on SS18 condensates and hijack that in a phase separation manner, resulting in the relocation of protein SS18 to the H2AK119ub modification targeted by SS18-SSX1. Consequently, this leads to the downregulation of tumor suppressor genes occupied by SS18 physiologically, like CAV1 and DAB2. These results reveal the underlying mechanism of genomic disorder and tumorigenesis caused by the remodeling of oncoprotein SS18-SSX1 condensates at the macroscopic level.

Adaptive rewiring: a general principle for neural network development.

The nervous system, especially the human brain, is characterized by its highly complex network topology. The neurodevelopment of some of its features has been described in terms of dynamic optimization rules. We discuss the principle of adaptive rewiring, i.e., the dynamic reorganization of a network according to the intensity of internal signal communication as measured by synchronization or diffusion, and its recent generalization for applications in directed networks. These have extended the principle of adaptive rewiring from highly oversimplified networks to more neurally plausible ones. Adaptive rewiring captures all the key features of the complex brain topology: it transforms initially random or regular networks into networks with a modular small-world structure and a rich-club core. This effect is specific in the sense that it can be tailored to computational needs, robust in the sense that it does not depend on a critical regime, and flexible in the sense that parametric variation generates a range of variant network configurations. Extreme variant networks can be associated at macroscopic level with disorders such as schizophrenia, autism, and dyslexia, and suggest a relationship between dyslexia and creativity. Adaptive rewiring cooperates with network growth and interacts constructively with spatial organization principles in the formation of topographically distinct modules and structures such as ganglia and chains. At the mesoscopic level, adaptive rewiring enables the development of functional architectures, such as convergent-divergent units, and sheds light on the early development of divergence and convergence in, for example, the visual system. Finally, we discuss future prospects for the principle of adaptive rewiring.

A novel spine tester TO GO.

Often after large animal experiments in spinal research, the question arises-histology or biomechanics? While biomechanics are essential for informed decisions on the functionality of the therapy being studied, scientists often choose histological analysis alone. For biomechanical testing, for example, flexibility, specimens must be shipped to institutions with special testing equipment, as spine testers are complex and immobile. The specimens must usually be shipped frozen, and, thus, biological and histological investigations are not possible anymore. To allow both biomechanical and biological investigations with the same specimen and, thus, to reduce the number of required animals, the aim of the study was to develop a spine tester that can be shipped worldwide to test on-site. The "Spine Tester TO GO" was designed consisting of a frame with three motors that initiate pure moments and rotate the specimen in three motion planes. A load cell and an optical motion tracking system controlled the applied loads and measured range of motion (ROM) and neutral zone (NZ). As a proof of concept, the new machine was validated and compared under real experimental conditions with an existing testing machine already validated employing fresh bovine tail discs CY34 (n = 10). The new spine tester measured reasonable ROM and NZ from hysteresis curves, and the ROM of the two testing machines formed a high coefficient of determination R 2 = 0.986. However, higher ROM results of the new testing machine might be explained by the lower friction of the air bearings, which allowed more translational motion. The spine tester TO GO now opens up new opportunities for on-site flexibility tests and contributes hereby to the 3R principle by limiting the number of experimental animals needed to obtain full characterization of spine units at the macroscopic, biomechanical, biochemical, and histological level.

Hollow Superhelices Based on Chiral Europium Coordination Polymers.

