Search Results (5,730)

Acid mine drainage (AMD) and acid-generating mine wastes exhibit low pH, high sulfate levels, and complex multi-metal loads that strain conventional treatment. Thermoacidophilic red algae of the order Cyanidiales, particularly Galdieria sulphuraria (G. sulphuraria), have attracted interest as a biological option because they tolerate extreme acidity and elevated temperatures, grow under low light in mixotrophic or heterotrophic modes, and display rapid metal binding at the cell surface. This review synthesizes about two decades of peer-reviewed work to clarify how G. sulphuraria can be deployed as a practical module within mine water treatment trains. We examine the mechanisms of biosorption and bioaccumulation and show how they map onto two distinct configurations. Processed freeze-dried biomass functions as a regenerable sorbent for rare earth elements (REEs) and selected transition metals in packed beds with acid elution for recovery. Living cultures serve as polishing units for divalent metals and, when present, nutrients or dissolved organics under low light. We define realistic operating windows centered on pH 2–5 and temperatures of approximately 25–45 °C, and we identify matrix effects that govern success, including competition from ferric iron and aluminum, turbidity and fouling risks, ionic strength from sulfate, and suppression of REE uptake by phosphate in living systems. Building on laboratory studies, industrial leachate tests, and ecosystem observations, we propose placing G. sulphuraria upstream of bulk neutralization and outline reporting practices that enable cross-site comparison. The goal is an actionable framework that reduces reagent use and sludge generation while enabling metal capture and potential recovery of valuable metals from mine-influenced waters. Full article

Ground-source heat pump (GSHP) systems are widely regarded as an energy-efficient solution for building heating and cooling. However, their actual performance in large commercial buildings is often limited by rigid control strategies, insufficient equipment coordination, and suboptimal load matching. In the Liuzhou Fengqing Port commercial complex, the seasonal coefficient of performance (SCOP) of the GSHP system remains at a relatively low level of 3.0–3.5 under conventional operation. To address these challenges, this study proposes a gray-box-model-based cooperative optimization and group control strategy for GSHP systems. A hybrid gray-box modeling approach (YFU model), integrating physical-mechanism modeling with data-driven parameter identification, is developed to characterize the energy consumption behavior of GSHP units and variable-frequency pumps. On this basis, a multi-equipment cooperative optimization framework is established to coordinate GSHP unit on/off scheduling, load allocation, and pump staging. In addition, continuous operational variables (e.g., chilled-water supply temperature and circulation flow rate) are globally optimized within a hierarchical control structure. The proposed strategy is validated through both simulation analysis and on-site field implementation, demonstrating significant improvements in system energy efficiency, with annual electricity savings of no less than 3.6 × 10⁵ kWh and an increase in SCOP from approximately 3.2 to above 4.0. The results indicate that the proposed framework offers strong interpretability, robustness, and engineering applicability. It also provides a reusable technical paradigm for intelligent energy-saving retrofits of GSHP systems in large commercial buildings. Full article

Solar-driven hydrogen production via photocatalytic water splitting represents a promising route toward sustainable and low-carbon energy systems. Among emerging photocatalysts, porphyrin-based framework materials, specifically porphyrinic metal–organic frameworks (PMOFs) and porphyrinic covalent organic frameworks (PCOFs), have attracted increasing attention owing to their strong visible-light absorption, tunable electronic structures, permanent porosity, and well-defined catalytic architectures. In these systems, porphyrins function as versatile photosensitizers whose photophysical properties can be precisely tailored through metalation, peripheral functionalization, and integration into ordered frameworks. This review provides a comprehensive, design-oriented overview of recent advances in PMOFs, PCOFs, and hybrid porphyrinic architectures for photocatalytic H₂ evolution. We discuss key structure–activity relationships governing light harvesting, charge separation, and hydrogen evolution kinetics, with particular emphasis on the roles of porphyrin metal centers, secondary building units, linker functionalization, framework morphology, and cocatalyst integration. Furthermore, we highlight how heterojunction engineering through coupling porphyrinic frameworks with inorganic semiconductors, metal sulfides, or single-atom catalytic sites can overcome intrinsic limitations related to charge recombination and limited spectral response. Current challenges, including long-term stability, reliance on noble metals, and scalability, are critically assessed. Finally, future perspectives are outlined, emphasizing rational molecular design, earth-abundant catalytic motifs, advanced hybrid architectures, and data-driven approaches as key directions for translating porphyrinic frameworks into practical photocatalytic hydrogen-generation technologies. Full article

