Search Results (4,210)

Dehumidification plays a vital role across industrial, commercial, and residential settings, where controlling moisture is essential for maintaining air quality, protecting materials, and ensuring comfort. Calcium chloride (CaCl₂) is a widely used, low-cost desiccant, but it suffers from a critical drawback: under humid conditions, particles tend to agglomerate, which reduces their ability to absorb water. In addition, when the salt dissolves in hydration water, its contact surface with moist air decreases, and corrosive liquid leakage can occur. Embedding CaCl₂ into hydrophilic porous matrices offers a solution by dispersing particles more effectively, preventing agglomeration, increasing the contact area, and retaining liquid within the pore network to suppress leakage. In this study, we introduce a novel approach for fabricating carbon-based foams impregnated with CaCl₂, produced through the thermal decomposition of glucose under self-induced pressure. These foams exhibit a composite architecture that integrates CaCl₂ and calcium carbonate, enabling controlled porosity through selective dissolution. Importantly, the in situ transformation of CaCl₂ into calcite refines the internal structure, improving both stability and acids absorption performance. FTIR confirmed the strong hydrophilicity of the foam walls, which enhances water vapor uptake while preventing leakage of saturated salt solutions. The carbon matrix further suppresses salt particle agglomeration during moisture absorption, resulting in high efficiency. These multifunctional foams not only capture water vapor and volatile acids but also show potential as phase change materials. Mechanical testing revealed tunable behavior among the fabricated foams, ranging from high-stiffness structures with superior energy absorption (e.g., C2) to more compliant foams with extended strain capacity (e.g., A2), illustrating their versatility for practical applications. Full article

The nexus of sustainable manufacturing and Industry 4.0 technologies is redefining modern industrial practices. Conventional manufacturing, characterized by intensive energy use, resource depletion, and waste generation, is increasingly unsustainable in the face of environmental pressures and evolving regulations. Industry 4.0 technologies—including IoT, artificial intelligence, data analytics, cloud computing platforms, and, recently, digital twins—provide opportunities to embed sustainability by enabling real-time monitoring, predictive analytics, and adaptive decision-making. This paper addresses key methods and strategies for sustainability and Industry 4.0 nexus. It involves IoT systems for data-driven monitoring, AI for process optimization, cloud platforms for supply chain sustainability, and emphasizes the use of digital twins for predictive maintenance. Organizational strategies such as cross-functional collaboration, customized software, dual-focus performance metrics, and workforce reskilling are explored, alongside barriers including high capital costs, cybersecurity risks, and system integration challenges. The findings present a structured perspective on harmonizing sustainability and Industry 4.0, demonstrating how this nexus can reduce environmental impact, enhance efficiency, and support long-term industrial resilience. Full article

Conventional alligator farming, characterized by reliance on chilled fish meat, faces significant challenges, including risks of bacterial contamination and nutritional imbalances. These issues heighten increasing disease susceptibility and threaten industry sustainability, underscoring the critical need for developing nutrient-dense, low-pathogenicity compound feeds. This study conducted a comparative analysis of newborn Siamese crocodiles fed either chilled fish meat or compound feed formulation. Intestinal microbial samples from both cohorts underwent 16S rRNA gene high-throughput sequencing to evaluate differences in microbial composition, diversity, and predicted functionality. The compound feed, specifically formulated for this investigation, possessed the following nutritional composition: crude protein 52.42%; digestible crude protein/digestible energy 16 mg/kcal; crude fat 12.31%; ash 17.42%; crude fiber 0.45%; starch 7.69%; digestible energy 3450 kcal/kg; lysine 3.66%; threonine 1.92%; methionine 1.27%; arginine 3.07%; total essential amino acids 22.97%; calcium 2.51%; total phosphorus 1.8%; available phosphorus 0.98%. Bioinformatics analysis revealed that the compound feed group exhibited numerically higher richness and alpha diversity indices within the intestinal microbiota compared to the chilled fish group. The microbial communities in both groups were dominated by the phyla Proteobacteria, Bacteroidetes, Fusobacteriota, and Firmicutes, collectively representing over 50% of the relative abundance. Functional prediction indicated that the compound feed group possessed the highest relative abundance in metabolic pathways associated with cofactor and vitamin metabolism, carbohydrate metabolism, amino acid metabolism, terpenoid and polyketide metabolism, lipid metabolism, and replication and repair. In contrast, the chilled fish group exhibited significant functional alterations in glycan biosynthesis and metabolism, translation, nucleotide metabolism, transcription, and biosynthesis of other secondary metabolites. Histomorphological analysis demonstrated greater villus height and crypt depth in the compound diet group compared to chilled fish group, although no significant differences were observed in crypt depth or the villus-to-crypt depth ratio. Collectively, these findings indicate that the compound feed enhances intestinal microbial diversity and optimizes its functional structure. Furthermore, while no statistically significant difference in small intestinal villus height was detected, the results suggest a potential positive influence on intestinal development. This investigation provides a scientific foundation for compound feed development, supporting sustainable breeding practices for Siamese crocodiles. Full article

