Concentration Distribution Research Articles

Spatial and Temporal Characteristics of Land Use Changes in the Yellow River Basin from 1990 to 2021 and Future Predictions

Studying spatial and temporal characteristics of land use changes and the driving factors in the Yellow River Basin as well as simulating and predicting future land use is crucial for resource management, ecological protection, and regional sustainable development in the Yellow River Basin. Based on the China Land Cover Dataset (CLCD) of the Yellow River Basin from 1990 to 2021, this study employs various methods such as the Mann–Kendall test and sliding t-test, land use dynamics, the land use transfer matrix, the standard deviation ellipse, the center of gravity migration model, and a geographic detector to explore the spatial and temporal characteristics of land use changes and driving forces in the Yellow River Basin over the past 30 years. Additionally, the study predicts land use types in the study area for the year of 2030 by using the Future Land Use Simulation (FLUS) model. The results show the following: (1) From 1990 to 2021, the area of forest, grassland, water, and impervious surfaces increased significantly, while the area of cropland, shrub, barren land, and wetlands decreased significantly. The most actively changing land use types are cropland, grassland, barren land, and impervious surfaces. (2) The center of gravity for shrub and impervious surfaces shifted westward, while wetlands showed a trend of obvious concentrated distribution, and the remaining land use types exhibited stable directional distributions. (3) Economic factors had a stronger driving effect on land use changes than topographic and climatic factors. The land use changes in the Yellow River Basin are influenced by the coordinated driving forces of multiple factors. (4) In 2030, the main land use types in the Yellow River Basin are still expected to be cropland, grassland, and forest. However, there will be a significant expansion of impervious surfaces and forest land, with substantial encroachment on cropland and grassland.

Mathematical modelling for viscoinelastic nanofluid flow over a stretching sheet with machine learning: an application to tissue adhesive

ABSTRACT The medicated tissue adhesive on a stretching surface through Prandtl–Eyring nanofluid flow is emphasized in the current article to estimate the heat transfer rate and optimize the adhesion process. The effects of Brownian motion and thermophoresis on electrically conducting viscoinelastic nanofluid are taken into consideration with the sight of convective states. The modeled governing equations are nondimensionalized by operating similarity variables to stimulate the optimization process. The result of an executed model is solved scientifically by employing the NDSolve technique. The influence of various parameters on the fluid momentum, thermal, and concentration distributions is accentuated through graphs. Furthermore, the machine learning approach is enhanced to analyze the physical quantities of interest in the entire region. The outcomes are validated to those that have already been published in the pertinent area literature to determine the effectiveness of viscoinelastic nanofluid. The findings revealed that the Prandtl–Eyring fluid parameter enhances the momentum, while the Hartmann number indicates the reverse trend. In addition, it delivers that the proposed machine learning model is capable of forecasting the physical quantities with lower error ( 10 − 3 ) and is a powerful engineering tool that can be effectively employed in a viscoinelastic nanofluid.

Analysis of Brownian motion and thermophoresis effects with chemical reaction on Williamson nanofluid flow over permeable stretching sheet

This study warrants a broad analysis on the simultaneous effects of Brownian and thermophoresis on the chemically reacting Williamson nanofluid flow via a permeable expanding sheet. The analysis includes the properties of slip velocity and thermal radiation, providing a more representative picture of the physical phenomena. The differential equations governed by demonstrating the fluid flow phenomena are renewed to a set of nonlinear differential equations utilizing similarity rules. These transformed equations are subsequently handled numerically adopting the fourth-order Runge-Kutta method. The results reveal the significant control of Brownian motion together with thermophoresis and additionally, the inclusion of velocity slip and thermal radiation remarkably affect the velocity, temperature, and concentration distributions. Parametric studies show the validation of the present system to variations in the Williamson fluid parameter, slip parameter, radiative heat parameter, Brownian motion parameter, and thermophoresis parameter.

