Stimuli-Responsive Codelivery System-Embedded Polymeric Nanofibers with Synergistic Effects of Growth Factors and Low-Intensity Pulsed Ultrasound to Enhance Osteogenesis Properties.

The present work aims to develop optimized scaffolds for bone repair by incorporating mesoporous nanoparticles into them, thereby combining bioactive factors for cell growth and preventing rapid release or loss of effectiveness. We synthesized biocompatible and biodegradable scaffolds designed for the controlled codelivery of curcumin (CUR) and recombinant human bone morphogenic protein-2 (rhBMP-2). Active agents in dendritic silica/titania mesoporous nanoparticles (DSTNs) were incorporated at different weight percentages (0, 2, 5, 7, 9, and 10 wt %) into a matrix of polycaprolactone (PCL) and polyethylene glycol (PEG) nanofibers, forming the CUR-BMP-2@DSTNs/PCL-PEG delivery system (S0, S2, S5, S7, S9, and S10, respectively, with the number showing the weight percentage). To enhance the formation process, the system was treated using low-intensity pulsed ultrasound (LIPUS). Different advanced methods were employed to assess the physical, chemical, and mechanical characteristics of the fabricated scaffolds, all confirming that incorporating the nanoparticles improves their mechanical and structural properties. Their hydrophilicity increased by approximately 25%, leading to ca. 53% enhancement in their water absorption capacity. Furthermore, we observed a sustained release of approximately 97% for CUR and 70% for BMP-2 for the S7 (scaffold with 7 wt % DSTNs) over 28 days, which was further enhanced using ultrasound. In vitro studies demonstrated accelerated scaffold biodegradation, with the highest level observed in S7 scaffolds, approximately three times higher than the control group. Moreover, the cell viability and proliferation on DSTNs-containing scaffolds increased when compared to the control group. Overall, our study presents a promising nanocomposite scaffold design with notable improvements in structural, mechanical, and biological properties compared to the control group, along with controlled and sustained drug release capabilities. This makes the scaffold a compelling candidate for advanced bone tissue engineering and regenerative therapies.

Infusion Simulation of Graphene-Enhanced Resin in LCM for Thermal and Chemo-Rheological Analysis.

The present numerical study proposes a framework to determine the heat flow parameters-specific heat and thermal conductivity-of resin-graphene nanoplatelets (GNPs) (modified) as well as non-modified resin (with no GNPs). This is performed by evaluating the exothermic reaction which occurs during both the filling and post-filling stages of Liquid Composite Moulding (LCM). The proposed model uses ANSYS Fluent to solve the Stokes-Brinkman (momentum and mass), energy, and chemical species conservation equations to a describe nano-filled resin infusion, chemo-rheological changes, and heat release/transfer simultaneously on a Representative Volume Element (RVE). The transient Volume-of-Fluid (VOF) method is employed to track free-surface propagation (resin-air interface) throughout the computational domain. A User-Defined Function (UDF) is developed together with a User-Defined Scaler (UDS) to incorporate the heat generation (polymerisation), which is added as an extra source term into the energy equation. A separate UDF is used to capture intra-tow (microscopic) flow by adding a source term into the momentum equation. The numerical findings indicate that the incorporation of GNPs can accelerate the curing of the resin system due to the high thermal conductivity of the nanofiller. Furthermore, the model proves its capability in predicting the specific heat and thermal conductivity of the modified and non-modified resin systems utilising the computed heat of reaction data. The analysis shows an increase of ∼15% in the specific heat and thermal conductivity due to different mould temperatures applied (110-170 °C). This, furthermore, stresses the fact that the addition of GNPs (0.2 wt.%) improves the resin-specific heat by 3.68% and thermal conductivity by 58% in comparison to the non-modified thermoset resin. The numerical findings show a satisfactory agreement with and in the range of experimental data available in the literature.

MXene-Embedded Porous Carbon-Based Cu2O Nanocomposites for Non-Enzymatic Glucose Sensors.

This work explores the use of MXene-embedded porous carbon-based Cu2O nanocomposite (Cu2O/M/AC) as a sensing material for the electrochemical sensing of glucose. The composite was prepared using the coprecipitation method and further analyzed for its morphological and structural characteristics. The highly porous scaffold of activated (porous) carbon facilitated the incorporation of MXene and copper oxide inside the pores and also acted as a medium for charge transfer. In the Cu2O/M/AC composite, MXene and Cu2O influence the sensing parameters, which were confirmed using electrochemical techniques such as cyclic voltammetry, electrochemical impedance spectroscopy, and amperometric analysis. The prepared composite shows two sets of linear ranges for glucose with a limit of detection (LOD) of 1.96 µM. The linear range was found to be 0.004 to 13.3 mM and 15.3 to 28.4 mM, with sensitivity values of 430.3 and 240.5 µA mM-1 cm-2, respectively. These materials suggest that the prepared Cu2O/M/AC nanocomposite can be utilized as a sensing material for non-enzymatic glucose sensors.

A Numerical Thermo-Chemo-Flow Analysis of Thermoset Resin Impregnation in LCM Processes.

This paper presents a numerical framework for modelling and simulating convection-diffusion-reaction flows in liquid composite moulding (LCM). The model is developed in ANSYS Fluent with customised user-defined-functions (UDFs), user-defined-scalar (UDS), and user-defined memory (UDM) codes to incorporate the cure kinetics and rheological characteristics of thermoset resin impregnation. The simulations were performed adopting volume-of-fluid (VOF)-a multiphase flow solution-based on finite volume method (FVM). The developed numerical approach solves Darcy's law, heat transfer, and chemical reactions in LCM process simultaneously. Thereby, the solution scheme shows its ability to provide information on flow-front, viscosity development, degree of cure, and rate of reaction at once unlike existing literature that commonly focuses on impregnation stage and cure stage in isolation. Furthermore, it allows online monitoring, controlled boundary conditions, and injection techniques (for design of manufacturing) during the mould filling and curing stages. To examine the validity of the model, a comparative analysis was carried out for a simple geometry, in that the numerical results indicate good agreement-3.4% difference in the degree of cure compared with previous research findings.