Search Results (1,631)

Room-temperature sodium–sulfur (RT Na–S) batteries offer a superior, high-energy-density solution for rechargeable batteries using earth-abundant materials. However, conventional RT Na–S batteries typically use sulfur as the cathode, which suffers from severe volume expansion and requires pairing with a sodium metal anode, raising significant safety concerns. Utilizing Na₂S as the cathode material addresses these issues, yet challenges such as Na₂S’s low conductivity as well as the shuttle effect of polysulfide still hinder RT Na–S battery development. Herein, we present a simple and cost-effective method to fabricate a Na₂S–Na₆CoS₄/Co@C cathode, wherein Na₂S nanoparticles are embedded in a conductive carbon matrix and coupled with dual catalysts, Na₆CoS₄ and Co, generated via the in situ carbothermal reduction of Na₂SO₄ and CoSO₄. This approach creates a three-dimensional porous composite cathode structure that facilitates electrolyte infiltration and forms a continuous conductive network for efficient electron transport. The in situ formed Na₆CoS₄/Co electrocatalysts, tightly integrated with Na₂S, exhibit strong catalytic activity and robust physicochemical stabilization, thereby accelerating redox kinetics and mitigating the polysulfide shuttle effect. As a result, the Na₂S–Na₆CoS₄/Co@C cathode achieves superior capacity retention, demonstrating a discharge capacity of 346 mAh g⁻¹ after 100 cycles. This work highlights an effective strategy for enhancing Na₂S cathodes with embedded catalysts, leading to enhanced reaction kinetics and superior cycling stability. Full article

A smart viscose fabric with temperature and pH responsiveness and proactive antibacterial and UV protection was developed. PNCS (poly-(N-isopropylakrylamide)/chitosan) hydrogel was used as the carrier of silver nanoparticles (Ag NPs), synthesised in an environmentally friendly manner using AgNO₃ and a sumac leaf extract. PNCS hydrogel and Ag NPs were applied to the viscose fabric by either in situ synthesis of Ag NPs on the surface of viscose fibres previously modified with PNCS hydrogel, or by the direct immobilisation of Ag NPs by the dehydration/hydration of the PNCS hydrogel with the nanodispersion of Ag NPs in the sumac leaf extract and subsequent application to the viscose fibres. Compared to the pre-functionalised PNCS application method, the in situ functionalisation imparted much higher concentration of Ag NPs on the fibres, colouring the samples brown to brown-green. These samples showed more than 90% reduction in the test bacteria E. coli and S. aureus and provided excellent UV protection. In this case, the PNCS hydrogel acted as a reservoir for Ag NPs, whose release was based on a diffusion-controlled mechanism. Despite the Ag NPs decreasing the responsiveness of the PNCS hydrogel, the moisture management was still preserved in the modified samples. Accordingly, the PNCS hydrogel is a suitable carrier for biosynthesized Ag NPs to tailor the protective smart surface of viscose fibres. Full article

This work focuses on the preparation and application of silver nanoparticles/organophilic clay/polyethylene glycol for the catalytic reduction of the contaminants methylene blue (MB) and 4-nitrophenol (4-NP) in a simple and binary system. Algerian clay was subjected to a series of treatments including acid treatment, ion exchange with the surfactant hexadecyltrimethylammonium bromide (HTABr), immobilization of polyethylene glycol polymer, and finally dispersion of AgNPs. The molecular weight of polyethylene glycol was varied (100, 200, and 4000) to study its effect on the stabilization of silver nanoparticles (AgNPs) and the catalytic activity of the resulting samples. The results showed that the catalyst with the highest molecular weight of polyethylene glycol had the highest AgNP content. Catalyst mass, NaBH₄ concentration, and type of catalyst were shown to have a significant influence on the conversion and rate constant. The material with the highest silver nanoparticle content was identified as the optimal catalyst for the reduction of both pollutants. The measured rate constants for the reduction of methylene blue (MB) and 4-nitrophenol (4-NP) were 164 × 10⁻⁴ s⁻¹ and 25 × 10⁻⁴ s⁻¹, respectively. The reduction of MB and 4-NP in the binary system showed high selectivity for MB dye, with rate constants of 64 × 10⁻⁴ s⁻¹ and 9 × 10⁻⁴ s⁻¹ for MB and 4-NP, respectively. The reuse of the best catalyst via MB dye reduction for four cycles showed good results without loss of performance. Full article

