Search Results (1,572)

The energy transition is an issue of fundamental importance in the current global context, as an increasing number of countries are committed to searching for minerals and elements essential for the storage, distribution, and supply of energy derived from new renewable and sustainable sources. In some countries, these elements (such as boron, lithium, and strontium) are considered to be critical raw materials (CRMs) because of their limited occurrence within their own borders and are commonly found in minerals and geothermal–formation waters, especially in brackish to brine waters. In the Italian territory, CRM-rich waters have already been identified by previously published studies (i.e., with mean concentrations in the Salsomaggiore Terme of 390 mg/L of boron, 76 mg/L of lithium, and 414 mg/L of strontium); however, their extraction is hampered by several knowledge gaps. In particular, a comprehensive understanding of the origin, accumulation processes, and migration pathways of these CRM-rich waters is still lacking. These factors are closely linked to the geological framework and evolutionary history of each specific area. To address these gaps, we investigated the Salsomaggiore Structure that is located at the northwestern front of the Apennine in Italy by integrating geological data with hydrogeochemical results. We constructed new preliminary distribution maps of the most significant CRMs around the Salsomaggiore Structure, which can be used in the future for the National Mineral Exploration Program drawn up in accordance with the European Critical Raw Materials Act. These maps, combined with the interpretation of seismic reflection profiles calibrated with surface geology and wells, allowed us to establish a close relationship between water geochemistry/CRM contents and the geological evolution of the Salsomaggiore Structure. This structure can be considered representative of the frontal ranges of the Northwestern Apennine and other mountain chains associated with the foreland basin systems. Full article

Geopolymers represent an innovative and environmentally sustainable alternative to traditional construction materials, offering significant potential for reducing anthropogenic CO₂ emissions. Among these, phosphoric acid-activated metakaolin-based systems have attracted increasing attention for their chemical and thermal resilience. In this study, we present a comprehensive structural and mechanical evaluation of metakaolin-based geopolymers synthesized across a wide range of Al/P molar ratios (0.8–4.0). Six formulations were systematically prepared and analyzed using X-ray powder diffraction (XRPD), small-angle X-ray scattering (SAXS), Fourier-transform infrared spectroscopy (FTIR), solid-state nuclear magnetic resonance (ssNMR), and complementary mechanical testing. The novelty of this work lies in the integrated mapping of composition–structure–property relationships across the broad Al/P spectrum under controlled synthesis, combined with the rare application of SAXS to reveal composition-dependent nanoscale domains (~18–50 nm). We identify a stoichiometric window at Al/P ≈ 1.5, where complete acid consumption leads to a structurally homogeneous AlVI–O–P network, yielding the highest compressive strength. In contrast, acid-rich systems exhibit divergent flexural and compressive behaviors, with enhanced flexural strength linked to hydrated silica domains arising from metakaolin dealumination, quantitatively tracked by ²⁹Si MAS NMR. XRPD further reveals the formation of uncommon Si–P crystalline phases (SiP₂O₇, Si₅P₆O₂₅) under low-temperature curing in acid-rich compositions. Together, these findings provide new insights into the nanoscale structuring, phase evolution, and stoichiometric control of silica–alumino–phosphate geopolymers, highlighting strategies for optimizing their performance in demanding thermal and chemical environments. Full article

This paper provides an in-depth analysis of the microstructural characteristics and the chemical content of Polybutylene Terephthalate (PBT) composites that have different contents of Glass Fiber (GF). Blending of VALOX 420 (30 wt% GF/PBT) with unreinforced VALOX 310 allowed the composites to be prepared, with control of the concentration and distribution of the GF. The GF reinforcement and PBT matrix were characterized by an advanced microstructural spectrum and spatial analysis to show the influence of fiber density, dispersion, and chemical composition on performance. Findings indicate that GF content has a profound effect on microstructural properties and damage processes, especially traction effects in various regions of the specimen. These results highlight the significance of accurate control of GF during fabrication to maximize durability and performance, which can be used to inform the design of superior PBT/GF composites in challenging engineering applications. The implications of these results are relevant to a number of high-performance sectors, especially in automotive, electrical, and consumer electronic industries, where PBT/GF composites are found in extensive use because of their outstanding mechanical strength, dimensional stability, and thermal resistance. The main novelty of the current research is both the microstructural and chemical assessment of PBT/GF composites in different fiber contents, and this aspect is rather insufficiently studied in the literature. Although the mechanical performance or macro-level aging effects have been previously assessed, the Literature usually did not combine elemental spectroscopy or spatial microstructural mapping to correlate the fiber distribution with the damage mechanisms. Further, despite the importance of GF reinforcement in achieving the right balance between mechanical, thermal, and electrical performance, not much has been conducted in detail to describe the correlation between the microstructure and the evolution of damage in short-fiber composites. Conversely, this paper will use the superior spatial elemental analysis to bring out the effects of GF content and dispersion on micro-mechanisms like interfacial traction, cracking of the matrix, and fiber fracture. We, to the best of our knowledge, are the first to systematically combine chemical spectrum analysis with spatial mapping of PBT/GF systems with varied fiber contents—this allows us to give actionable information on material design and optimized manufacturing procedures. Full article