Laboratory study on the relationship between the vertical distribution of radon concentration in soil and the exhalation rate of radon from the soil surface

Laboratory study on the relationship between the vertical distribution of radon concentration in soil and the exhalation rate of radon from the soil surface

A zero-background fluorescent probe for sensing and imaging of glutathione via the "covalent-assembly" approach.

Developing selective and sensitive fluorescent probes for the detection of glutathione (GSH) concentration and intracellular distribution is of great significance for early diagnosis and treatment of diseases such as liver injury and cancer since GSH plays irreplaceable roles in regulating intracellular redox homeostasis. Herein, we present a new fluorescent probe that can be specifically activated by GSH through the conjugate addition and hydrolysis induced covalent-assembly approach for achieving zero-background interference fluorescence off-on sensing. Besides, the probe exhibited prominent selectivity and sensitivity, a low detection limit and cytotoxicity, thus successfully realizing specific real-time monitoring and tracking of GSH levels in living cells. As a consequence, this work might provide a potentially promising candidate for validating the function of GSH in various physiological and pathological processes, which is beneficial for early diagnosis and therapeutics of related diseases.

Selection functions of strong lens finding neural networks

Abstract We show that Convolution Neural Networks trained to find strong gravitational lens systems are biased towards systems with larger Einstein radii and large concentrated sources. This selection function is key to fully realising the potential of the large samples of strong gravitational lens systems that will be found in upcoming wide-field surveys. In this paper, we use a CNN and three training datasets to quantify the network selection function and its implication for the many scientific applications of strong gravitational lensing. We use CNNs with similar architecture as is commonly found in the literature. The networks preferentially select systems with larger Einstein radii and larger sources with more concentrated source-light distributions. Increasing the detection significance threshold to 12σ from 8σ results in 50percnt of the selected strong lens systems having Einstein radii θE ≥ 1.04 arcsec from θE ≥ 0.879 arcsec, source radii RS ≥ 0.194 arcsec from RS ≥ 0.178 arcsec and source Sérsic indices $n_{\mathrm{Sc}}^{\mathrm{S}}$ ≥ 2.62 from $n_{\mathrm{Sc}}^{\mathrm{S}}$ ≥ 2.55. The model trained to find lensed quasars shows a stronger preference for higher lens ellipticities than those trained to find lensed galaxies. The selection function is independent of the slope of the power-law of the mass profiles, hence measurements of this quantity will be unaffected. The lens finder selection function reinforces that of the lensing cross-section, and thus we expect our findings to be a general result for all galaxy-galaxy and galaxy-quasar lens finding neural networks.

Research on Key Roof-Cutting Parameters for Surrounding Rock Stability Control in Gob-Side Entry Retention without Coal Pillars in Karst Mountainous Area

The differential distribution of original rock stress and stress concentration caused by the variation in coal seam depth in karst topography are critical factors influencing the selection of roof-cutting parameters. Based on this, this study explores a method to determine reasonable roof-cutting parameters by incorporating the characteristics of coal seam depth variation in karst mountainous areas. A mechanical model of the cantilever beam structure for roof cutting in gob-side entry retention (GSER) is constructed, and the critical values and reasonable ranges of roof-cutting height and angle under different burial depths are derived. Furthermore, the displacement and stress evolution characteristics of surrounding rocks in gob-side entry retention under different coal seam burial depths, roof-cutting heights, and roof-cutting angles within the reasonable range of roof-cutting parameters are analyzed. The results show that there is a positive correlation between roof-cutting height and tensile stress in the uncut portion of the main roof, while roof-cutting angle and coal seam depth are negatively correlated with tensile stress. From the perspective of impact, roof-cutting height has a greater impact than roof-cutting angle, followed by coal seam depth. As for the distribution characteristics of the reasonable roof-cutting parameter range, the fan-shaped area of reasonable roof-cutting parameters gradually decreases with increasing coal seam depth. Taking the geological conditions of Anshun Coal Mine as an example, when the burial depth increases from 350 m to 550 m, adjusting the roof-cutting height to 6 m, 7 m, and 8 m, respectively, and setting the roof-cutting angle at 10° can effectively achieve the stability of the surrounding rock in the GSER. The research findings can provide a scientific basis and engineering references for selecting roof-cutting parameters in mines with similar geological conditions.

