Multiscale Morphology Research Articles

Correlating the 3D Morphology of Polymer-Based Battery Electrodes with Effective Transport Properties.

Polymer-based batteries represent a promising candidate for next-generation batteries due to their high power densities, decent cyclability, and environmentally friendly synthesis. However, their performance essentially depends on the complex multiscale morphology of their electrodes, which can significantly affect the transport of ions and electrons within the electrode structure. In this paper, we present a comprehensive investigation of the complex relationship between the three-dimensional (3D) morphology of polymer-based battery electrodes and their effective transport properties. In particular, focused ion beam scanning electron microscopy (FIB-SEM) is used to characterize the 3D morphology of three polymer-based electrodes which differ in material composition. The subsequent segmentation of FIB-SEM image data into active material, carbon-binder domain and pore space enables a comprehensive statistical analysis of the electrode structure and a quantitative morphological comparison of the electrode samples. Moreover, spatially resolved numerical simulations allow for computing effective properties of ionic and electronic transport. The obtained results are used for establishing analytical regression formulas which describe quantitative relationships between the 3D morphology of the electrodes and their effective transport properties. To the best of our knowledge, this is the first time that the 3D structure of polymer-based battery electrodes is quantitatively investigated at the nanometer scale.

Reusable SERS Platform of Femtosecond Laser Processed Substrate for Detection of Malachite Green.

This study presents the development of highly efficient Surface-Enhanced Raman Scattering (SERS) substrates through femtosecond (fs) laser processing of crystalline silicon (Si), resulting in mountain-like microstructures. These microstructures, when decorated with gold nanoparticles (Au NPs), exhibit remarkable SERS performance due to the creation of concentrated hotspots. The enhanced Raman signals originate from the excitation of localized surface plasmon resonance (LSPR) of the Au NPs and the multi-scale rough morphology of the Si substrates. Finite-element method simulations confirm the electromagnetic field enhancement in narrow gaps, supporting the experimental observations. The fabricated substrates show high uniformity, oxidation resistance, long-term stability, and exceptional reproducibility, making them ideal for molecular detection, especially in food safety applications. A remarkable enhancement factor (EF) of 1010 is attained in the detection of Malachite Green (MG), boasting a limit of detection (LoD) as low as 10-14 M. This underscores the immense potential of this technique for achieving highly sensitive and dependable SERS-based sensing capabilities.

Study on the variability of oxygen adsorption behavior in coal gangue based on pore size structure

To investigate the adsorption and migration behavior of oxygen in coal gangue with different pore sizes, this study characterizes the multiscale morphology of coal gangue pores and fractures using SEM NMR, and low-temperature N2 (77 K) adsorption methods. As well, the study simulates the adsorption and diffusion behavior of O2 within the pore structures of coal gangue based on GCMC and MD methods. The study examines the oxygen adsorption characteristics across different pore sizes and analyzes the mass transfer mechanisms during oxygen diffusion. Results indicate that the pores in coal gangue are predominantly micropores within the 1–10 nm range. As pore size increases, the absolute adsorption amount of oxygen increases while the isosteric heat of adsorption decreases. The isosteric heat of adsorption in coal gangue models with different pore sizes is less than 42 kJ/mol, indicating the physical adsorption of oxygen within the pores. Oxygen shows adsorption layering perpendicular to the coal gangue surface, with adsorption density distribution differences decreasing and adsorption capacity weakening as pore size increases. The O2 adsorption isotherms in coal gangue follow Langmuir adsorption principles, with oxygen saturation adsorption decreasing as absolute adsorption increases. The confinement effect of coal gangue on oxygen weakens as pore size increases, and the impact of increasing pore size on oxygen diffusion is stronger than adsorption. These findings are significant for understanding the microscopic adsorption and diffusion behavior of O2 in coal gangue pores and fractures and are crucial for accurately preventing and managing spontaneous combustion in coal gangue piles.

Circadian Rhythms Tied to Changes in Brain Morphology in a Densely Sampled Male.

