Extreme Conditions Research Articles

Polymorphism in Type-II Dirac Semimetal WSi2 under Pressure: Structural, Mechanical, and Electronic Insights.

The type-II Dirac candidate semimetal WSi2 is a promising candidate for electronic devices, quantum computing, and topological materials research, owing its distinct electronic structure and superior mechanical properties. Here, we synthesized high-quality WSi2 materials and systematically investigated their compressive behavior, and structural and electronic properties under high pressure using in-situ high pressure experiments, complemented by first-principles calculations. The results confirms that WSi2 has the properties of a type-II Dirac semimetal. Our results demonstrate that WSi2 maintains structural stability under high pressure but undergoes an electronic phase transition from a semimetal to a metal around 40 GPa. Additionally, the mechanical hardness softens discontinuously at this pressure. The structural stability of WSi2 under high pressure is attributed to the strong hybridization of Si-3p and W-5d electrons, the rigid crystal lattice, and the adaptable electronic structure. The pressure-induced electronic phase transition and softening are primarily governed by the energy band reconstruction and W-5d orbitals. This study provides valuable insights into the high-pressure behavior of type-II Dirac semimetal, highlighting their potential for advanced applications in electronic devices and topological quantum computing under extreme conditions by elucidating their structural stability and electronic phase transition mechanisms.

Disaster Management Strategy Through Social Capital Development

Indonesia is one of the countries with the highest level of natural disaster vulnerability in the world due to extreme geographical and climate conditions. In facing this risk, the government has implemented a disaster management policy based on community involvement. One important aspect in this approach is social capital, which includes social participation, interpersonal trust, and community networks, as a crucial component to strengthen community resilience. This study explores the role of the Meteorology, Climatology, and Geophysics Agency (BMKG) in Lampung Province in developing weather literacy and disaster mitigation programs through digital media to build social capital in the community. The results of the study show that although BMKG has succeeded in increasing community participation through digital channels, community involvement is currently still passive and limited. To achieve comprehensive disaster preparedness, it is necessary to increase active community participation, strengthen trust through two-way communication, and expand community networks. With a social capital-based approach, BMKG is expected to be able to create a more responsive and adaptive mitigation program, making the community an active partner in facing the threat of disasters in the future.

Spatial analysis with unoccupied aircraft systems data in wheat breeding yield trials

AbstractAn important aspect of reliable cultivar development is good field trial evaluations. Unoccupied aircraft systems (UAS also known as drones or UAVs) are a popular high‐throughput phenotyping tool that has been used to successfully evaluate plant stress and other canopy characteristics in a field. In precision agriculture applications, UAS imagery has been used to identify spatial variability in field settings. Here, we use UAS spectral imagery to improve field trial spatial analysis, better control spatial variability, and reduce errors for more reliable selections. UAS imagery data were collected across 47 breeding trials planted in an augmented complete block design (ACBD) or alpha‐lattice replicated designs from 2020 through 2023. Trials were evaluated using three spatial analysis strategies: linear models incorporating block effect, row‐column effect, or 2D splines. UAS‐derived spectral reflectance indices (SRI) were combined with each model as covariates. Modeling strategies were used across all trials and evaluated for autocorrelation, model fitness, and coefficient of variation (CV). Akaike information criterion (AIC) was used to assess model fitness. For spatial analysis trials, SRIs significantly lowered model AIC by an average of 38.4 for alpha‐lattice trials and 69.1 for ACBD trials. CV scores were also lowered when SRIs were utilized, with average CV values being 2.6 lower for alpha‐lattice and 2.1 for ACBD trials. This study highlights the potential for SRIs to improve the analyses of field breeding trials despite extreme environmental variables and climate conditions, improving experiment reliability and changing the way breeders plan and implement breeding experiments.

Multi-Timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks

Demand side management is a critical issue in the energy sector. Recent events such as the global energy crisis, costs, the necessity to reduce greenhouse emissions, and extreme weather conditions have increased the need for energy efficiency. Thus, accurately predicting energy consumption is one of the key steps in addressing inefficiency in energy consumption and its optimization. In this regard, accurate predictions on a daily, hourly, and minute-by-minute basis would not only minimize wastage but would also help to save costs. In this article, we propose intelligent models using ensembles of convolutional neural network (CNN), long-short-term memory (LSTM), bi-directional LSTM and gated recurrent units (GRUs) neural network models for daily, hourly, and minute-by-minute predictions of energy consumptions in smart buildings. The proposed models outperform state-of-the-art deep neural network models for predicting minute-by-minute energy consumption, with a mean square error of 0.109. The evaluated hybrid models also capture more latent trends in the data than traditional single models. The results highlight the potential of using hybrid deep learning models for improved energy efficiency management in smart buildings.

