Properties Of Matrix Research Articles

Concentration of a Sparse Bayesian Model With Horseshoe Prior in Estimating High‐Dimensional Precision Matrix

ABSTRACTPrecision matrices are crucial in many fields such as social networks, neuroscience and economics, representing the edge structure of Gaussian graphical models (GGMs), where a zero in an off‐diagonal position of the precision matrix indicates conditional independence between nodes. In high‐dimensional settings where the dimension of the precision matrix exceeds the sample size and the matrix is sparse, methods like graphical Lasso, graphical SCAD and CLIME are popular for estimating GGMs. While frequentist methods are well‐studied, Bayesian approaches for (unstructured) sparse precision matrices are less explored. The graphical horseshoe estimate, applying the global‐local horseshoe prior, shows superior empirical performance, but theoretical work for sparse precision matrix estimations using shrinkage priors is limited. This paper addresses these gaps by providing concentration results for the tempered posterior with the fully specified horseshoe prior in high‐dimensional settings. Moreover, we also provide novel theoretical results for model misspecification, offering a general oracle inequality for the posterior. A concise set of simulations is performed to validate our theoretical findings.

On the role of non-equilibrium heat transfer in the filled fracture-rock matrix system

Understanding heat transfer processes in fractured porous media is critical for characterizing thermal behaviors and improving the operation efficiency of geothermal energy. The field observations have shown that many fractures are filled with sediment infilling rather than being fully open, but heat transfer in such a filled fracture-rock matrix system has received less attention to date. Since multiple domains are involved in such a system and the fracture is highly permeable even with sediment infilling, local thermal equilibrium may not be reached instantaneously. In this study, a fully coupled analytical model is proposed and developed to investigate the role of transient local heat transfer in the filled fracture-rock matrix system and analyze the spatiotemporal thermal distributions in each domain. The results indicate that multiple thermal properties of the rock matrix, such as average volumetric thermal capacity and thermal conductivity, have significant impacts on the spatiotemporal thermal distribution in the fracture. The temperatures of the pore water and sediment infilling in the fracture are sensitive to flow velocity, thermal conductivity of the rock matrix, and volumetric thermal capacities of the fluid and rock matrix. The thermal transfer coefficient has moderate interaction effects with other parameters according to the sensitivity analysis.

3D‐Printing of Highly Piezoelectric Barium Titanate Polymer Nanocomposites with Surface‐Modified Nanoparticles at Low Loadings

AbstractThis work describes the development and characterization of tetragonal barium titanate nanoparticles (BTO NPs) and their surface functionalization with dopamine dodecylamine (DDA), a lipophilic organic ligand. The so‐obtained lipophilic NPs (BTO‐DDA) are then formulated at low loadings (< 5 wt.%) into liquid photocurable resins for vat photopolymerization (VP) and 3D printed into solid objects. The printed composites are mechanically characterized in order to assess the effect of the nanomaterial on the mechanical properties of the 3D printed polymer, revealing no significant variations in the mechanical properties (tensile or flexural) of the nanocomposites compared to the original polymer matrix. In light of these results, the printed nanocomposites are studied in terms of their capacity to generate a separation of charge by the piezoelectric effect, typical of the BTO crystal structure. This study reveals that BTO‐loaded nanocomposites display outstanding piezoelectric coefficients as high as 50 pC/N when BTO‐DDA is formulated at 3.0 wt.%, only slightly less than one‐third of the piezoelectric coefficient previously reported for bulk BTO, while preserving the mechanical properties of the polymer matrix.

Enhanced Mechanical Properties and Degradation Control of Poly(Lactic) Acid/Hydroxyapatite/Reduced Graphene Oxide Composites for Advanced Bone Tissue Engineering Application.