Cognitive Odor Source Localization (OSL) strategies are reliable search strategies for turbulent environments, where chemical cues are sparse and intermittent. These methods estimate a probabilistic belief over the source location using Bayesian inference and guide the searching movement by evaluating expected entropy reduction at candidate new positions. By maximizing expected information gain, agents make informed decisions rather than simply reacting to sensor readings. However, computing entropy reductions is computationally expensive, making real-time implementation challenging for resource-constrained platforms. Interestingly, search trajectories produced by cognitive algorithms often resemble those of small insects, suggesting that informative movement patterns might be replicated using simpler, bio-inspired searching strategies. This work investigates that possibility by analysing spatial distribution of entropy reductions across the entire search area. Rather than focusing on searching algorithms and local decisions, the analysis maps information gain over the full environment, identifying consistent high-gain regions that may serve as navigational cues. Results show that these regions often emerge near the source and along plume borders and that expected entropy reduction is strongly influenced by prior belief shape and sensor observations. This global perspective enables identification of spatial patterns and high-gain regions that remain hidden when analysis is restricted to local neighborhoods. These insights enable synthesis of hybrid search strategies that preserve cognitive effectiveness while significantly reducing computational cost. Full article

The utilization of biomass as a renewable energy source has the potential to play a role in mitigating climate change. Furthermore, biomass gasification represents a sustainable solution for the management of lignocellulosic waste. Topics related to the different types of gasification reactors, biomass, and economic feasibility, along with tar formation and its removal in the product gas, are discussed as general aspects in the gasification. A detailed analysis of capital and operational expenditures, the net present value, the payback period, and the internal rate of return of downdraft gasifiers has been conducted. A bibliometric analysis has been conducted; the results are presented in the form of visual maps based on keywords, and likely future trends in gasification modeling were identified. Since modeling is crucial to optimize the production or quality of the syngas, this paper discloses some important aspects related to biomass gasification carried out on downdraft gasifiers. The modeling section encompasses a range of approaches, including those based on chemical equilibrium, both stoichiometric and non-stoichiometric, kinetic models, and computational fluid dynamics. A substantial section is devoted to the modeling of the downdraft reactor, incorporating the primary conservation equations for mass, energy, and momentum. The modeling framework aims to provide a comprehensive overview for researchers seeking to simulate downdraft gasifiers. This enables researchers to utilize a summary of equations and conditions that are pertinent to their own modeling and simulations. Full article

Corrosion of refractory materials in NaCl–KCl melts is a major issue affecting the service life of linings in aluminum metallurgy, where these salts serve as the basis for covering and refining mixtures. The aim of this study was to comprehensively evaluate the corrosion resistance of alumina-silicate refractory materials (ASRM) with a high SiO₂/Al₂O₃ ratio in contact with melts of varying NaCl–KCl ratios. Static crucible corrosion tests were conducted in accordance with the technical specification CEN/TS 15418:2006. Macro- and microscopic analysis, chemical analysis (AAS), and semi-quantitative EDX analysis enabled detailed monitoring of the depth of melt infiltration, microstructural changes, and element distribution within the material. The results demonstrated that as the NaCl content in the melt increased, there was a significant rise in both the depth of infiltration and the degree of material degradation. A linear regression model confirmed a very strong positive correlation between NaCl content and the extent of damage (R² = 0.967). Chemical analysis revealed that the silicon content decreases in the infiltrated zone, while aluminum remains stable, indicating superior corrosion resistance of Al₂O₃ compared to SiO₂. EDX analysis also confirmed increased concentrations of sodium and chlorine in the infiltrated areas, complementing the AAS results and providing more precise mapping of the distribution of corrosion products within the material structure. These findings provide a quantitative basis for optimizing the composition of refractory materials and designing protective strategies to extend their service life under the aggressive operating conditions of aluminum production. Full article