Modeling soil acidity (pH) dynamics under extreme agroclimatic conditions in Horro Guduru Wallaga Zone, northwestern Ethiopia.

Soil plays a critical role in nutrient availability, microbial activity, and fertility in agriculture. However, the effects of agroclimatic conditions on soil pH are not well understood, particularly in the Horro Guduru Zone of Ethiopia. This study aimed to investigate the soil pH under extremely wet and dry conditions across 3 shared socioeconomic pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. Baseline agroclimatic data (1981-2010) and future projections (2041-2070) were obtained from the European Commission Climate Change Services. Soil pH data at a 250m resolution were extracted from the FAO-UNESCO global soil map. Missing values, multicollinearity, and outliers were addressed before modeling. Predictive models, including neural networks, generalized regression, and bootstrap forests, were validated, with the generalized regression model showing the best performance. The results indicate that soil pH decreases under consecutive dry‒wet conditions and increases with increasing maximum day temperatures across all scenarios. Soil pH is significantly influenced by the number of consecutive dry days, consecutive wet days, and maximum day temperature. The SSP1-2.6 and SSP2-4.5 scenarios resulted in improved pH levels, whereas SSP5-8.5 led to a decrease in soil pH, averaging 5.79 and decreasing to 5.54. These findings suggest that under SSP5-8.5, soil health and farming productivity may be compromised. This study emphasizes the need to adjust soil management practices based on prevailing climatic conditions to ensure soil health and agricultural sustainability.

К ВОПРОСУ О ПРАКТИКЕ ПРИМЕНЕНИЯ ПОЛИТИКО – ВОСПИТАТЕЛЬНОЙ РАБОТЫ В ОРГАНАХ ВНУТРЕННИХ ДЕЛ РОССИЙКОЙ ФЕДЕРАЦИИ

Organization of political and educational work in extreme and special conditions of service is a complex and responsible process. Understanding the importance of this activity and the effective use of appropriate methods and tools are an integral part of ensuring national security and stability in society. This article examines the most pressing problems facing the law enforcement system and arising in the constantly improving realities associated with the emergence of special conditions of service and the possibility of conducting political and educational work in these conditions. The article also touches upon the issue and problems of the lack of methods for conducting political and educational work with personnel in special conditions, as well as issues of legal consolidation of some mechanisms for working in these conditions. The issue of the significance of special and extreme conditions, which to this day remain unfixed in the conceptual and categorical apparatus of the legislator, is considered. The object of the study is the process of organizing political and educational work with personnel in special conditions. The subject of this study will be the content and methods with the help and on the basis of which the methodology for conducting political and educational work is formed. The purpose of examining this issue will be to analyze the practice of applying political and educational work in the Russian Federation's Internal Affairs Bodies in order to identify its current problems, effective methods and areas for improvement by developing a memo on organizing political and educational work with the personnel of the Internal Affairs Bodies in special service conditions.

Факторная модель личностного выбора соучастия в экстремальном добровольчестве и его диагностика