Population Distribution in Guizhou’s Mountainous Cities: Evolution of Spatial Pattern and Driving Factors

Guizhou is a typical mountainous province and is also one of the lowland regions in China that has attracted a population influx. Here, using population density data from 2000 to 2020 as the basic dataset and the coefficient of variation method and standard deviation ellipse analysis, we investigated the spatial characteristics across different years. The results show: Firstly, Guizhou’s population has a distinct spatial distribution, characterized by a lower population density in the southeast and a higher density in the northwest as well as an increasing polarization of population concentration toward the centers of prefecture-level cities and provincial capitals. Fluctuations in population density resemble a central siphon effect, which is particularly pronounced in the provincial capital and show a significant gravitational pull. Secondly, the coefficient of variation in population density across Guizhou’s counties is spatially divided by Guiyang, showing higher values in the east and lower values in the west. Furthermore, the ellipse of the standard deviation of population density is gradually shrinking, indicating an increasingly concentrated population distribution. Thirdly, the explanatory power of the population and socio-economic systems on the population distribution in Guizhou is significantly greater than that of the natural systems. Population distribution and migration patterns have shifted from purely “economic driven” to coexisting with “economic and comfort-oriented” trends, and there is an urgent need to improve the comfort level of public services as a typical supply, in order to boost Guizhou’s population attraction.

Thermosolutal Marangoni convective flow of MHD tangent hyperbolic hybrid nanofluids with elastic deformation and heat source

Abstract In this work, the Marangoni convective flow of magnetohydrodynamic tangent hyperbolic ( F e 3 O 4 − Cu / {{\rm{F}}{{\rm{e}}}_{3}{\rm{O}}}_{4}-{\rm{Cu}}/ ethylene glycol) hybrid nanofluids over a plate dipped in a permeable material with heat absorption/generation, heat radiation, elastic deformation and viscous dissipation is discussed. The impact of activation energy is also examined. Hybrid nanofluids are regarded as advanced nanofluids due to the thermal characteristics and emerging advantages that support the desire to augment the rate of heat transmission. The generalized Cattaneo–Christov theory, which takes into account the significance of relaxation times, is modified for the phenomena of mass and heat transfer. The fundamental governing partial differential equations are converted to ordinary differential equations (ODEs) by adopting similarity variables. The Runge–Kutta–Fehlberg-45 technique is utilized to solve nonlinear ODEs. Regarding the non-dimensional embedded parameters, a graphic investigation of the thermal field, concentration distribution, and velocity profile is performed. The results show that the increasing Marangoni ratio parameter enhances velocity and concentration distributions while decreases the temperature distribution. The velocity profile is decreased and the efficiency of heat transfer is improved as the porosity parameter is increased. Nusselt number is diminished with the rising values of the porosity variable.

Examination of nonideal film growth in batch atomic layer deposition for plasma-resistant coatings

