Search Results (2,967)

Herein, we propose an ultrasensitive electrochemical immunosensor based on glucose oxidase labeling and enzyme-catalyzed Au staining. In brief, the primary antibody (Ab₁), bovine serum albumin, an antigen and then a bionanocomposite that contains a second antibody (Ab₂), poly(3-anilineboronic acid) (PABA), Au nanoparticles (AuNPs) and glucose oxidase (GOx) are modified on a glassy carbon electrode coated with multiwalled carbon nanotubes, yielding a corresponding sandwich-type immunoelectrode. In the presence of glucose, a chemical reduction of NaAuCl₄ by enzymatically generated H₂O₂ can precipitate a lot of gold on the Ab₂-PABA-AuNPs-GOx immobilized immunoelectrode. In situ anodic stripping voltammetry (ASV) detection of gold in 8 μL 1.0 M aqueous HBr-Br₂ is conducted for the antigen assay, and the ASV detection process takes approximately 6 min. This method is employed for the assay of human immunoglobulin G (IgG) and human carbohydrate antigen 125 (CA125), which demonstrates exceptional sensitivity, high selectivity and fewer required reagents/samples. The achieved limits of detection (S/N = 3) by the method are 0.25 fg mL⁻¹ for IgG (approximately equivalent to containing 1 IgG molecule in the 1 microlitre of the analytical solution) and 0.1 nU mL⁻¹ for CA125, which outperforms many previously reported results. Full article

The rising demand for electric vehicles (EVs) has driven a significant increase in nickel consumption, a critical element in EV battery production. An industrially viable hydrometallurgical process was developed for the selective recovery of nickel from a copper-rich industrial intermediate, containing approximately 70 wt.% Cu and 6 wt.% Ni, predominantly as sulfides alongside minor impurities. Approximately 90% of nickel was selectively extracted via single-stage atmospheric pressure leaching using HCl and H₂O₂ at 95 °C for 12 h, with the majority of copper retained in the leach residue, which can be utilized as a valuable feedstock for copper smelters. The selectivity of nickel over copper was analyzed in detail through corresponding Pourbaix diagrams, and an appropriate leaching mechanism was proposed. The leachate was subsequently purified through a sequence of cementation, selective precipitation, and solvent extraction steps to remove residual copper, iron, and cobalt, achieving an overall separation efficiency of 99% with nickel losses below 2%. In the final stage, nickel carbonate was precipitated with >99% purity using sodium carbonate, potentially suitable for battery applications. The optimal conditions at each stage were determined through batch-type laboratory-scale experiments, which may need to be verified by continuous pilot-scale testing in the future. This process offers dual advantages by meeting the growing nickel demand for battery applications while simultaneously providing additional copper feedstocks for smelters. Full article

Droughts are increasingly threatening ecological balance, agricultural productivity, and socio-economic resilience—especially in semi-arid regions like the Inner Mongolia segment of China’s Yellow River Basin. This study presents a hybrid drought forecasting framework integrating machine learning (ML) and deep learning (DL) models with high-resolution historical and downscaled future climate data. TerraClimate observations (1985–2014) and bias-corrected CMIP6 projections (2030–2050) under SSP2-4.5 and SSP5-8.5 scenarios were utilized to develop and evaluate the models. Among the tested ML algorithms, Random Forest (RF) demonstrated the best trade-off between accuracy and interpretability and was selected for feature importance analysis. The top-ranked predictors—precipitation, solar radiation, and maximum temperature—were used to train a Long Short-Term Memory (LSTM) network. The LSTM outperformed all ML models, achieving high predictive skill (R² = 0.766, CC = 0.880, RMSE = 0.885). Scenario-based projections revealed increasing drought severity and variability under SSP5-8.5, with mean PDSI values dropping below −3 after 2040 and deepening toward −4 by 2049. The high-emission scenario also exhibited broader uncertainty bands and amplified interannual anomalies. These findings highlight the value of hybrid AI–climate modeling approaches in capturing complex drought dynamics and supporting anticipatory water resource planning in vulnerable dryland environments. Full article

