Search Results (12,466)

This study proposes a circular economy strategy to recover phenolic compounds by valorizing shrimp shell waste into a chitosan biosorbent (CH-B). Its adsorption efficiency was evaluated compared to commercial activated carbon (AC) and synthetic XAD-4 resin. Kinetic analysis revealed that while both pseudo-first-order (PFO) and pseudo-second-order (PSO) models exhibited high correlations (R²$\ge$ 0.96), both CH-B and XAD-4 resin were best described by the PFO model. This aligns with diffusion-controlled processes consistent with the porous and physical nature of these adsorbents. In contrast, AC followed the PSO model. Isotherm modeling indicated that CH-B and AC fit the Temkin model, reflecting heterogeneous surfaces, whereas XAD-4 followed the Langmuir model (monolayer adsorption). Notably, CH-B exhibited a maximum adsorption capacity (q_m) of 229.2 mg/g, significantly outperforming XAD-4 (104.8 mg/g) and AC (90.2 mg/g). Thermodynamic and kinetic modeling confirmed that the adsorption mechanism was governed by a combination of electrostatic interactions, π–π stacking, and hydrogen bonding between the hydroxyl/amine groups of chitosan and phenolic compounds. Optimization using Box–Behnken design for CH-B showed optimal acidic pH and moderate temperature but non-significant effect of CH-B dose in the experimental domain. Optimisation results showed unexpected high removal efficiency at low CH-B dosages. A tentative explanation may be adsorbent aggre-gation, which needs to be confirmed by further experimental evidence. Full article

This review synthesizes the state of the art in flapping foil technology and bridges the distinct engineering domains of bio-inspired propulsion and power generation via flow energy harvesting. This review is motivated by the observation that propulsion and power-generation studies are frequently presented separately, even though they share common unsteady vortex dynamics. Accordingly, we adopt a unified unsteady-aerodynamic perspective to relate propulsion and energy-extraction regimes within a common framework and to clarify their operational duality. Within this unified framework, the feathering parameter provides a theoretical delimiter between momentum transfer and kinetic energy extraction. A critical analysis of experimental foundations demonstrates that while passive structural flexibility enhances propulsive thrust via favorable wake interactions, synchronization mismatches between deformation and peak hydrodynamic loading constrain its benefits in power generation. This review extends the analysis to complex and non-homogeneous environments and identifies that density stratification fundamentally alters the hydrodynamic performance. Specifically, resonant interactions with the natural Brunt–Väisälä frequency of the fluid shift the optimal kinematic regimes. The present study also surveys computational methodologies and highlights a paradigm shift from traditional parametric sweeps to high-fidelity three-dimensional (3D) Large-Eddy Simulations (LESs) and Deep Reinforcement Learning (DRL) to resolve finite-span vortex interconnectivities. Finally, this review outlines the critical pathways for future research. To bridge the gap between computational idealization and physical reality, the findings suggest that future systems prioritize tunable stiffness mechanisms, multi-phase environmental modeling, and artificial intelligence (AI)-driven digital twin frameworks for real-time adaptation. Full article

Finding Noether symmetries for time-dependent damped dynamical systems remains a significant challenge. This paper introduces a complete geometric algorithm for determining all Noether point symmetries and first integrals for the general class of Lagrangians $L=A\left(t\right)L_0$, which model motion with general linear damping in a Riemannian space. We derive and prove a central Theorem that systematically links these symmetries to the homothetic algebra of the kinetic metric defined by $L_0$. The power of this method is demonstrated through a comprehensive analysis of the damped Kepler problem. Beyond recovering known results for constant damping, we discover new quadratic first integrals for time-dependent damping $\varphi \left(t\right)=\gamma /t$ with $\gamma =-1$ and $\gamma =-1/3$. We also include preliminary results on the Noether symmetries of the damped harmonic oscillator. Finally, we clarify why a time reparameterization that removes damping yields a physically inequivalent system with different Noether symmetries. This work provides a unified geometric framework for analyzing dissipative systems and reveals new integrable cases. Full article