A comprehensive study on the distribution characteristics and exploitation strategies of remaining oil was carried out in the Ordovician ultra-deep fault-controlled fractured–vuggy carbonate reservoir within the Shunbei No. 1 strike–slip fault zone. This research addresses challenges such as severe watered-out and gas channeling encountered during multi-stage development, marking a shift toward a development phase focused on residual oil recovery. By integrating seismic attributes, drilling, logging, and production performance data—and building upon previous methodologies of “hierarchical constraint and genetic modeling”—a three-dimensional geological model was constructed with a five-tiered architecture: strike–slip fault affected zone, fault-controlled unit, cave-like structure, cluster fillings, and fracture zone. Numerical simulations were subsequently performed based on this model. The results demonstrate that the distribution of remaining oil is dominantly controlled by the coupling between key geological factors—including fault kinematics, reservoir architecture formed by karst evolution, and fracture–vug connectivity—and the injection–production well pattern. Three major categories with five sub-types of residual oil distribution patterns were identified: (1) local low permeability, weak hydrodynamics; (2) shielded connectivity pathways; and (3) Well Pattern-Dependent. Accordingly, two types of potential-tapping measures are proposed: improve well control through optimized well placement and sidetrack drilling and reservoir flow field modification via adjusted injection–production parameters and sealing of high-permeability channels. Techniques such as gas (nitrogen) huff-and-puff, gravity-assisted segregation, and injection–production pattern restructuring are recommended to improve reserve control and sweep efficiency, thereby increasing ultimate recovery. This study provides valuable guidance for the efficient development of similar ultra-deep fractured–vuggy carbonate reservoirs. Full article

A dew-point evaporative air cooler incorporating a novel segmented heat exchange design, demarcated according to the humidity state of moist air, is proposed. The system employs a porous fibrous material to create a wetted evaporative surface, which is continuously maintained in a moistened condition through a self-wicking water supply mechanism to enhance latent heat transfer. Circular fins are installed on the heat exchanger’s partition surface once the moist air reaches saturation, thereby improving sensible heat exchange between the dry and wet channels. The performance of a prototype was evaluated under controlled conditions in a standard enthalpy chamber. Experimental results indicate that, under typical summer conditions (inlet dry-bulb and wet-bulb temperatures of 33.8 °C and 25.4 °C, respectively), with an air mass flow ratio of 0.7 and an air velocity of 1.5 m/s, the wet-bulb effectiveness reaches 114.4% and the dew-point effectiveness achieves 84.8%. The maximum temperature reduction occurs in the sensible heat exchange section, reaching up to 6.1 °C, demonstrating its substantial sensible heat recovery capability. The device exhibits an energy efficiency ratio (EER) ranging from 9.1 to 31.8. The proposed compact configuration not only enhances energy efficiency but also reduces material costs by approximately 15.4%, providing a valuable reference for the future development of dew-point evaporative cooling systems in residential buildings. Full article

This study builds on previous research into the surface modification of beech wood with polyethyleneimine (PEI) prior to finishing it with a waterborne coating. Poly(diallyldimethylammonium chloride) (PDADMAC) is introduced as an alternative cationic polyelectrolyte for the pretreatment of beech wood surfaces. Wood samples were treated with aqueous 1% PDADMAC solutions of low (LMW—8000 g mol⁻¹) and high (HMW—100,000–200,000 g mol⁻¹) molecular weights, with or without NaCl addition. The effects of the treatments on wood surface chemistry, wettability, surface energy, water absorption, coating penetration, coating adhesion strength, and surface roughness of coated wood were analysed using FTIR, fluorescence microscopy, SEM/EDS, and standardised tests commonly used in wood surface finishing. The results showed that polyelectrolyte pretreatment modified the surface properties of wood, reducing water absorption and surface roughness without significantly affecting coating adhesion strength. PDADMAC formed a more uniform surface layer of wood with limited coating penetration, and NaCl addition improved wood surface smoothness (reducing surface roughness parameters of coated wood by 23%–29%, in samples treated with PDADMAC LMW with 0.01 M NaCl). These findings confirm that cationic polyelectrolyte pretreatment enhances the compatibility and performance of waterborne coatings, offering an environmentally friendly approach to improving wood–waterborne coating interactions. Full article