The development of sustainable and high-performance oxygen reduction reaction (ORR) electrocatalysts is fundamental to fuel cell implementation. Non-precious transition metal oxides present interesting electrocatalytic behavior, and their incorporation into N-doped carbon supports leads to excellent ORR performance. Herein, we prepared a shrimp shell-derived biochar (CC), which was doped with nitrogen via a ball milling approach (N-CC), and then used as support for Co₃O₄ nanoparticles growth (N-CC@Co₃O₄). Co₃O₄ loading was optimized using three different amounts of cobalt precursor: 1.56, 2.33 and 3.11 mmol in N-CC@Co₃O₄_1, N-CC@Co₃O₄_2 and N-CC@Co₃O₄_3, respectively. Interestingly, all prepared electrocatalysts, including the initial biochar CC, presented electrocatalytic activity towards ORR. Both N-doping and the introduction of Co₃O₄ NPs had a significant positive effect on ORR performance. Meanwhile, the three composites showed distinct ORR behavior, demonstrating that it is possible to tune their electrocatalytic performance by changing the Co₃O₄ loading. Overall, N-CC@Co₃O₄_2 achieved the most promising ORR results, displaying an E_onset of 0.84 V vs. RHE, j_L of −3.45 mA cm⁻² and excellent selectivity for the 4-electron reduction (n = 3.50), besides good long-term stability. These results were explained by a combination of high content of pyridinic-N and graphitic-N, high ratio of pyridinic-N/graphitic-N, and optimized Co₃O₄ loading interacting synergistically with the porous N-CC support. Full article

As a sustainable alternative technology to the cost- and energy-intensive Haber–Bosch method, electrochemical nitrogen (N₂) reduction offers direct conversion of N₂ to NH₃ under ambient conditions. Direct use of noble metals or non-noble metals as electrocatalytic materials results in unsatisfactory electrocatalytic properties because of their low electrical conductivity and stability. Herein, three-dimensional flexible carbon nanofiber (CNF/TiO₂@CoS) nanostructures were prepared on the surface of CNF by using electrospinning, a hydrothermal method, and in situ growth. We investigated the behavior of CNFs/TiO₂@CoS as an electrocatalytic material in 0.1 M sodium sulfate. The highest ammonia yield of the material was 4.61 × 10⁻¹¹ mol s⁻¹ cm⁻² at −0.45 V vs. RHE, and the highest Faraday efficiency, as well as superior long-term durability, was 8.3% at −0.45 V vs. RHE. This study demonstrates the potential of firecracker-shaped nanofiber templates for loading varied noble metals or non-noble metals as a novel development of hybrid composites for electrocatalytic nitrogen reduction. Full article

Satellite precipitation estimates are crucial for managing climate-related risks such as droughts and floods. However, these datasets often contain systematic errors due to the observation methods used. The accuracy of these estimates can be enhanced by integrating spatial and temporal resolution data from in situ observations. Nevertheless, the accuracy of the merged dataset is influenced by the density and distribution of rain gauges, which can vary regionally. This paper presents an approach to improve satellite precipitation data (SPD) over Burkina Faso. Two bias correction methods, Empirical Quantile Mapping (EQM) and Time and Space-Variant (TSV), have been applied to the SPD to yield a bias-corrected dataset for the period 1991–2020. The most accurate bias-corrected dataset is then combined with in situ observations using the Regression Kriging (RK) method to produce a merged precipitation dataset. The findings show that both bias correction methods achieve similar reductions in RMS error, with higher correlation coefficients (approximately 0.8–0.9) and a normalized standard deviation closer to 1. However, EQM generally demonstrates more robust and consistent performance, particularly in terms of correlation and RMS error reduction. On a monthly scale, the superiority of EQM is most evident in June, September, and October. Following the merging process, the final dataset, which incorporates satellite information in addition to in situ observations, demonstrates higher performance. It shows improvements in the coefficient of determination by 83%, bias by 11.4%, mean error by 96.7%, and root-mean-square error by 95.5%. The operational implementation of this approach provides substantial support for decision-making in regions heavily reliant on rainfed agriculture and sensitive to climate variability. Delivering more precise and reliable precipitation datasets enables more informed decisions and significantly enhances policy-making processes in the agricultural and water resources sectors of Burkina Faso. Full article

