Technobius Physics

Emerging directions in thermal physics, materials science, and data-driven modeling

Technobius Physics — 2026-06-30

The articles presented in this issue collectively illustrate the increasingly interdisciplinary character of contemporary physics research, where classical physical principles are being complemented by advanced computational techniques, artificial intelligence, and materials engineering. Although each contribution addresses a distinct scientific problem, together they demonstrate how modern approaches are reshaping the study of thermal phenomena across multiple length scales and application domains.

The issue opens with an investigation of building energy demand and energy systems, highlighting the importance of heat transfer analysis for improving energy efficiency in the built environment. As global efforts continue toward sustainable development and carbon reduction, accurate thermal modeling remains essential for optimizing building performance and supporting evidence-based engineering decisions.

The second contribution explores defect-mediated charge transport in anisotropic two-dimensional semiconductors using machine-learning-assisted modeling. This work exemplifies the growing integration of data-driven methodologies with condensed matter physics, demonstrating how artificial intelligence can accelerate the understanding of complex electronic transport mechanisms while reducing computational cost.

Artificial intelligence also plays a central role in the third article, which presents the application of Physics-Informed Neural Networks (PINNs) to model heat diffusion in anisotropic solids. By embedding governing physical laws directly into neural network architectures, PINNs represent a promising alternative to conventional numerical methods, offering new opportunities for solving complex inverse and forward problems in heat transfer and continuum physics.

The final contribution investigates the thermal stability and fire resistan-ce of vinyl ester resin composites containing various fillers. The study provides valuable insight into the relationship between material composition, thermal degradation mechanisms, and flame-retardant performance, contributing to the development of safer and more durable composite materials for engineering applications.

Taken together, these studies emphasize several important trends that are shaping current physical research. First, advanced numerical simulations continue to play a fundamental role in understanding thermal transport and energy-related processes. Second, machine learning and physics-informed artificial intelligence are rapidly becoming powerful research tools capable of complementing traditional theoretical and experimental approaches. Finally, the development of advanced functional materials remains closely connected with thermal analysis, reliability assessment, and sustainability considerations.

The Editorial Board hopes that the contributions presented in this issue will encourage further interdisciplinary collaboration among physicists, materials scientists, computational researchers, and engineers. The convergence of physics-based modeling, artificial intelligence, and materials innovation is expected to remain one of the defining directions of scientific progress in the coming years.

Finally, we invite our readers to actively engage with the research presented in this issue. Scientific progress is driven by the careful exchange of ideas, critical discussion, and the appropriate acknowledgment of previous work. We hope that the articles published in Technobius Physics will serve as valuable references for future investigations and contribute to the continued advancement of physics and related disciplines. We also encourage readers to share these contributions within their professional networks, helping to expand their visibility and foster new scientific collaborations.

We sincerely thank all authors for their valuable contributions and the reviewers for their careful evaluations, constructive recommendations, and commitment to maintaining high scientific standards. Their collective efforts ensure the quality and integrity of every issue published by Technobius Physics.

We trust that the research published in this issue will stimulate further scientific discussion, inspire new investigations, and contribute to the continued advancement of thermal physics, condensed matter research, computational modeling, and materials science.

Thermal Stability and Fire Resistance of Vinyl Ester Resin Composites: Effect of Various Fillers on Thermal Degradation and Flame Retardancy

Marzhan Akhmetova — 2026-06-29

This work systematizes published data on the thermal stability and fire resistance of vinyl ester resin (VER)-based composites filled with various inorganic and organic flame retardants, including aluminum trihydrate Al (OH)₃, Mg (OH)₂, dimethyl methylphosphonate immobilized on diatomaceous earth (DE@DMMP), ammonium polyphosphate, expandable graphite, montmorillonite, zinc borate, phosphate esters, and hybrid fillers. The study describes the synthesis and curing methods for ten representative VER composite systems and presents a comparative analysis of data from thermogravimetry, differential scanning calorimetry, heat release rate, and the oxygen index. It is shown that mineral hydroxide fillers increase the LOI to 30–31%, reduce the pHRR by 42–45%, and raise the decomposition onset temperature by 17–28 °C, whereas immobilized DMMP systems achieve an oxygen index of 50% with a UL-94 V-0 rating. For each type of filler, the trade-off between flame resistance and mechanical properties is discussed. The work is supplemented with data from publications over the past 3–5 years, covering bio-based VERs (based on magnolol, vanillin, protocatechuic aldehyde, and bisgwayacol), new-generation reactive phosphorus-containing modifiers (VIDOP, VIDHP/VIDPP, flexible-chain DOPO, DPPO, APP-MDO), and hybrid interphase solutions for fiber-reinforced composites, as well as a critical analysis of discrepancies in the literature and an assessment of the economic feasibility and scalability of the systems under consideration.