Ensuring reliable inspection and quality control in complex industrial settings remains a significant challenge, particularly when traditional manual methods are applied to dynamic, multi-object environments. This paper presents and validates a new hybrid framework that integrates Object-Centric Process Mining (OCPM) with Support Vector Machines (SVMs) to improve industrial safety and quality assurance. The aims are: (1) to uncover and model the complex, multi-object processes characteristic of modern manufacturing using OCPM; (2) to assess these models in terms of conformance, performance, and the detection of bottlenecks; and (3) to design and embed a predictive layer based on Support Vector Regression (SVR) to anticipate process outcomes and support proactive control.The proposed methodology comprises a comprehensive pipeline: data fusion and OCEL structuring, OCPM for process discovery and conformance analysis, feature engineering, SVR for predictive modeling, and a multi-objective optimization layer. By applying this framework to a timber sawmill dataset, the study successfully modeled complex lumber drying operations, identified key object interactions, achieving a process conformance fitness score of 0.6905, and testing the integration of a predictive SVR layer. The SVR model’s predictive accuracy for production yield was found to be limited ($R^2=0.0255$) with the current feature set, highlighting the challenges of predictive modeling in this complex, multi-object domain. Despite this predictive limitation, the multi-objective optimization effectively balanced defect rates, energy consumption, and process delays, yielding a mean objective function value of 0.0768. These findings demonstrate the framework’s capability to provide deep, object-centric process insights and support data-driven decision-making for operational improvements in Industry 4.0. Future research will focus on improving predictive model performance through advanced feature engineering and exploring diverse machine learning techniques. Full article

Stainless steels are commonly used in coastal structures and in seawater desalination and treatment systems, so understanding their corrosion behaviour under different salinity conditions is important to ensure the durability and reliability of the material. In this study, the behaviour of AISI 304L, AISI 316L, and 2205 duplex stainless steels (DSS) was tested in three media with different salinities: brackish water (BSW), seawater (SW), and concentrated seawater bittern (CSW). Testing was conducted using classical electrochemical methods (open circuit potential, linear, and potentiodynamic polarization) supplemented by surface analyses (optical microscopy, SEM/EDS, and optical profilometry). Corrosion resistance increased in the order AISI 304L < AISI 316L < 2205 DSS. Duplex steel 2205 performed best in all media: it exhibited the most positive open circuit potential, the highest polarization resistance, the lowest corrosion current density, and the widest passive range. Unexpectedly, CSW showed improved corrosion resistance compared to SW, which is explained by the reduced chloride content characteristic of seawater bittern after NaCl crystallisation and the presence of magnesium, calcium, and sulphate ions that promote the formation of protective deposits on the metal surface. Pronounced pitting was observed on AISI 304L steel in seawater, while surface degradation in brackish and concentrated seawater was significantly less, and 2205 DSS remained almost unchanged. The results obtained can serve as guidelines for the design and selection of materials for equipment and structures in industries operating in aggressive marine and coastal environments, such as desalination plants, shipbuilding, and energy systems. Full article