This study explores the enhancement of poly(lactic acid) (PLA) matrix using calcium hydroxyapatite (cHAP) and reduced graphene oxide (rGO) for developing composite scaffolds aimed at bone regeneration applications. The PLA composites were fabricated through solvent evaporation and melt extrusion and characterized by various techniques, including thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and mechanical testing. The incorporation of cHAP and rGO significantly improved the thermal, mechanical, and morphological properties of the PLA matrix. Mechanical testing revealed that adding 10% cHAP and varying amounts of rGO (0.1%, 0.3%, 0.5%) enhanced tensile and compressive strengths, with the highest improvements observed at 0.5% rGO content. Thermal analysis showed increased thermal stability with higher degradation temperatures for the composites. Spectroscopic analyses confirmed the effective integration of cHAP and rGO into the PLA matrix with characteristic peaks of the fillers identified in the composite spectra. In vitro, degraded action tests in phosphate-buffered saline (PBS) at pH 7.4 over 12 months indicated that composites with higher rGO content exhibited lower mass loss and better mechanical stability. Furthermore, finite element analysis (FEA) simulations were performed to validate the experimental results, demonstrating a strong correlation between simulated and experimental compressive strengths. This novel approach demonstrates the potential of PLA/cHAP/rGO composites in creating effective and biocompatible scaffolds for tissue engineering, providing a comprehensive analysis of the synergistic effects of cHAP and rGO on the PLA matrix and offering a promising material for bone regeneration applications.

Study on the tribological properties of MXene reinforced polyurethane composites under dry conditions

The mechanical properties of the polymer matrix are crucial for forming a stable and dense transfer film between the friction pairs, facilitating friction reduction and wear resistance. This work introduces a novel polyurethane composite (PUC) system, prepared by incorporating the MXene (Ti3C2Tx) into a polyurethane elastomer, which exhibits a robust strength of ∼ 64 MPa and an excellent toughness of ∼ 155.8 MJ/m3. The PUC containing 0.5 wt% MXene (PUC0.5) exhibited excellent tribological properties, with the friction coefficient and wear rate (66 N, 0.54 m/s) reduced by 45.1 % and 206 % in comparison to pure PU, respectively. The excellent tribological properties of PUC0.5 are attributed to exceptional mechanical properties and low shear of MXene.

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.

Evolution of mechanical properties of organic-rich shale during thermal maturation

Accurate assessment of the mechanical properties of organic matter, clay matrix, and bulk shale during maturation remains a challenge. Here, we aim to assess the mechanical properties of organic-rich shale during maturation using a combination of nanoindentation methods and various geochemical analyses, i.e., mineral composition, mass loss rate, chemical structure of organic matter, and Rock-Eval analyses. Results show that the evolution of mechanical properties of organic matter in shale during maturation can be divided into: the main oil-generation stage, and the condensate oil and gas generation stage. The stiffening of organic matter in the shale is mainly due to increased aromaticity and condensation of aromatic groups. The clay matrix experiences a slight decrease in hardness and Young’s modulus at low maturity levels due to the generation of liquid hydrocarbons. However, overall, the clay matrix becomes stiffer as the shale matures due to shale dehydration, expulsion or cracking of liquid hydrocarbons, transformation of clay minerals, and hardening of organic matter. The Young’s modulus and hardness of bulk shale generally increase with increasing maturity. This is closely related to the hardening of organic matter and clay matrix, as well as the development of the more compact and dense microstructure in the shale.

Dynamic Behaviors of Composite-Laminated Concentrically Stiffened Rectangular Plates by Substructure Modal Synthesis

Existing numerical analysis methods often incur high computational costs when directly solving multi-degree-of-freedom systems due to computer capacity limitations. To address this issue, this paper took the composite laminated concentrically stiffened rectangular plate as the research object. The modal synthesis method was employed to reduce the degrees of freedom of the model, enabling the solution of complex dynamic characteristics. In the research process, the domain decomposition theory was used to divide the research object into plate and rib substructures. Based on the spectral Chebyshev method, the substructure dynamic model of the composite laminated rectangular plate was constructed. Subsequently, the function expressions of the complex load excitation on each substructure were derived, and the fixed modal synthesis method was introduced to construct the spectral Chebyshev reduced model of the substructures. According to the artificial spring theory equivalent, the interface force, and the whole reduction model of the composite laminated concentrically stiffened rectangular plate were obtained by assembling the substructure models. The time–domain and frequency–domain dynamic behaviors of composite laminated concentrically stiffened rectangular plates are yielded by solving the whole reduced model. The novelty of this work lies in combining the spectral Chebyshev method with the modal synthesis method. The established reduced model no longer depended on mesh quality and required fewer matrix dimensions and computation time compared to the whole model, while still achieving sufficiently accurate results. Additionally, based on the reduced model, an effective parametric study scheme was proposed to analyze the impact of dimensions and material properties of rib substructures on the dynamic behaviors of the composite laminated concentrically stiffened rectangular plate. These findings can be used to guide the optimal design of laminated concentrically stiffened rectangular plates in engineering practice.

