Search Results (1,786)

Eutrophication has become the primary water quality issue for most of the freshwater and coastal marine ecosystems in the world. Caused by excessive nitrogen (N) and phosphorus (P) inputs, it has a significant impact on aquatic ecosystems, resulting in algal blooms, oxygen depletion, and biodiversity loss. Zeolites have been identified as effective adsorbents for removal of these pollutants, improving water quality and ecosystem health. Kinetic and isotherm adsorption experiments were conducted to examine the adsorption efficiency of four zeolites of various origins (Greek, Slovakian, Turkish, and Bulgarian) and a specific modification (ZeoPhos) to determine the most effective material for N and P removal. The aim of the study is to discover the best zeolite for chemical adsorption in eutrophic waters by comparing their adsorption capacities and pollutant removal efficiencies along with SEM, TEM, and X-RD spectrographs. Slovakian ZeoPhos has been identified as the best-performing material for long-term and efficient water treatment systems for eutrophication management. Full article

Over the past few decades, lithium-ion batteries (LIBs) have gained significant attention due to their inherent potential for environmental sustainability and unparalleled energy storage efficiency. Meanwhile, polymer electrolytes have gained popularity in several fields due to their ability to adapt to various battery geometries, enhanced safety features, greater thermal stability, and effectiveness in reducing dendrite growth on the anode. However, their relatively low ionic conductivity compared to liquid electrolytes has limited their application in high-performance devices. This limitation has led to recent studies revolving around the development of poly(ionic liquids) (PILs), particularly imidazolium-mediated polymer backbones as novel electrolyte materials, which can increase the conductivity with fine-tuning structural benefits, while maintaining the advantages of both solid and gel electrolytes. In this study, a curated dataset of 120 data points representing eight different polymers was used to predict ionic conductivity in imidazolium-based PILs as well as the emerging ionene substructures. For this purpose, four ML models: CatBoost, Random Forest, XGBoost, and LightGBM were employed by incorporating chemical structure and temperature as the models’ inputs. The best-performing model was further employed to estimate the conductivity of novel ionenes, offering insights into the potential of advanced polymer architectures for next-generation LIB electrolytes. This approach provides a cost-effective and intelligent pathway to accelerate the design of high-performance electrolyte materials. Full article

Efficient and accurate panel assignment is critical in expert and peer review processes. Traditional methods—based on manual preferences or Heuristic rules—often introduce bias, inconsistency, and scalability challenges. We present an automated framework that combines transformer-based document similarity modeling with optimization-based reviewer assignment. Using the all-mpnet-base-v2 from model (version 3.4.1), our system computes semantic similarity between proposal texts and reviewer documents, including CVs and Google Scholar profiles, without requiring manual input from reviewers. These similarity scores are then converted into rankings and integrated into an Integer Linear Programming (ILP) formulation that accounts for workload balance, conflicts of interest, and role-specific reviewer assignments (lead, scribe, reviewer). The method was tested across 40 researchers in two distinct disciplines (Chemical Engineering and Philosophy), each with 10 proposal documents. Results showed high self-similarity scores (0.65–0.89), strong differentiation between unrelated fields (−0.21 to 0.08), and comparable performance between reviewer document types. The optimization consistently prioritized top matches while maintaining feasibility under assignment constraints. By eliminating the need for subjective preferences and leveraging deep semantic analysis, our framework offers a scalable, fair, and efficient alternative to manual or Heuristic assignment processes. This approach can support large-scale review workflows while enhancing transparency and alignment with reviewer expertise. Full article

Accurate estimation of biomass higher heating value (HHV) is crucial for designing efficient bioenergy systems. In this study, we developed a Backpropagation artificial neural network (ANN) that predicts HHV from routine proximate/ultimate composition data. The network (9-6-6-1 architecture, trained for 15,000 epochs with learning rate 0.3 and momentum 0.4) was calibrated on 99 diverse Spanish biomass samples (inputs: moisture, ash, volatile matter, fixed carbon, C, H, O, N, S). The optimized ANN achieved strong predictive accuracy (validation R² ≈ 0.81; mean squared error ≈ 1.33 MJ/kg; MAE ≈ 0.77 MJ/kg), representing a substantial improvement over 54 analytical models despite the known complexity and variability of biomass composition. Importantly, in direct comparisons it significantly outperformed 54 published analytical HHV correlations—the ANN achieved substantially higher R² and lower prediction error than any fixed-form formula in the literature. A sensitivity analysis confirmed chemically intuitive trends (higher C/H/FC increase HHV; higher moisture/ash/O reduce it), indicating the model learned meaningful fuel-property relationships. The ANN thus provided a computationally efficient and robust tool for rapid, accurate HHV estimation from compositional data. Future work will expand the dataset, incorporate thermal pretreatment effects, and integrate the model into a user-friendly decision-support platform for bioenergy applications. Full article