Waterborne polyurethane (WPU) and waterborne poly(urethane-urea) (WPUU) dispersions allow safer and more sustainable manufacturing of rechargeable batteries via water-based processing, while offering tunable adhesion and segmented-domain mechanics. Beyond conventional roles as binders and coatings, WPU/WPUU chemistries also support separator/interlayer and polymer-electrolyte designs for lithium-ion and lithium metal systems, where interfacial integrity, stress accommodation, and ion transport must be balanced. Here, we review WPU/WPUU fundamentals (building blocks, dispersion stabilization, morphology, and film formation) and review prior studies through a battery-centric structure–processing–property lens. We point out key performance-limiting trade-offs—adhesion versus electrolyte uptake and ionic conductivity versus storage modulus—and relate them to practical formulation variables, including soft-/hard-segment selection, ionic center/counterion design, molecular weight/topology control, and crosslinking strategies. Applications are reviewed for (i) electrode binders (graphite/Si; cathodes such as LFP and NMC), (ii) separator coatings and functional interlayers, and (iii) gel/solid polymer electrolytes and hybrid composites, with a focus on practical design guidelines for navigating these trade-offs. Future advancements in WPU/WPUU chemistries will depend on developing stable, low-impedance interlayers, enhancing electrochemical behavior, and establishing application-specific design guidelines to optimize performance in lithium metal batteries (LMB). Full article

Urban leaf litter represents an underutilized biomass resource with potential applications in sustainable building materials. This study investigates the suitability of dried, comminuted leaves collected from municipal green areas as a loose-fill thermal insulation material. The material was characterized in terms of thermal conductivity, settlement behavior, fire reaction, resistance to mold growth, water vapor diffusion, hygroscopic sorption, and short-term water absorption. Tests were conducted following relevant DIN and ISO standards, with both untreated and flame-retardant-treated samples examined. Results indicate that the thermal conductivity of leaf-based insulation (λ = 0.041–0.046 W/m·K) is comparable to other bio-based loose-fill materials such as cellulose and wood fiber. Optimal performance was achieved for particles sized 2–16 mm, showing settlement below 1%. All variants, including untreated material, fulfilled the fire resistance requirements of class E, while selected treatments further improved fire resistance. The material exhibited moderate vapor permeability (μ ≈ 4–5), low water absorption, and moisture buffering behavior similar to that of other bio-based insulation materials. Resistance to mold growth was satisfactory under standardized conditions. Overall, the results demonstrate that leaf litter can serve as an effective and environmentally favorable loose-fill insulation material, offering an innovative recycling pathway for urban green waste. Full article

One of the important stages of energy efficiency measures for buildings is the optimal choice of heating and cooling systems, as well as that of heat/cold production sources, so as to minimize dependence on conventional fuels and the national energy system. Currently, the option for integrating renewable energy sources addressed in the building efficiency stage is the installation of solar systems. In the context of the need to reduce energy consumption and reduce pollutant emissions, and the need to integrate renewable energy sources, the article addresses the implementation of a hybrid system with a water-to-water heat pump and photovoltaic panels, capable of achieving the decarbonization of the heating/cooling of a University Politehnica Timisoara teaching/research laboratory. The analyzed hybrid system uses the thermal potential of the Bega channel as a heat/cold source, and as a source for electricity production, a PV system, supported by the national energy system. Following the analysis of the operation of the building’s hybrid system for heating and cooling, during 4 November 2024–12 September 2025, an energy independence of approximately 90.7% and a reduction in CO₂ emissions of 4.17 t/year was found. Full article