Zeolites are a class of porous aluminosilicates possessing high surface area, good hydrothermal stability, strong sorption, and high ion-exchanging potential, and which frequently serve as efficient catalytic materials. The composites which integrate zeolite with alternative substances like metal oxides or carbon-based materials steadily outperform individual constituents. Recently, the application of zeolite-based composites in the field of photocatalytic oxidation and electrocatalytic oxidation/reduction, which is mainly focused on pollution treatment in sewage and air, have garnered significant attention. Several synthesis strategies for zeolite-based composites including post-treatment and in situ hydrothermal synthesis methods are explicated. Meanwhile, multifarious types of zeolite-based photoelectric catalytic composites are also summarized. Finally, we highlight the advancements improving the performance of zeolite-based composites in the photocatalytic treatment of organic pollutants in wastewater and the electrocatalytic reduction of CO₂ and organics. Full article

An atmosphere–ocean–wave coupled regional model, the UWIN-CM, began its operational trial in real time at the Hong Kong Observatory (HKO) in the second half of 2024. Its performance in the analysis of three selected tropical cyclones, Severe Tropical Storm Prapiroon, Super Typhoon Gaemi, and Super Typhoon Yagi, are studied in this paper. The forecast track and intensity of the tropical cyclones were verified against the operational analysis. It is shown that the track error of the UWIN-CM was lower than other regional numerical weather prediction (NWP) models in operation at the HKO, with a reduction in mean direct positional error of up to 50% for the first 48 forecast hours. For cyclone intensity, the performance of the UWIN-CM was the best out of the available global and regional models at HKO for Yagi at forecast hours T + 36 to T + 84 h. The model captured the rapid intensification of Yagi over the SCS with a lead time of 24 h or more. The forecast winds were compared with the in situ measurements of buoy and with the wind field analysis obtained from synthetic-aperture radar (SAR). The correlation of forecast winds with measurements from buoy and SAR ranged between 65–95% and 50–70%, respectively. The model was found to perform generally satisfactorily in the above comparisons. Full article

Recently, there has been increasing interest in organic carbon (OC) certification of soil as an incentive for farmers to adopt sustainable agricultural practices. In this context, this pilot project combines geochemical and geophysical methods to map the distribution of OC contents in agricultural fields, allowing us to detect variations in time and space. Here we demonstrated a relationship between soil OC contents estimated in the laboratory and the apparent electrical conductivity (ECa) measured in the field. Specifically, geochemical elemental analyses were used to evaluate the OC content and relative isotopic signature in collected soil samples from a hazelnut orchard in the Emilia–Romagna region of Northeastern Italy, while the geophysical Electromagnetic Induction (EMI) method enabled the in situ mapping of the ECa distribution in the same soil field. According to the results, geochemical and geophysical data were found to be reciprocally related, as both the organic matter and soil moisture were mainly incorporated into the fine sediments (i.e., clay) of the soil. Therefore, such a relation was used to create a map of the OC content distribution in the investigated field, which could be used to monitor the soil C sequestration on small-scale farmland and eventually develop precision agricultural services. In the future, this method could be used by farmers and regional and/or national policymakers to periodically certify the farm’s soil conditions and verify the effectiveness of carbon sequestration. These measures would enable farmers to pursue Common Agricultural Policy (CAP) incentives for the reduction of CO₂ emissions. Full article