Cholesterol blood levels in low-density lipoproteins (LDLs) are a key parameter for assessing the risk of cardiovascular diseases. Direct quantification of LDL cholesterol at the point of care would be possible if all other lipoproteins, particularly the high-density lipoproteins (HDLs), could be removed prior to measurement. Here, we investigated whether a molecularly imprinted membrane (MIM) could be used for the solid-phase affinity extraction (SPAE) of HDL in a paper-based lateral flow test. Samples traveled by capillarity through the MIM before reaching a detection zone where LDL cholesterol was quantified enzymatically. MIMs were produced by impregnation of the membrane with a dispersion of molecularly imprinted polymers (MIPs) selective for HDL. MIPs were synthesized using precipitation polymerization and exhibited good selectivity for HDL compared with LDL and an uptake capacity of 5.0–7.0 µg of HDL-C/mg of MIP. The MIM enabled the removal of HDL with an efficiency of typically 68%. However, quantification of LDL cholesterol suffered from strong non-specific binding of LDL, likely due to its inherent colloidal instability. Overall, our results highlight the challenges associated with SPAE of colloidal particles. Furthermore, our study demonstrates a novel, efficient, and potentially generic modality to integrate SPAE into paper-based POC diagnostic tests. Full article

Climate change research is increasingly important in regions vulnerable to extreme hydrometeorological events like droughts, which pose significant socio-economic and environmental challenges. This study examines future variability of meteorological drought in northernmost South America using the Standardized Precipitation Index (SPI) and precipitation projections from CMIP6 models. We first evaluated model performance by comparing historical simulations with observational data from the Climate Hazards Group InfraRed Precipitation with Station dataset for 1981–2014. Among the models, CNRM-CM6-1-HR was selected for its superior accuracy, demonstrated by the lowest errors and highest correlation with observed data—specifically, a correlation coefficient of 0.60, a normalized root mean square error of 1.08, and a mean absolute error of 61.37 mm/month. Under SSP1-2.6 and SSP5-8.5 scenarios, projections show decreased rainfall during the wet months in the western Perijá mountains, with reductions of 3% to 26% between 2025 and 2100. Conversely, the Sierra Nevada of Santa Marta is expected to see increases of up to 33% under SSP1-2.6. During dry months, northern Colombia and Venezuela—particularly coastal lowlands—are projected to experience rainfall decreases of 10% to 17% under SSP1-2.6 and 13% to 20% under SSP5-8.5. These areas are likely to face severe drought conditions in the mid and late 21st century. These findings are essential for guiding water resource management, enabling adaptive strategies, and informing policies to mitigate drought impacts in the region. Full article

Coal ash represents an abundant secondary resource of rare earth elements (REEs), but their recovery is hindered by low concentrations in leachates and the presence of large amounts of competing matrix elements such as Fe, Al, Ca, and Mg. In this study, a stepwise pH-controlled precipitation approach was applied to real sulfuric acid coal ash leachates for selective recovery of Ce and La. The process combined impurity scrubbing, oxidative precipitation of Ce, and phosphate precipitation of La. Nearly complete recovery was achieved, with >95% of both Ce and La recovered and >99% phase purity. Selectivity analysis further demonstrated strong discrimination of REEs over matrix elements, with Ce showing >400 selectivity over Fe, Al, and Ca, and La showing ~170 over the same ions and ~17 over Ce. These results show the efficiency of the approach under realistic multi-element conditions. Full article

Satellite Precipitation Products (SPPs) play a crucial role in hydrological modeling, particularly in data-scarce and climate-sensitive basins such as the Magat River Basin (MRB), Philippines—one of Southeast Asia’s most typhoon-prone and infrastructure-critical watersheds. This study presents the first full-cycle evaluation of nine widely used multi-source precipitation products (2000–2024), integrating raw validation against rain gauge observations, bias correction using quantile mapping, and post-correction re-ranking through an Entropy Weight Method–TOPSIS multi-criteria decision analysis (MCDA). Before correction, SM2RAIN-ASCAT demonstrated the strongest statistical performance, while CHIRPS and ClimGridPh-RR exhibited robust detection skills and spatial consistency. Following bias correction, substantial improvements were observed across all products, with CHIRPS markedly reducing systematic errors and ClimGridPh-RR showing enhanced correlation and volume reliability. Biases were decreased significantly, highlighting the effectiveness of quantile mapping in improving both seasonal and annual precipitation estimates. Beyond conventional validation, this framework explicitly aligns SPP evaluation with four critical hydrological applications: flood detection, drought monitoring, sediment yield modeling, and water balance estimation. The analysis revealed that SM2RAIN-ASCAT is most suitable for monitoring seasonal drought and dry periods, CHIRPS excels in detecting high-intensity and erosive rainfall events, and ClimGridPh-RR offers the most consistent long-term volume-based estimates. By integrating validation, correction, and application-specific ranking, this study provides a replicable blueprint for operational SPP assessment in monsoon-dominated, data-limited basins. The findings underscore the importance of tailoring product selection to hydrological purposes, supporting improved flood early warning, drought preparedness, sediment management, and water resources governance under intensifying climatic extremes. Full article