Radiative cooling (RC) offers a passive pathway to reduce surface and system temperatures by emitting thermal radiation through the atmospheric window, yet its daytime effectiveness is often constrained by geometry, angular solar exposure, and practical integration limits. This work experimentally investigates the use of passive non-imaging optics, specifically compound parabolic concentrators (CPCs), as enhancers of RC performance under realistic conditions. A three-tier experimental methodology is followed. First, controlled indoor screening using an infrared lamp quantifies the intrinsic heat gain suppression of a commercial RC film, showing a temperature reduction of nearly 88 °C relative to a black-painted reference. Second, outdoor rooftop experiments on aluminum plates assess partial RC coverage, with and without CPCs, under varying orientations and tilt angles, revealing peak daytime temperature reductions close to 8 °C when CPCs are integrated. Third, system-level validation is conducted using a modified GUNT ET-202 solar thermal unit to evaluate the transfer of RC effects to a water circuit absorber. While RC strips alone produce modest reductions in water temperature, the addition of CPC optics amplifies the effect by factors of approximately three for ambient water and nine for water at 70 °C. Across all configurations, statistical analysis confirms stable, repeatable measurements. These results demonstrate that coupling commercially available RC materials with non-imaging optics provides consistent and measurable performance gains, supporting CPC-assisted RC as a scalable and retrofit-friendly strategy for urban and building energy applications while calling for longer-term experiments, durability assessments, and techno-economic analysis before deriving definitive deployment guidelines. Full article

The global shipping industry is surging ahead, and with it, a quiet revolution is taking place on the water: marine lithium-ion batteries have emerged as a crucial clean energy carrier, powering everything from ferries to container ships. When these vessels dock, they increasingly rely on shore power charging systems to refuel—essentially, plugging in instead of idling on diesel. But predicting how much power they will need is not straightforward. Think about it: different ships, varying battery sizes, mixed charging technologies, and unpredictable port stays all come into play, creating a load profile that is random, uneven, and often concentrated—a real headache for grid planners. So how do you forecast something so inherently variable? This study turned to the Monte Carlo method, a probabilistic technique that thrives on uncertainty. Instead of seeking a single fixed answer, the model embraces randomness, feeding in real-world data on supply modes, vessel types, battery capacity, and operational hours. Through repeated random sampling and load simulation, it builds up a realistic picture of potential charging demand. We ran the numbers for a simulated fleet of 400 vessels, and the results speak for themselves: load factors landed at 0.35 for conventional AC shore power, 0.39 for high-voltage DC, 0.33 for renewable-based systems, 0.64 for smart microgrids, and 0.76 when energy storage joined the mix. Notice how storage and microgrids really smooth things out? What does this mean in practice? Well, it turns out that Monte Carlo is not just academically elegant, it is practically useful. By quantifying uncertainty and delivering load factors within confidence intervals, the method offers port operators something precious: a data-backed foundation for decision-making. Whether it is sizing infrastructure, designing tariff incentives, or weighing the grid impact of different shore power setups, this approach adds clarity. In the bigger picture, that kind of insight matters. As ports worldwide strive to support cleaner shipping and align with climate goals—China’s “dual carbon” ambition being a case in point—achieving a reliable handle on charging demand is not just technical; it is strategic. Here, probabilistic modeling shifts from a simulation exercise to a tangible tool for greener, more resilient port energy management. Full article

Underwater images often suffer from luminance attenuation, structural degradation, and color distortion due to light absorption and scattering in water. The variations in illumination and color distribution across different water bodies further increase the uncertainty of these degradations, making traditional enhancement methods that rely on fixed parameters, such as underwater dark channel prior (UDCP) and histogram equalization (HE), unstable in such scenarios. To address these challenges, this paper proposes a multi-operator underwater image enhancement framework with adaptive parameter optimization. To achieve luminance compensation, structural detail enhancement, and color restoration, a collaborative enhancement pipeline was constructed using contrast-limited adaptive histogram equalization (CLAHE) with highlight protection, texture-gated and threshold-constrained unsharp masking (USM), and mild saturation compensation. Building upon this pipeline, an adaptive multi-operator parameter optimization strategy was developed, where a unified scoring function jointly considers feature gains, geometric consistency of feature matches, image quality metrics, and latency constraints to dynamically adjust the CLAHE clip limit, USM gain, and Gaussian scale under varying water conditions. Subjective visual comparisons and quantitative experiments were conducted on several public underwater datasets. Compared with conventional enhancement methods, the proposed approach achieved superior structural clarity and natural color appearance on the EUVP and UIEB datasets, and obtained higher quality metrics on the RUIE dataset (Average Gradient (AG) = 0.5922, Underwater Image Quality Measure (UIQM) = 2.095). On the UVE38K dataset, the proposed adaptive optimization method improved the oriented FAST and rotated BRIEF (ORB) feature counts by 12.5%, inlier matches by 9.3%, and UIQM by 3.9% over the fixed-parameter baseline, while the adjacent-frame matching visualization and stability metrics such as inlier ratio further verified the geometric consistency and temporal stability of the enhanced features. Full article