The mineral composition in coalbed methane (CBM) reservoirs significantly influences fracture morphology, making the description of reservoir heterogeneity challenging. This study develops a fracture propagation model for CBM reservoirs that incorporates the varying mineral properties within the reservoir’s lithology. Dynamic logging data are considered to characterize rock mechanical properties, which form the basis for in situ stress estimation. Using an adjusted critical circumferential stress calculation for coal rock, the model considers the impact of complex lithology on fracture propagation. A comprehensive fractal index is introduced to capture the influence of different minerals on fracture morphology and propagation randomness. Models representing clay, quartz, and pyrite with varied compositions were constructed to explore the effects of each mineral on fracture characteristics. In single-component models, clay-rich reservoirs exhibited the highest induced fracture density, with quartz and pyrite showing approximately 65% and 20% of the fracture density observed in clay, respectively. Fractures primarily propagated toward quartz-rich regions, while pyrite significantly inhibited fracture growth. In mixed-mineral models, increasing the quartz proportion by 40% resulted in a 20 m increase in fracture length and a 30% reduction in fracture density. Fractures predominantly propagated around pyrite boundaries, demonstrating pyrite’s resistance to fracture penetration. Clay and quartz promote fracture development, whereas pyrite hinders fracture formation. The fracture inversion model presented here effectively captures the influence of complex mineral distributions on fracture morphology, offering valuable insights for optimizing fracturing production strategies. Full article

The Fischer–Tropsch Synthesis process is projected to have a significant impact in the near future due to its potential for synthesizing sustainable fuels from biomass, carbon dioxide and organic wastes. In this catalytic process, catalyst activation plays a major role in the overall performance of Fischer–Tropsch Synthesis. Catalyst activation temperatures are considerably higher than the typical operating conditions of industrial reactors. Consequently, ex situ activation is often required for industrial Fischer–Tropsch Synthesis processes. This study evaluated the influence of different activation approaches (in situ, ex situ, passivation and low-temperature activation). Catalytic experiments were conducted in a fixed-bed reactor at 230 °C and 20 bar·g using a reference supported Co/γ-Al₂O₃ catalyst. Experimental results demonstrate that catalysts can be effectively reduced ex situ. This work reveals that re-activation of the catalyst after ex situ reduction is unnecessary, as the reaction conditions themselves re-reduce any superficial oxides formed, owing to the reducing nature of the reactant mixture. This approach could simplify reactor design by enabling temperature requirements to match operating conditions (e.g., 230 °C), thereby reducing both investment and operational costs and eliminating additional catalyst preparation steps. Full article

The Plasma Immersion Ion Implantation (PIII) nitriding was used to form a modified layer rich in expanded austenite (γ_N) and expanded ferrite (α_N) phases in super duplex steel. The thermal stability of these phases was investigated through the in situ synchrotron X-ray diffraction. All the surfaces were analyzed by SEM, EDS, and nanoindentation. During the heating stage of the thermal treatments, the crystalline structure of the γ_N phase expanded thermally up to a temperature of 350 °C and, above this temperature, a reduction in the lattice parameter was observed due to the diffusion of nitrogen into the substrate. During the isothermal heating, the gradual diffusion of nitrogen continued and the lattice parameter of the γ_N phase decreased. Increasing the treatment temperature from 450 °C to 550 °C, a greater reduction in the lattice parameter of the γ_N phase occured and the peaks related to the CrN, α, and α_N phases became more evident in the diffractograms. This phenomenon is associated with the decomposition of the γ_N phase into CrN + α + α_N. After the heat treatments, the thickness of the modified layers increased and the hardness values close to the surface decreased, according to the diffusion of the nitrogen to the substrate. Full article