Heat transfer-based analysis of building energy demand and energy systems

Aida Nazarova — 2026-06-23

This study examines the thermal performance of buildings and the efficiency of alternative energy supply systems under cold climatic conditions, with a focus on Eastern Kazakhstan. The objective is to quantify the impact of building envelope insulation and energy system configuration on heating demand, primary energy consumption, carbon emissions, and economic performance. An analytical methodology based on standardized building physics approaches was applied to determine heat demand, accounting for transmission and ventilation losses as well as internal and solar heat gains. Climatic conditions were incorporated using the heating degree-day method. Several energy supply configurations, including conventional fossil-based systems and renewable-integrated solutions, were evaluated in terms of energy, environmental, and economic indicators. The results indicate that improving the insulation standard reduces the specific heating demand from approximately 185 kWh/m²·a to 63 kWh/m²·a, corresponding to a reduction exceeding 60%. Transmission losses represent about 70% of total heat losses. Primary energy consumption decreases from approximately 280 kWh/m²·a for coal-based systems to about 110 kWh/m²·a for renewable-based configurations. Similarly, carbon emissions are reduced from 0.34 kg/kWh to 0.04 kg/kWh. Systems with higher initial investment demonstrate improved life-cycle economic performance due to lower operating costs. The findings confirm that enhanced thermal insulation combined with efficient and low-carbon energy systems constitutes a key strategy for reducing energy demand and environmental impact in cold climate regions. The proposed analytical framework provides a consistent and reproducible basis for evaluating building energy performance and supporting energy planning decisions.

Machine-learning-assisted modeling of defect-mediated charge transport in anisotropic 2D semiconductors

Zhang Wei — 2026-06-25

This study investigates defect-mediated charge transport in anisotropic two-dimensional semiconductors using combined transport modeling and machine-learning analysis. The objective of the work was to determine how structural defects and lattice anisotropy influence electrical conductivity and carrier mobility in low-dimensional quantum materials. Monolayer transition-metal dichalcogenides, black phosphorus, and ReS₂-based systems with various defect configurations were analyzed. Transport behavior was modeled within a semiclassical framework incorporating anisotropic carrier mobility and defect-assisted scattering mechanisms. Machine-learning models, including random forest regression, gradient boosting regression, and artificial neural networks, were applied to predict conductivity variations using structural and electronic descriptors. The results demonstrated that conductivity decreases nonlinearly with increasing defect concentration, particularly above 10¹³ cm⁻², where localization effects become dominant. Strongly anisotropic materials exhibited enhanced sensitivity to vacancy-induced disorder due to directional carrier confinement. Among the evaluated algorithms, the artificial neural network achieved the highest predictive accuracy with an R² value of 0.972. Feature-importance analysis revealed that defect concentration, effective mass, and lattice anisotropy ratio are the primary factors governing transport degradation. The study confirms that accurate prediction of transport properties in anisotropic quantum materials requires simultaneous consideration of defect physics and directional electronic transport. The proposed approach may be useful for the design and optimization of next-generation nanoelectronic and flexible semiconductor devices.

Physics-Informed Neural Networks for modeling heat diffusion in anisotropic solids

Saira Sakhabayeva — 2026-06-26

Accurate simulation of heat diffusion in anisotropic solids is essential for the design and optimization of advanced engineering materials and thermal management systems. However, conventional numerical approaches often require computationally intensive mesh generation and repeated solution procedures, particularly for transient problems involving directional heat transport. This study investigates the application of Physics-Informed Neural Networks for modeling transient heat diffusion in anisotropic solid materials. The proposed framework incorporates the governing heat conduction equation, initial conditions, and boundary conditions directly into the neural network training process, enabling physically consistent predictions without extensive labeled datasets. The model was trained using a combination of Adam and L-BFGS optimization algorithms and validated against finite element method simulations. The influence of thermal anisotropy was evaluated for conductivity ratios ranging from isotropic conditions to strongly anisotropic cases. The developed Physics-Informed Neural Networks demonstrated excellent agreement with finite element method solutions, achieving a root mean square error of 0.014 K, a mean absolute error of 0.010 K, a maximum absolute error of 0.061 K, and a coefficient of determination of 0.9997. Although prediction errors increased slightly with increasing anisotropy, the model maintained high accuracy and stable convergence across all investigated scenarios. The results confirm that Physics-Informed Neural Networks provide an accurate and physically consistent alternative to traditional numerical methods for anisotropic heat transfer analysis. The proposed approach offers significant potential for rapid thermal simulations, inverse heat transfer problems, digital twin development, and real-time engineering applications involving complex anisotropic materials.