Plastic waste accumulation is one of the most pressing environmental challenges of the 21st century, owing to the widespread use of synthetic polymers and the limitations of conventional recycling methods. Among available strategies, chemical upcycling via depolymerization has emerged as a promising circular approach that converts plastic waste back into valuable monomers and chemical feedstocks. This article provides an in-depth narrative review of recent progress in the upcycling of major plastic types such as PET, PU, PS, and engineering plastics through thermal, chemical, catalytic, biological, and mechanochemical depolymerization methods. Each method is critically assessed in terms of efficiency, scalability, energy input, and environmental impact. Special attention is given to innovative catalyst systems, such as microsized MgO/SiO₂ and Co/CaO composites, and emerging enzymatic systems like engineered PETases and whole-cell biocatalysts that enable low-temperature, selective depolymerization. Furthermore, the conversion pathways of depolymerized products into high-purity monomers such as BHET, TPA, vanillin, and bisphenols are discussed with supporting case studies. The review also examines life cycle assessment (LCA) data, techno-economic analyses, and policy frameworks supporting the adoption of depolymerization-based recycling systems. Collectively, this work outlines the technical viability and sustainability benefits of depolymerization as a core pillar of plastic circularity and monomer recovery, offering a path forward for high-value material recirculation and waste minimization. Full article

Accurate delineation of within-field management zones (MZs) is essential for implementing precision agriculture, particularly in spatially heterogeneous environments. This study evaluates the spatiotemporal consistency and practical value of MZs derived from three complementary data sources: electromagnetic conductivity (EM38-MK2), basic soil chemical properties (pH, humus, P₂O₅, K₂O, nitrogen), and vegetation/surface indices (NDVI, SAVI, LCI, BSI) derived from Sentinel-2 imagery. Using kriging, fuzzy k-means clustering, percentile-based classification, and Weighted Overlay Analysis (WOA), MZs were generated for a five-year period (2018–2022), with 2–8 zone classes. Stability and agreement were assessed using the Cohen Kappa, Jaccard, and Dice coefficients on systematic grid samples. Results showed that EM38-MK2 and humus-weighted BSP data produced the most consistent zones (Kappa > 0.90). Sentinel-2 indices demonstrated strong alignment with subsurface data (r > 0.85), offering a low-cost alternative in data-scarce settings. Optimal zoning was achieved with 3–4 classes, balancing spatial coherence and interpretability. These findings underscore the importance of multi-source data integration for robust and scalable MZ delineation and offer actionable guidelines for both data-rich and resource-limited farming systems. This approach promotes sustainable agriculture by improving input efficiency and allowing for targeted, site-specific field management. Full article

Background: Urine microbial analysis is a frequently requested test that is often associated with contamination during specimen collection or storage, which leads to false-positive diagnoses and delayed reporting. In the era of digitalization, machine learning (ML) can serve as a valuable tool to support clinical decision-making. Methods: This study investigates the application of a simple artificial neural network (ANN) to pre-identify negative and contaminated (false-positive) specimens. An ML model was developed using 8181 urine samples, including cytology, dipstick tests, and culture results. The dataset was randomly split 2:1 for training and testing a multilayer perceptron (MLP). Input variables with a normalized importance below 0.2 were excluded. Results: The final model used only microbial and either urine color or urobilinogen pigment analysis as inputs; other physical, chemical, and cellular parameters were omitted. The frequency of positive and negative specimens for bacteria was 6.9% and 89.6%, respectively. Contaminated specimens represented 3.5% of cases and were predominantly misclassified as negative by the MLP. Thus, the negative predictive value (NPV) was 96.5% and the positive predictive value (PPV) was 87.2%, leading to 0.82% of the cultures being unnecessary microbial cultures (UMC). Conclusions: These results suggest that the MLP is reliable for screening out negative specimens but less effective at identifying positive ones. In conclusion, ANN models can effectively support the screening of negative urine samples, detect clinically significant bacteriuria, and potentially reduce unnecessary cultures. Incorporating morphological information data could further improve the accuracy of our model and minimize false negatives. Full article