The water consumption processes of vegetation play an important role in water resource management in semiarid regions, while the difference in water consumption between native and exotic species is unclear. In this study, the exotic Pinus sylvestris L. var. mongholica Litv. (Pinus) and the native Salix psammophila (Salix) in Mu Us Sandy Land were selected as the research objects, and their water consumption characteristics were studied via in situ experiment and stable isotopes (δ²H and δ¹⁸O). Results revealed that vegetation water consumption caused spatial variation in soil moisture, allowing the soil profile to be divided into active, stable, capillary support and saturated zones. Pinus primarily used water from the active and stable zones, whereas Salix relied more on the capillary support and saturated zones. Water consumption patterns also varied seasonally, for example, at the beginning of growth (May–June), Salix and Pinus mainly use shallow soil water and begin to use deep soil water and groundwater with growth. During July–September, they absorb soil water mainly in the active zone and stable zone. Both Salix and Pinus can freely switch water sources between deep and shallow layers according to water demand. The seasonal fluctuations in precipitation and groundwater level were the main factors driving the seasonal changes in the water consumption of the two vegetation types. Pinus has better strategies to adapt to droughts than Salix, but its water consumption is higher than that of Salix. Therefore, proper management is needed to control the reasonable density of Pinus plantation to balance the water consumption of vegetation and groundwater recharge. The results can provide a scientific basis for the reasonable vegetation reconstruction in the Mu Us Sandy Land. Full article

The current state of the art research, and latest engineering technology application in the synthesis of the aragonite crystalline phase of calcium carbonate is presented here. Aragonite crystalline products are highly valuable in selected industries, such as medical and personal care, and in food additives using MgCl₂ as a chemical inducer. The outcome of this literature review provides the outlook of the available research whitespace opportunity in optimizing the current process parameters and in ensuring that sustainable and economically feasible continuous production of aragonite products could be achieved. One of the major improvements proposed in this study is to investigate the methods of synthesizing aragonite crystalline particles using a continuous mineral carbonation reactor system and optimizing the operating parameters. An experimental design was established to identify all the main effects to maximize aragonite production. The three main effects investigated are the effect of feedstock or reactant concentration, the effect of reaction temperature, and the effect of reaction time towards aragonite yield in the final products synthesized. An optimized operating parameter for maximum aragonite yield at 95% purity was proposed at the reaction temperature T of 90 °C, reaction time t of 10 min, and feedstock ratio Mg-to-Ca of 0.4. Subsequently, the continuous reactor system was designed, operated, and tested for at least 50 h operation, where the lime CaO(s) feed was successfully converted into aragonite products with purity between 75 and 81%. The properties and quality of the aragonite produced were analytically characterized from the following laboratory methods which include the thermalgravimetric analysis, TGA; X-Ray Diffraction, XRD; scanning electron microscopy, SEM; and induction coupled plasma, ICP. TGA mass balance after decomposition suggests that 44% of the mass balance represents the weight of CO₂ sequestered in the aragonite crystalline carbonates. Hence, the aragonite crystalline carbonates can be labeled as a green product which sequesters 0.44 kg of CO₂ per 1 kg of precipitated aragonite products synthesized. Interestingly, SEM microscopy characterization results revealed that the aragonite precipitate has a physical morphology of needle-like shape with a good aspect ratio (length/diameter) AR of between 8.67 micron and 11.35 micron. The properties were found to be suitable for paper making fillers, medical, personal care, and food additive applications. Full article