Atomic layer deposition (ALD) can be used to fabricate protective coatings including moisture barrier layers for organic light emitting diodes, anticorrosion layers for photoelectrodes, and plasma-resistant coating for semiconductor manufacturing equipment, which necessitates the deposition of large and thick ALD films via batch ALD. However, batch ALD for the fabrication of large-area and thick coatings exhibits nonideal film growth, a phenomenon that cannot solely be explained by transient concentration distribution within the deposition chamber. This paper describes the application of precursor “exposure” (in the unit of Langmuir, or Pa s), defined as the integral of concentration over time, as a metric to assess the growth per cycle (GPC) distribution under nonideal ALD conditions, demonstrating that the local GPC correlates well with the cumulative precursor exposure at that site. Consequently, this measure can effectively predict the nonuniformity (NU) distribution of film thickness and facilitate the determination of optimal operating conditions that ensure maximal uniformity of exposure. Under this condition, the intrafilm NU of ALD-grown Al2O3 film (nominal thickness 300 nm) was reduced to 1.2%, and the interfilm NU is diminished to as low as 3.3%. These values represent reductions of 40% and 45%, respectively, compared to the NU levels observed under nonideal conditions (insufficient trimethylaluminum, TMA exposure downstream). The plasma etch rate of ALD-deposited films is merely 4.3 nm/min, representing a reduction of one-half compared to films deposited under nonideal conditions (9.8 nm/min) with overload TMA exposure downstream leading to chemical vapor deposition-like reactions.

Application of Instance Segmentation to Identifying Insect Concentrations in Data from an Entomological Radar

Entomological radar is one of the most effective tools for monitoring insect migration, capable of detecting migratory insects concentrated in layers and facilitating the analysis of insect migration behavior. However, traditional entomological radar, with its low resolution, can only provide a rough observation of layer concentrations. The advent of High-Resolution Phased Array Radar (HPAR) has transformed this situation. With its high range resolution and high data update rate, HPAR can generate detailed concentration spatiotemporal distribution heatmaps. This technology facilitates the detection of changes in insect concentrations across different time periods and altitudes, thereby enabling the observation of large-scale take-off, landing, and layering phenomena. However, the lack of effective techniques for extracting insect concentration data of different phenomena from these heatmaps significantly limits detailed analyses of insect migration patterns. This paper is the first to apply instance segmentation technology to the extraction of insect data, proposing a method for segmenting and extracting insect concentration data from spatiotemporal distribution heatmaps at different phenomena. To address the characteristics of concentrations in spatiotemporal distributions, we developed the Heatmap Feature Fusion Network (HFF-Net). In HFF-Net, we incorporate the Global Context (GC) module to enhance feature extraction of concentration distributions, utilize the Atrous Spatial Pyramid Pooling with Depthwise Separable Convolution (SASPP) module to extend the receptive field for understanding various spatiotemporal distributions of concentrations, and refine segmentation masks with the Deformable Convolution Mask Fusion (DCMF) module to enhance segmentation detail. Experimental results show that our proposed network can effectively segment concentrations of different phenomena from heatmaps, providing technical support for detailed and systematic studies of insect migration behavior.

The Causes of Waterlogging in Chinese Southern Hilly Cities Based on Hydrological Processes

The various hydrological processes that cause waterlogging exhibit regional differences. Studies on the causes of waterlogging in Chinese southern hilly cities from the perspective of urban regional hydrological processes are needed. This article examines Changsha Central City to study the hydrological processes of Chinese southern hilly cities based on waterlogging point data from 2015 to 2017, analyzing the relationships between the degree of waterlogging and changes in the material elements of runoff sources, runoff convergence terminals, and runoff convergence processes using correlation analysis, principal component analysis and comparative analysis. These results show that the urban waterlogging in Chinese southern hilly cities is caused by the hardening of convergence spaces, concentrated water distribution, a decrease in the connectivity of rainwater corridors, complex topographic slopes, and a lag in the construction of drainage facilities. The expansion of impervious surfaces, particularly in areas intended for convergence terminals, has significantly reduced the number of these critical structures. Additionally, disordered changes in topographic slopes, the division caused by roads, and the hardening of underlying surfaces in rainwater corridors have collectively diminished the structural and ecological connectivity of the rainwater corridor system. This obstruction of surface runoff into concentrated water bodies has rendered the runoff regulation function of these water bodies ineffective, making their size a leading cause of urban waterlogging. To mitigate the risk of waterlogging, Chinese southern hilly cities should prevent urban development from encroaching on natural runoff areas. They should also implement dispersed water body layouts, enhance both the structural and ecological connectivity of rainwater corridors and their underlying surfaces, and improve the design standards of drainage facilities.