Circadian, infradian, and seasonal changes in steroid hormone secretion have been tied to changes in brain volume in several mammalian species. However, the relationship between circadian changes in steroid hormone production and rhythmic changes in brain morphology in humans is largely unknown. Here, we examined the relationship between diurnal fluctuations in steroid hormones and multiscale brain morphology in a precision imaging study of a male who completed 40 MRI and serological assessments at 7 A.M. and 8 P.M. over the course of a month, targeting hormone concentrations at their peak and nadir. Diurnal fluctuations in steroid hormones were tied to pronounced changes in global and regional brain morphology. From morning to evening, total brain volume, gray matter volume, and cortical thickness decreased, coincident with decreases in steroid hormone concentrations (testosterone, estradiol, and cortisol). In parallel, cerebrospinal fluid and ventricle size increased from A.M. to P.M. Global changes were driven by decreases within the occipital and parietal cortices. These findings highlight natural rhythms in brain morphology that keep time with the diurnal ebb and flow of steroid hormones.

Multi-scale correlation analysis between geometric parameters and solar radiation in high density urban environment——Case study in Nanjing

This study explores the impact of multi-scale urban morphology on solar radiation in high-density environments, focusing on Nanjing as a case study. Using Grasshopper, the research decomposed urban models into four scales—district, block group, block, and plot—and analyzed eight morphological indicators, including site coverage (SC), floor area ratio (FAR), and block surface ratio (BSR). Solar radiation was simulated for the entire year, the summer solstice, and the winter solstice. Key findings reveal that different scales exhibit varying influences on solar radiation. At the district scale, BSR shows the highest correlation with annual solar radiation (R2 = 0.96), while at the neighborhood cluster scale, the minimum distance between buildings (DBmin) is most correlated (R2 = 0.7). At the block scale, BSR significantly correlates with summer solar radiation (R2 = 0.55). At the plot scale, no single indicator strongly correlates with solar radiation, but a combination of indicators is more relevant. These findings enable rapid solar performance assessments in urban design, promoting efficient solar energy use.

Deciphering the structure-function-quality improvement role of starch gels by wheat bran insoluble dietary fibers obtained from different fermentation patterns and its potential mechanisms

Insoluble dietary fiber (IDF) isolated through co-fermented bran from probiotics may improve starch gel-based foods. This work aimed to elucidate the comprehensive impact of different IDF samples (CK, unfermented; NF, natively fermented; YF, yeast fermented; LF, Lactobacillus plantarum fermented; and MF, mix-fermented) and their addition ratios (0.3–0.9%) on gel structure-property function. Results indicated that IDF introduction altered the starch pasting behavior (decreased the viscosity and advanced the pasting time). Also, YF, LF, and MF showed less effect on gel multiscale morphology (SEM and CLSM); however, their excessively high ratio resulted in network structure deterioration. Moreover, FT-IR, XRD, and Raman characterization identified the composite gels interaction mechanisms mainly by hydrogen bonding forces, van der Waals forces, water competition, and physical entanglement. This modulation improved the composite gel water distribution, rheological/stress-strain behavior, textural properties, color, stability, and digestive characteristics. The obtained findings may shed light on the construction and development of whole-grain gel-based food products with new perspectives.

Formation of Ca, P, and Zn-doped ZrO2/TiO2 coating layer via plasma electrolytic oxidation and magnetic sputtering: Improving surface characteristics and biocompatibility of Ti–6Al–4V alloy

The study demonstrated the Ca, P, and Zn-doped ZrO2/TiO2 coating layer via plasma electrolytic oxidation (PEO) and radiofrequency magnetron sputtering (RF-MS) for improving surface characteristics and biocompatibility of Ti–6Al–4V alloy. The process involved sandblasting (SB) to create micro-scale irregularities on the alloy surface, followed by RF-MS and PEO. The coating's morphology, elemental composition, phase composition, and chemical analysis were examined by FE-SEM with energy-dispersive spectroscopy, X-ray diffraction, X-ray photoelectron spectroscopy, and Raman analysis. The dual surface treatment resulted in the formation of composite coatings with TiO2, ZnO, ZrO2, and HA presence and displayed anisotropic multi-scale porous morphology while the surface texturing created by the SB process facilitated mechanical interlocking during the oxide layer growth. In the SB-Zr + PEO-Zn sample, the phase analysis indicated the strain-induced stabilization of t-ZrO2 in the PEO coating layer due to the incorporation of Ca2+ ions, and the formation of zinc-substituted hydroxyapatite was observed that showed enhanced roughness and wettability. Also, reduced hardness and elastic modulus (comparable to that of cortical bone) were observed due to transformation toughening under stress from t-ZrO2 to m-ZrO2. The presence of TiO2, ZrO2, ZnO, and HA within the coating, all act as protective barriers in effectively reducing corrosion rates. The biological analysis demonstrated better biocompatibility, including promoting cell proliferation, cell adhesion, osteoblast differentiation, ALP activity, and mRNA expression of several osteogenic markers of the cells cultured on the SB-Zr + PEO-Zn sample. These results demonstrated the higher potential of the PEO coating system with higher biocompatibility and osseointegration ability for dental implants.