Soil amendments play a critical role in improving soil health and supporting sustainable crop production, especially under declining soil fertility and climate-related stress. However, their impact varies because each amendment influences the soil through different biogeochemical processes rather than a single universal mechanism. This review synthesizes current knowledge on a wide range of soil amendments, including compost, biosolids, green and animal manure, biochar, hydrochar, bagasse, humic substances, algae extracts, chitosan, and newer engineered options such as metal–organic framework (MOF) composites, highlighting their underlying principles, modes of action, and contributions to soil function, crop productivity, and soil carbon dynamics. Across the literature, three main themes emerge: improvement of soil physicochemical properties, enhancement of nutrient cycling and nutrient-use efficiency, and reinforcement of plant resilience to biotic and abiotic stresses. Organic nutrient-based amendments mainly enrich the soil and build organic matter, influencing soil carbon inputs and short- to medium-term increases in soil organic carbon stocks. Biochar, hydrochar, and related materials act mainly as soil conditioners that improve structure, water retention, and soil function. Biostimulant-type amendments, such as algae extracts and chitosan, influence plant physiological responses and stress tolerance. Humic substances exhibit multifunctional effects at the soil–root interface, contributing to improved nutrient efficiency and, in some systems, enhanced carbon retention. The review highlights that no single amendment is universally superior, with outcomes governed by soil–crop context. Its novelty lies in its mechanism-based, cross-amendment synthesis that frames both yield and carbon outcomes as context-dependent rather than universally transferable. Within this framework, humic substances and carbon-rich materials show potential for climate-smart soil management, but long-term carbon sequestration effects remain uncertain and context-dependent. Full article

The aim of this article is to present the social–ecological system (SES) as a unit of analysis for sustainable water management under conditions of anthropogenic pressure in Poland. In the face of accelerating climate change and growing human impacts, Polish water systems are exposed to increasing ecological stress and to material and immaterial losses affecting local communities. The SES approach provides an integrative analytical framework that links ecological and social components, enabling a holistic view of adaptive and governance processes at multiple spatial scales, from municipalities to areas that transcend administrative boundaries. Methodologically, this study triangulates three complementary approaches to strengthen explanatory inference. This conceptual SES review defines the analytical categories used in the paper, the bibliometric mapping (Scopus database with VOSviewer) identifies dominant research streams and underexplored themes, and the qualitative Polish case studies operationalize these categories to diagnose mechanisms, feedbacks, and governance vulnerabilities under anthropogenic pressure. The bibliometric analysis identifies the main research streams at the intersection of SES, water management and sustainable development, revealing thematic clusters related to climate change adaptation, environmental governance, ecosystem services and hydrological extremes. The case studies - the 2024 flood, the 2022 ecological disaster in the Odra River, and water deficits associated with lignite opencast mining in Eastern Wielkopolska - illustrate how anthropogenic pressure and climate-related hazards interact within local SES and expose governance gaps. Particular attention is paid to attitudes and social participation, understood as configurations of behaviors, knowledge and emotions that shape decision-making in local self-government, especially at the municipal level. This study argues that an SES-based perspective can contribute to building the resilience of water systems, improving the integration of ecological and social dimensions and supporting more sustainable water management in Poland. Full article

Hospitals are among the most energy-intensive buildings, yet their heating systems often operate below optimal efficiency due to outdated controls and limited sensing. Existing facilities often provide only a few accessible measurement points, many of which are locked within proprietary master controllers and not integrated into the Building Automation System (BAS). To address these limitations, this study proposes a data-driven feature selection approach that supports the development of gray-box emulators for complex, real-world central heating plants. A year of operational and weather data from a large hospital was used to train multiple machine learning models to predict the heating demand and return water temperature of a hospital heating plant system. The model’s performance was evaluated under reduced-sensor conditions by intentionally removing unpredictable values such as the VFD speed and flow rate. XGBoost achieved the highest accuracy with full sensor data and maintained a strong performance when critical sensors were omitted. An explainability analysis using Shapley Additive Explanations (SHAP) is applied to interpret the models, revealing that outdoor temperature and time of day (as an occupancy proxy) are the dominant predictors of boiler load. The results demonstrate that, even under sparse instrumentation, an AI-driven digital twin of the heating plant can reliably capture system dynamics. Full article