The article is devoted to improving psychological support for volunteering in extreme conditions. The emphasis is placed on the establishment of predictors of personal choice of participation in volunteerism, the development of an integrative model and diagnostic tools for assessing readiness for participating activities. The subject of the study is a model of personal choice of participation in extreme volunteerism. The aim is to substantiate an integrative model of personal choice of participation in extreme volunteerism, to develop an appropriate diagnostic methodology based on it. The methodological basis is based on axiological, holistic, activity-based and subjective approaches. Semantic analysis of diagnostic constructs, as well as expert assessment and psychological testing were used (questionnaire «Scale of dispositional egoism» (K. Muzdybaev); questionnaire «Motivation for help» (S.K. Nartova-Bochaver); questionnaire of value orientations (adaptation of M. Rokich's methodology); indicative questionnaire «Personality orientation» (V. Smekal, M. Kucera adaptation by N.M. Peisakhov); questionnaire on motivation of volunteer activity (K.V. Gil); methodology for diagnosing achievement motivation (A. Mehrabian); methods of diagnosing personal maturity (V.A. Ruzhenkov); test of resilience (S. Muddy, adaptation by D.A. Leontiev, E.I. Rasskazova); questionnaire «Coping behavior» (R. Lazurus, S. Folkman, adaptation by S. Hobfall); value questionnaire (Sh. Schwartz); original author's methods – a questionnaire for assessing the propensity to extreme volunteering, an assessment of manifestations of professional personality deformation, a questionnaire for evaluating the choice of participation in extreme volunteerism). The sample size is 218 people. Applied: descriptive statistics, factor analysis, meta-analysis of data. According to the results of the study, the 16 factor structure of the choice of complicity is justified, which includes: «Prosocial value orientations», «Ideality», «Integrity of worldview», «Voluntary sense orientation», «Motivation», «Interest in volunteerism», «Propensity to extreme activity», «Dedication», «Critical thinking», «Extreme perceptual congruence», «Emotional stenicity», «Self-control», «Activity in extreme conditions», «Extreme vitality», «Self-organization», «Reliability». The novelty lies in the development of an integrative model and a questionnaire for evaluating the choice of participation in volunteerism. The results can be applied by psychologists of law enforcement agencies and volunteer organizations in conducting psychological selection and training of volunteers.

Regulating Interface Dipole Interaction between Ethers and Active Species toward Highly Stable Li-SPAN Batteries.

Sulfurized polyacrylonitrile (SPAN) is recognized as a promising organic cathode for long-lifespan lithium metal batteries. Nevertheless, the irreversible cleavage/formation of multiple sulfur-sulfur (S-S) bonds of SPAN within conventional ether-based electrolytes results in loss of active S species, severe capacity fading and shuttle effects. Herein, we propose a new electrolyte based on dipropyl ether (PE) solvent for Li-SPAN batteries. Benefiting from the particular chain-coordination structure and weak dipole interactions with Li+ and active species, the resulting electrolyte not only achieves low desolvation energy barrier and high Li+ transference number, but also displays stable electrolyte-electrode interface (EEI). Consequently, the full cells utilizing this electrolyte exhibit good cyclability, outstanding capacity retention and superior extreme-temperature (-50°C to 50 °C) performance. Furthermore, the Ah-scale pouch cell with lean electrolyte (2.5 g Ah-1) achieves record cycle stability with 96.5% capacity retention after 75 cycles, which deliver an initial specific energy density of 150 Wh kg-1 (based on the weight of the entire cell). Impressively, this strategy demonstrates universality in a series of organic electrodes employing with PE-based electrolytes. This work highlights the strategy for modulating the dipole interaction at EEI for long-lifespan Li-organic batteries at extreme conditions.

Near-complete extraction of maximum stored energy from large-core fibers using coherent pulse stacking amplification of femtosecond pulses

High field science relies on ultrashort pulse lasers with multi-joule pulse energies for studying light–matter interactions under extreme conditions and for driving particle accelerators and secondary radiation sources of x rays, gamma rays, neutrons, positrons, muons, and protons. Next-generation laser drivers will require a 103−104 times increase in pulse repetition rates, producing multi-joule energies at multi-kilowatt average powers to enable practical applications in nuclear engineering, advanced materials, medicine, biology, homeland security, and high-energy physics. Spatially coherently combined femtosecond fiber lasers are recognized as a pathway to these next-generation drivers, with significant practical advantages including high efficiency and the possibility of compact integration. However, chirped pulse amplification in fibers is capable of extracting only a small fraction (usually ∼1%) of the maximum stored energy. Here we demonstrate near-complete maximum stored energy extraction with low accumulated nonlinearity from a large-core fiber amplifier using coherent pulse stacking amplification. We have amplified a 81-pulse stacking burst in a 85 µm core chirally coupled core Yb-doped fiber, extracting up to 9.5 mJ (∼90% of stored energy) with <4.5 radians of accumulated nonlinear phase, temporally combined this burst into a single pulse, and achieved 4.2 mJ pulses of 313 fs bandwidth-limited duration after compression. This represents, to our knowledge, the highest energy extracted and compressed into a femtosecond pulse from a single fiber amplifier, enabling approximately two orders of magnitude size reduction of future high-energy coherently spatially combined fiber laser arrays.