The influences of temperature on the macroscopic elastoplastic behaviors of heterogeneous materials

The microstructure of heterogeneous materials affects importantly their macroscopic mechanical behavior. For a thermal-mechanical coupling problem, the temperature also has a significant effect. Firstly, the effect of temperature on the elastic behavior of heterogeneous material has been investigated in the present work with the consideration of its microstructure. Then, an explicit expression of the macroscopic yield criterion has been derived for the plastic behavior of porous material by using the homogenization approach. This yield criterion takes into account explicitly and simultaneously the influence of porosity and the effect of temperature on its overall mechanical behavior. To consider the pressure sensitivity of the matrix, the Drucker–Prager type criterion is adopted at the microscopic scale. After that, the heterogeneous material with a matrix reinforced by rigid inclusions has been studied. The microstructure information, such as the inclusion content, matrix property and the temperature have been considered explicitly in the obtained yield function. The influences of temperature and the confining pressure on the macroscopic material strength are captured by the obtained criterion. Then, the obtained result is applied to describe the temperature-dependent mechanical behaviors of sandstone.

Online kernel sliced inverse regression

Online dimension reduction techniques are widely utilized for handling high-dimensional streaming data. Extensive research has been conducted on various methods, including Online Principal Component Analysis, Online Sliced Inverse Regression (OSIR), and Online Kernel Principal Component Analysis (OKPCA). However, it is important to note that the exploration of online supervised nonlinear dimension reduction techniques is still limited. This article presents a novel approach called Online Kernel Sliced Inverse Regression (OKSIR), which specifically tackles the challenge of dealing with the increasing dimension of the kernel matrix as the sample size grows. The proposed method incorporates two key components: the approximate linear dependence condition and dictionary variable sets. These components enable a reduced-order approach for online variable updates, improving the efficiency of the process. To solve the OKSIR problem, we formulate it as an online generalized eigen-decomposition problem and employ stochastic optimization techniques to update the dimension reduction directions. Theoretical properties of this online learner are established, providing a solid foundation for its application. Through extensive simulations and real data analysis, we demonstrate that the proposed OKSIR method achieves performance comparable to that of batch processing kernel sliced inverse regression. This research significantly contributes to the advancement of online dimension reduction techniques, enhancing their effectiveness in practical applications.

Penalized Sparse Covariance Regression with High Dimensional Covariates

Covariance regression offers an effective way to model the large covariance matrix with the auxiliary similarity matrices. In this work, we propose a sparse covariance regression (SCR) approach to handle the potentially high-dimensional predictors (i.e., similarity matrices). Specifically, we use the penalization method to identify the informative predictors and estimate their associated coefficients simultaneously. We first investigate the Lasso estimator and subsequently consider the folded concave penalized estimation methods (e.g., SCAD and MCP). However, the theoretical analysis of the existing penalization methods is primarily based on i.i.d. data, which is not directly applicable to our scenario. To address this difficulty, we establish the non-asymptotic error bounds by exploiting the spectral properties of the covariance matrix and similarity matrices. Then, we derive the estimation error bound for the Lasso estimator and establish the desirable oracle property of the folded concave penalized estimator. Extensive simulation studies are conducted to corroborate our theoretical results. We also illustrate the usefulness of the proposed method by applying it to a Chinese stock market dataset.