Calcium (Ca²⁺)signaling dysfunction is a central contributor to neuronal hyperexcitability and seizure propagation in epilepsy, yet the intracellular mechanisms underlying the actions of valproic acid (VPA) remain incompletely understood. In this study, we investigated whether VPA modulates Ca²⁺ homeostasis at the level of the endoplasmic reticulum (ER) and how this action influences cytosolic Ca²⁺ dynamics associated with epileptiform activity. ER Ca²⁺ levels were directly measured using ER-targeted aequorin in HeLa and PC12 cells, while cytosolic Ca²⁺ signals were monitored by fura-2 fluorescence imaging in bovine chromaffin cells exposed to veratridine, a model of sustained sodium channel activation and Ca²⁺ oscillations. VPA induced a concentration-dependent release of Ca²⁺ from the ER, with an IC₅₀ of approximately 17 µM. This effect was preserved in permeabilized cells and exhibited activation kinetics comparable to those elicited by inositol 1,4,5-trisphosphate (InsP₃). Pharmacological inhibition of InsP₃ receptors (InsP₃Rs), but not ryanodine receptors or SERCA, abolished VPA-induced ER Ca²⁺ release, supporting a selective InsP₃R-mediated mechanism. Functionally, VPA suppressed the repetitive cytosolic Ca²⁺ oscillations induced by veratridine, while simultaneously producing a sustained elevation of cytosolic Ca²⁺ originating from ER stores and facilitating depolarization-evoked catecholamine secretion. Together, these results support the conclusion that VPA induces InsP₃R-mediated Ca²⁺ mobilization from the endoplasmic reticulum and identify ER Ca²⁺ release as a previously unrecognized intracellular mechanism contributing to its modulatory effects on Ca²⁺ signaling and excitability in epilepsy. Full article

Direct ethanol fuel cells (DEFCs) have gained considerable attention as promising power sources for sustainable energy conversion due to their high energy density, low toxicity, and renewable ethanol feedstock. However, the sluggish ethanol oxidation reaction (EOR) kinetics and the formation of strongly adsorbed intermediates (e.g., CO*, CH_x*) severely hinder catalytic efficiency and durability. Rhodium (Rh)-based catalysts stand out for their balanced intermediate adsorption, efficient C–C bond cleavage, and superior CO tolerance arising from their unique electronic structure. This review summarizes recent advances in Rh-based EOR catalysts, including monometallic Rh nanostructures, Rh-based alloys, and Rh–support composites. The effects of morphology, alloying, and metal–support interactions on activity, selectivity, and stability are discussed in detail. Strategies for structural and electronic regulation—such as nanoscale design, alloying modulation and interfacial engineering—are highlighted to enhance catalytic performance. Finally, current challenges and future directions are outlined, emphasizing the need for Rh-based catalysts with high activity, selectivity and stability, integrating in situ characterization with theoretical modeling. This work provides insights into the structure–activity relationships of Rh-based catalysts and guidance for designing efficient and durable anode catalysts for practical DEFC applications. Full article

In this study, the nonlinear and non-monotonic behavior of amperometric bienzyme biosensors employing an enzymatic trigger reaction is investigated analytically and computationally using a two-compartment model comprising an enzymatic layer and an outer diffusion layer. The trigger enzymatic reaction is coupled with a cyclic electrochemical–enzymatic conversion (CEC) process. The model is formulated as a system of reaction–diffusion equations incorporating nonlinear Michaelis–Menten kinetics and interlayer partitioning effects. Exact steady-state analytical solutions for substrate and product concentrations, as well as for the output current, are obtained for specific cases of first- and zero-order reaction kinetics. At the transition conditions, biosensor performance is further analyzed numerically using the finite difference method. The CEC biosensor exhibits the highest signal gain when the first enzyme has low activity and the second enzyme has high activity; however, under these conditions, the response time is the longest. When the first enzyme possesses a higher substrate affinity (lower Michaelis constant) than the second, the biosensor demonstrates severalfold higher current and gain compared to the reverse configuration under identical diffusion limitations. Furthermore, increasing external mass transport resistance or interfacial partitioning can enhance the apparent signal gain. Full article