Fractional wavelet combined with multi-scale morphology and PCNN hybrid algorithm for grayscale image fusion

Fractional wavelet combined with multi-scale morphology and PCNN hybrid algorithm for grayscale image fusion

Integrated database of granular soils under triaxial shear and its application in the prediction of stress–strain relationship

This study presents a novel data generation framework that generates a large database for machine learning (ML)-based soil model predictions. The dataset comprised 216 sets of triaxial tests on morphologically mutated and gene-decayed granular samples. This database was then estimated using five widely utilized ML algorithms to predict the stress-strain relationship of granular soils. They include the support vector machine (SVM), bagged trees, Gaussian process regression (GPR), and back propagation neural network (BPNN) algorithms. Following the hyperparameter settlement, model training, and testing, all the trained models captured the effects of the multiscale particle morphology, initial packing state, and confining stress. The excellent training and testing performances indicate the superior quality of the generated dataset. The fine tree, exponential GPR, and BPNN outperformed the Gaussian SVM and bagged trees in terms of the predictive performance. Among them, the exponential GPR exhibits the best model performance in reflecting the particle morphology effect, whereas the fine tree and BPNN generally exhibit good predictive performance for missing local information. Furthermore, all the models are tested by the micro-tomography (μCT) experimental data. The findings of this study were validated through a comparison between the DEM and model prediction results.

Characterization of road surface topography changes during polishing process with characteristic roughness parameter

In this paper, three-dimensional structure feature points of the asphalt mixture were obtained based on the industrial CT. The surface morphology of the asphalt mixture was then extracted through image morphology processing and evolutionary matching algorithms. A series of accelerated polishing tests were utilized to simulate the long-term polishing effect of the tire load on the asphalt pavement. Combining with digital image analysis, the full-life morphology evolution trend of the asphalt pavement was analyzed, and the full-life surface morphology datasets of the asphalt pavement were constructed. By comparing the power spectrum density of the asphalt pavement morphology at different wear stages, a segmented evaluation method based on the spectral fractal method was proposed. Combining the spectral fractal parameters and statistical geometric parameters, a multi-scale asphalt pavement surface morphology feature evaluation index was proposed. This provides a more intricate and detailed analysis of the surface structure of asphalt mixtures.

A novel hierarchical approach to insight to spectral characteristics in surface water of karst wetlands and estimate its non-optically active parameters using field hyperspectral data

Wetlands cover only around 6 % of the Earth's land surface, and are recognized as one of the three major ecosystems, alongside forests and oceans. The ecological structure and function of karst wetlands are unique due to the influence of geologic structure. At present, the unclear spectral morphology of surface water in karst wetlands poses a significant challenge in remote sensing estimation of non-optically active water quality parameters (NAWQPs). This study proposed a novel multi-scale spectral morphology feature extraction (MSFE) method to insight to spectral characteristics in surface water of karst wetlands, and further screen the sensitive features of NAWQPs. Then we constructed three remote sensing inversion strategies for NAWQPs (TN, TP, NH3_N, DO), including direct estimation, indirect estimation, and auxiliary estimation. Finally, we constructed a novel pH-based hierarchical analysis framework (pH_HA) to thoroughly explore the influence of alkalinity-biased characteristics of karst water on the spectral domain of NAWQPs and its estimation accuracy using in-situ hyperspectral data, respectively. We found that the spectral characteristics of karst waters at the first reflectance peak (580 nm) differed significantly from other water body types. The MSFE successfully captured the sensitive spectral domains for NAWQPs, and focused on between 500 and 600 nm and 900–960 nm. The sensitive features captured by MSFE improved estimation accuracy of NAWQPs (R2 >0.9). Direct estimation presented more stable performance compared to the auxiliary estimation (average RMSE of 0.366 mg/L), and the auxiliary estimation model further improved the retrieval accuracy of TN compared to direct estimation model (R2 increasing from 0.43 to 0.56). The novel hierarchical framework clearly revealed the notable changes in the sensitive spectral domains of NAWQPs under different pH values, and enabled more precise determination of spectral subdomains of NAWQPs, and identified the optimal spectral features. The pH_HA framework effectively improved the estimation accuracy of NAWQPs (R2 increased from 0.514 to over 0.9), and the estimation accuracies (R2) of four NAWQPs were all more than 0.9 when the pH value was over 8.5. Our works provide an effective approach for monitoring water quality in karst wetlands.

