Radiation Synergy Unlocks Complex Material Behaviours

Scientists are increasingly focused on understanding synergistic effects in materials science, particularly how radiation interacts with other physical phenomena to produce non-additive outcomes. Boris Oksengendler, Muhsin Ashurov, and Sultan Suleymanov, working with colleagues from the Institute of Materials Science of the Academy of Sciences of the Republic of Uzbekistan, the Institute of Nuclear Physics of the Academy of Sciences of the Republic of Uzbekistan, and the Nanotechnology Development Center at the National University of Uzbekistan named after M. Ulugbek, present a detailed investigation into radiation synergism and its fundamental and applied aspects. Their research introduces graphical techniques for identifying these non-additive effects, proposing a parameter expression to quantify them, and drawing parallels with Tsallis’ q parameter from Complexity science. This work significantly advances radiation physics by offering new avenues for exploring radiation’s impact on both biological and non-biological systems, potentially leading to innovative material design and radiation protection strategies.

Scientists are increasingly recognising that combining different physical influences can yield results far greater than the sum of their parts. This principle, termed synergism, is now being applied to understanding how materials respond to radiation exposure. The work offers a new framework for designing more resilient materials and optimising radiation-based technologies.

Researchers at the Institute of Materials Science of the Academy of Sciences of the Republic of Uzbekistan, alongside colleagues, have developed new graphical and mathematical tools to quantify this non-additive behaviour, termed ‘radiation synergism’. Their findings reveal that the combined impact of radiation and other factors isn’t merely a sum of parts, but a qualitatively new phenomenon with implications for diverse fields.

This study builds upon 170 years of advancements in radiation physics of condensed matter, a field that has consistently progressed through the development of new materials, radiation sources, and conceptual frameworks. The team’s work centres on the idea of ‘Complexity’, a modern scientific ideology encompassing concepts like self-organization and dynamic chaos, and specifically focuses on how these principles manifest in radiation interactions.

By identifying and measuring non-additive effects, they’ve established a parameter expression that mirrors a concept, the ‘q parameter’ , originating in the broader science of Complexity, suggesting a unifying principle governing these interactions in both living and non-living systems. The core of this research lies in understanding how multiple ‘causes’ can combine to produce emergent properties, moving beyond traditional linear models.

Researchers propose that synergism represents a distinct pathway to emergence, differing from the well-studied transitions to ‘synergetic’ states which require both nonlinearity and strong non-equilibrium conditions. This expanded concept of Complexity highlights the importance of identifying mechanisms that lead to amplified or diminished effects when multiple factors are at play. The team’s approach offers a robust methodology for quantifying these synergistic effects, exemplified by their analysis of how ionizing radiation and temperature combine to enhance the destruction of cancer cells, demonstrating a nearly two-fold amplification of the effect.

Quantifying synergistic effects using Tsallis statistics and non-additivity parameters

Analysis of combined radiation and heat exposure to cancerous tumors reveals a quantifiable level of non-additivity, specifically measured by the segments defining |N4(t) − N3(t)|. This parameter establishes the degree to which the combined effect exceeds simple summation, indicating a positive interaction between radiation and heat as the tumor is destroyed.

Increases in this value with increasing dose demonstrate that the synergistic effect intensifies with greater treatment intensity. The research further connects this non-additivity to Tsallis’ q-deformation statistics and thermodynamics, a framework for understanding complex systems. Specifically, the ratio Q = |N4(t)−N3(t)| / |N4(t)+N3(t)| is defined as the value of q, establishing a link between experimental radiation exposure and Tsallis’ theoretical model.

This q-value, now understood as a q-triplet, provides a novel approach to radiation science applicable to both living and non-living systems. Detailed analysis of SARS-2V RNA destruction by Auger electrons predicts a 103-fold increase in effectiveness compared to ordinary biopolymers. This prediction aligns with published medical results demonstrating the efficacy of low-dose irradiation for Covid-pneumonia.

However, even low doses of X-ray radiation (below 1 Gray) present risks due to the potential for Auger electron-induced mutations. The study identifies a natural synergism that mitigates this risk, whereby the “Coulomb explosion” resulting from the Auger cascade disperses molecular fragments and clears a volume of approximately 1.5 ∙ 104 Å3 of metastable states.

This clearing action effectively eliminates precursors to oncological mutations near the site of viral RNA destruction. The estimated cleared volume is determined by the ratio of the “Coulomb explosion” energy (Ecoul) to the metastability barrier energy (Emet), applied to the initial volume of the Auger charge (d0 3).

Quantifying Non-additivity in Radiation Interactions via Parameterised Graphical Analysis

Graphical techniques form the core of this work, designed to identify and quantify non-additivity in the combined effects of radiation and other physical influences. The study meticulously examines scenarios involving combinations of 2x, 3x, and 4x radiation alongside other contributing factors, moving beyond single-variable analyses to explore complex interactions.

This approach directly addresses the central tenet of radiation synergism, that the total effect of multiple stimuli is not the sum of their individual contributions. To establish a robust methodology for detecting this non-additivity, researchers developed a parameter expression intended to capture the degree to which the overall radiation effect deviates from a linear summation.

This parameter is purposefully analogous to the ‘q’ parameter introduced by Tsallis within the broader field of Complexity science, facilitating a conceptual link between radiation physics and more general theories of complex systems. The rationale behind this connection is to leverage established mathematical frameworks for analysing non-extensive systems, potentially unlocking new insights into radiation interactions with both living and non-living matter.

Recognising the importance of emergent phenomena, the research distinguishes between pathways leading to new qualities, specifically highlighting synergism as a distinct mechanism beyond traditional synergetics, which requires both nonlinearity and strong non-equilibrium conditions. This nuanced differentiation is visually represented in a schematic diagram illustrating the relationship between Complexity, synergistics, and synergetics, emphasizing the role of nonlinearity in both.

Quantifying synergistic radiation effects using a statistical mechanics framework

Scientists have long sought to understand how multiple physical factors interact, rather than adding up their individual effects. This pursuit, now framed within the broader field of complexity science, has historically been hampered by the difficulty of isolating and quantifying these synergistic interactions. Traditional linear models struggle to account for non-additive behaviours, where the combined impact of several stimuli is either greater or lesser than the sum of their parts.

This work offers a compelling, graphical approach to identifying and parameterising these non-additive radiation effects, drawing a parallel with established concepts from statistical mechanics, specifically, Tsallis’ q parameter, to provide a quantifiable measure of synergy. The implications extend beyond fundamental physics. Understanding how radiation interacts with other agents, be they chemical compounds, biological tissues, or material structures, is crucial for optimising treatments in fields like cancer therapy and materials science.

The ability to predict non-additive effects could lead to more targeted and efficient radiation protocols, minimising damage to healthy tissue or enhancing the desired properties of irradiated materials. However, the current methodology appears largely descriptive, focused on identifying synergy rather than predicting it a priori. Future work must address this predictive gap.

The development of computational models capable of forecasting synergistic outcomes, based on the characteristics of the interacting factors, would be a significant step forward. Furthermore, extending this framework to encompass a wider range of radiation types and combined stressors, including, for example, the interplay between radiation and temperature or pressure, is crucial.