Optical aspects of Born-Infeld BTZ black holes in massive gravity

We explore the dynamics of thin accretion disks, the radius of black hole shadows, observed intensities, and the visual characteristics of Born-Infeld BTZ black holes in massive gravity. We find out the relations for angular velocity, specific energy, and angular momentum of particles around the black hole. We observe that intense Born-Infeld electromagnetic effects lead to a reduction in the rotational motion of particles within the accretion disk, and the massive gravity slows down the orbital motion of these particles. We reveal that the influence of massive gravity parameter correlates with a reduction in the black hole’s shadow size, which suggests that massive gravity effects intensify the gravitational fields, thereby reducing the angular diameter of the shadows. On the other hand, a higher Born-Infeld parameter enlarges the black hole’s shadow, which manifests a visual relationship between the black hole’s physical dimensions and its gravitational influence. Moreover, we also uncover the optical characteristics of Born-Infeld BTZ black holes, which show that the Born-Infeld parameter greatly influences the electromagnetic field around the black hole, which affects energy distribution in the space. Finally, we observe that massive gravity significantly influences the spacetime structure near black holes, which is crucial for grasping gravitational lensing and the dynamics of accretion disks under such extreme conditions.

Regulating Interface Dipole Interaction between Ethers and Active Species toward Highly Stable Li‐SPAN Batteries

Sulfurized polyacrylonitrile (SPAN) is recognized as a promising organic cathode for long‐lifespan lithium metal batteries. Nevertheless, the irreversible cleavage/formation of multiple sulfur‐sulfur (S‐S) bonds of SPAN within conventional ether‐based electrolytes results in loss of active S species, severe capacity fading and shuttle effects. Herein, we propose a new electrolyte based on dipropyl ether (PE) solvent for Li‐SPAN batteries. Benefiting from the particular chain‐coordination structure and weak dipole interactions with Li+ and active species, the resulting electrolyte not only achieves low desolvation energy barrier and high Li+ transference number, but also displays stable electrolyte‐electrode interface (EEI). Consequently, the full cells utilizing this electrolyte exhibit good cyclability, outstanding capacity retention and superior extreme‐temperature (‐50°C to 50 °C) performance. Furthermore, the Ah‐scale pouch cell with lean electrolyte (2.5 g Ah‐1) achieves record cycle stability with 96.5% capacity retention after 75 cycles, which deliver an initial specific energy density of 150 Wh kg‐1 (based on the weight of the entire cell). Impressively, this strategy demonstrates universality in a series of organic electrodes employing with PE‐based electrolytes. This work highlights the strategy for modulating the dipole interaction at EEI for long‐lifespan Li‐organic batteries at extreme conditions.

Three-state p-SOS models on binary Cayley trees

Abstract We consider a version of the solid-on-solid model on the Cayley tree of order two in which vertices carry spins of value 0 , 1 or 2 and the pairwise interaction of neighboring vertices is given by their spin difference to the power p > 0. We exhibit all translation-invariant splitting Gibbs measures (TISGMs) of the model and demonstrate the existence of up to seven such measures, depending on the parameters. We further establish general conditions for extremality and non-extremality of TISGMs in the set of all Gibbs measures and use them to examine selected TISGMs for a small and a large p. Notably, our analysis reveals that extremality properties are similar for large p compared to the case p = 1, a case that has been explored already in previous work. However, for the small p, certain measures that were consistently non-extremal for p = 1 do exhibit transitions between extremality and non-extremality.

Data-Driven Surrogate Modeling with Microstructure-Sensitivity of Viscoplastic Creep in Grade 91 Steel

AbstractTo support the development of advanced steel alloys tailored to withstand extreme conditions, it is imperative to account for the mechanical performance of components, while considering the influence of local microstructure on the macroscopic response. To this end, this study focuses on the development of microstructure-sensitive constitutive models for the mechanical response of Grade 91 steel exposed to extreme thermo-mechanical environments. Polynomial chaos expansion (PCE) surrogates are used to emulate high-fidelity polycrystal simulations of the viscoplastic response of Grade 91 steel as a function of the microstructure fingerprint (e.g., dislocations and precipitates). To cover a wide temperature–stress domain, two separate PCE surrogates—one that captures softening and the other that captures hardening behavior—are combined using another (sparse) Gaussian process regression model. The resulting constitutive creep surrogate model is integrated within the MOOSE finite element framework to simulate the intricate effects of microstructure, in particular MX-phase precipitates, on a component with a graded microstructure. Surrogate sensitivity analysis is applied to quantify the relevant impact of spatially varying microstructure on the creep response in a test-case involving a Grade 91 alloy with a prototypical weld.