A novel Z-scheme Dy₂TmSbO₇/BiHoO₃ heterostructure photocatalyst was synthesized with the ultrasound-assisted solvothermal method. The Dy₂TmSbO₇/BiHoO₃ heterojunction photocatalyst (DBHP) reflected wonderful separation efficiency of photogenerated electrons and photogenerated holes owing to the efficient direct Z-scheme heterojunction structure characteristic. The lattice parameter and the bandgap energy of the Dy₂TmSbO₇ were 10.52419 Å and 2.58 eV, simultaneously, the lattice parameter and the bandgap energy of the BiHoO₃ were 5.42365 Å and 2.25 eV, additionally, the bandgap energy of the DBHP was 2.32 eV. Above results indicated that DBHP, Dy₂TmSbO₇ or BiHoO₃ possessed an excellent ability for absorbing visible light energy, therefore, DBHP, Dy₂TmSbO₇ or BiHoO₃ owned superior photocatalytic activity for degrading the sulphamethoxypyridazine (SMP) under visible light irradiation. The removal rate of the SMP after visible light irradiation of 135 min with the DBHP was 99.47% for degrading the SMP during the photocatalytic degradation (PADA) process, correspondingly, the removal rate of the total organic carbon (TOC) concentration after visible light irradiation of 135 min with the DBHP was 98.02% for degrading the SMP during the PADA process. The removal rate of the SMP after visible light irradiation of 135 min with the DBHP was 1.15 times, 1.29 times or 2.60 times that with Dy₂TmSbO₇, BiHoO₃ or nitrogen-doped TiO₂ (N-T). Therefore, the DBHP displayed higher photocatalytic activity for degrading the SMP under visible light irradiation compared with Dy₂TmSbO₇, BiHoO₃ or N-T. Specifically, the mineralization rate for removing the TOC concentration during the PADA process of the SMP with the DBHP was 1.18 times, 1.32 times or 2.79 times that with Dy₂TmSbO₇, BiHoO₃ or N-T. In addition, the stability and reusability of the DBHP were systematically evaluated, confirming that the DBHP owned potential applicability for degrading the antibiotic pollutant, which derived from the practical industrial wastewater. Trapping radicals experiments and the electron paramagnetic resonance measurement experiments were conducted for identifying the reactive radicals, such as the hydroxyl radicals (•OH), the superoxide anions (•O₂⁻) and the photogenerated holes (h⁺), which were generated with the DBHP for degrading the SMP during the PADA process under visible light irradiation, as a result, the •O₂⁻ possessed the maximal oxidative capability compared with the •OH or the h⁺. Above results indicated the degradation mechanism and the degradation pathways which were related to the SMP. In conclusion, this study makes a significant contribution for the development of the efficient Z-scheme heterostructure photocatalysts and provides a key opinion to the development of the sustainable remediation method with the view of mitigating the antibiotic pollution. Full article

Distributed fiber-optic photoacoustic non-destructive testing (DFP-NDT) represents a paradigm shift from passive sensing to active probing, fundamentally transforming structural health monitoring through integrated fiber-based ultrasonic generation and detection capabilities. This review systematically examines DFP-NDT’s evolution by following the technology’s natural progression from fundamental principles to practical implementations. Unlike conventional approaches that require external excitation mechanisms, DFP-NDT leverages photoacoustic transducers as integrated active components where fiber-optical devices themselves generate and detect ultrasonic waves. Central to this technology are photoacoustic materials engineered to maximize conversion efficiency—from carbon nanotube-polymer composites achieving 2.74 × 10⁻² conversion efficiency to innovative MXene-based systems that combine high photothermal conversion with structural protection functionality. These materials operate within sophisticated microstructural frameworks—including tilted fiber Bragg gratings, collapsed photonic crystal fibers, and functionalized polymer coatings—that enable precise control over optical-to-thermal-to-acoustic energy conversion. Six primary distributed fiber-optic photoacoustic transducer array (DFOPTA) methodologies have been developed to transform single-point transducers into multiplexed systems, with low-frequency variants significantly extending penetration capability while maintaining high spatial resolution. Recent advances in imaging algorithms have particular emphasis on techniques specifically adapted for distributed photoacoustic data, including innovative computational frameworks that overcome traditional algorithmic limitations through sophisticated statistical modeling. Documented applications demonstrate DFP-NDT’s exceptional versatility across structural monitoring scenarios, achieving impressive performance metrics including 90 × 54 cm² coverage areas, sub-millimeter resolution, and robust operation under complex multimodal interference conditions. Despite these advances, key challenges remain in scaling multiplexing density, expanding operational robustness for extreme environments, and developing algorithms specifically optimized for simultaneous multi-source excitation. This review establishes a clear roadmap for future development where enhanced multiplexed architectures, domain-specific material innovations, and purpose-built computational frameworks will transition DFP-NDT from promising laboratory demonstrations to deployable industrial solutions for comprehensive structural integrity assessment. Full article