The construction of helical nanotubes based on chiral coordination polymers (CPs) is an intriguing but challenging task, which is important for the development of functional materials that combine macroscopic chirality with tube-related properties. Here, we selected a chiral europium phosphonate system, e.g., Eu(NO3)3/R-,S-pempH2, and carried out a systematic work. By controlling the hydrothermal reaction conditions such as the pH value of the reaction mixture, the molar ratio and concentration of the reactants, we obtained block-like crystals of R/S-1b, rod-like crystals ofR/S-3r, hollow superhelices of R/S-2hh, and solid superhelices of R/S-4sh. In the latter two cases, the chirality has been successfully transferred and amplificated from the molecular level to the macroscopic level. Interestingly, compounds R/S-2hh and R/S-4sh have the same chemical composition of Eu(R/S-pempH)3×2H2O and show identical PXRD patterns, thus can be considered as the same material except for different morphologies. We further investigatedtheir circularly polarized luminescence (CPL) properties and found that the hollow superhelix of R/S-2hh had a larger dissymmetry factor than the solid superhelix of R/S-4sh. This study not only provides the first example of hollow superhelices of chiral CPs, but also offers the possibility of modulating the chiroptical properties of CPs through morphological control.

Spatio-temporal dynamics and recovery of commuting activities via bike-sharing around COVID-19: A case study of New York

The COVID-19 has led to significant changes in urban travel behaviors, with commuting being one of the most affected travel modes. Commuting cycling by bike-sharing systems (BSS) is regarded as a new transportation mode that is low-carbon and low-cost. However, its dynamic changes and spatiotemporal characteristics in different periods of COVID-19 still lack exploration. Therefore, this study adopts machine learning methods to identify commuter bike-sharing activities and develops a combined analysis method to analyze commuting cycling data via temporal, spatial, and spatiotemporal aggregation. Finally, we select the bike-sharing data in New York City from periods before, during, and after COVID-19 to conduct experiments. It has been found that commuting cycling experienced a “decrease-rebound” trend at the macroscopic level under the pandemic impact. However, at the micro level, urban mobility driven by this travel mode failed to fully recover, as evidenced by significant changes in spatial and temporal mobility patterns. The findings shall not only help traffic operators and managers discover the BSS commuting patterns but also reveal the pandemic impact on the travel behavior of urban residents, promoting the development of intelligent services for urban emergency management and traffic management.

Maxwell boundary condition for discrete velocity methods: Macroscopic physical constraints and Lagrange multiplier-based implementation

In this paper, we propose an algorithm that imposes macroscopic physical constraints with Lagrange multiplier approach in implementing the Maxwell boundary condition within the framework of the discrete velocity method. For the simulation of rarefied gas flows in the presence of solid walls with complex geometry, the distribution function in the reflection region of the wall surface needs to be constructed in the discrete velocity space, to fulfill the specular reflection in the Maxwell boundary condition. The construction process should not consist of interpolation only, but include certain macroscopic physical constraints at the wall surface, so as to correctly account for gas-surface interaction on a macroscopic level. We demonstrate that for the specular reflection, keeping the symmetry of the first three moments of the distribution function between the incident and reflected region is sufficient for maintaining the conservation of mass, momentum, and energy at the wall surface. Furthermore, to strictly satisfy macroscopic physical constraints, a Lagrange multiplier method is introduced into the construction of the distribution function to correct the pure interpolation solution. In addition, the construction process requires the inversion of a large and sparse matrix (of dimension N × N, where N is the number of points in the velocity space). To improve the computational efficiency, the matrix inversion is converted into that of a much smaller matrix, i.e. (D + 2) × (D + 2) in the d-dimensional physical space. A series of numerical experiments are conducted to examine the performance of the proposed algorithm under different flow conditions. We demonstrate that the results obtained by the proposed algorithm are more accurate than the pure interpolation solution, comparing with the benchmark data. Moreover, after the validation of our results with previous studies, we find that the method significantly enhances the conservation of total mass and energy, especially for flows in an enclosed domain.