Solvents represent the quiet majority in biomolecular systems, yet modeling their influence with both speed and ri:gor remains a central challenge. This study maps the state of the art in implicit solvent theory and practice, spanning classical continuum electrostatics (PB/GB; DelPhi, APBS), modern nonpolar and cavity/dispersion treatments, and quantum–continuum models (PCM, COSMO/COSMO-RS, SMx/SMD). We highlight where these methods excel and where they falter, namely, around ion specificity, heterogeneous interfaces, entropic effects, and parameter sensitivity. We then spotlight two fast-moving frontiers that raise both accuracy and throughput: machine learning-augmented approaches that serve as PB-accurate surrogates, learn solvent-averaged potentials for MD, or supply residual corrections to GB/PB baselines, and quantum-centric workflows that couple continuum solvation methods, such as IEF-PCM, to sampling on real quantum hardware, pointing toward realistic solution-phase electronic structures at emerging scales. Applications across protein–ligand binding, nucleic acids, and intrinsically disordered proteins illustrate how implicit models enable rapid hypothesis testing, large design sweeps, and long-time sampling. Our perspective argues for hybridization as a best practice, meaning continuum cores refined by improved physics, such as multipolar water, ML correctors with uncertainty quantification and active learning, and quantum–continuum modules for chemically demanding steps. Full article

Weeds are one of the main problems in cultivation of medicinal and aromatic plants (MAPs); they negatively affect yield (herba and essential oil), and the overall quantity and quality of biomass, flowers, roots, seeds, and secondary metabolites. This review evaluates mulching as a sustainable, non-chemical method for weed management in the cultivation of MAPs and examines how effectively organic, synthetic, and living mulches reduce weeds and increase yields. Regarding different mulch materials such as straw, sawdust, bark, needles, compost, polyethylene, and biodegradable films, the basic processes of mulch activity, including light interception, physical suppression, and microclimate adjustment, are examined. The review further analyzes the impact of mulching on soil parameters (moisture, temperature, pH, chlorophyll content) and the biosynthesis of secondary metabolites. The findings consistently indicate that mulching substantially reduces weed biomass, improves crop performance, and supports organic farming practices. However, there are still issues with cost, material availability, and possible soil changes, and the efficacy is affected by variables including cultivated plant species, mulch type, and application thickness. The review highlights the importance of further research to optimize the selection of mulch and MAPs and their application across various agroecological conditions, and indicates that mulching is a potential, environmentally friendly technique for weed control in MAP cultivations. Full article

The Eastern Kazakhstan Gold Belt is a major black-shale-hosted gold province in Central Asia where the main types of deposits comprise mineralized zones with auriferous sulfides (micro- and nano-inclusions of gold and refractory gold) and quartz veins with visible gold. The quartz vein deposits are economically less important but may potentially represent the upper parts of bigger ore systems concealed at depth. In this work, the mineralogy of the quartz vein deposits and related wall rock alteration zones was studied using microscopy and SEM-EDS analysis, and the geochemical dispersion of the ore elements in primary alteration haloes was documented utilizing spatial distribution maps and statistical treatment methods. The studied auriferous quartz veins are classified as epizonal black-shale-hosted orogenic gold deposits. The veins generally have linear shapes with an average width of ca. 1 m and length up to 150 m and contain high-grade native gold with minor amounts of sulfides. In supergene oxidation zones, the native gold is closely associated with Fe-hydroxide minerals cementing brecciated zones within the veins. The auriferous quartz veins are usually enclosed by the wall rock alteration envelopes, where two types of alteration are distinguished. Proximal phyllic alteration (sericite-albite-pyrite ± chlorite, Fe-Mg-Ca carbonates, arsenopyrite, and pyrrhotite) develops as localized alteration envelopes, and pervasive carbonation accompanied by chlorite ± sericite and albite is the dominant process in the distal alteration zones. The rocks within the alteration zones are enriched in Au and chalcophile elements, and three groups of chemical elements showing significant positive mutual correlation have been identified: (1) an early geochemical assemblage includes V, P, and Co (±Ni), which are the chemical elements characteristic for black shale formations, (2) association of Au, As, and other chalcophile elements is distinctly overprinting, and manifests the main stage of sulfide-hosted Au mineralization, and (3) association of Bi and Hg (±Sb and U) includes the chemical elements that are mobile at low temperatures, and can be explained by activity of the late-stage hydrothermal or supergene fluids. The chalcophile elements show negative slopes from proximal to distal alteration zones and form overlapping positive anomalies on spatial distribution mono-elemental maps. Thus, the geochemical methods can provide useful tools to delineate the ore elemental associations and to outline reproducible anomalies for subsequent regional gold prospecting. Full article