The structural origin of the germanate anomaly in glasses, which involves complex Ge–O coordination environments, is frequently studied using crystalline analogs. This study aims to provide reliable spectroscopic fingerprints by performing a detailed structural and thermal analysis of crystalline A₄Ge₉O₂₀ model systems with A = Li, Na, K. The compounds were synthesized via melt crystallization and characterized using powder X-ray diffraction (PXRD), differential scanning calorimetry (DSC), and Raman spectroscopy techniques. The results demonstrate clear cation-dependent crystallization pathways. The Li-containing system predominantly forms Li₂Ge₇O₁₅ in mixture with Li₄Ge₉O₂₀, indicating a preference for thermodynamically stable phases. The Na-system successfully yields the target Na₄Ge₉O₂₀ compound. In contrast, the K-system primarily produces the likely metastable K₂Ge₄O₉ phase with a significant amorphous fraction, highlighting the role of kinetic limitations. This comparative study demonstrates that the size of the alkali cation is a critical factor for controlling phase formation under identical stoichiometric and thermal conditions. Full article

This study investigated the effects of microwave (MW) pre-treatment (45 kJ total irradiated microwave energy) and magnetic nanoparticles (MPs) on the anaerobic digestion (AD) of meat-processing sludge, integrating biokinetic modeling with dielectric parameter measurements. Five different sludge variants were examined: native (non-treated control); MP-only control; microwave pre-treated sludge, and MW + MP combination with the nanoparticles either retained in the fermentation medium or removed prior to anaerobic digestion. Cumulative biomethane production was evaluated using the modified Gompertz, Logistic, and Weibull models, and key kinetic parameters (maximum achievable methane yield, maximum rate of product formation, and λ-values) were compared across the different treatments. The results revealed that the highest production rate, along with the highest biomethane potential, could be achieved when combining MW treatment with magnetic nanoparticles which were retained in the fermentation medium during AD. Based on the biokinetic analysis, this combined method increased biomethane potential by 52% to 390 mL CH₄/gVS and maximum methane production rate by 85% to 37 mL CH₄/gVS/day compared to the untreated control. The measurement of relative permittivity (${\epsilon }^{\prime }$) exhibited progressive changes during digestion, and the maximum rate of change in ${\epsilon }^{\prime }$ strongly correlated with the maximum methane production rate across all samples (R² > 0.98). These results highlight the potential of microwave–metal oxide nanoparticle pre-treatment for process enhancement and to demonstrate the suitability of dielectric parameter measurement as a rapid, non-invasive indicator of biochemical activity during anaerobic digestion. Full article

The increasing urgency to mitigate plastic pollution has accelerated the shift from linear manufacturing toward circular systems. This review synthesizes current advances in mechanical, chemical, biological, and upcycling pathways, emphasizing how artificial intelligence (AI) is reshaping decision-making, performance prediction, and system-level optimization. Intelligent sensing technologies—such as FTIR, Raman spectroscopy, hyperspectral imaging, and LIBS—combined with Machine Learning (ML) classifiers have improved material identification, reduced reject rates, and enhanced sorting precision. AI-assisted kinetic modeling, catalyst performance prediction, and enzyme design tools have improved process intensification for pyrolysis, solvolysis, depolymerization, and biocatalysis. Life Cycle Assessment (LCA)-integrated datasets reveal that environmental benefits depend strongly on functional-unit selection, energy decarbonization, and substitution factors rather than mass-based comparisons alone. Case studies across Europe, Latin America, and Asia show that digital traceability, Extended Producer Responsibility (EPR), and full-system costing are pivotal to robust circular outcomes. Upcycling strategies increasingly generate high-value materials and composites, supported by digital twins and surrogate models. Collectively, evidence indicates that AI moves from supportive instrumentation to a structural enabler of transparency, performance assurance, and predictive environmental planning. The convergence of AI-based design, standardized LCA frameworks, and inclusive governance emerges as a necessary foundation for scaling circular plastic systems sustainably. Full article