Global energy markets have experienced persistent dispersion in real energy prices, creating structural competitiveness pressures that standard indicators often fail to capture in real time. These pressures have intensified as energy-intensive sectors face asymmetric exposure across advanced and emerging economies. This study addresses two critical gaps in international energy cost competitiveness. The first is a frequency gap: conventional indicators such as the Real Unit Energy Cost (RUEC) are typically published with delays of 2–5 years, limiting their usefulness for timely policy evaluation. Here, both RUEC and the Real Price Level Index for energy (Real PLI)—the ratio of the Purchasing Power Parity (PPP) for energy to that for GDP—are measured with only a 2–3-month lag for nine countries—four in Asia, four in Europe, and the U.S. The second is a competitiveness gap that calls for policy responses. Real PLIs indicate that the energy price disadvantages of Japan, Korea, France, Germany, Italy, and the UK have widened from 1.76–2.91 times the U.S. level before the pandemic to 2.14–3.28 times by Q3 2025, with the gaps relative to China and India also widening. Once country-specific thresholds are exceeded, output in energy-intensive and trade-exposed (EITE) industries tends to contract disproportionately. These findings highlight that sustainable transitions require not only internationally differentiated burden-sharing but also structural reforms to avoid persistent widening of energy price gaps. The Real PLI framework provides a timely indicator of competitiveness and an early-warning tool, signaling when growing asymmetries may undermine policy feasibility. Policy implications include the need to monitor real energy price dispersion as a core source of competitiveness risk, to strengthen structural measures that stabilize marginal energy costs, and to design transition pathways that account for heterogeneous adjustment pressures across countries. Full article

Magnetic flux leakage (MFL) signals in steel wire rope defect detection are often corrupted by structural noise and environmental interference, leading to reduced defect recognition accuracy. This study proposes a denoising approach combining synchrosqueezing wavelet transform (SST) with dynamic time–frequency masking to enhance signal quality. The method first employs SST to redistribute time–frequency coefficients, improving resolution and highlighting defect-related energy concentrations. A dynamic masking strategy is then introduced to adaptively suppress noise by leveraging local energy statistics. Experimental results on a constructed dataset show that the proposed method achieves a signal-to-noise ratio (SNR) improvement compared to traditional wavelet denoising. This approach provides an effective solution for real-time monitoring of wire rope defects in industrial applications. Full article

Accurately forecasting the operating temperature of lithium-ion batteries (LIBs) is essential for preventing thermal runaway, extending service life, and ensuring the safe operation of electric vehicles and stationary energy-storage systems. This work introduces a unified, physics-informed, and data-driven temperature-prediction framework that integrates mathematically governed preprocessing, electrothermal decomposition, and sequential deep learning architectures. The methodology systematically applies the governing relations to convert raw temperature measurements into trend, seasonal, and residual components, thereby isolating long-term thermal accumulation, reversible entropy-driven oscillations, and irreversible resistive heating. These physically interpretable signatures serve as structured inputs to machine learning and deep learning models trained on temporally segmented temperature sequences. Among all evaluated predictors, the Bidirectional Long Short-Term Memory (BiLSTM) network achieved the highest prediction fidelity, yielding an RMSE of 0.018 °C, a 35.7% improvement over the conventional Long Short-Term Memory (LSTM) (RMSE = 0.028 °C) due to its ability to simultaneously encode forward and backward temporal dependencies inherent in cyclic electrochemical operation. While CatBoost exhibited the strongest performance among classical regressors (RMSE = 0.022 °C), outperforming Random Forest, Gradient Boosting, Support Vector Regression, XGBoost, and LightGBM, it remained inferior to BiLSTM because it lacks the capacity to represent bidirectional electrothermal dynamics. This performance hierarchy confirms that LIB thermal evolution is not dictated solely by historical load sequences; it also depends on forthcoming cycling patterns and entropic interactions, which unidirectional and memoryless models cannot capture. The resulting hybrid physics-data-driven framework provides a reliable surrogate for real-time LIB thermal estimation and can be directly embedded within BMS to enable proactive intervention strategies such as predictive cooling activation, current derating, and early detection of hazardous thermal conditions. By coupling physics-based decomposition with deep sequential learning, this study establishes a validated foundation for next-generation LIB thermal-management platforms and identifies a clear trajectory for future work extending the methodology to module- and pack-level systems suitable for industrial deployment. Full article

