Designer biochar with enhanced functionality for efficient removal of radioactive cesium and strontium from water

Cesium adsorption from an aqueous medium for environmental remediation: A comprehensive analysis of adsorbents, sources, factors, models, challenges, and opportunities

Considering the widespread and indispensable nature of nuclear energy for future power generation, there is a concurrent increase in the discharge of radioactive Cs into water streams. Recent studies have demonstrated that adsorption is crucial in removing Cs from wastewater for environmental remediation. However, the existing literature lacks comprehensive studies on various adsorption methods, the capacities or efficiencies of adsorbents, influencing factors, isotherm and kinetic models of the Cs adsorption process. A bibliometric and comprehensive analysis was conducted using 1179 publications from the Web of Science Core Collection spanning from 2014 to 2023. It reviews and summarizes current publication trends, active countries, adsorption methods, adsorption capacities or efficiencies of adsorbents, tested water sources, influencing factors, isotherm, and kinetic models of Cs adsorption. The selection of suitable adsorbents and operating parameters is identified as a crucial factor. Over the past decade, due to their notable capacity for Cs adsorption, considerable research has focused on novel adsorbents, such as Prussian blue, graphene oxide, hydrogel, and nanoadsorbents (NA). However, there remains a need for further development of application-oriented laboratory-scale experiments. Future research directions should encompass exploring adsorption mechanisms, developing new adsorbents or their combinations, practical applications of lab-scale studies, and recycling radioactive Cs from wastewater. Drawing upon this literature review, we present the most recent research patterns concerning adsorbents to remove Cs, outline potential avenues for future research, and delineate the obstacles hindering effective adsorption. This comprehensive bibliometric review provides valuable insights into prevalent research focal points and emerging trends, serving as a helpful resource for researchers and policymakers seeking to understand the dynamics of adsorbents for Cs removal from water.

Recent trends in the phytoremediation of radionuclide contamination of soil by cesium and strontium: Sources, mechanisms and methods: A comprehensive review

This comprehensive review examines recent trends in phytoremediation strategies to address soil radionuclide contamination by cesium (Cs) and strontium (Sr). Radionuclide contamination, resulting from natural processes and nuclear-related activities such as accidents and the operation of nuclear facilities, poses significant risks to the environment and human health. Cs and Sr, prominent radionuclides involved in nuclear accidents, exhibit chemical properties that contribute to their toxicity, including easy uptake, high solubility, and long half-lives. Phytoremediation is emerging as a promising and environmentally friendly approach to mitigate radionuclide contamination by exploiting the ability of plants to extract toxic elements from soil and water. This review focuses specifically on the removal of 90Sr and 137Cs, addressing their health risks and environmental implications. Understanding the mechanisms governing plant uptake of radionuclides is critical and is influenced by factors such as plant species, soil texture, and physicochemical properties. Phytoremediation not only addresses immediate contamination challenges but also provides long-term benefits for ecosystem restoration and sustainable development. By improving soil health, biodiversity, and ecosystem resilience, phytoremediation is in line with global sustainability goals and environmental protection initiatives. This review aims to provide insights into effective strategies for mitigating environmental hazards associated with radionuclide contamination and to highlight the importance of phytoremediation in environmental remediation efforts.

Molten salt synthesis of MXene-derived hierarchical titanate for effective strontium removal

The removal and recovery of radioactive Sr(II) from wastewater and seawater has been of great concern due to the negative environmental impacts of nuclear energy development and the potential risk of nuclear accidents. Herein, a facile molten salt synthesis strategy was developed to systematically investigated the reaction of different types of MXenes with nitrates. Among the products, K+ intercalated hierarchical titanate nanostructures (K-HTNs) obtained from the direct chemical transformation of multilayered Ti3C2Tx exhibited unique layered structures, good physicochemical properties, and outstanding adsorption performance for Sr(II). The maximum adsorption capacity of Sr(II) by K-HTNs reached 204 mg·g-1 at ambient temperature, and the good regeneration and reusability of the titanate was also demonstrated. K-HTNs showed preferential selectivity for Sr(II) in different environmental media containing competing ions, and the removal efficiency of Sr(II) in real seawater was as high as 93.3 %. The removal mechanism was elaborated to be the exchange of Sr2+ with K+/H+ in the interlayers of K-HTNs, and the adsorbed Sr(II) had a strong interaction with Ti-O- termination on the titanate surface. Benefiting from the merits of rapid and scalable synthesis and excellent adsorption performance, MXene-derived K-HTNs have broad application prospects for the purification of 90Sr-contaminated wastewater and seawater.