Agrobiodiversity and food security: challenges and sustainable solutions

Objective: Agricultural biodiversity, also known as agrobiodiversity, encompasses the variety of plants, animals, and microorganisms that are directly or indirectly involved in agriculture. This diversity is the result of millennia of selection, management, and domestication of species, which has allowed societies to adapt to different environmental and cultural conditions. However, in recent decades, the loss of agrobiodiversity, accelerated by industrial agriculture, the expansion of monocultures, and the reduction of varieties, has put global food security at risk. This diversity is crucial to ensuring the resilience of agricultural systems in the face of challenges such as climate change, emerging pests, and the depletion of natural resources. This paper examines the importance of conserving agricultural biodiversity from the perspective of food security. It emphasizes how agrobiodiversity not only contributes to the stability of food production but also improves human diets by diversifying crops and providing essential micronutrients. Design/Methodology/Approach: A search was conducted on the following scientific information platforms: Web of Science database and Google Scholar. A systematic search for publications related to agrobiodiversity systems was carried out in the WoS database and Google Scholar over the last 49 years (1975–2024). Results: Genetic erosion is particularly concerning because genetic diversity is essential for crops to face environmental challenges such as climate change, pests, and diseases. The loss of traditional varieties, which are selected by local farmers to adapt to specific conditions, increases agriculture's vulnerability to external disruptions. These landraces, having been cultivated in genetically diverse mosaics, offer protection against catastrophic losses in the event of crop failures due to extreme conditions or diseases. Findings/Conclusions: Genetic diversity allows for the development of sustainable solutions to pests and diseases, reducing dependence on pesticides and promoting more environmentally friendly farming practices. However, challenges related to biodiversity conservation persist, making it essential to implement public policies that promote agrobiodiversity and address the socioeconomic issues that limit its adoption.

Innovative strategies and challenges mosquito-borne disease control amidst climate change

The revival of the transmission dynamics of mosquito-borne diseases grants striking challenges to public health intensified by climate change worldwide. This inclusive review article examines multidimensional strategies and challenges linked to climate change and the epidemiology of mosquito-borne diseases such as malaria, dengue, Zika, chikungunya, and yellow fever. It delves into how the biology, pathogenic dynamics, and vector distribution of mosquitoes are influenced by continuously rising temperatures, modified rainfall patterns, and extreme climatic conditions. We also highlighted the high likelihood of malaria in Africa, dengue in Southeast Asia, and blowout of Aedes in North America and Europe. Modern predictive tools and developments in surveillance, including molecular gears, Geographic Information Systems (GIS), and remote sensing have boosted our capacity to predict epidemics. Integrated data management techniques and models based on climatic conditions provide a valuable understanding of public health planning. Based on recent data and expert ideas, the objective of this review is to provide a thoughtful understanding of existing landscape and upcoming directions in the control of mosquito-borne diseases regarding changing climate. This review determines emerging challenges and innovative vector control strategies in the changing climatic conditions to ensure public health.

Thermodynamics of the Primordial Universe

This review delves into the pivotal primordial stage of the universe, a period that holds the key to understanding its current state. To fully grasp this epoch, it is essential to consider three fundamental domains of physics: gravity, particle physics, and thermodynamics. The thermal history of the universe recreates the extreme high-energy conditions that are critical for exploring the unification of the fundamental forces, making it a natural laboratory for high-energy physics. This thermal history also offers valuable insights into how the laws of thermodynamics have governed the evolution of the universe’s constituents, shaping them into the forms we observe today. Focusing on the Standard Cosmological Model (SCM) and the Standard Model of Particles (SM), this paper provides an in-depth analysis of thermodynamics in the primordial universe. The structure of the study includes an introduction to the SCM and its strong ties to thermodynamic principles. It then explores equilibrium thermodynamics in the context of the expanding universe, followed by a detailed analysis of out-of-equilibrium phenomena that were pivotal in shaping key events during the early stages of the universe’s evolution.