Long-term grazing changed the spatial distributions of dominant species in typical steppe of Inner Mongolia

Dominant species occupy a pivotal role in plant community, influencing the structure and function of the ecosystem. The spatial distributions of dominant species can react to the effect of different grazing intensities, thereby reflecting their tolerance and adaptive strategies toward grazing. In this study, geostatistical methods were mainly used to study the spatial distribution characteristics of Stipa krylovii Roshev. and Leymus chinensis (Trin.) Tzvel. species at two interval scales (quadrat size 5 m × 5 m, 10 m × 10 m) and two treatments (free grazing, FG, 1.66 sheep·ha− 1·a− 1; control, CK, 0 sheep·ha− 1·a− 1) in typical steppe of Inner Mongolia. A systematic sampling method was used in each 100 m × 100 m representative sample plots to obtain the height, coverage, and density of all species in the community. The results showed that grazing altered the concentrated distribution of S. krylovii and the spatial mosaic distribution pattern of S. krylovii and L. chinensis while having no effect on the spatial clumped distribution of L. chinensis. It also found that the spatial distributions of dominant species are primarily affected by structural factors, and random factors such as long-term grazing led to a transition of S. krylovii from a concentrated distribution to a small patchy random pattern should not be overlooked. Our findings suggest that long-term grazing alters the spatial distribution pattern of dominant species and that adaptive strategies may be the key for maintaining the dominant role of structural factors.

Molecular simulation of CO2/N2 injection on CH4 adsorption and diffusion

As an efficient and clean energy, coalbed methane development and utilization have deep significance in promoting energy conservation and emission reduction, reducing greenhouse gas emissions. Therefore, molecular simulation was utilized to study the influence of N2/CO2 on the adsorption and diffusion of methane in coal under different gas injection methods and to elucidate the influence of varying gas injection methods on the efficiency of coalbed methane extraction, which provides a basis for the efficient development of coalbed methane. The results show that the adsorption effect of gases in coal decreases with the increase of temperature and increases with the rise of pressure, and the adsorption performance of the three gases in coal shows the law of CO2 > CH4 > N2. In addition, the injection of CO2/N2 had an obvious inhibition effect on CH4 adsorption, and the inhibition effect of CO2 was more significant, and the inhibition effect on CH4 adsorption reached the maximum when the two gases were mixture injected. In terms of diffusion, compared with separate injection, mixed injection of N2 + CO2 promotes CH4 diffusion more effectively, which can be reflected in the relative concentration distribution and velocity distribution. The injection of N2 helps to increase the porosity of coal, and the injection of CO2 and N2 + CO2 will lead to the decrease of porosity, but the mixed gas injection has less effect than the injection of CO2 alone.

Quantifying concentration distributions in redox flow batteries with neutron radiography

The continued advancement of electrochemical technologies requires an increasingly detailed understanding of the microscopic processes that control their performance, inspiring the development of new multi-modal diagnostic techniques. Here, we introduce a neutron imaging approach to enable the quantification of spatial and temporal variations in species concentrations within an operating redox flow cell. Specifically, we leverage the high attenuation of redox-active organic materials (high hydrogen content) and supporting electrolytes (boron-containing) in solution and perform subtractive neutron imaging of active species and supporting electrolyte. To resolve the concentration profiles across the electrodes, we employ an in-plane imaging configuration and correlate the concentration profiles to cell performance with polarization experiments under different operating conditions. Finally, we use time-of-flight neutron imaging to deconvolute concentrations of active species and supporting electrolyte during operation. Using this approach, we evaluate the influence of cell polarity, voltage bias and flow rate on the concentration distribution within the flow cell and correlate these with the macroscopic performance, thus obtaining an unprecedented level of insight into reactive mass transport. Ultimately, this diagnostic technique can be applied to a range of (electro)chemical technologies and may accelerate the development of new materials and reactor designs.