Currently, the mainstream methods for dye removal internationally include advanced oxidation, catalytic degradation, and adsorption. Catalytic and oxidation methods are costly and unsuitable for large-scale application. While adsorption is straightforward, selecting and modifying raw materials poses significant challenges. Therefore, identifying readily available and inexpensive adsorbents is crucial for dye removal. This study utilized Type A coal as raw material to prepare a series of specialized activated carbon (JA) for adsorbing methyl orange from wastewater, followed by optimization. The optimized screening results indicated that JA-12 exhibited the highest methyl orange removal rate (90.54%). This performance is attributed to its larger micropore structure and increased pore volume. Further analysis revealed that the adsorption process follows pseudo-second-order kinetics and the Langmuir adsorption isotherm model (R² ≈ 0.999). Compared to the theoretical adsorption capacity calculated based on specific surface area, the adsorption capacity calculated based on pore volume (270.66 mg/g) was closer to the actual adsorption capacity, indicating that the pore structure of JA-12 plays a dominant role in the adsorption process. Combined with the Langmuir adsorption model, it can be inferred that dye molecules in solution adsorb onto the inner surface of JA-12 in a monolayer form. Surface functional group analysis revealed that protonation enhances JA-12’s adsorption capacity for the azo dye methyl orange. Collectively, our findings elucidate the removal mechanism of methyl orange using readily available coal as raw material to prepare low-cost specialty activated carbon, providing a framework for cost-effective, large-scale dye removal. Full article

Li₃PO₄ is a promising raw material for the low-cost synthesis of high-performance LiFePO₄. Reactive crystallization from low-concentration lithium-rich brine is a key process for the efficient preparation of high-quality Li₃PO₄ products. The effect of operating conditions (temperature/supersaturation/impurities/ultrasonic) on the induction time was investigated using a focused beam reflectance measurement. The evaluation of the primary nucleation, growth kinetics, and parameters for the extraction of Li₃PO₄ from low-concentration lithium-rich brine was conducted using an induction time method. The dominant mechanisms at different stages were inferred through online monitoring of the particle size distribution during the Li₃PO₄ crystallization process. Results show that induction time decreases with increasing operating conditions (temperature/supersaturation/ultrasonic frequency), indicating that their increases all promote nucleation. Impurities (NaCl/KCl) did not significantly affect the induction time, whereas Na₂SO₄ and Na₂B₄O₇ significantly increased it, with Na₂B₄O₇ showing the most notable effect. Classical nucleation theory was applied to determine kinetic parameters (nucleation activation energy/interfacial tension/contact angle/critical nucleus size/surface entropy factor). Results indicate that Li₃PO₄ mainly nucleates through heterogeneous nucleation, with a temperature increase weakening the role of heterogeneous nucleation. Fitted models indicate that Li₃PO₄ predominantly follows the secondary nucleation and spiral growth mechanism. Our findings are crucial for crystallization design and control in producing high-quality Li₃PO₄ from lithium-rich brines. Full article

To enhance the screening efficiency of bioactive peptides, an AI-driven approach was employed to screen DPP-IV inhibitory peptides from goat blood proteins by an integrated in silico, in vitro, and machine learning strategy. Furthermore, the inhibitory mechanism of DPP-IV inhibitory peptides was elucidated by kinetics, molecular docking and simulation. Additionally, their in vitro digestive stability was assessed. In silico results revealed that goat blood proteins were promising precursors of DPP-IV inhibitory peptides, while bromelain was the optimal protease. Their peptide sequences were further identified by peptidomics and predicted by self-developed machine learning models (LightGBM) to identify the potent DPP-IV inhibitory peptides. Two novel DPP-IV inhibitory peptides were identified (FPL and FPHFDL). Enzyme kinetics, molecular docking and molecular simulation data indicated that FPL served as a competitive inhibitor, whereas FPHFDL was a non-competitive inhibitor. Overall, the integrative in silico and in vitro strategy is feasible for rapid screening of DPP-IV inhibitory peptides from goat blood proteins. Full article