Genetic variation in surface temperature measured using infra-red thermography and genetic associations with production traits in grazing dairy cattle

ABSTRACT Increasing global temperatures and the incidence of extreme weather conditions will result in heat stress becoming a greater issue in production animals. Genetic selection and breeding for heat-tolerant animals have been promoted as a possible mitigation strategy in dairy cattle. The objectives of this study were to obtain in-field skin temperature measurements of the eye, muzzle and udder using infra-red thermography to examine the genetic variation in skin temperature within cows of a dairy herd and to estimate the genetic correlations between skin temperature and production traits. Thermal images and herd test records were obtained for the dairy herd at Massey University’s dairy farm 1. Estimates of (co)variances were obtained using the JWAS program with univariate and bivariate animal models. The heritability estimates for the eye, muzzle and udder temperature were low to moderate at 0.20, 0.24 and 0.39, respectively. All genetic correlations between production and temperature traits were positive except for eye temperature with milk yield and protein yield which was negative and weak. These results indicate that it may be possible to select for a greater skin temperature, however, these results need to be validated using a larger sample size.

TPGPred: A Mixed-Feature-Driven Approach for Identifying Thermophilic Proteins Based on GradientBoosting

Thermophilic proteins maintain their stability and functionality under extreme high-temperature conditions, making them of significant importance in both fundamental biological research and biotechnological applications. In this study, we developed a machine learning-based thermophilic protein GradientBoosting prediction model, TPGPred, designed to predict thermophilic proteins by leveraging a large-scale dataset of both thermophilic and non-thermophilic protein sequences. By combining various machine learning algorithms with feature-engineering methods, we systematically evaluated the classification performance of the model, identifying the optimal feature combinations and classification models. Trained on a large public dataset of 5652 samples, TPGPred achieved an Accuracy score greater than 0.95 and an Area Under the Receiver Operating Characteristic Curve (AUROC) score greater than 0.98 on an independent test set of 627 samples. Our findings offer new insights into the identification and classification of thermophilic proteins and provide a solid foundation for their industrial application development.

Localized Water Restriction in Ternary Eutectic Electrolytes for Ultra-Low Temperature Hydrogen Batteries.

Proton batteries are promising candidates for next-generation large-scale energy storage in extreme conditions due to the small ionic radius and efficient transport of protons. Hydrogen gas, with its low working potentials, fast kinetics, and stability, further enhances the performance of proton batteries but necessitates the development of novel electrolytes with low freezing points and reduced corrosion. This work introduces a localized water restriction strategy by incorporating a tertiary component with a high donor number, which forms strong bonds with water molecules. This approach restricts free water molecules and reduces the average hydrogen bond ratio and strength. As-prepared ternary eutectic electrolytes lowered the freezing point to -103 °C, significantly lower than the traditional binary electrolyte (9.5 m H3PO4, -93 °C). This electrolyte is highly compatible with the Cu0.79Co0.21[Fe(CN)6]0.64·4H2O (CoCuHCF) cathode, reducing material dissolution and current collector corrosion. The H2||CoCuHCF battery using this electrolyte demonstrated a high-power density of 23664.3 W kg-1, excellent performance at -80 °C, and stable cyclability over 1000 cycles (> 30 days) at -50 °C. These findings provide a framework for proton electrolytes, highlighting the potential of hydrogen batteries in challenging environments.

Recyclable Robust Plastic Scintillation Resin Achieving the Exceptional Separation and Detection of Technetium-99.

Rapid detection and absorption of 99TcO4 ⁻ contamination in the environment are critical due to its high radioactivity, long half-life, and significant environmental mobility. Resins have been demonstrated effective bifunctional properties for both the detection and separation of 99TcO4 ⁻. However, the poor stability of these compounds limits their practical application. Here, a chemical grafting strategy is presented to synthesize ultra-stable plastic scintillation resin, in which 4-vinylpyridine and divinylbenzene are cross-linked as matrix polymer to withstand extreme conditions and a fluorophore "shield" to convert beta radiation into detectable signals. As expected, the as-obtained resin exhibits a high adsorption capacity of 549.2 mg g⁻¹ for 99TcO4 ⁻ with a rapid kinetic response of just 10 min as well as superior selectivity at 1000 times excess of interfering ions and full reusability. Moreover, it showed remarkable stability under 800 kGy, 3.0 mol L-1 HNO3 or 2.5 L solution continuous leaching, consistently maintaining high separation and detection efficiency after recycling 10 times. This strategy paves a new way to develop stable resin for the rapid capture and accurate measurement of 99TcO4 ⁻, which owns great potential for practical application.