A comparative study on the Cs adsorption/desorption and structural changes in different clay minerals

We investigated the structural changes in clay minerals after Cs adsorption and understood their low desorption efficiency using an ion-exchanger. We focused on the role of interlayers in Cs adsorption and desorption in 2:1 clay minerals, namely illite, hydrobiotite, and montmorillonite, using batch experiments and XRD and EXAFS analyses. The adsorption characteristics of the clay minerals were analyzed using cation exchange capacity (CEC), maximum adsorption isotherms (Qmax), and radiocesium interception potential (RIP) experiments. Although illite showed a low CEC value, it exhibited high selectivity for Cs with a relatively high RIP/CEC ratio. The Cs desorption efficiency after treatment with a NaCl ion exchanger was the highest for illite (74.3%), followed by hydrobiotite (45.5%) and montmorillonite (30.3%); thus, Cs adsorbed onto planar sites, rather than on interlayers or frayed edge sites (FESs), is easily desorbed. After NaCl treatment, XRD analysis showed that the low desorption efficiency was due to the collapse of the interlayer-fixed Cs, which tightly narrowed the interlayers' hydrobiotite due to the ion exchange of divalent cations (Mg2+ or Ca2+) into the monovalent cation (Na+). Moreover, EXAFS analysis showed that hydrobiotite formed inner-sphere structures after NaCl desorption, indicating that it was difficult to remove Cs from NaCl desorption due to the collapsed hydrobiotite and montmorillonite interlayers as well as the strong bonding in FESs of illite. In contrast, chelation desorption using oxalic acid effectively dissolved the narrowed interlayers of hydrobiotite (98%) and montmorillonite (85.26%), enhancing the desorption efficiency. Therefore, low desorption efficiency for Cs clays using an ion exchanger was caused by the collapsed interlayer due to the exchange between monovalent cation and divalent cation.

Predicting the distribution coefficient of cesium in solid phase groups using machine learning

The migration and retention of radioactive contaminants such as 137Cesium (137Cs) in various environmental media pose significant long-term storage challenges for nuclear waste. The distribution coefficient (Kd) is a critical parameter for assessing the mobility of radioactive contaminants and is influenced by various environmental conditions. This study presents machine-learning models based on the Japan Atomic Energy Agency Sorption Database (JAEA-SDB) to predict the Kd values for Cs in solid phase groups. We used three different machine learning models: random forest (RF), artificial neural network (ANN), and convolutional neural network (CNN). The models were trained on 14 input variables from the JAEA-SDB, including factors such as the Cs concentration, solid-phase properties, and solution conditions, which were preprocessed by normalization and log-transformation. The performances of the models were evaluated using the coefficient of determination (R2) and root mean squared error (RMSE). The RF, ANN, and CNN models achieved R2 values greater than 0.97, 0.86, and 0.88, respectively. We also analyzed the variable importance of RF using an out-of-bag (OOB) and a CNN with an attention module. Our results showed that the environmental media, initial radionuclide concentration, solid phase properties, and solution conditions were significant variables for Kd prediction. Our models accurately predict Kd values for different environmental conditions and can assess the environmental risk by analyzing the behavior of radionuclides in solid phase groups. The results of this study can improve safety analyses and long-term risk assessments related to waste disposal and prevent potential hazards and sources of contamination in the surrounding environment.

Removal of 241Am from Aqueous Solutions by Adsorption on Sponge Gourd Biochar

Luffa cylindrica biomass was converted to biochar and the removal of 241Am by pristine and oxidized biochar fibers was investigated in laboratory and environmental water samples. This species has the added advantage of a unique microsponge structure that is beneficial for the production of porous adsorbents. The main purpose of this study was to valorize this biomass to produce an efficient adsorbent and investigate its performance in radionuclide-contaminated waters. Following the preparation of Am3+ solutions at a concentration of 10−12 mol/L, the adsorption efficiency (Kd) was determined as a function of pH, adsorbent mass, ionic strength, temperature, and type of aqueous solution by batch experiments. At the optimum adsorbent dose of 0.1 g and pH value of 4, a log10Kd value of 4.2 was achieved by the oxidized biochar sample. The effect of temperature and ionic strength indicated that adsorption is an endothermic and entropy-driven process (ΔH° = −512 kJ mol−1 and ΔS° = −1.2 J K−1 mol−1) leading to the formation of inner-sphere complexes. The adsorption kinetics were relatively slow (24 h equilibrium time) due to the slow diffusion of the radionuclide to the biochar surface and fitted well to the pseudo-first-order kinetic model. Oxidized biochar performed better compared to the unmodified sample and overall appears to be an efficient adsorbent for the treatment of 241Am-contaminated waters, even at ultra-trace concentrations.