Antimicrobial Resistance (AMR), i.e., the evolution of microbes to become resistant to chemicals used to control them, is a global public health concern that can make bacterial diseases untreatable. Inputs including antibiotics, metals, and biocides can create an environment in the agrifood chain that selects for AMR. Consumption of food represents a potential exposure route to AMR microbes and AMR genes (ARGs), which may be present in viable bacteria or on free DNA. Ready-to-eat (RTE) foods are of particular interest because they are eaten without further cooking, so AMR bacteria or ARGs that are present may be consumed intact. They also represent varied production systems (fresh produce, cooked meat, dairy, etc.). An evidence gap exists regarding the diversity and consumption of ARGs in RTE food, which this study begins to address. We sampled 1001 RTE products at retail sale in the UK, in proportion to their consumption by the UK population, using National Diet and Nutrition Survey data. Bacterial DNA content of sample extracts was assessed by 16S metabarcoding, and 256 samples were selected for metagenomic sequencing for identification of ARGs based on consumption and likely bacterial DNA content. A total of 477 unique ARGs were identified in the samples, including ARGs that may be involved in resistance to important antibiotics, such as colistin, fluoroquinolones, and carbapenems, although phenotypic AMR was not measured. Based on the incidence of ARGs in food types, ARGs are estimated to be present in a high proportion of average diets. ARGs were detected on almost all RTE food types tested (48 of 52), and some efflux pump genes are consumed in 97% of UK diets. Full article

The rapid growth of plant-based biodegradable tableware, driven by plastic restrictions, necessitates rigorous safety assessments of potential chemical contaminants like per- and polyfluoroalkyl substances (PFASs). This study comprehensively evaluated PFAS contamination risks in commercial sugarcane pulp tableware, focusing on the residues of five target PFASs (PFOA, PFOS, PFNA, PFHxA, PFPeA) and their migration behavior under simulated use and takeout conditions. An analysis of 22 samples revealed elevated levels of total fluorine (TF: 33.7–163.6 mg/kg) exceeding the EU limit (50 mg/kg) in 31% of products. While sporadic PFOA residues surpassed the EU single compound limit (0.025 mg/kg) in 9% of samples (16.1–25.5 μg/kg), the levels of extractable organic fluorine (EOF: 4.9–17.4 mg/kg) and the low EOF/TF ratio (3.19–10.4%) indicated inorganic fluorides as the primary TF source. Critically, the migration of all target PFASs into food simulants (water, 4% acetic acid, 50% ethanol, 95% ethanol) under standardized use conditions was minimal (PFOA: 0.52–0.70 μg/kg; PFPeA: 0.54–0.63 μg/kg; others < LOQ). Even under aggressive simulated takeout scenarios (50 °C oscillation for 12 h + 12 h storage at 25 °C), PFOA migration reached only 0.99 ± 0.01 μg/kg in 95% ethanol. All migrated levels were substantially (>15-fold) below typical safety thresholds (e.g., 0.01 mg/kg). These findings demonstrate that, despite concerning residue levels in some products pointing to manufacturing contamination sources, migration during typical and even extended use scenarios poses negligible immediate consumer risk. This study underscores the need for stricter quality control targeting PFOA and inorganic fluoride inputs in sugarcane pulp tableware production. Full article

Aluminothermic reduction is an alternative processing route for the circular economy because Al is produced electrochemically in the Hall–Héroult process with minimal CO₂ emissions if the electricity input is sourced from non-fossil fuel energy sources. This circular processing option attracts increased research attention in the aluminothermic production of manganese and silicon alloys. The Al₂O₃ product must be recycled through hydrometallurgical processing, with leaching as the first step. Recent work has shown that the NaAlO₂ compound is easily leached in water. In this work, a suitable slag formulation is applied in the aluminothermic reduction of manganese ore to form a Na₂O-based slag of high Al₂O₃ solubility to effect good alloy–slag separation. The synergistic effect of carbon and silicon reductants with aluminium is illustrated and compared to the test result with only carbon reductant. The addition of small amounts of carbon reductant to MnO₂-containing ore ensures rapid pre-reduction to MnO, facilitating aluminothermic reduction. At 1350 °C, a loosely sintered mass formed when carbon was added alone. The alloy and slag chemical analyses are compared to the thermochemistry predicted phase chemistry. The alloy consists of 66% Mn, 22–28% Fe, 2–9% Si, 0.4–1.4% Al, and 2.2–3.5% C. The higher %Si alloy is formed by adding Si metal. Although the product slag has a higher Al₂O₃ content (52–55% Al₂O₃) compared to the target slag (39% Al₂O₃), the fluidity of the slags appears sufficient for good alloy separation. Full article

