Gold-Based Nanomaterials for Antimicrobial and Anticancer Applications: A Critical Review
Keywords:
Gold Nanoparticles, Antimicrobial Agents, Cancer Therapeutics, NanomedicineAbstract
The rapid emergence of multidrug-resistant pathogens and the inherent limitations of conventional cancer therapeutics have catalyzed the search for novel, multifunctional therapeutic agents. Gold-based nanomaterials have garnered unprecedented attention in biomedical research due to their unique physicochemical properties, exceptional biocompatibility, and highly tunable surface chemistry. This comprehensive review critically evaluates the dual applications of gold nanomaterials in combating microbial infections and malignant tumors. We systematically explore the foundational synthesis methods, ranging from traditional chemical reduction techniques to emerging biologically mediated green synthesis approaches. The mechanisms underlying the antimicrobial efficacy of gold nanoparticles, including physical membrane disruption, oxidative stress induction through reactive oxygen species generation, and intracellular biomolecular interference, are thoroughly analyzed. Furthermore, the anticancer potential of these nanomaterials is examined through the lenses of targeted drug delivery, localized photothermal therapy, and photodynamic therapy, leveraging the enhanced permeability and retention effect as well as active targeting strategies. Despite their immense clinical potential, the translation of gold nanomaterials from the laboratory bench to the bedside faces significant hurdles, primarily concerning long-term in vivo toxicity, biodistribution, and stringent regulatory requirements. By synthesizing current empirical evidence and highlighting existing knowledge gaps, this review aims to provide a robust framework for future research directions in gold-based nanomedicine.
References
1. Yao, Y., Zhang, W., & Li, M. (2026). Cloud-Edge Federated Incremental Learning Framework for PMSM Efficiency Optimization with Lightweight CNN-LSTM Models and OTA Differential Deployment. Journal of Computer Science and Frontier Technologies, 3(1), 98-111.
2. Xing, J., & Xu, J. (2022). An improved convolutional neural network for recognition of incipient faults. IEEE Sensors Journal, 22(16), 16314-16322.
3. Gu, D., Pan, J., & Luković, M. (2025). Understanding shear-resisting mechanism in reinforced engineered cementitious composite (ECC) beams using distributed strain measurements. Engineering Structures, 327, 119612.
4. Zhang, P., Zhu, S., Wang, C., Zhao, Y., & Lam, E. Y. (2024). Neuromorphic imaging with super-resolution. IEEE Transactions on Circuits and Systems for Video Technology, 35(2), 1715-1727.
5. Li, K., Ren, Y., Fan, D., Liu, L., Wang, Z., & Ma, Z. (2018). Enhance GO methodology for reliability analysis of the closed-loop system using Cyclic Bayesian Networks. Mechanical Systems and Signal Processing, 113, 237-252.
6. Lin, J., Jiang, B., Gu, D., Hu, J., He, Q., Xu, L., & Pan, J. (2025). Shear transfer mechanism of headed stud connectors in engineered cementitious composites (ECC): Experimental observation, numerical investigation and analytical model. Journal of Building Engineering, 99, 111565.
7. Zhou, X., Wang, P., Al-Dhaifallah, M., Rawa, M., & Khadimallah, M. A. (2022). A machine learning-based model for the estimation of the critical thermo-electrical responses of the sandwich structure with magneto-electro-elastic face sheet. Advances in nano research, 12(1), 81-99.
8. Chen, Q., & Li, B. (2025). Explainable artificial intelligence (XAI)-driven probabilistic image-based structural health monitoring of reinforced concrete beams with shear reinforcements. Automation in Construction, 180, 106549.
9. Dai, Y., Chen, B., Wang, Y., Ye, Q., Luo, L., Li, B., & Hai, L. (2025, January). Parametric study and new design recommendations for high-strength stainless steel plate girders under patch loading. In Structures (Vol. 71, p. 108064). Elsevier.
10. Gu, D., Xu, H., Huang, Y., Zhu, Y., Pan, J., & Luković, M. (2024). Shear crack kinematics in reinforced engineered cementitious composite (ECC) beams. Case Studies in Construction Materials, 21, e03587.
11. Wei, G., Zhang, W., Bi, M., Sun, P., Li, S., Ouyang, X., ... & Tian, X. (2024). Trade-offs and synergies pattern evolution of ecosystem structure-resilience-activity-services (SRAS) in the Belt and Road Initiative region. Resources, Conservation and Recycling, 211, 107883.
12. Hawi, P., Yao, Z., Aitharaju, V., Mahishi, J., & Ghanem, R. (2024, October). Reliability-Based Design and Certification of Hybrid Composites. In American Society for Composites (ASC) Annual Technical Conference (pp. 117-131). Cham: Springer Nature Switzerland.