A new Schiff base, (E)-2-(((1,10-phenanthrolin-5-yl)imino)methyl)-4,6-di-tert-butylphenol (Fen-IHB), was designed to incorporate an intramolecular hydrogen bond (IHB) between the phenolic OH and the azomethine nitrogen with the goal of modulating its physicochemical and biological properties. Fen-IHB was synthesized by condensation of 5-amino-1,10-phenanthroline with 3,5-di-tert-butyl-2-hydroxybenzaldehyde and exhaustively characterized by HR-ESI-MS, FTIR, 1D/2D NMR (¹H, ¹³C, DEPT-45, HH-COSY, CH-COSY, D₂O exchange), and UV–Vis spectroscopy. Cyclic voltammetry in anhydrous CH₃CN revealed a single irreversible cathodic peak at −1.43 V (vs. Ag/Ag⁺), which is consistent with the intramolecular reductive coupling of the azomethine moiety. Density functional theory (DFT) calculations, including MEP mapping, Fukui functions, dual descriptor analysis, and Fukui potentials with dual descriptor potential, identified the exocyclic azomethine carbon as the principal nucleophilic site and the phenolic ring (hydroxyl oxygen and adjacent carbons) as the main electrophilic region. Noncovalent interaction (NCI) analysis further confirmed the strength and geometry of the intramolecular hydrogen bond (IHB). In vitro antimicrobial assays indicated that Fen-IHB was inactive against Gram-negative facultative anaerobes (Salmonella enterica serovar Typhimurium and Typhi, Escherichia coli) and strictly anaerobic Gram-positive species (Clostridioides difficile, Roseburia inulinivorans, Blautia coccoides), as any growth inhibition was indistinguishable from the DMSO control. Conversely, Fen-IHB displayed measurable activity against Gram-positive aerobes and aerotolerant anaerobes, including Bacillus subtilis, Streptococcus pyogenes, Enterococcus faecalis, Staphylococcus aureus, and Staphylococcus haemolyticus. Overall, these comprehensive characterization results confirm the distinctive chemical and electronic properties of Fen-IHB, underlining the crucial role of the intramolecular hydrogen bond and electronic descriptors in defining its reactivity profile and selective biological activity. Full article

Multiwavelength modelling of the light variations in the chemically peculiar star CU Vir is presented. The modelling is based on the recent Doppler Imaging of CU Vir, which provides maps of the surface distribution of Si, Fe, He, and Cr. Intensity maps in both individual photometric filters and in the wide wavelength range from UV to NIR were calculated, taking into account the individual chemical abundances on the stellar surface. Comparison with observations revealed good agreement of both the light curves and their amplitude along the spectrum. Additionally, we analysed changes in the photometric period of the CU Vir from 1955 to 2022, including TESS measurements. The data of the last decades clearly indicate a gradual decrease in this period. Measurements of the CU Vir period over the next two decades will be crucial for verifying or refuting the periodic nature of its variations. Full article

Water and chemical use in textile dye production are exacerbating water pollution and extraction across Dhaka, Narayanganj, and Gazipur in Bangladesh, where these industries are concentrated. However, the ability to cope with water-related challenges is influenced by multiple factors. This study applies descriptive spatial analysis to map textile dye clusters, river pollution, and water insecurity. As vulnerability is multidimensional and fluctuates across subdistricts, this study develops a Water Vulnerability Index (WVI) consisting of 25 indicators across demographics, socioeconomics, gender, health, WASH, and climate dimensions. The index is based on Multidimensional Vulnerability Assessment (MDVA) and constructed through multicriteria analysis (MCA). The study highlights that the Shitalakhya, Turag-Tongi Khal, Buriganga, and Balu Rivers are highly polluted, with average biochemical oxygen demand (BOD), chemical oxygen demand (COD), and dissolved oxygen (DO) levels exceeding safe limits. Central Dhaka is identified as being extremely water insecure, characterized by significant inequalities in water insecurity across subdistricts. The WVI finds that Gazipur Sadar and Kaliakair subdistricts, housing several textile dye factories, face the highest water vulnerability of the 57 subdistricts. This study furthers the case that Dhaka, Narayanganj, and Gazipur host numerous textile hubs, confront serious water challenges, such as river pollution and water insecurity, and are marked by significant spatial disparities in vulnerability. By exploring anthropogenic pollution alongside multidimensional water vulnerability, this study can inform targeted policy responses, such as stricter regulatory limits, more frequent monitoring and enforcement, and tailored support in high-vulnerability areas. Full article