Macroscopic State-Level Analysis of Pavement Roughness Using Time–Space Econometric Modeling Methods

This paper used pavement condition data collected by the Federal Highway Administration (FHWA) between 2001 and 2006 aggregated by U.S. states to identify macroscopic factors affecting pavement roughness in time and space. To account for prior pavement conditions and preservation expenditure over time, time autocorrelation parameters were introduced in a spatial modeling scheme that accounted for spatial autocorrelation and heterogeneity. The proposed framework accommodates data aggregation in network-level pavement deterioration models. Because pavement roughness across different roadway classes is anticipated to be affected by different explanatory parameters, separate time–space models are estimated for nine roadway classes (rural interstate roads, rural collectors, urban minor arterials, urban principal arterials, and other freeways). The best model specifications revealed that different time–space models were appropriate for pavement performance modeling across the different roadway classes. Factors that were found to affect state-level pavement roughness in time and space included preservation expenditure, predominant soil type, and predominant climatic conditions. The results have the potential to assist governmental agencies in planning effectively for pavement preservation programs at a macroscopic level.

Study of cellulose nanocrystals -enhanced natural gas foam performance and profile control & oil displacement mechanism

ABSTRACT To address the poor performance of conventional natural gas foam flooding in the tight conglomerate reservoirs of the Mahu region in Xinjiang, a high-activity green material, cellulose nanocrystals (CNC), was introduced to improve foam performance based on a conventional AOS+SDS foam system. However, the impact of CNC on the performance of the conventional foam system and the synergistic effects between CNC and surfactants are not yet clear. Therefore, this study evaluates the improvement effects of CNC on foam performance from both macroscopic and microscopic perspectives by analyzing the foam composite index and the synergistic effects between CNC and surfactants. The study also investigates the factors affecting foam performance and uses a planar visualization model to evaluate the enhanced gas foam flooding effects from a microscopic mechanism perspective. The results indicate that CNC increases the foam composite index by 26,850 mL·min on a macroscopic level. Microscopically, it makes the foam more compact, thickens the foam liquid film by 0.1 mm, improves the wettability reversal by 10%, and reduces the oil-water interfacial tension by 0.26 mN/m. After mixing 1 vol% crude oil, the foam volume and half-life decrease by 12% and 4%, respectively. At reservoir temperature and pressure, the foam volume decreases by 12.7% and 10.5%, but the half-life increases by 4 hours and 3.7 hours, respectively. At formation water mineralization conditions, the foam volume and half-life decrease by 6.6% and 1.7 hours, respectively. In summary, it still has good performance under reservoir conditions. The visualization model demonstrates from a microscopic perspective that the foam system has good gas shutoff capability. After foam flooding, the gas sweep efficiency increases by 54.12%, and the foam flooding and subsequent gas flooding improve the crude oil recovery by 20.32%.

Autonomous tracking of honey bee behaviors over long-term periods with cooperating robots.

Digital and mechatronic methods, paired with artificial intelligence and machine learning, are transformative technologies in behavioral science and biology. The central element of the most important pollinator species-honey bees-is the colony's queen. Because honey bee self-regulation is complex and studying queens in their natural colony context is difficult, the behavioral strategies of these organisms have not been widely studied. We created an autonomous robotic observation and behavioral analysis system aimed at continuous observation of the queen and her interactions with worker bees and comb cells, generating behavioral datasets of exceptional length and quality. Key behavioral metrics of the queen and her social embedding within the colony were gathered using our robotic system. Data were collected continuously for 24 hours a day over a period of 30 days, demonstrating our system's capability to extract key behavioral metrics at microscopic, mesoscopic, and macroscopic system levels. Additionally, interactions among the queen, worker bees, and brood were observed and quantified. Long-term continuous observations performed by the robot yielded large amounts of high-definition video data that are beyond the observation capabilities of humans or stationary cameras. Our robotic system can enable a deeper understanding of the innermost mechanisms of honey bees' swarm-intelligent self-regulation. Moreover, it offers the possibility to study other social insect colonies, biocoenoses, and ecosystems in an automated manner. Social insects are keystone species in all terrestrial ecosystems; thus, developing a better understanding of their behaviors will be invaluable for the protection and even the restoration of our fragile ecosystems globally.