Sol-gel derived silicate-phosphate glass SiO2–P2O5–CaO–TiO2: The effect of titanium isopropoxide on porosity and thermomechanical stability

Despite of several decades lasting extensive research of bioactive and bioresorbable glasses the systematic parametrization and determination of the key factors affecting porosity and thermomechanical characteristics still remains challenging. Here, we present silica-phosphate glasses, with the composition 70SiO2–20P2O5–(10-x)CaO–xTiO2 (mol%; x = 0, 2.5, 5, and 7.5), prepared by sol-gel method and reinforced by titanium dioxide via titanium isopropoxide (TTIP) incorporation which demonstrated tunable variation of porosity from micro-to macro-region and superb mechanical integrity during the calcination process. The presence of 7.5 mol% TiO2 promotes dimensional stability up to 1000 °C as investigated by thermomechanical analysis. The XRD showed the dominant presence of silicon phosphate [Si(P2O7)], titanium phosphate [Ti(P2O7)] and calcium phosphates [β-Ca(P2O6) and γ- Ca2(P2O7)]. The effect of TiO2 doping on the multiscale morphology and porosity was investigated by means of SEM, MIP, μCT, N2 adsorption and USAXS/SAXS. Increasing TiO2 content leads to the formation of open porosity up to 70 vol% and drives the formation of a refined interconnected macroporosity of 2–30 μm. In contrast, mesoporosity with a dominance of 3–6 nm pores decreases in all samples with increasing TiO2 content. USAXS/SAXS revealed an increase in primary particle size with increasing TiO2 content which is in good agreement with the nitrogen physisorption analysis showing that microporosity decreases with increasing TiO2 content.

The effects of laser line energy on welding strength of PASF/PC using magnesium zinc alloy powders as absorbents in laser transmission welding

The metal powder absorbents have the advantage of fabricating clean and high-strength joints in the process of laser transmission welding dissimilar transparent thermoplastics. However, the joining behavior between metal powders and thermoplastics is vague. Herein, polyarylsulfone (PASF) and polycarbonate (PC) are selected as model materials to investigate the joining behavior using magnesium zinc alloy (MZA) powders at different laser line energies. The shear force test was performed to reveal the effect of laser line energy on the welding. The µ-CT images reveal the evaluation of welding strength is attributed to the specific joint morphology. The multi-scale morphology characterization shows that the fractured surface height increases with increasing the laser line energy. The voids between MZA powders and resins increase the risks of interface failure, and the bubbles in the resins increase the risks of cohesion failure. The effects of laser line energy on the joint characteristics are elaborated based on the thermo-physical property test and transient temperature field analysis. The above results indicate the optimized welding temperature locates the melting temperature of PASF, because of the good fusion of PASF and less decomposition of PC. The study can provide a guideline for choosing proper process parameters when choosing metal powders as laser absorbents in welding dissimilar transparent thermoplastics.

Solid Additive Dual-Regulates Spectral Response Enabling High-Performance Semitransparent Organic Solar Cells.

Semi-transparent organic solar cells (ST-OSCs) possess significant potential for applications in vehicles and buildings due to their distinctive visual transparency. Conventional device engineering strategies are typically used to optimize photon selection and utilization at the expense of power conversion efficiency (PCE); moreover, the fixed spectral utilization range always imposes an unsatisfactory upper limit to its light utilization efficiency (LUE). Herein, a novel solid additive named1,3-diphenoxybenzene (DB) is employed to dual-regulate donor/acceptor molecular aggregation and crystallinity, which effectively broadens the spectral response of ST-OSCsin near-infrared region. Besides, more visible light is allowed to pass through the devices, which enables ST-OSCs to possess satisfactory photocurrent and high average visible transmittance (AVT) simultaneously. Consequently, the optimal ST-OSC based on PP2+DB/BTP-eC9+DB achieves a superior LUE of 4.77%, representing the highest value within AVT range of 40-50%, which also correlates with the formation of multi-scale phase-separated morphology. Such results indicate that the ST-OSCs can simultaneously meet the requirements for minimum commercial efficiency and plant photosynthesis when integrated with the roofs of agricultural greenhouses. This work emphasizes the significance of additives to tune the spectral response in ST-OSCs, and charts the way for organic photovoltaics in economically sustainable agricultural development.