In wheat pest and disease control methods, pesticide application occupies a dominant position, and the use of UAVs for precise pesticide application is a key technology in precision agriculture. However, it is difficult for existing UAV spraying systems to accurately achieve variable spraying according to crop growth conditions, resulting in pesticide waste and environmental pollution. To address this issue, this paper proposes a LiDAR-assisted UAV variable-speed spraying system. Firstly, a biomass estimation model based on LiDAR data and RGB data is constructed, LiDAR point cloud data and RGB data are extracted from the target farmland, and, after preprocessing, key parameters including LiDAR feature variables, canopy cover, and visible-light vegetation indices are extracted from the two types of data. Using these key parameters as model inputs, multiple machine learning methods are employed to build a wheat biomass estimation model, and a variable spraying prescription map is generated based on the spatial distribution of biomass. Secondly, the variable-speed spraying system is constructed, which integrates a prescription map interpretation module and a PWM control module. Under the guidance of the variable spraying prescription map, the spraying rate is adjusted to achieve real-time variable spraying. Finally, a comparative experiment is designed, and the results show that the LiDAR-assisted UAV variable spraying system designed in this study performs better than the traditional constant-rate spraying system; while maintaining equivalent spraying effects, the usage of chemical agents is significantly reduced by 30.1%, providing a new technical path for reducing pesticide pollution and lowering grain production costs. Full article

The objective of this study was to characterize austenitic stainless steel 310 produced by Wire and Arc Additive Manufacturing (WAAM), addressing a gap in the literature regarding this alloy. Microstructural, chemical, and mechanical analyses were performed. Optical and electron microscopy revealed a predominantly columnar grain structure with characteristic tracks along the deposition direction. Point and mapping EDS analyses indicated a homogeneous distribution of iron, chromium, and nickel; however, point measurements suggested a possible underestimation of nickel, likely due to high relative error. Tensile tests demonstrated anisotropic mechanical behavior, with yield strength meeting standards at 45° and 90°, but lower at 0°. Ultimate tensile strength and elongation were below conventional requirements, with a maximum elongation of 15% at 90°. Additionally, the sample exhibited a total porosity of approximately 0.89%, which contributes to the reduction in mechanical properties, especially in the direction parallel to the deposition tracks. Overall, the WAAM-produced 310 stainless steel presented a microstructure similar to hot-rolled and annealed AISI 310 steel, but with distinctive features related to the additive process, such as mechanical anisotropy and microstructural directionality. These limitations highlight the need for process optimization to improve mechanical performance but reinforce the alloy’s structural potential in additive manufacturing. Full article

The recently proposed concept of “precursory fingerprint” is a logical consequence of the commonsense statement that seismic structures are unique and that their expected preshock behaviors, including precursory phenomena, are also unique. Our new prediction-related research strategy is conceptually based on the principles of (1) the uniqueness of seismogenic structures, (2) interconnected and interacting geospheres, and (3) non-equivalence of Earth’s surface spots in terms of precursory signal receptivity. The precursory fingerprint of a given seismic structure is a unique assemblage of precursory signals of various natures (seismic, physical, chemical, and biological), detectable in principle by using a system of proper monitoring equipment that consists of a matrix of n sensors placed on the ground at “sensitive” spots identified beforehand and on orbiting satellites. In principle, it is composed of a combination of signals that are emitted by the “responsive sensors”, in addition to the “non-responsive sensors”, coming from the sensor matrix, monitoring as many virtual precursory processes as possible by continuously measuring their relevant parameters. Each measured parameter has a pre-established (by experts) threshold value and an uncertainty interval, discriminating between background and anomalous values that are visualized similarly to traffic light signals (green, yellow, and red). The precursory fingerprint can thus be viewed as a particular configuration of “precursory signals” consisting of anomalous parameter values that are unique and characteristic to the targeted seismogenic structure. Presumably, it is a complex entity that consists of pattern, space, and time components. The “pattern component” is a particular arrangement of the responsive sensors on the master board of the monitoring system yielding anomalous parameter value signals, that can be re-arranged, after a series of experiments, in a spontaneously understandable new pattern. The “space component” is a map position configuration of the signal-detecting sensors, whereas the “time component” is a characteristic time sequence of the anomalous signals including the order, occurrence time before the event, transition time between yellow and red signals, etc. Artificial intelligence using pattern-recognition algorithms can be used to follow, evaluate, and validate the precursory signal assemblage and, finally, to judge, together with an expert board of human operators, its “precursory fingerprint” relevance. Signal interpretation limitations and uncertainties related to dependencies on sensor sensibility, focal depth, and magnitude can be established by completing all three phases (i.e., experimental, validation, and implementation) of the precursory fingerprint-based earthquake prediction research strategy. Full article