Tensor Based Semi-Blind Channel Estimation for Reconfigurable Intelligent Surface-Aided Multiple-Input Multiple-Output Communication Systems

Reconfigurable intelligent surfaces (RISs) are a promising technology for sixth-generation (6G) wireless networks. However, a fully passive RIS cannot independently process signals. Wireless systems equipped with it often encounter the challenge of large channel matrix dimensions when acquiring channel state information using pilot-assisted algorithms, resulting in high pilot overhead. To address this issue, this article proposes a semi-blind joint channel and symbol estimation receiver without a pilot training stage for RIS-aided multiple-input multiple-output (MIMO) (including massive MIMO) communication systems. In a semi-blind system, a transmission symbol matrix and two channel matrices are coupled within the received signals at the base station (BS). We decouple them by building two parallel factor (PARAFAC) tensor models. Leveraging PARAFAC tensor decomposition, we transform the joint channel and symbol estimation problem into least square (LS) problems, which can be solved by Alternating Least Squares (ALSs). Our proposed scheme allows duplex communication. Compared to recently proposed pilot-based methods and semi-blind receivers, our results demonstrate the superior performance of our proposed algorithm in estimation accuracy and speed.

Advancement of Graphite-Clay Composite Electrodes via Different Compositions to Improve the Synergetic Matrix Effect Towards General Electroanalytical and Energy Storage Applications

Modern electrode technology trends for enhancing multifunctional electrodes, prioritizing energy storage and electroanalytical prospects. This study anticipates fabricating electrodes by incorporating cement and colloidal graphite, to improve the performance via synergetic interactions of the composite matrix. Graphite-montmorillonite-cement ternary composite electrode (GMMTCeCE) and graphite-colloidal graphite-montmorillonite-cement quaternary composite electrode (GCGMMTCeCE) are fabricated, as two types of electrodes. GMMTCeCE reflects the lowest peak-to-peak separation in Ce3+/Ce4+ (0.098 V), greater sensitivity towards [Fe(CN)6]4- (2.68 A·m·mol-1) and [Fe(CN)6]3- (2.64 A·m·mol-1), showing comparatively better performance in analyte detection than GCGMMTCeCE. Multilayered polyaniline (PANI) nanofibers network, which is unique in GMMTCeCE electropolymerization, accounts for very low serial (0.38 Ω) and charge transfer (0.59 Ω) resistances, while densely packed nanofibers of PANI in GCGMMTCeCE is responsible for the lowest ohmic resistance (1.31 Ω). Supercapacitor device fabricated by PANI-GMMTCeCE, achieved higher capacitance (1,002 F·g-1 at 5 mV·s-1) and showed greater cyclic stability, in contrast to relatively lower specific capacitance (685 F·g-1 at 5 mV·s-1) attained by PANI-GCGMMTCeCE, demonstrates the superiority of the supercapacitor of GMMTCeCE over GCGMMTCeCE for energy-storage purposes. Therefore, GMMTCeCE outperformed GCGMMTCeCE in electroanalysis and energy storage, confirming better synergetic interactions of the ternary composite matrix over the quaternary one. Besides, further improvements in graphite-clay composite matrix properties facilitate the advancement of electrochemical sensors and supercapacitor devices.

Experimental & numerical investigations of ultra-high-speed dynamics of optically induced droplet cavitation in soft materials

Perfluorocarbon (PFC) droplets represent a novel class of phase-shift contrast agent with promise in applications in biomedical and bioengineering fields. PFC droplets undergo a fast liquid-gas transition upon exposure to acoustic or optical triggering, offering a potential adaptable and versatile tool as contrast agent in diagnostic imaging and localized drug delivery vehicles in therapeutics systems. In this paper, we utilize advanced imaging techniques to investigate ultra-high-speed inertial dynamics and rectified quasi-static (low-speed) diffusion evolution of optically induced PFC droplet vaporization within three different hydrogels, each of different concentrations, examining effects such as droplet size and PFC core on bubble dynamics and material viscoelastic properties. Gelatin hydrogels reveal concentration-dependent impacts on bubble expansion and material elasticity. Embedding PFC droplets in gelatin increases internal pressure, resulting in higher equilibrium radius and continuous bubble growth during quasi-static evolution. Similar trends are observed in fibrin and polyacrylamide matrices, with differences in bubble behavior attributed to matrix properties and droplet presence. Interestingly, droplet size exhibits minimal impact on bubble expansion during inertial dynamics but influences quasi-static evolution, with larger droplets leading to continuous growth beyond 60 s. Furthermore, the core composition of PFC droplets significantly affects bubble behavior, with higher boiling point droplets exhibiting higher maximum expansion and faster quasi-static dissolution rates. Overall, the study sheds light on the intricate interplay between droplet characteristics, matrix properties, and multi-timescale bubble dynamics, offering valuable insights into their behavior within biomimetic hydrogels.

