Amorphous Carbon Coatings for Total Knee Replacements-Part I: Deposition, Cytocompatibility, Chemical and Mechanical Properties

Photocrosslinkable Cellulose Derivatives for the Manufacturing of All-Cellulose-Based Architectures

Replacing petroleum-based polymers with biopolymers such as polysaccharides is essential for protecting our environment by saving fossil resources. A research field that can benefit from the application of more sustainable and renewable materials is photochemistry. Therefore, cellulose-based photoresists that could be photocrosslinked via UV irradiation (λ = 254 nm and λ = 365 nm) were developed. These biogenic polymers enable the manufacturing of sustainable coatings, even with imprinted microstructures, and cellulose-based bulk materials. Thus, herein, cellulose was functionalized with organic compounds containing carbon double bonds to introduce photocrosslinkable side groups directly onto the cellulose backbone. Therefore, unsaturated anhydrides such as methacrylic acid anhydride and unsaturated and polyunsaturated carboxylic acids such as linoleic acid were utilized. Additionally, these cellulose derivatives were modified with acetate or tosylate groups to generate cellulose-based polymers, which are soluble in organic solvents, making them suitable for multiple processing methods, such as casting, printing and coating. The photocurable resist was basically composed of the UV-crosslinkable biopolymer, an appropriate solvent and, if necessary, a photoinitiator. Moreover, these bio-based photoresists were UV-crosslinkable in the liquid and solid states after the removal of the solvent. Further, the manufactured cellulose-based architectures, even the bulk structures, could be entirely regenerated into pure cellulose devices via a sodium methoxide treatment.

Subject-specific tribo-contact conditions in total knee replacements: a simulation framework across scales

Fundamental knowledge about in vivo kinematics and contact conditions at the articulating interfaces of total knee replacements are essential for predicting and optimizing their behavior and durability. However, the prevailing motions and contact stresses in total knee replacements cannot be precisely determined using conventional in vivo measurement methods. In silico modeling, in turn, allows for a prediction of the loads, velocities, deformations, stress, and lubrication conditions across the scales during gait. Within the scope of this paper, we therefore combine musculoskeletal modeling with tribo-contact modeling. In the first step, we compute contact forces and sliding velocities by means of inverse dynamics approach and force-dependent kinematic solver based upon experimental gait data, revealing contact forces during healthy/physiological gait of young subjects. In a second step, the derived data are employed as input data for an elastohydrodynamic model based upon the finite element method full-system approach taking into account elastic deformation, the synovial fluid's hydrodynamics as well as mixed lubrication to predict and discuss the subject-specific pressure and lubrication conditions.

Discussion of Orientation and Performance of Crosslinked Ultrahigh-Molecular-Weight Polyethylene Used for Artificial Joints

Previously, the orientation structure of ultrahigh-molecular-weight polyethylene (UHMWPE) for artificial joints was considered to be unchanged after irradiation crosslinking. Therefore, much of the research related to the long-term failure of artificial joints has focused on material improvements. In this study, ultrasmall-angle X-ray scattering (USAXS) and the small/wide-angle X-ray scattering (SAXS-WAXS) combined technique reveal that the orientation structures of UHMWPE materials at all scales (nanoscale to microscale) are responsible for the long-term failure of artificial joints. To further illustrate the formation of these hierarchical oriented structures, a simple model is presented. In this model, first, the migration of free radicals plays a vital role, and the different steric hindrances in different directions directly lead to uneven migration behavior of free radicals. Second, the uneven migration of free radicals contributes to an inhomogeneous concentration of free radicals, thus resulting in observable crosslinking nonuniformities. Finally, all the hierarchical structural nonuniformities promote long-term failure of artificial joints after long-term wear.

Recent Advancements in Materials and Coatings for Biomedical Implants

Metallic materials such as stainless steel (SS), titanium (Ti), magnesium (Mg) alloys, and cobalt-chromium (Co-Cr) alloys are widely used as biomaterials for implant applications. Metallic implants sometimes fail in surgeries due to inadequate biocompatibility, faster degradation rate (Mg-based alloys), inflammatory response, infections, inertness (SS, Ti, and Co-Cr alloys), lower corrosion resistance, elastic modulus mismatch, excessive wear, and shielding stress. Therefore, to address this problem, it is necessary to develop a method to improve the biofunctionalization of metallic implant surfaces by changing the materials’ surface and morphology without altering the mechanical properties of metallic implants. Among various methods, surface modification on metallic surfaces by applying coatings is an effective way to improve implant material performance. In this review, we discuss the recent developments in ceramics, polymers, and metallic materials used for implant applications. Their biocompatibility is also discussed. The recent trends in coatings for biomedical implants, applications, and their future directions were also discussed in detail.

Design of Amorphous Carbon Coatings Using Gaussian Processes and Advanced Data Visualization

In recent years, an increasing number of machine learning applications in tribology and coating design have been reported. Motivated by this, this contribution highlights the use of Gaussian processes for the prediction of the resulting coating characteristics to enhance the design of amorphous carbon coatings. In this regard, by using Gaussian process regression (GPR) models, a visualization of the process map of available coating design is created. The training of the GPR models is based on the experimental results of a centrally composed full factorial 23 experimental design for the deposition of a‑C:H coatings on medical UHMWPE. In addition, different supervised machine learning (ML) models, such as Polynomial Regression (PR), Support Vector Machines (SVM) and Neural Networks (NN) are trained. All models are then used to predict the resulting indentation hardness of a complete statistical experimental design using the Box–Behnken design. The results are finally compared, with the GPR being of superior performance. The performance of the overall approach, in terms of quality and quantity of predictions as well as in terms of usage in visualization, is demonstrated using an initial dataset of 10 characterized amorphous carbon coatings on UHMWPE.