Effect of temperature and moisture composite environments on the mechanical properties and mechanisms of woven carbon fiber composites

AbstractTemperature and moisture are important environmental conditions governing the usage of carbon fiber‐reinforced plastics (CFRP). In this study, tensile, compression, and shear tests of woven CFRP composites were conducted in four composite environments: cold‐temperature dry state, room‐temperature dry state, elevated‐temperature dry state, and elevated‐temperature wet state. Fourier transform infrared (FTIR) spectroscopy was used to analyze the molecular composition and structure under these environments. The fracture morphology was also analyzed using scanning electron microscopy (SEM) to understand the deterioration mechanism of the material properties at the macro and micro scales. Finally, a two‐parameter Weibull statistical model was used to evaluate the scattering of the composites. The results demonstrate that the tensile, compressive, and shear properties of the material decrease under harsh conditions, and the degradation effect on the ultimate strength is much greater than that of the modulus. Cold‐temperature dry and elevated‐temperature wet conditions are particularly severe. Composite environments significantly affect the macroscopic failure process and ultimate failure mode of materials. In general, the temperature and humidity of the matrix system acts at the core of the deterioration mechanism of composites, affecting the properties of the fiber/matrix and orthogonal woven fiber interfaces, and eventually resulting in a loss of their constitutive strength and bearing capacity. The theoretical ultimate strength were consistent with the experimental values.Highlights Comprehensive mechanical property testing in multiple composite environments. Degradation mechanisms were revealed from multiscale morphology analysis. Scattering of composites were evaluated using statistical analyses. Cold temperature dry and elevated temperature wet were the worst states. Deterioration effect of environment on matrix and interface is the core failure mechanism.

Synchrotron Resonant X-Ray Scattering Reveals New Multiscale Ionomer Morphology

Ion-conducting polymer, or ionomer, membranes play a key role as the ion-conducting electrolyte in electrochemical devices, including fuel cells and electrolyzers. The performance of ionomers, for example, its proton conductivity, is defined by its unique molecular structure and multiscale morphology. Perfluorinated sulfonic acid ionomers are the most used ionomers, and they are cast into membranes and thin films from alcohol-rich dispersions. In this work, we examine the connections between the dispersion solvent used for casting and resulting membrane properties. Understanding and controlling the hierarchical phase separated morphology of ionomer membranes and elucidating its relationship to transport properties is crucial for improving device performance. Synchrotron-based x-ray scattering is well-suited to probe the morphology in polymers that lack a high degree of long-range order, including ionomer membranes. However, it is still challenging for conventional high energy hard x-ray scattering to resolve all the morphological details in chemically heterogeneous ionomers. Here, we use emerging synchrotron tender resonant x-ray scattering (TReXS) near the sulfur K-edge to improve scattering contrast and enhance chemical specificity to the sulfonate groups in perfluorinated sulfonic acid ionomers. We find that TReXS reveals unique energy-dependent mesoscale morphological features not apparent in hard x-ray scattering, and these features depend on dispersion composition and ionomer chemistry. These structure-property relationships can be used to guide design of next-generation ionomers for energy applications. Furthermore, this work highlights synchrotron resonant x-ray scattering as an emerging tool for structural characterization of ionomers and electrochemically active polymers in general.

Data-driven constitutive modelling of granular soils considering multiscale particle morphology

This paper adopts a micro-tomography (μCT)-based discrete element method (DEM) technique to generate a database for the constitutive modelling of granular soils. A large DEM database comprising 217 DEM simulations of morphologically gene-mutated and gene-decayed samples was generated. Based on this database, three neural network models, i.e., the backpropagation neural networks (BPNN), long short-term memory neural networks (LSTM), and gate recurrent unit neural networks (GRU) were utilised to predict the constitutive behaviours of granular soils. After training and testing, all trained models can reasonably predict granular soils' deviatoric stress-volumetric strain-axial strain relationship. It is found that: 1) the effects of particle morphology at different length scales, sample initial packing state, and confining stress condition can be well captured by all these models; 2) LSTM and GRU outperform BPNN in predictive performance with more local information; 3) With fewer weights and biases, more efficient computation, and more stable and even error distributions for different stages of axial strains, GRU shows the best predictive performance, followed by LSTM and BPNN. Furthermore, all three models are tested by the μCT experimental data. The excellent consistency between model prediction and experimental results reflects these algorithms' feasibility, capability and generalization for the constitutive modelling of granular soils.