Financial supply side structural reform (FSSR) serves as a key for advancing the low-carbon transformation of industrial energy (LTIE) and supporting the dual carbon strategic goals. By using provincial panel data from China for the period of 2008–2022 and leveraging the national financial comprehensive reform pilot zones as a quasi-natural experiment, this study uses the difference-in-differences method to examine empirically the effect of FSSR on the LTIE and the underlying mechanisms. Research findings indicate that, first, FSSR can significantly advance the LTIE, which remained unchanged after other policies, omitted variables, and other potential influencing factors were controlled. Second, the mechanism tests indicate that FSSR can drive the LTIE by increasing green financial support, fostering green industrial development, and promoting green technological innovation. Third, the heterogeneity tests reveal that the benchmark effect is pronounced in regions with weak environmental regulation and a low level of financial development. This study provides theoretical and empirical evidence to understand the crucial role of FSSR in advancing the LTIE and insights for relevant policy formulation. Full article

Mixed amine/sulfolane (TMS) biphasic solutions have gained attention for their adjustable structure–activity relationships and lower regeneration energy. In this study, monoethanolamine (MEA) is employed as the main absorbent and polyamine as the co-absorbent, which are subsequently mixed with the phase separation promoter sulfolane (TMS) to form ternary biphasic solvent systems. Polyamine co-absorbents include 3-Dimethylaminopropylamine (DMAPA), 3-Diethylaminopropylamine (DEAPA), and Diethylenetriamine (DETA). Phase separation, absorption, and desorption performances were systematically studied. Reaction and phase separation mechanisms were elucidated through ¹³C nuclear magnetic resonance (NMR) spectroscopy. The overall mass transfer coefficients (K_G) were measured using a wetted wall column (WWC). Variations in the amine-to-sulfolane concentration ratio showed minimal impact on phase volume, while temperature and solvent composition significantly influenced phase separation behavior. All three solvents exhibited superior CO₂ capture performance, with CO₂ loadings in the rich phases ranging from 4.09 to 4.71 mol/L and over 96.82% of CO₂ concentrated in them, cyclic capacities reached or exceeded 3 mol/L, and regeneration energy consumption was 29.63–55.51% lower than 5 M MEA. ¹³C NMR analysis indicated that multiple N atoms in polyamines promoted the formation of additional ionic species during CO₂ absorption, thereby enhancing phase separation completeness. Furthermore, K_G values for the ternary systems exceeded that of conventional MEA, with the MEA/DEAPA/TMS system exhibiting a 1.7-fold increase. These findings demonstrated the industrial potential of MEA/polyamine/TMS biphasic solvents for efficient CO₂ capture. Full article

