The rapid pace of development of the world economy requires the progressive improvement of modern energy storage devices such as lithium-ion batteries (LIBs). The main limiting factor in the specific capacity of LIBs is the anode material, which is traditionally graphite. One possible solution is to use SiOx, which has a higher theoretical capacity than commercial carbon. Despite this, SiO2 has several disadvantages that limit its widespread use as an anode material for LIBs due to low electrical conductivity and short cycling life associated with volume changes during alloying–dealloying of lithium ions during charge-discharge. We have proposed an inexpensive and simple method for obtaining amorphous SiO2 particles in a carbon shell to solve these shortcomings. SiO2 was synthesized from biological waste material - rice husks, and the carbon shell was obtained from sucrose, which is also obtained from biomass. Surface morphology, structural analysis, and chemical composition were examined using XRD, thermogravimetric analysis, TEM, SEM, Raman and FTIR. The resulting composite shows a high performance of 450 mAh/g at a current density of 50 mA/g after 50 cycles and high cyclic stability compared to pure SiO2, which at 50 cycles showed 295 mAh/g at a current density of 50 mA/g. The proposed inexpensive and easily scalable method for obtaining a SiO2/C hybrid composite is a possible solution for creating next-generation LIBs. © 2022 Elsevier Ltd

Back-contact design for the architecture of devices is a promising approach to develop high-performance perovskite solar cells. Here, numerical simulation methods are used to investigate device properties of back-contact perovskite solar cells (BC–PSCs) with the quasi-interdigitated, flat-interdigitated, and interdigitated electrode designs. The results highlight the principal differences in the designs of the electrodes and provide an investigation and analysis of the impact of these electrodes on the photovoltaic properties of their BC-PSCs. The effect of the perovskite photoactive layer electronic properties on the performance of BC-PSCs is also investigated. It is revealed that while BC-PSCs with the quasi-interdigitated electrode design can potentially produce power conversion efficiencies (PCEs) well above 25 %, BC-PSCs with the flat-interdigitated and interdigitated electrode designs are more tolerant to electronic imperfections in the perovskite layer and can produce PCEs higher than those in devices with the quasi-interdigitated electrode design. Manufacturing and prospective use of electrodes with the quasi-interdigitated, flat-interdigitated, and interdigitated designs in developing BC-PSCs are discussed from the experimental standpoint. © 2021 Elsevier B.V.

This study presents a comprehensive computational analysis of flow rate efficiency during uranium extraction via the In Situ Recovery method. Using field data from a deposit located in Southern Kazakhstan, a series of mathematical models were developed to evaluate the distribution and balance of leaching solution. A reactive transport model incorporating uranium dissolution kinetics and acid–rock interactions were utilized to assess the accuracy of both traditional and proposed methods. The results reveal a significant spatial imbalance in sulfuric acid distribution, with up to 239.1 tons of acid migrating beyond the block boundaries. To reduce computational demands while maintaining predictive accuracy, two alternative methods, a streamline-based and a trajectory-based approach were proposed and verified. The streamline method showed close agreement with reactive transport modeling and was able to effectively identify the presence of intra-block reagent imbalance. The trajectory-based method provided detailed insight into flow dynamics but tended to overestimate acid overflow outside the block. Both alternative methods outperformed the conventional approach in terms of accuracy by accounting for geological heterogeneity and well spacing. The proposed methods have significantly lower computational costs, as they do not require solving complex systems of partial differential equations involved in reactive transport simulations. The proposed approaches can be used to analyze the efficiency of mineral In Situ Recovery at both the design and operational stages, as well as to determine optimal production regimes for reducing economic expenditures in a timely manner. © 2025 by the authors.

This review explores the potential of MXenes, a novel class of two-dimensional (2D) materials, in advancing energy storage and conservation technologies. MXenes exhibit exceptional physicochemical properties, including a high specific surface area (∼390 m² g⁻¹ for MXene@PPy-800), outstanding electrical conductivity, and robust chemical stability, making them ideal for energy-related applications. In supercapacitors, MXene-based electrodes have demonstrated capacitances exceeding 700 F g⁻¹ at 1 mV s⁻¹, with retention of over 90 % of their initial performance after 10,000 charge/discharge cycles. For lithium-ion batteries, MXenes achieve theoretical capacities ranging from 390 to 600 mAh g⁻¹, depending on the type of MXene material, with experimental reversible capacities often exceeding 400 mAh g⁻¹ at 1C rates and high cycling stability. This review synthesizes recent research efforts on the synthesis, structural characterization, and integration of MXenes into energy storage systems. Findings highlight their versatility as electrode materials for supercapacitors, lithium-ion batteries, and fuel cells, as well as their catalytic potential in solar energy conversion. Despite these advancements, challenges remain unresolved. Scalability of MXene synthesis through selective etching methods continues to be a significant technical and economic barrier. Moreover, while MXene-based devices show high initial performance, further work is needed to improve long-term stability in operational and harsh chemical environments. By providing a comprehensive overview of MXene-based energy systems, this review identifies critical gaps in understanding their electrochemical mechanisms, particularly ion transport and surface interaction dynamics. Addressing these challenges will be key to optimizing MXene properties and enabling their widespread application in efficient and sustainable energy technologies. © 2025