Heavy metal ion pollution poses a serious threat to the natural environment and human health. Photoreduction through Bi-based photocatalysts is regarded as an advanced green technology for solving environmental problems. However, their photocatalytic activity is limited by the rapid recombination of photogenerated e⁻ and h⁺ pairs and a low photo-quantum efficiency. In this work, an optimal precursor of Bi-based MOFs was identified by using different solvents, and rod-like Bi₂O₃ materials were derived by in situ oxidation of Bi atoms in the precursor. The adsorption and photocatalytic reduction efficiency of the prepared Bi₂O₃ materials for Cr(VI) were evaluated under visible light irradiation. The results showed that the prepared materials had a large specific surface area and enhanced visible light absorption. Bi₂O₃(DMF/MeOH-3)-400 had a large specific surface area and many active adsorption sites, and it had the highest adsorption of Cr(VI) (49.13%) among the materials. Bi₂O₃(DMF/MeOH-3)-400 also had the highest photocatalytic reduction efficiency, and it achieved 100% removal of 10 mg·L⁻¹ Cr(VI) within 90 min under light. In addition, the material showed remarkable stability after three consecutive photocatalytic cycles. The enhanced photocatalytic performance was mainly attributed to the fast separation of electron–hole pairs and efficient electron transfer in the MOF-derived materials, which was confirmed by electrochemical tests and PL spectroscopy. Reactive species trapping experiments confirmed that electrons were the main active substances; accordingly, a possible photocatalytic mechanism was proposed. In conclusion, this work provides a new perspective for designing novel photocatalysts that can facilitate the removal of Cr(VI) from water. Full article

Accurate identification and monitoring of aerosol properties is crucial for understanding their sources and impacts on human health and the environment. Therefore, we propose a feature extraction and attribute recognition method from in situ light-scattering measurements based on Bayesian Optimization, wavelet scattering transform, and long short-term memory neural network (BO-WST-LSTM), with empirical mode decomposition (EMD) and independent component analysis (ICA) algorithm for signal preprocessing. In this study, an experimental platform was utilized to gather light-scattering signals from particles with varying characteristics. The signals are then processed using the EMD-ICA noise reduction technique. Then, the wavelet scattering network is used to realize the adaptive extraction of the characteristics of the particle light-scattering signal, and the Bayesian Optimization model is used to optimize the hyperparameters of the LSTM neural network. The extracted scattering coefficient matrix is input into the LSTM for iterative training. Finally, the SoftMax layer’s probability classification method is applied to the identification of particle attributes. The results show that the multi-angle particle light-scattering signal detection system designed and built in this study performs well and is capable of effectively collecting particle light-scattering signals. At the same time, the proposed new method for particle property recognition demonstrates good classification performance for six different types of particles with a particle size of 2 μm, achieving a classification accuracy of 98.83%. This proves its effectiveness in recognizing particle properties and provides a solid foundation for particle identification. Full article

Background/Objectives: With regard to excision of pigmented lesions for detection of malignant melanoma (MM), the number needed to treat (NNT) describes the number of melanocytic nevi that need to be biopsied/excised to detect one MM. The aim should be a low NNT. Methods: Single-center data analysis, including dermatohistopathological records of all nevi and MM cases during 2004–2013 at the Department of Dermatology, University Hospital Regensburg (UKR), was performed. We calculated the NNT, correlating it with the patient’s age and referring physician. The MM to MM in situ ratio was calculated to quantify early detection. As a secondary objective, we stratified into a pre- and post-2008 dataset, coinciding with the introduction of statutory skin cancer screening in Germany. Results: The overall NNT of 118,668 pigmented lesions was 17.2. We found a linear decrease in NNT towards older patients (R² = 62%; p < 0.001). The impact of skin cancer screening in 2008 was marked by a reduction in biopsies/excisions, a shift in age distribution, and a decrease in the NNT from 20.3 to 14.7. Office-based dermatologists had an NNT of 22.3, UKR-based dermatologists had an NNT of 8.0, and non-dermatologists had an NNT of 16.5. Conclusions: The age-related decrease in the NNT emphasizes the importance of age stratification for pigmented lesions. The NNT differed between professional settings. The implementation of skin cancer screening in 2008 was associated with a reduced NNT. Full article