Next-Generation Biomaterials for Load-Bearing Tissue Interfaces: Sensor-Integrated Scaffolds and Mechanoadaptive Constructs for Skeletal Regeneration
Abstract
1. Introduction
2. Advanced Fabrication Techniques for Load-Bearing Scaffolds
2.1. Gradient Scaffold Fabrication
2.2. Hybrid Material Systems
3. Sensor-Integrated Scaffolds for Real-Time Monitoring
3.1. Microsensor Networks for Strain Detection
3.2. Biofilm Detection and Infection Control
3.3. pH and Metabolite Tracking
3.4. Antibacterial Nanostructures
4. Computational Approaches and Future Directions
4.1. Multi-Objective Optimization Models
4.2. Personalized Scaffold Platforms
4.3. In Silico Mechanobiological Testing
5. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Category | Core Technologies | Functional Capabilities | Clinical Applications | Emerging Innovations |
---|---|---|---|---|
Microsensor Networks for Strain Detection | Piezoresistive carbon nanocomposites, optical fiber Bragg gratings, graphene sensors, MEMS | Detect micromotion, stress shielding, load transmission; strain <0.1 μm resolution | Monitor spinal fusion, arthroplasty loosening, fracture nonunion strain development | Wireless telemetry, piezoelectric energy harvesting, microbatteries, AI-guided data interpretation |
Biofilm Detection and Infection Control | EIS, SERS, pH/thermal sensors, quorum sensing probes, machine learning classifiers | Detect early colonization, identify bacterial species, map infection progression | Early diagnosis of implant-associated infections, trigger-localized antimicrobial release | Smart hydrogels, dynamic antimicrobial coatings, multimodal sensing with AI |
pH and Metabolite Tracking | Fluorophores, ion-selective electrodes, enzymatic glucose/lactate sensors, optical oxygen phosphors | Monitor pH, O2, glucose, lactate, cytokines, MMPs; define metabolic/inflammatory profiles | Detect ischemia, inflammation, regeneration quality, scaffold remodeling | Integrated microfluidics, hydrogel multiplex sensing, colorimetric optical diagnostics |
Antibacterial Nanostructures | ZnO nanowires, nanopillars, silver/copper nanostructures, AMPs, TiO2 ROS platforms | Direct bacterial kill via rupture/ROS/ions; prevent adhesion with antifouling coatings | Prevent implant infection without antibiotics, avoid resistance, preserve healing | Hybrid multi-modal platforms, light-triggered antimicrobials, biomimetic surface chemistry |
Computational Paradigm | Core Functions | Innovations Introduced | Unresolved Challenges | Future Research Vectors |
---|---|---|---|---|
Multi-Objective Optimization (MOO) | Design space exploration; performance trade-off balancing | Pareto-optimal scaffold configurations; data-driven optimization replacing trial-and-error | Integration with real-time clinical feedback; interpretability of high-dimensional design spaces | Reinforcement learning-guided optimization; AI-human co-design platforms |
Finite Element Analysis (FEA)-Driven Optimization | Simulating mechanical behavior under physiological loads | Stress-shielding minimization via spatially distributed material properties | Accurate modeling of anisotropy and viscoelasticity in scaffold-tissue interfaces | Coupling with time-dependent degradation models and real patient load profiles |
In Silico Mechanobiological Simulation | Predicting biological outcomes (e.g., osteogenesis, vascularization) via mechanical–biochemical coupling | Multiscale modeling of cell–matrix interaction; digital twin of healing environments | Experimental validation of cellular mechanosensitivity at tissue scale | Hybrid models combining agent-based systems with deep mechanotransduction networks |
Personalized Scaffold Modeling | Subject-specific optimization based on anatomical and loading data | Patient-matched design using computational pipelines from imaging to 3D printing | Scalability of personalization; integration of biological remodeling processes | Closed-loop biofabrication using real-time sensor feedback and AI correction algorithms |
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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Kumar, R.; Sporn, K.; Prabhakar, P.; Paladugu, P.; Khanna, A.; Ngo, A.; Gowda, C.; Waisberg, E.; Jagadeesan, R.; Zaman, N.; et al. Next-Generation Biomaterials for Load-Bearing Tissue Interfaces: Sensor-Integrated Scaffolds and Mechanoadaptive Constructs for Skeletal Regeneration. J. Funct. Biomater. 2025, 16, 232. https://doi.org/10.3390/jfb16070232
Kumar R, Sporn K, Prabhakar P, Paladugu P, Khanna A, Ngo A, Gowda C, Waisberg E, Jagadeesan R, Zaman N, et al. Next-Generation Biomaterials for Load-Bearing Tissue Interfaces: Sensor-Integrated Scaffolds and Mechanoadaptive Constructs for Skeletal Regeneration. Journal of Functional Biomaterials. 2025; 16(7):232. https://doi.org/10.3390/jfb16070232
Chicago/Turabian StyleKumar, Rahul, Kyle Sporn, Pranay Prabhakar, Phani Paladugu, Akshay Khanna, Alex Ngo, Chirag Gowda, Ethan Waisberg, Ram Jagadeesan, Nasif Zaman, and et al. 2025. "Next-Generation Biomaterials for Load-Bearing Tissue Interfaces: Sensor-Integrated Scaffolds and Mechanoadaptive Constructs for Skeletal Regeneration" Journal of Functional Biomaterials 16, no. 7: 232. https://doi.org/10.3390/jfb16070232
APA StyleKumar, R., Sporn, K., Prabhakar, P., Paladugu, P., Khanna, A., Ngo, A., Gowda, C., Waisberg, E., Jagadeesan, R., Zaman, N., & Tavakkoli, A. (2025). Next-Generation Biomaterials for Load-Bearing Tissue Interfaces: Sensor-Integrated Scaffolds and Mechanoadaptive Constructs for Skeletal Regeneration. Journal of Functional Biomaterials, 16(7), 232. https://doi.org/10.3390/jfb16070232