Analysis of leakage artifacts and their impact on convergence of algebraic reconstruction in multi-contrast magnetic particle imaging

Objective.Magnetic particle imaging (MPI) is a tracer-based medical imaging modality with great potential due to its high sensitivity, high spatiotemporal resolution, and ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem, which the use of regularization methods can address. Multi-contrast MPI reconstructs the signal from different tracer materials or environments separately, resulting in multi-channel images that enable quantification of, for example, temperature or viscosity. Single- and multi-contrast MPI reconstructions produce different kinds of artifacts. The objective of this work is threefold: first, to present the concept of multi-contrast specific MPI channel leakage artifacts; second, to ascertain the source of these leakage artifacts; and third, to introduce a method for their reduction.Approach.A definition for leakage artifacts is established, and a quantification method is proposed. A comprehensive analysis is conducted to establish a connection between the properties of the multi-contrast MPI system matrix and the leakage artifacts. Moreover, a two-step measurement and reconstruction method is introduced to reduce channel leakage artifacts between multi-contrast MPI channels.Main results.The severity of these artifacts correlates with the system matrix shape and condition number and depends on the similarity of the corresponding frequency components. Using the proposed two-step method on both semi-simulated and measured data a significant leakage reduction and speed up the convergence of the multi-contrast MPI reconstruction was observed.Significance.The multi-contrast system matrix analysis we conducted is essential for understanding the source of the channel leakage artifacts and finding methods to reduce them. Our proposed two-step method is expected to improve the potential for real-time multi-contrast MPI applications.

Compressed sensing measurement matrix construction method based on uniform chaotic sequence and matrix factorization

Based on the limitations of traditional measurement matrix reconstruction performance in non-ideal environments, as well as the high complexity and slow speed of matrix construction, large initial variables, and memory space requirements, this study proposes a measurement matrix construction method based on chaotic sequences. This method utilizes chaotic sequences as the foundation for constructing the measurement matrix, leveraging their pseudo-randomness and determinism to achieve the construction of a deterministic matrix. The conventional approach to creating a measurement matrix from chaotic sequences has limitations in sampling intervals, resulting in data redundancy. To address this issue, this paper presents a deterministic compressed sensing measurement matrix construction method based on a uniform chaotic sequence. Initially, an irreversible function is employed to conduct binary conversion on the chaotic sequence without interval sampling. This nonlinear transformation reduces the complexity and storage challenges of the sequence while decreasing correlation. Subsequently, the binary sequence is stored in a Toeplitz matrix with structured characteristics, further diminishing storage costs. Moreover, the nonlinear transformation guarantees the linear independence of each column of the Toeplitz matrix, serving as a foundation for Singular Value Decomposition (SVD) processing. SVD is utilized to produce a high-dimensional orthogonal matrix from a low-dimensional Toeplitz matrix. In comparison to alternative orthogonalization methods like Gram-Schmidt orthogonalization and eigenvalue decomposition, SVD does not necessitate the matrix to be square. It operates at a faster speed and requires lower matrix dimensions under identical conditions. This creates an orthogonal measurement matrix that performs well. A significant number of simulation experiments demonstrate that the measurement matrix formulated in this paper exhibits excellent performance.