Global warming poses a growing threat to winter wheat production in Henan Province, a critical region for China’s food security, necessitating a quantitative assessment of climate impacts. This study aimed to quantify the dominant climatic drivers of winter wheat yield and assess its spatiotemporal evolution and future risks under climate change, thereby providing a scientific basis for targeted adaptation strategies. Thus, the APSIM model in combination with the Geodetector method was applied to quantify the spatiotemporal response of winter wheat yield to climate change in Henan Province under historical (1957–2020) and SSP245 scenarios. The study results demonstrated significant trends in climatic factors during the winter wheat growing season: precipitation decreased by an average of 3.09 mm/decade, sunshine hours declined by 36 h/decade, wind speed reduced by 0.447 m/(s·decade), and evaporation decreased by 14.7 mm/decade. In contrast, the accumulated temperature ≥ 0 °C significantly increased by 70.9 °C·d/decade. Geodetector analysis further identified accumulated temperature as the dominant climatic driver (q = 0.548), followed by precipitation (q = 0.340) and sunshine hours (q = 0.261). Yield simulations from 1960 to 2018 indicated that most regions maintained stable or slightly increasing yields (<50 kg·ha⁻¹·decade⁻¹), though some areas experienced fluctuating declines. Under future scenarios, major production regions in Henan Province (Zhengzhou, Xinxiang, Luoyang) are projected to see substantial yield increases, with growth rates of 147.2–148.9 kg·ha⁻¹·decade⁻¹. Specifically, Xinxiang is expected to achieve yields of 6200 kg·ha⁻¹. The frequency of climate-induced negative yield years decreased by approximately 35% after 2003, highlighting the role of improved agricultural technologies in enhancing climate resilience. This study clarifies how multiple climatic factors jointly affect winter wheat yield, identifying rising accumulated temperature and water stress as key future constraints. It recommends optimizing varietal selection and cultivation practices according to regional climate patterns to improve policy relevance and local applicability. Full article

Inadequate screening of features and insufficient extraction of multi-source time-series data potentially result in insensitivity to historical noise and poor extraction of features for PM_2.5 concentration prediction models. Precipitable water vapor (PWV) data obtained from the Global Navigation Satellite System (GNSS), along with air quality and meteorological data collected in Suzhou city from February 2021 to July 2023, were employed in this study. The Spearman correlation analysis and Random Forest (RF) feature importance assessment were used to select key input features, including PWV, PM₁₀, O₃, atmospheric pressure, temperature, and wind speed. Based on RF, Long Short-Term Memory (LSTM), and Multilayer Perceptron (MLP) algorithms, four PM_2.5 concentration prediction models were developed using sliding window and fusion algorithms. Experimental results show that the root mean square error (RMSE) of the 1 h PM_2.5 concentration prediction model using the RF-LSTM fusion algorithm is 4.36 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, while its mean absolute error (MAE) and mean absolute percentage error (MAPE) values are 2.63 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$ and 9.3%. Compared to the individual LSTM and MLP algorithms, the RMSE of the RF-LSTM PM_2.5 prediction model improves by 34.7% and 23.2%, respectively. Therefore, the RF-LSTM fusion algorithm significantly enhances the prediction accuracy of the 1 h PM_2.5 concentration model. As for the 2 h, 3 h, 6 h, 12 h, and 24 h PM_2.5 prediction models using the RF-LSTM fusion algorithm, their RMSEs are 5.6 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, 6.9 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, 9.9 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, 12.6 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, and 15.3 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, and their corresponding MAPEs are 13.8%, 18.3%, 28.3%, 38.2%, and 48.2%, respectively. Their prediction accuracy decreases with longer forecasting time, but they can effectively capture the fluctuation trends of future PM_2.5 concentrations. The RF-LSTM PM_2.5 prediction models are efficient and reliable for early warning systems in Suzhou city. Full article

Net primary productivity (NPP) is a vital indicator of carbon sequestration and ecosystem resilience. However, the dynamics of NPP across different land use types and especially the interactive function of natural drivers remain insufficiently quantified in regions with significant land use change. Therefore, this study selected Heilongjiang Province in China as the research area. Utilizing multi-source data from 2001 to 2022, it identified the primary land use types, analyzed the mean values and trends of vegetation NPP for each type, and quantified the driving effects of natural factors on NPP across these land types. Results show that forests had the highest mean NPP (514.01 gC m⁻²·a⁻¹) and shrub–grass–wetland composites the lowest (269.2 gC m⁻²·a⁻¹); cropland-to-forest transitions boosted NPP most notably. Critically, precipitation–temperature interactions dominated NPP variation, while elevation acted mainly through modulating other factors. This study offers a strategic framework for spatial planning and ecosystem management, supporting climate mitigation and carbon sequestration policies. Full article