This paper presents a numerical analysis of two hot water storage tank configurations—one equipped with an external heat exchanger (Tank-1) and the other with an internal heat exchanger (Tank-2). The objective is to evaluate and compare their thermal performance during charging and discharging processes. The numerical model is developed by solving a system of ordinary differential equations using the 4th-order Runge–Kutta method, implemented in the Python programming language. The results indicate that Tank-1 demonstrated a higher charging efficiency of 94.6%, achieving full charge in approximately 2 h and 20 min. In comparison, Tank-2 required 3 h and 47 min to reach full charge, with a charging efficiency of 85.9%. During discharge, both configurations exhibited similar behavior, with an efficiency of 13.63% over approximately 33 min. The analysis showed that the external heat exchanger configuration led to more effective thermal stratification, supported by the Richardson number analysis, which indicated a significant effect of buoyancy during charging. This design advantage makes Tank-1 particularly suitable for applications requiring rapid heating and minimal heat loss, such as in cold climates or intermittent demand systems. The numerical model demonstrated reliable predictive accuracy, achieving an RMSE of 6.1% for the charging process and 6.8% for the discharging process, thereby validating the model’s reliability. These findings highlight the superior performance of the external heat exchanger configuration for fast and efficient energy storage, particularly for applications in cold climates. © 2024 by the authors.

In situ leaching represents an efficient and safe method for uranium mining, where a suboptimal well flow rate distribution leads to solution imbalances between wells, forming stagnant zones that increase operational costs. This study examines a real technological block from the Budenovskoye deposit, applying reactive transport modeling to optimize well flow rates and reduce operational time and reagent consumption. A reactive transport model was developed based on mass conservation and Darcy’s laws coupled with chemical kinetics describing sulfuric acid interactions with uranium minerals ((Formula presented.) and (Formula presented.)). The model simulated a technological block with 4 production and 18 injection wells arranged in hexagonal cells over 511–542 days to achieve 90% uranium recovery. Six approaches for well flow rate redistribution were compared, based on different weighting factor calculation methods: advanced traditional, linear distance, squared distance, quadrilateral area, and two streamline-based approaches utilizing the minimum and average time of flight. The squared distance method achieved the highest efficiency, reducing operational costs by 5.7% through improved flow redistribution. The streamline-based methods performed comparably and offer potential advantages for heterogeneous conditions by automatically identifying hydraulic connections. The reactive transport modeling approach successfully demonstrated that multi-criteria optimization methods can improve ISL efficiency by 3.9%–5.7% while reducing operational costs. © 2025 by the authors.

Biomass-based carbon nanofibers (CNF) were synthesized using lignin extracted from sawdust and polyacrylonitrile (PAN) (30:70) with the help of the electrospinning method and subsequent stabilization at 220 °C and carbonization at 800, 900, and 1000 °C. The synthesized CNFs were studied by scanning electron microscopy, energy-dispersive X-ray analysis, Raman spectroscopy, and the Brunauer–Emmett–Teller method. The temperature effect shows that CNF carbonized at 800 °C has excellent stability at different current densities and high capacitance. CNF 800 in the first test cycle at a current density of 100 mA/g shows an initial capacity of 798 mAh/g and an initial coulomb efficiency of 69.5%. The CNF 900 and 1000 show an initial capacity of 668 mAh/g and 594 mAh/g, and an initial Coulomb efficiency of 52% and 51%. With a long cycle (for 500 cycles), all three samples at a current density of 500 mA/g show stable cycling in different capacities (CNF 800 in the region of 300–400 mAh/g, CNF 900 and 1000 in the region of 100–200 mAh/g). © 2022 by the authors.

This work presents the preliminary experimental data on the study of gas release from two-phase lithium ceramics Li4SiO4-Li2TiO3 under neutron irradiation conditions. Experiments were carried out at the WWR-K research reactor (Almaty, Kazakhstan) for ∼4.3 days. The total neutron fluence during the irradiation was ∼1.8·1019n/cm2. In the course of irradiation, two experiments on ceramics heating during irradiation and two experiments with hydrogen isotopes (H2 and D2) supply into the experimental chamber with the sample were performed at a temperature of 680 °C and reactor power of 6 MW. During the entire irradiation, the gas composition in the continuously evacuated ampoule device with samples was recorded. The obtained dependences of the release of tritium-containing molecules and helium during the experiment were qualitatively analyzed. © 2022 The Authors

В статье рассмотрены возможности использования геомагнитных данных и магнитных свойств горных пород при решении задач геонаук. Показано, что магнитные параметры, включая намагниченность, магнитную восприимчивость и магнитную проницаемость, могут отражать состав, состояние и изменения геологической среды. Обобщены примеры применения магнитометрии, петрофизических связей, палеомагнитных данных и параметров геомагнитного поля для изучения глубинного строения геологических структур, прогноза месторождений полезных ископаемых и анализа природных явлений, включая сейсмическую активность. Отмечено, что совершенствование методов наблюдений, обработки и интерпретации геомагнитных данных повышает вероятность получения более однозначных результатов при изучении геологической среды и прогнозировании природных процессов.

В статье выполнен сравнительный анализ возможностей алгоритмов машинного и глубокого обучения при решении геологических и геопрогнозных задач. Рассмотрены основные типы геоданных, включая геолого-геофизические, геохимические, дистанционные, скважинные и текстовые материалы, а также алгоритмы ML, DL, CNN, RNN, GAN и NLP. Показано, что методы искусственного интеллекта могут использоваться для автоматизации обработки и интерпретации геологических данных, выявления скрытых закономерностей, прогнозирования месторождений полезных ископаемых и оценки геологических рисков. Отмечены ключевые ограничения, связанные с дефицитом качественных геоданных, сложностью интерпретации моделей и необходимостью экспертной валидации результатов.