Akademik Veri Yönetim Sistemi
IEEE Transactions on Plasma Science, 2026 (SCI-Expanded, Scopus)
Cold atmospheric plasma (CAP) has emerged as a promising nonthermal therapeutic modality for cancer treatment, owing to its capacity to selectively target malignant cells while preserving surrounding healthy tissue. Given that the therapeutic efficacy of CAP is intrinsically linked to its interaction with the dielectric properties of biological tissues, a detailed understanding of frequency-dependent permittivity and conductivity is essential for optimizing treatment protocols and enhancing clinical outcomes. This study presents a robust experimental–computational framework for evaluating the dielectric response of breast tissue-mimicking phantoms before and after CAP exposure. Custom-engineered phantoms, designed to replicate the dielectric behavior of both healthy and malignant breast tissues, were fabricated using tunable formulations. Baseline dielectric properties were measured via a vector network analyzer (VNA), with reflection coefficient (S11) processed in MATLAB to extract complex permittivity and conductivity over the 2.5–50 MHz frequency range. A multipole Debby model was implemented in COMSOL Multiphysics to accurately capture dispersive dielectric behavior. CAP was applied to malignant phantoms using a custom-built flyback driver under two protocols: single and cyclic exposure, the latter involving repeated plasma treatments with cooling intervals. Posttreatment analysis revealed a significant reduction in static permittivity and conductivity, especially under cyclic exposure, indicating cumulative plasma-induced effects. These alterations are attributed to chemical, structural, and compositional changes driven by reactive plasma species. In addition, the distinct dielectric signatures observed between healthy and malignant phantoms underscore the potential of dielectric profiling as a noninvasive diagnostic and therapeutic monitoring tool. The proposed methodology offers reproducibility, tunability, and clinical relevance, forming a strong foundation for predictive modeling and future in vivo applications in plasma medicine.