The exponential growth of lithium-ion batteries (LIBs) in electric vehicles and energy storage systems has amplified the urgent need for sustainable recycling strategies. Conventional pyrometallurgical and hydrometallurgical methods for LIB recycling are energy-intensive, chemically demanding, and fail to preserve the structural integrity of cath-ode materials. Closed-loop recycling, in contrast, enables the recovery of layered oxides with minimal processing steps, reducing environmental footprint and supporting a circular economy. This study provides a systematic comparison of two regeneration approaches—sol–gel synthesis and hydroxide co-precipitation—for closed-loop recycling of layered NCM (LiNi_xCo_yMn_zO₂) cathode materials recovered from spent LIBs. Spent cells were mechani-cally processed and leached using malic acid to recover Ni, Co, Mn, which were subsequently used to synthesize NCM622 cathode powders. The regenerated materials were characterized using SEM/EDX, XRD, and electrochemical testing in CR2032 coin cells. Both methods successfully produced phase-pure layered oxides with the R-3m structure, with distinct differences in structural ordering and electrochemical behavior. The sol–gel-derived NCM622 displayed higher crystallinity and reduced cation mixing, evidenced by an I(003)/I(104) ratio of 1.896 compared to 1.720 for the co-precipitated sample, and delivered a high initial discharge capacity of 170 mAh/g at 0.1 C. However, it exhibited significant capacity fade, retaining only 60 mAh/g after 40 cycles. In contrast, the co-precipitation route produced hierarchical porous spherical agglomerates that offered superior cycling stability, maintaining ~150 mAh/g after 40 cycles with lower polarization (ΔE_p = 0.16 V). Both materials demonstrated electrochemical performance comparable to commercial NCM. Overall, hydroxide co-precipitation emerged as the most industrially viable method due to scalable processing, compositional robustness, and improved long-term stability of regenerated cathodes. This work highlights the critical influence of synthesis route selection in LIB closed-loop recycling and provides a technological framework for industrial recovery of high-value NCM cathode materials. Full article

The increasing global demand for cleaner transportation has intensified the importance of efficient AdBlue^® (AUS32) production, a key chemical in selective catalytic reduction (SCR) systems that reduces nitrogen oxides (NOx) emissions from diesel engines. This work presents a computational fluid dynamics (CFD) simulation study of the urea–water mixing process within a high shear mixer (HSM), aiming to enhance the sustainability of AdBlue^® manufacturing. The model evaluates the hydrodynamic characteristics critical to optimising the dissolution of urea pellets in deionised water, which conventionally requires significant preheating. Experimental validation was conducted by comparing pressure drop simulation results with operational data from an active industrial facility in the United Kingdom. Therefore, this study validates the CFD model against an industrial two-stage Rotor–stator under real operating conditions. The computational framework combines a refined mesh with the k-ω SST turbulent model to resolve flow structures and capture near-wall effects and shear stress transport in complex flow domains. The results reveal opportunities for process optimisation, particularly in reducing thermal energy input without compromising solubility, thus offering a more sustainable pathway for AdBlue^® production. The main contribution of this work is to close existing gaps in industrial practice and propose and computationally validate strategies to improve the numerical design of HSM for solid dissolution. Full article

This study investigates polypropylene (PP)–based biocomposites reinforced with systematically varied jute and glass fiber ratios as sustainable, lightweight alternatives for semi-structural automotive parts. Four formulations (J20/G0, J15/G5, J10/G10, J5/G15) with a constant 20 wt% total fiber were produced by injection molding and characterized through mechanical, thermal, and morphological analyses. Tensile, flexural, and Charpy impact tests showed progressive improvements in strength, stiffness, and energy absorption with increasing glass fiber content, while ductility was maintained or slightly enhanced. SEM revealed a transition from fiber pull-out in jute-rich systems to fiber rupture and stronger matrix adhesion in glass-rich hybrids. Thermal analyses confirmed the benefits of hybridization: heat deflection temperature increased from 75 °C (J20/G0) to 103 °C (J5/G15), and thermogravimetry indicated improved stability and higher char residue. DSC showed negligible changes in crystallization and melting, confirming that fiber partitioning does not significantly affect PP crystallinity. Benchmarking demonstrated mechanical and thermal performance comparable to acrylonitrile–butadiene–styrene (ABS) and acrylonitrile–styrene–acrylate (ASA), widely used in automotive components. Finally, successful molding of a prototype exterior mirror cap from J20/G0 validated industrial processability. These findings highlight jute–glass hybrid PP composites as promising, sustainable alternatives to conventional engineering plastics for automotive engineering applications. Full article