Soil carbon stocks and nutrient stratification in a volcanically active coffee-dominated landscape in south-central Guatemala

The volcanoclastic mountains of Central America offer elevations ideal for coffee production. Both vulcanism and land use can have significant impacts on soil formation and properties. We evaluated how the interactive impacts of soil formation and coffee-dominated land use modulate soil storage and vertical distribution of organic carbon and nutrient elements in a volcanically active landscape dominated by coffee agriculture in south-central Guatemala. Thirty-seven pedons under coffee production (n = 29), forest cover (n = 5) and pasture (n = 3) were characterized and classified, and concentrations and stocks of organic carbon and macronutrient and micronutrient elements were quantified across genetic horizons. Additionally, soil organic carbon (SOC) stock values calculated using the fixed depth (FD) and equivalent soil mass (ESM) methodologies were compared. The active stratovolcano in the region had a strong effect on the development of andic properties and soil horizon burial, with thirty-one pedons classified as Andisols, three as Inceptisols and three as Entisols. Land use management practices and soil horizon burial by deposition of volcanic material were partly reflected in the vertical distribution of SOC concentrations and stocks. The vertical distribution of macronutrients was generally more sensitive to land use than the vertical distribution micronutrients, which could potentially reflect differences in inputs via fertilizers and vegetation cover and in outputs with biomass harvest. Soil organic carbon stocks were similar when calculated by FD and ESM, reflecting relatively consistent depth-wise bulk density. These results demonstrate that in volcanically active landscapes, land use should be considered in concert with soil-forming factors to comprehensively understand organic carbon and nutrient element storage in soils.

Modeling and validation of purification of pharmaceutical compounds via hybrid processing of vacuum membrane distillation

This study provides an in-depth examination of forecasting the concentration of pharmaceutical compounds utilizing the input features (coordinates) r and z through a range of machine learning models. Purification of pharmaceuticals via vacuum membrane distillation process was carried out and the model was developed for prediction of separation efficiency based on hybrid approach. Dataset was collected from mass transfer analysis of process to obtain concentration distribution in the feed side of membrane distillation and used it for machine learning models. The dataset has undergone preprocessing, which includes outlier detection using the Isolation Forest algorithm. Three regression models were used including polynomial regression (PR), k-nearest neighbors (KNN), and Tweedie regression (TWR). These models were further enhanced using the Bagging ensemble technique to improve prediction accuracy and reduce variance. Hyper-parameter optimization was conducted using the Multi-Verse Optimizer algorithm, which draws inspiration from cosmological concepts. The Bagging-KNN model had the highest predictive accuracy (R2 = 0.99923) on the test set, indicating exceptional precision. The Bagging-PR model displayed satisfactory performance, with a slightly reduced level of accuracy. In contrast, the Bagging-TWR model showcased the least accuracy among the three models. This research illustrates the effectiveness of incorporating bagging and advanced optimization methods for precise and dependable predictive modeling in complex datasets.

Development of a local wall concentration model for the design of single pass tangential flow filtration (SPTFF) systems with viral vector surrogates