The priming effect (PE), a microbially mediated process, critically regulates the balance between carbon sequestration and mineralization. This study used soils from different soil depths (0–20 cm, 20–40 cm, and 40–60 cm) under Picea schrenkiana forest in the Tianshan Mountains as the research object. An indoor incubation experiment was conducted by adding three concentrations (1% SOC, 2% SOC, and 3% SOC) of ¹³C-labelled glucose. We applied ¹³C isotope probe-phospholipid fatty acid (PLFA-SIP) technology to investigate the influence of readily labile organic carbon inputs on soil priming effect (PE), microbial community shifts at various depths, and the mechanisms underlying soil PE. The results indicated that the addition of ¹³C-labeled glucose accelerated the mineralization of soil organic carbon (SOC); CO₂ emissions were highest in the 0–20 cm soil layer and decreased trend with increasing soil depth, with significant differences observed across different soil layers (p < 0.05). Soil depth had a positive direct effect on the cumulative priming effect (CPE); however, it showed negative indirect effects through physico-chemical properties and microbial biomass. The CPE of the 0–20 cm soil layer was significantly positively correlated with ¹³C-Gram-positive bacteria, ¹³C-Gram-negative bacteria, and ¹³C-actinomycetes. The CPE of the 20–40 cm and 40–60 cm soil layers exhibited a significant positive correlation with cumulative mineralization (CM) and microbial biomass carbon (MBC). Glucose addition had the largest and most significant positive effect on the CPE. Glucose addition positively affected PLFAs and particularly microbial biomass. This study provides valuable insights into the dynamics of soil carbon pools at varying depths following glucose application, advancing the understanding of forest soil carbon sequestration. Full article

Robots are increasingly emerging as effective platforms to overcome a wide range of challenges in agriculture. Beyond functioning as standalone systems, agricultural robots are proving valuable as collaborative platforms, capable of supporting and integrating with humans and other technologies and agricultural activities. In this study, we designed and implemented an automated system embedded in a ground-based robotic platform to support spraying drone operations. The system consists of a robotic platform that carries the spraying drone along with all necessary support devices, including a water tank, chemical reservoirs, a mixer, generators for drone battery charging, and a top landing pad. The system is controlled with a mobile app that calculates the total amount of water and chemicals required and sends commands to the platform to prepare the application mixture. The input information in the app includes the field area, application rate, and up to three chemical dosages simultaneously. Additionally, the platform allows the drone to take off from and land on it, enhancing both safety and operability. A set of pumps was used to deliver water and chemicals as specified in the mobile app. To automate pump control, we used Arduino technology, including both the microcontroller and a programming environment for coding and designing the mobile app. To validate the system’s effectiveness, we individually measured the amount of water and chemical delivered to the mixer tank and compared it with conventional manual methods for calculating chemical quantities and preparation time. The system demonstrated consistent results, achieving high precision and accuracy in delivering the correct amount. This study advances the field of agricultural robotics by highlighting the role of collaborative platforms. Particularly, the system presents a valuable and low-cost solution for small farms and experimental research. Full article

Most industrial processes depend on heat, electricity, demineralized water, and chemical inputs, which themselves are produced through energy- and resource-intensive industrial activities. In this work, acetic acid (AA) production from syngas (CO, CO₂, and H₂) fermentation is explored and compared against a thermochemical fossil benchmark and other thermochemical/biological processes across four main Key Performance Indicators (KPI)—electricity use, heat use, water consumption, and carbon footprint (CF)—for the years 2023 and 2050 in Portugal and France. CF was evaluated through transparent and public inventories for all the processes involved in chemical production and utilities. Spreadsheet-traceable matrices for hotspot identification were also developed. The fossil benchmark, with all the necessary cascade processes, was 0.64 kg CO_2-eq/kg AA, 1.53 kWh/kg AA, 22.02 MJ/kg AA, and 1.62 L water/kg AA for the Portuguese 2023 energy mix, with a reduction of 162% of the CO₂-eq in the 2050 energy transition context. The results demonstrated that industrial practices would benefit greatly from the transition from fossil to renewable energy and from more sustainable chemical sources. For carbon-intensive sectors like steel or cement, the acetogenic syngas fermentation appears as a scalable bridge technology, converting the flue gas waste stream into marketable products and accelerating the transition towards a circular economy. Full article