Strain-softening model for granite and sandstone based on experimental and discrete element methods

Combing macroscopic experimental method and mesoscopic numerical method, this study analyses the strain-softening behaviours of granite and sandstone. From the macroscopic perspective, the stress–strain curves of granite and sandstone under different confining pressures are studied by laboratory triaxial compression test. Variations of post-peak reduction modulus and critical plastic shear strain versus confining stress are obtained. Evolution of strength parameters at peak, residual and strain-softening stage are proposed. Then a method to develop the strain-softening model of hard and soft rocks is presented. From the mesoscopic perspective, based on the laboratory test results, the parameters of discrete element method PFC for the samples of the granite and sandstone are calibrated. Comparing the basically consistent results of laboratory experiment and numerical simulation, the feasibility of discrete element method is verified. Evolutions of mesoscopic crack propagation and mesoscopic particle displacement field in the complete failure process are analysed. Typical stresses of granite sample and sandstone sample in the failure stage are investigated. Above combined macroscopic experimental method and mesoscopic numerical method systematically analyse the characteristics of hard rock and soft rock in the strain-softening stage. Failure process and mechanical property of hard rock and soft rock are revealed at the macroscopic and mesoscopic levels. The initiation and propagation process of micro-cracks in rock are thoroughly investigated. The research results provide a scientific foundation for the analyse of strain-softening behaviour of hard rock and soft rock. The result shows that both the mesoscopic numerical method and macroscopic experimental method indicate that the failure pattern of sandstone is influenced by both confining pressure and axial stress, while granite is mainly affected by axial stress.

Investigation of Improving Stability in Desulfurized-Rubber-Modified Asphalt Using Cerium-Stearate Incorporation

To enhance the compatibility of high-content desulfurized-rubber-modified asphalt (DRMA), the innovative selection of cerium hard acid is employed in this study to investigate its impact on the compatibility of DRMA. The results indicate that when the optimal content of cerium stearate is 1%, the compatibility of the modified asphalt is improved. On the microscopic level, it is observed that the total heat absorption of the modified asphalt decreases and thermal storage stability is enhanced as measured by differential scanning calorimetry. On the macroscopic level, it gives the smallest difference in softening point, optimal storage stability, and best compatibility of the modified asphalt. Additionally, dissolution tests and adhesion tests demonstrate that the inclusion of cerium stearate increases the solubility of desulfurized rubber in asphalt and reduces the stripping rate of desulfurized-rubber particles on the asphalt surface, thereby decreasing phase separation and promoting the compatibility of DRMA. Furthermore, rheological tests indicate that cerium stearate improves both high-temperature and low-temperature performance of the modified asphalt. The addition of cerium stearate increases the dynamic shear modulus of the modified asphalt, maintaining good stability and elasticity under shear loading, with minimal change in low-temperature rheological properties and lower temperature sensitivity, indicating the best compatibility at this point. Finally, molecular-dynamics simulation data indicate that the addition of cerium stearate decreases the parameter difference in the dissolution of modified asphalt and enhances the binding energy of the modified asphalt, demonstrating the feasibility of cerium stearate in promoting the compatibility of modified asphalt.

Biomimetic Microstructural Materials for Intervertebral Disk Degeneration Repair

The intervertebral disks (IVD) serve as shock absorbers in the spine. As the largest avascular tissue in the human body, it has a limited capacity for regeneration. To address this issue, various innovative biomimetic materials have been explored to facilitate IVD regeneration at both microscopic and macroscopic levels. Techniques such as electrostatic spinning and fiber‐winding machines have been employed to prepare biomimetic materials. In this review, the physiological structure of the IVD is described, and advanced studies on its microstructure are summarized. The techniques used in biomimetic biomaterial development are further investigated, and biomimetic materials that facilitate IVD regeneration are systematically explored. Specifically, this article provides a detailed description and summary of the key features of biomimetic materials, including the types of loads they can withstand and their regenerative effects. Finally, a prospective outlook for the development and application of biomimetic materials in IVD regeneration is presented.