Southwestern China, characterized by highly undulating terrain and mountainous areas, faces frequent landslide disasters. However, previous studies in this region mostly neglected the role of extreme rainfall in landslide susceptibility assessment and the socio-economic risks threatened by landslides. To address these gaps, this study integrated 688 recorded landslides for Tengchong City in the southwest of China and 10 influencing factors (topography, lithology, climate, vegetation, and human activities), particularly two extreme precipitation indices of maximum consecutive 5 day precipitation (Rx5day) and maximum length of wet spell (CWD). These influencing factors were selected after ensuring variable independence via multicollinearity analysis. Four machine learning models were then built for landslide susceptibility assessment. The Random Forest model performed the best with an Area Under Curve (AUC) of 0.88 and identified elevation, normalized difference vegetation index (NDVI), lithology, and CWD as the four most important influencing factors. Landslides in Tengchong are concentrated in areas with low NDVI (<0.57), indicating increased vegetation cover might reduce landslide frequency. Landslide risk was further quantified via the Analytic Hierarchy Process (AHP) by integrating multiple socio-economic factors. High-risk zones were pinpointed in central-southern Tengchong (e.g., Heshun and Tuantian townships) due to their high social exposure and vulnerability. Overall, this study highlights extreme rainfall and vegetation as key modifiers of landslide susceptibility and identifies the regions with high landslide risk, which provides targeted scientific support for regional early-warning systems and risk management. Full article

Ti-bearing blast furnace slag (TBS), a byproduct of vanadium–titanium magnetite smelting, serves as an important secondary resource for titanium recovery. However, the complex mineralogical composition and finely dispersed nature of titanium in TBS present significant challenges for efficient extraction. This review systematically examines four major titanium extraction routes: hydrometallurgical leaching, pyrometallurgical smelting, molten salt electrolysis, and selective precipitation, focusing on their limitations and recent improvements. For instance, conventional acid leaching suffers from acid mist release, a colloidal formation that hinders titanium recovery, and waste acid pollution. The adoption of concentrated sulfuric acid roasting activation effectively suppresses acid mist emission and prevents colloidal generation. Pyrometallurgical approaches are hampered by high energy consumption and substantial carbon emissions, which can be alleviated through the use of gaseous reductants to enhance reaction efficiency and reduce environmental impact. Molten electrolysis faces issues such as polarization and undesirable dendritic deposition; these are mitigated by employing liquid metal cathodes integrated with vacuum distillation to achieve high-purity titanium products. Selective precipitation struggles with strict crystallization conditions and low separation efficiency, though advanced techniques like supergravity separation show improved extraction performance. We propose an integrated technical strategy termed “Online conditioning driven by waste heat-mineral phase reconstruction-directional crystallization-optimized liberation.” This approach utilizes the inherent waste heat of slag combined with electromagnetic stirring to enhance homogeneity and promote efficient titanium recovery, offering a sustainable and scalable solution for industrial TBS treatment. Full article

Fluctuations in precipitation usually affect the ecological environment and human socioeconomics through events such as floods and droughts, resulting in substantial economic losses. The high-resolution models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) are vital for simulating precipitation patterns in China; however, significant uncertainties still exist. This study utilized the quantile mapping (QM) method to correct biases in nine high-resolution Earth System Models (ESMs) and then comprehensively evaluated their precipitation simulation capabilities over the Chinese mainland from 1985 to 2014. Based on the selected models, the Bayesian Model Averaging (BMA) method was used to integrate them to obtain the spatial–temporal variation in precipitation over the Chinese mainland. The results showed that the simulation performance of nine models for precipitation from 1985 to 2014 was significantly improved after the bias correction. Six out of the nine high-resolution ESMs were selected to generate the BMA ensemble model. For the BMA model, the precipitation trend and the locations of significant points were more closely aligned with the observational data in the summer than in other seasons. It overestimated precipitation in the spring and winter, while it underestimated it in the summer and autumn. Additionally, both the BMA model and the worst multi-model ensemble (WMME) model exhibited a negative mean bias in the summer, while they displayed a positive mean bias in the winter. And the BMA model outperformed the WMME model in terms of mean bias and bias range in both the summer and winter. Moreover, high-resolution models delivered precipitation simulations that more closely aligned with observational data compared to low-resolution models. Full article