Cosmic web dissection in fuzzy dark matter cosmologies

ABSTRACT On large cosmological scales, anisotropic gravitational collapse is manifest in the dark cosmic web. Its statistical properties are little known for alternative dark matter (DM) models such as fuzzy dark matter (FDM). In this work, we assess for the first time the relative importance of cosmic nodes, filaments, walls, and voids in a cosmology with primordial small-scale suppression of power. We post-process N-body simulations of FDM-like cosmologies with varying axion mass m at redshifts z ∼ 1.0−5.6 using the NEXUS+ Multiscale Morphology Filter technique at smoothing scale Δx = 0.04 h−1 Mpc. The formation of wall and void halos is more suppressed than naively expected from the half-mode mass M1/2. Also, we quantify the mass- and volume-filling fractions of cosmic environments and find that 2D cosmic sheets host a larger share of the matter content of the Universe as m is reduced, with an ∼8−12 per cent increase for the m = 7 × 10−22 eV model compared to cold dark matter (CDM). We show that in FDM-like cosmologies, filaments, walls, and voids are cleaner and more pronounced structures than in CDM, revealed by a strong mid-range peak in the conditioned overdensity PDFs P(δ). At high redshift, low-density regions are more suppressed than high-density regions. Furthermore, skewness estimates S3 of the total overdensity PDF in FDM-like cosmologies are consistently higher than in CDM, especially at high redshift z ∼ 5.6 where the m = 10−22 eV model differs from CDM by ∼6σ. Accordingly, we advocate for the usage of P(δ) as a testbed for constraining FDM and other alternative DM models.

On The Multiscale Structure and Morphology of PVDF‐HFP@MOF Membranes in The Scope of Water Remediation Applications

AbstractPoly(vinylidene fluoride‐co‐hexafluoropropylene) (PVDF‐HFP) is a highly versatile polymer used for water remediation due to its chemical robustness and processability. By incorporating metal‐organic frameworks (MOFs) into PVDF‐HFP membranes, the material can gain metal‐adsorption properties. It is well known that the effectiveness of these composites removing heavy metals depends on the MOF's chemical encoding and the extent of encapsulation within the polymer. In this study, it is examined how the micro to nanoscale structure of PVDF‐HFP@MOF membranes influences their adsorption performance for CrVI. To this end, the micro‐ and nanostructure of PVDF‐HFP@MOF membranes are thoroughly studied by a set of complementary techniques. In particular, small‐angle X‐ray and neutron scattering allow to precisely describe the nanostructure of the polymer‐MOF complex systems, while scanning microscopy and mercury porosimetry give a clear insight into the macro and mesoporosity of the system. By correlating nanoscale structural features with the adsorption capacity of the MOF nanoparticles, different degrees of full encapsulation‐based on the PVDF‐HFP processing and structuration from the macro to nanometer scale are observed. Additionally, the in situ functionalization of MOF nanoparticles with cysteine is investigated to enhance their adsorption toward HgII. This functionalization enhanced the adsorption capacity of the MOFs from 8 to 30 mg·g−1.

Modeling and prediction of surface topography and surface roughness in magnetic-field-enhanced shear-thickening polishing of SiC mold

To fill the gap in the research on the surface morphology formation theory of small aspheric magnetic-field-enhanced shear-thickening polishing (MESTP), a theoretical framework for surface morphology formation during the MESTP process was proposed for the first time. Based on the macroscopic computational fluid dynamics and convolution iteration theory, a multiscale surface morphology prediction model was established. This model considers the mechanical mechanism of the ductile-brittle transition of hard and brittle materials and effective cutting abrasive distribution law. Through a series of experiments and simulations, the maximum relative error between the measured Sa data and theoretical prediction data was 9.1%, and the accuracy of the model was analyzed based on RMSE. The proposed model can predict the microsurface morphology in MESTP.