Recent advances in the use of viral vectors for gene therapy has created a need for efficient downstream processing of these novel therapeutics. Single-pass tangential flow filtration (SPTFF) can potentially improve final product quality via reductions in shear, and it can increase manufacturing productivity via simple implementation into continuous/intensified processes. This study investigated the impact of variations in pressure and flow rate along the length of the membrane on overall SPTFF performance. Constant-flux filtration experiments at feed fluxes from 14 to 420 L/m2/h (Reynolds numbers <20) were performed using Pellicon® 3 TFF cassettes with fluorescent nanoparticles as model viral vectors. The location of nanoparticle accumulation shifted towards the filter outlet at high conversion and was also a function of the permeate flow configuration. These phenomena were explained using a newly developed concentration polarization model that predicts the distribution in local wall concentration over the length of the membrane. The model accurately captured the observed nanoparticle accumulation trends, including the effects of the permeate flow profile (co-current, divergent, or convergent flow) on nanoparticle accumulation within the SPTFF module. Nanoparticle accumulation at moderate conversion was more uniform using convergent flow, but nanoparticle accumulation at 80 % conversion (5x concentration factor) can be minimized using a divergent flow configuration. The local wall concentration model was also used to evaluate the critical flux by assuming that fouling occurs when the nanoparticle concentration at any point along the membrane surface exceeds 15 % by volume. These results provide important insights for the design and operation of SPTFF technology for inline concentration of viral vectors.

Analysis of carrier dynamics and thermal effect of the GaAs photoconductive semiconductor switch in lock-on mode

Abstract The lock-on effect of the gallium arsenide photoconductive semiconductor switch (GaAs PCSS) at repetition rate aggravates the current crowding and electric field distortion, which significantly increases the risk of switch damage or even failure. Therefore, it is of great significance to investigate the carrier transport and the heat generation mechanism for improving the performance and longevity of GaAs PCSS in lock-on mode. The internal physical process of an opposed-electrode GaAs PCSS at low optical energy and strong electric field is analysed and discussed by experiment and simulation. A device-circuit hybrid simulation is employed to investigate the transient electric field, carrier concentration, and lattice temperature distribution within the GaAs PCSS in lock-on mode. The device temperature exhibits a positive correlation with the applied bias electric field, resulting in a peak temperature of 1037.25 K at an electric field of 38 kV/cm. The temperature distribution within the GaAs PCSS indicates a greater possibility for thermal breakdown and damage near the electrodes.

A Comparative Study on the Effects of Biodegradable High-Purity Magnesium Screw and Polymer Screw for Fixation in Epiphyseal Trabecular Bone

Abstract With mechanical strength close to cortical bone, biodegradable, and osteopromotive properties, magnesium (Mg)-based implants are promising biomaterials for orthopedic applications. However, during the degradation of such implants, there are still concerns on the potential adverse effects such as formation of cavities, osteolytic phenomena, and chronic inflammation. Therefore, to transform Mg-based implants into clinical practice, the present study evaluated the local effects of high-purity Mg screws (HP-Mg, 99.99 wt%) by comparing with clinically approved polylactic acid (PLA) screws in epiphyseal trabecular bone of rabbits. After implantation of screws at the rabbit distal femur, bone microstructural, histomorphometric, and biomechanical properties were measured at various time points (week 4, 8, and 16) using micro-CT, histology and histomorphometry, micro-indentation, and scanning electron microscope (SEM). High-purity Mg screws promoted peri-implant bone ingrowth with higher bone mass (BV/TV at week 4: 0.189 ± 0.022 in PLA group versus 0.313 ± 0.053 in Mg group), higher biomechanical properties (hardness at week 4: 35.045 ± 1.000 HV in PLA group versus 51.975 ± 2.565 HV in Mg group), more mature osteocyte LCN architecture, accelerated bone remodeling process, and alleviated inflammatory response (IRS score of Ram11 at week 4: 5.8 ± 0.712 in PLA group versus 3.75 ± 0.866 in Mg group) as compared to PLA screws. Furthermore, we conducted finite element analysis (FEA) to validate the superiority of high-purity Mg screws as orthopedic implants by demonstrating reduced stress concentration and uniform stress distribution around the bone tunnel, which led to lower risks of trabecular microfractures. In conclusion, high-purity Mg screws demonstrated greater osteogenic bioactivity and limited inflammatory response compared to PLA screws in the epiphyseal trabecular bone of rabbits. Our findings have paved a promising way for the clinical application of Mg-based implants.