Oncomatrix: Molecular Composition and Biomechanical Properties of the Extracellular Matrix in Human Tumors

The extracellular matrix is an organized three-dimensional network of protein-based molecules and other macromolecules that provide structural and biochemical support to tissues. Depending on its biochemical and structural properties, the extracellular matrix influences cell adhesion and signal transduction and, in general, can influence cell differentiation and proliferation through specific mechanisms of chemical and mechanical sensing. The development of body tissues during ontogenesis is accompanied by changes not only in cells but also in the composition and properties of the extracellular matrix. Similarly, tumor development in carcinogenesis is accompanied by a continuous change in the properties of the extracellular matrix of tumor cells, called ‘oncomatrix’, as the tumor matures, from the development of the primary focus to the stage of metastasis. In this paper, the characteristics of the composition and properties of the extracellular matrix of tumor tissues are considered, as well as changes to the composition and properties of the matrix during the evolution of the tumor and metastasis. The extracellular matrix patterns of tumor tissues can be used as biomarkers of oncological diseases as well as potential targets for promising anti-tumor therapies.

Micromechanical analyses of unidirectional (UD) discontinuous flax fiber reinforced composites

Prediction of the strength and damage behavior of natural fiber composites is a challenging task due to computational difficulties resulting from large fiber aspect ratio, non-uniform fiber length distribution, and interface modelling. A computational micromechanical model, which solves the above-mentioned challenges by incorporating a novel representative volume element (RVE) generation algorithm inspired by Lennard-Jones potential, is developed to predict the tensile behavior of unidirectional (UD) flax/epoxy composites. Effects of fiber aspect ratio, fiber spatial distribution, interfacial and matrix properties on longitudinal modulus, strength, and the damage behavior of the flax/epoxy composites are extensively studied using the numerical model. The modulus and strength predicted by numerical model were compared with both analytical models and experimental results, which not only validated the numerical model but identified the limitations of analytical model. The longitudinal strength of flax/epoxy composites initially increased with fiber aspect ratio. Upon reaching certain fiber aspect ratio (25 in this study), strength predicted by both RVE (RVEs with many fibers) and unit cell (unit cells with two fibers) are close to experimental value (240 MPa). This finding provides confidence and guidance on how to use simplified unit cells to predict the strength of natural fiber composites with acceptable accuracy and much lower computational cost.

A cooperative relaxation model with two physical parameters for investigating the temperature and cure dependence of relaxation mechanisms in resins

Modeling the relaxation properties of resin matrix during cure plays an important role in predicting process-induced residual stresses and final distortions of resin-based composites. This paper develops a physical characterization model, which explains the temperature and cure-degree dependence of relaxation behaviors in terms of the size of cooperatively rearranging region, and special emphasis is placed on investigating the general physical mechanism of cure degree affecting the relaxation properties. In this model, the relaxation time is governed by a modified Adam-Gibbs equation, which is extended here to include the cure dependence. In addition, the relaxation modulus is modeled in a chemo-rheologically simple manner (CSM) based on the free volume theory. Material characterization is carried out using experimental data of two typical resins. It is shown that two cure-dependent model parameters, i.e., the smallest size of the cooperatively rearranging region and the glass transition temperature, are sufficient in accounting for the effect of cure on the relaxation modulus, and could provide a physical explanation of the influence of cure on relaxation behaviors. Furthermore, the proposed model is numerically realized by incorporating ABAQUS with UMAT subroutine, and its validity in predicting the residual stresses and final distortion of composites is also numerically verified by comparing with the results available in literature.

Tribological and mechanical behaviors of Fe3O4NPs reinforced UHMWPE biocomposite

Ultra high molecular weight polyethylene (UHMWPE) filled with diverse contents of magnetite nanoparticles (Fe3O4NPs) were produced via hot compression molding. The effect of Fe3O4NPs on the tribological and mechanical properties of the UHMWPE matrix was studied. Wear and friction behaviors were studied using a reciprocating pin-on-disc tribometer under a sliding speed of 0.03 m.s−1 and an applied normal load of 20N. Mechanical behavior was investigated using tensile and hardness tests. SEM technique was used to explore the surface morphology and to investigate wear mechanisms. Results showed that reinforcement with Fe3O4NPs improved UHMWPE matrix hardness and wear resistance. A maximum hardness and wear resistance in addition to a good mechanical behavior in traction were obtained when adding 1 wt% of magnetite nanofiller. Reinforcement increased the coefficient of friction but there is no obvious correlation between nanofiller contents and friction. SEM analysis showed that the reinforcing rate of UHMWPE influences wear mechanisms.