Projects integrating Science, Technology, Engineering, and Mathematics (STEM) are essential to interdisciplinary research. This study presents a STEM (Science, Technology, Engineering, and Mathematics) methodology with the primary objective of designing, constructing, and validating a functional cannabinoid extraction device. To inform the device’s drying parameters, the dehydration kinetics of female hemp buds or flowering buds (FHB) were first analyzed using infrared drying at 100 °C for different durations. The plants were cultivated and harvested in accordance with good agricultural practices using Dinamed CBD Autoflowering seeds. The FHB were harvested and prepared by manually separating them from the stems and leaves. Six 5 g samples were prepared, each with a slab geometry of varying surface area and thickness. Two of these samples were ground: one into a fine powder and the other into a coarse powder. Mathematical fits were obtained for each resulting curve using either an exponential decay model or the logarithmic equation $y\left(t\right)=A\left(e^{-kt}\right)+y_0$ calculate the equilibrium moisture (m_E). The Moisture Rate (MR) was calculated, and by modelling with the logarithmic equation, the constant k and the effective diffusivity (Deff) were determined with the analytical solution of Fick’s second law. The Deff values (ranging from 10⁻⁷ to 10⁻⁵) were higher than previously reported. The coarsely ground powder sample yielded the highest k and Deff values and was selected for oil extraction. The device was then designed using Quality Function Deployment (QFD), specifically the House of Quality (HoQ) matrix, to systematically translate user requirements into technical specifications. A 200 g sample of coarsely ground, dehydrated FHB was prepared for ethanol extraction. Chemical results obtained by Liquid Chromatography coupled with Photodiode Array Detection (LC-PDA) revealed the presence of THC, CBN, CBC, and CBG. The extraction device design was validated using previous results showing the presence of CBD and CBDA. The constructed device successfully extracted cannabinoids, including Δ9-THC, CBG, CBC, and CBN, from coarsely ground FHB, validating the integrated STEM approach. This work demonstrates a practical framework for developing accessible agro-technical devices through interdisciplinary collaboration. Full article

This paper analyses several mathematical games developed 50 years ago by Manfred Eigen and Ruthild Winkler in their famous book “Laws of the Game: How the Principles of Nature Govern Chance,” published for the first time in German in 1975. These games are intended to represent the essential aspects of chemical selection processes via symmetry breaking in biological systems. Special attention is paid to games that model biochemical kinetics, in which a chessboard is used to represent different types of substrates. The time-dependent statistical outcomes of several games are studied by Monte Carlo techniques. Analytical stochastic models applied to these games relate game rules to partial differential equation problems with appropriate initial and boundary conditions: rationalizing their outcomes, they confirm the intuitions of the original authors and add new insights. The potential impact of game-based models on current research on biological homochirality is discussed. Full article

This paper presents an analytical model for the hereditary vibrations of a coupled circular plate system interconnected by viscoelastic creep layers. The system is represented as a discrete-continuous chain of thin, isotropic plates with time-dependent material properties. Based on the theory of hereditary viscoelasticity and D’Alembert’s principle, a system of partial integro-differential equations is derived and reduced to ordinary integro-differential equations using Bernoulli’s method and Laplace transforms. Analytical expressions for natural frequencies, mode shapes, and time-dependent response functions are obtained. The results reveal the emergence of multi-frequency vibration regimes, with modal families remaining temporally uncoupled. This enables the identification of resonance conditions and dynamic absorption phenomena. The fractional parameter serves as a tunable damping factor: lower values result in prolonged oscillations, while higher values cause rapid decay. Increasing the kinetic stiffness of the coupling layers raises vibration frequencies and enhances sensitivity to hereditary effects. This interplay provides deeper insight into dynamic behavior control. The model is applicable to multilayered structures in aerospace, civil engineering, and microsystems, where long-term loading and time-dependent material behavior are critical. The proposed framework offers a powerful tool for designing systems with tailored dynamic responses and improved stability. Full article