According to Victor Ernest Shelford’s ‘Law of Tolerance,’ organisms within ecosystems thrive optimally when environmental conditions are favorable. Applying this principle to ecosystems and agro-ecosystems facing water scarcity or environmental challenges can significantly enhance their productivity. In these ecosystems, phytocenosis adjusts its conditions by utilizing water with varying salinity levels. Moreover, establishing optimal drinking water conditions for human populations within an ecosystem can help mitigate future negative succession processes. The purpose of this study is to evaluate the quality of two distinct water sources in the Amudarya district of the Republic of Karakalpakstan, Uzbekistan: collector-drainage water and groundwater at depths of 10 to 25 m. This research is highly relevant in the context of climate change, as improper management of water salinity, particularly in collector-drainage water, may exacerbate soil salinization and degrade drinking water quality. The primary methodology of this study is as follows: The Food and Agriculture Organization of the United Nations (FAO) standard for collector-drainage water is applied, and the water quality index is assessed using the CCME WQI model. The Canadian Council of Ministers of the Environment (CCME) model is adapted to assess groundwater quality using Uzbekistan’s national drinking water quality standards. The results of two years of collected data, i.e., 2021 and 2023, show that the water quality index of collector-drainage water indicates that it has limited potential for use as secondary water for the irrigation of sensitive crops and has been classified as ‘Poor’. As a result, salinity increased by 8.33% by 2023. In contrast, groundwater quality was rated as ‘Fair’ in 2021, showing a slight deterioration by 2023. Moreover, a comparative analysis of CCME WQI values for collector-drainage and groundwater in the region, in conjunction with findings from Ethiopia, India, Iraq, and Turkey, indicates a consistent decline in water quality, primarily due to agriculture and various other anthropogenic pollution sources, underscoring the critical need for sustainable water resource management. This study highlights the need to use organic fertilizers in agriculture to protect drinking water quality, improve crop yields, and promote soil health, while reducing reliance on chemical inputs. Furthermore, adopting WQI models under changing climatic conditions can improve agricultural productivity, enhance groundwater quality, and provide better environmental monitoring systems. Full article

Aquatic plants, as sedentary lifestyle organisms that accumulate chemical substances from their surroundings, can serve as valuable indicators of long-term anthropogenic pressure. In Poland, water monitoring is limited both spatially and temporally, which hampers a comprehensive assessment of water quality. Since the implementation of the Water Framework Directive (WFD), biotic elements, including macrophytes, have played an increasingly important role in water monitoring. Moreover, running waters, due to their dynamic nature, are susceptible to episodic pollution inputs that may be difficult to detect during isolated, point-in-time sampling campaigns. The analysis of stable carbon (δ¹³C) and nitrogen (δ¹⁵N) isotope signatures in macrophytes enables the identification of elemental sources, including potential pollutants. Research conducted between 2008 and 2011 encompassed 38 sites along 15 rivers and 108 sites across 21 lakes in northern Poland. This study focused on the isotope signatures of three pondweed species: Stuckenia pectinata, Potamogeton perfoliatus, and Potamogeton crispus. The results revealed statistically significant differences in the δ¹³C and δ¹⁵N values of plant organic matter between river and lake environments. Higher δ¹⁵N values were observed in rivers, whereas higher δ¹³C values were recorded in lakes. Spearman correlation analysis showed a negative relationship between δ¹³C and δ¹⁵N, as well as correlations between δ¹⁵N and the concentrations of Ca²⁺ and HCO₃⁻. A positive correlation was also found between δ¹³C and dissolved oxygen levels. These findings confirm the utility of δ¹³C and, in particular, δ¹⁵N as indicators of anthropogenic eutrophication, including potentially domestic sewage input and its impact on aquatic ecosystems. Full article