13. Fan, J., Liang, W., & Zhang, W. Q. (2025). SARNet: A Spike-Aware consecutive validation Framework for Accurate Remaining Useful Life Prediction. arXiv preprint arXiv:2510.22955.
14. Wang, Y., Uy, B., Li, D., Thai, H. T., Mo, J., & Khan, M. (2023). Behaviour and design of coupled steel-concrete composite wall-frame structures. Journal of Constructional Steel Research, 208, 107984.
15. Chen, B., Dai, Y., Wang, Y., Wang, Y., Song, Y., Luo, L., & Hai, L. (2025). Patch-loading resistance of QN1803 and S32001 high-strength stainless steel plate girders: Experimental study and numerical simulation. Thin-Walled Structures, 206, 112681.
16. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2014). OverFeat: Integrated recognition, localization and detection using convolutional networks. In Proceedings of ICLR.
17. Liu, C., Liu, Y., Giannetti, B. F., de Almeida, C. M. V. B., Wei, G., Sevegnani, F., & Yan, X. (2024). Dynamics of ecosystem services and nonlinear responses to increased anthropogenic pressure. Ambio, 53(11), 1649-1663.
18. Gu, D., Sun, Y., Hu, J., Lu, R., & Pan, J. (2026). Rheology-and fracture-guided design of lightweight spray-applied engineered cementitious composites achieving structural-grade strength and high tensile ductility. Journal of Building Engineering, 116100.
19. Chen, Q., Yu, B., & Li, B. (2025). Quantifying seismic damage in RC walls with image analysis. Journal of Earthquake Engineering, 29(1), 156-179.
20. Bao, Zerui, et al. "SLOFetch: Compressed-Hierarchical Instruction Prefetching for Cloud Microservices." arXiv preprint arXiv:2511.04774 (2025).
21. Fan, D., Liu, M., Hai, X., Ren, Y., & Feng, Q. (2024). Higher-order rewiring strategy for enhancing robustness of multiplex aviation networks. IEEE transactions on network science and engineering, 11(6), 6417-6430.
22. Zhou, Yufan, et al. "Masked Temporal Interpolation Diffusion for Procedure Planning in Instructional Videos." arXiv preprint arXiv:2507.03393 (2025).
23. Yao, Z., Hawi, P., Aitharaju, V., Mahishi, J., & Ghanem, R. (2023). Cross scale simulation of fiber-reinforced composites with uncertainty in machine learning. In Proceedings of the American Society for Composites-Thirty-Eighth Technical Conference.
24. Zhang, W., Luo, M., & Chen, Z. (2024, October). Hybrid Forecasting: ML Predictions of Lake-Effect Regional Extreme Precipitations. In 2024 7th International Conference on Universal Village (UV) (pp. 1-11). IEEE.
25. Xing, J., & Xu, J. (2022). An improved incipient fault diagnosis method of bearing damage based on hierarchical multi-scale reverse dispersion entropy. Entropy, 24(6), 770.
26. Liu, Y., Liu, H., Wang, H., Meng, F., & Liu, M. (2023). BCAN: Bidirectional correct attention network for cross-modal retrieval. IEEE Transactions on Neural Networks and Learning Systems, 35(10), 14247-14258.
27. Huang, J., & Sandeep, H. (2025, October). RESEARCH ON REINFORCEMENT LEARNING FOR TRUSTWORTHY CLIMATE POLICY SIMULATION AND APPLICATION. In The 7th International scientific and practical conference “Development of modern scientific technologies in the era of globalization”(October 14-17, 2025) Berlin, Germany. International Science Group. 2025. 237 p. (p. 32).
28. Deng, Z., Xiang, Z., Zhu, S., Zhang, Y., Du, Y., Wang, S., ... & Zang, G. (2025, October). Switching Cathodic/Anodic Electrochemiluminescence of Ru (bpy) 32+ Precisely via Homogeneous Nickel Nanoparticles Crystal Facets Sites Modulated ORR/OER. In Exploration (Vol. 5, No. 5, p. 20250036).
29. Fan, L., Wang, K., & Fan, D. (2021). A combined universal generating function and physics of failure Reliability Prediction Method for an LED driver. Eksploatacja i Niezawodność, 23(1).
30. Liu, Z. Q., Xiong, Y. J., Yu, X., Lv, T., & Xu, H. Y. (2026). Female reproductive factors, genetic susceptibility and incident osteoporosis risk. Climacteric, 1-8.
31. Johnson, J., Alahi, A., & Fei-Fei, L. (2016). Perceptual losses for real-time style transfer and super-resolution. In Proceedings of ECCV.
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