Search Results (354)

Bio-CO₂ is part of the natural carbon cycle and represents a sustainable carbon source for the production of Renewable Fuels of Non-Biological Origin (RFNBOs), such as synthetic methanol. This study addresses the critical knowledge gap in aligning diverse biogenic CO₂ sources with e-methanol requirements in the EU by providing harmonized mapping, based on datasets, literature sources, and reported industrial statistics at the sectoral and country level. Bio-CO₂ streams from biogas and biogas upgrading, biomass combustion, pulp and paper, bioethanol production, and the food and beverage sector are evaluated for total emissions, CO₂ concentrations and purity, the geographical distribution, seasonality, and impurity profiles. Results show that approximately 350 Mtpa of bio-CO₂ are emitted across the EU, with highly heterogeneous characteristics. Biogas upgrading and fermentation-based processes generate highly pure CO₂ streams (>98–99%), yet their small and dispersed nature complicates logistics. In contrast, biomass-combustion and pulp and paper sectors provide large volumes (around 214.6–298.2 Mtpa and 73.9 Mtpa CO₂, respectively), but in diluted streams (typically 3–15% and 10–20%). Replacing just 10% of the EU maritime fuel demand with e-methanol would require 53.6 Mtpa of bio-CO₂ and 58 GW of electrolyzer capacity, a stark contrast to the current operational 385 MW. The findings highlight the need for infrastructure planning and aggregation hubs to enable the large-scale deployment of RFNBO methanol in the maritime sector. Full article

In this study, lightweight geopolymer mortars with low environmental impact, high thermal insulation performance, and strong resistance to elevated temperatures were developed. Fly ash, expanded perlite, and bio-based hemp fibers were employed as the binder, aggregate, and reinforcement, respectively. Hemp fibers were prepared in lengths of 1, 2, and 3 cm and incorporated into the mixtures at dosages of 0.50%, 0.75%, and 1.00% by weight of binder. Sodium hydroxide was used as the activator, and specimens were heat-cured at 90 °C for 24–48–72 h. The workability, unit weight, UPV, flexural, and compressive strength of the geopolymer mortars were determined. In addition, thermal conductivity, high-temperature resistance, microstructural characteristics, and environmental impacts of selected mixtures were evaluated. The results demonstrated that lightweight geopolymer mortars could be successfully produced using expanded perlite aggregate and that hemp fibers significantly enhanced mechanical performance up to 48% at one day. Moreover, fiber reinforcement improved thermal insulation capability by up to 5.5% and high-temperature resistance. FESEM, EDX, elemental mapping, and XRD analyses supported the mechanical and physical findings through detailed microstructural evidence. Furthermore, LCA results revealed that fiber incorporation improved the environmental performance of geopolymer mortars, resulting in approximately a 21% reduction in global warming potential compared with the reference mixture. Full article

Star polymers with dense shell structures exhibit unique advantages in molecule encapsulation. The incorporation of dendronized polymers as arms into star polymers enables the formation of spherical core–shell structures with high-density chain stacking, which is of great significance for enhancing their encapsulation capabilities. Here, we report on the synthesis of a new type of star dendronized polymer consisting of oligoethylene glycol (OEG)-based dendronized polymers as the arms and gold nanoparticles (AuNPs) as the core. Due to the thickness of individual dendronized polymer arms, the morphology of star dendronized polymers was directly visualized by an atomic force microscope (AFM). These star polymers inherit characteristic thermoresponsiveness from the OEG-based dendronized linear polymers, and their thermoresponsive behavior depends mainly on the grafting density of polymer chains on the AuNP cores and the molecular weights of the polymer arms. More importantly, these star dendronized polymers exhibit a tunable encapsulation capacity to guest molecules, which can be modulated through thermally induced aggregation. By virtue of these peculiarities, these thermoresponsive star dendronized polymers with tailorable release properties hold promise as smart nanoboxes for bio-applications, including drug delivery and biosensing. Full article

Premature deterioration of reinforced concrete is driven largely by moisture and chloride ingress, which accelerate steel corrosion and shorten service life. This study investigates a dual strategy to enhance durability while supporting circular-economy goals: (i) incorporating coal-fired biomass ash (CBA) as a fine-aggregate replacement (0%, 20%, and 50%) and (ii) applying bio-inspired surface treatments to reduce transport pathways. To capture variability in CBA performance across different environmental and material contexts, two concrete systems—produced in India and the UK—were evaluated, each subjected to a distinct coating approach: a bacterial self-healing treatment or a cinnamaldehyde (CNM) organic barrier. Mechanical, transport, and multi-scale characterization was performed, including compressive strength, capillary absorption, chloride migration (NT Build 492), SEM/EDS, XRF, and XRD. The 20% CBA mixes maintained or slightly improved strength, while higher CBA contents increased porosity but reduced chloride transport in the UK mix. The bacterial coating reduced long-term water absorption by over 80% through CaCO₃ mineralization, offering strong moisture resistance. The CNM coating decreased chloride migration by up to 68% via hydrophobic and ionic-blocking effects. Overall, moderate CBA with self-healing treatment enhances moisture control, whereas higher CBA with CNM provides effective chloride protection, extending the service life of CBA-based concrete. Full article

Stone Matrix Asphalt (SMA) has emerged as a premier high-performance paving solution for critical infrastructure applications. Its distinctive skeleton structure, composed of coarse aggregates bound by a fiber-stabilized bituminous mastic, delivers exceptional mechanical performance, including superior resistance to rutting (≤3 mm after 10⁶ load cycles) and fatigue cracking (>500,000 cycles to failure). While proven in demanding service environments, research has increasingly focused on enhancing the sustainability of SMA through key innovations: (1) the incorporation of recycled materials, such as 30–40% Reclaimed Asphalt Pavement (RAP) and 0.3–0.5% waste tire textile fibers (WTTF); (2) the development of bio-based binders derived from renewable sources; and (3) the adoption of Warm-Mix Asphalt (WMA) technologies that reduce production temperatures by 20–30 °C. These advancements yield significant environmental benefits, including approximately 25% lower CO₂ emissions and 15–20% reduced energy consumption compared to conventional SMA production. It is important to distinguish between these quantitatively demonstrated benefits, primarily from Life Cycle Assessment (LCA) studies of technologies like WMA and RAP, and the more qualitative sustainability claims associated with emerging materials like nanomaterials or novel bio-additives, which often lack comprehensive lifecycle inventories. Nevertheless, challenges persist, notably moisture susceptibility (manifesting as a 10–15% strength reduction after saturation) and uncertainties regarding the long-term performance of modified mixes. This review consequently identifies critical research priorities: optimizing mix designs with locally available materials to minimize transport emissions, employing nano-scale modifiers to enhance moisture resistance, and developing standardized lifecycle assessment protocols. Addressing these challenges is paramount to establishing SMA as a model sustainable pavement technology that robustly meets both structural performance benchmarks and ecological sustainability goals. Full article

This work introduces Hybrid Membrane–Neural P systems (HMN P systems), a computational model that integrates principles from membrane computing and spiking neural P systems. The resulting framework offers a versatile foundation for the development of bio-inspired arithmetic architectures. Within this setting, we propose a compact family of arithmetic kernels capable of executing signed addition, subtraction, multiplication, and division in both modular and non-modular arithmetic domains. By leveraging intrinsic spike aggregation, spike–anti-spike annihilation, and exhaustive rule application, the proposed designs achieve efficient and reliable arithmetic computation in a constant number of simulation steps under exhaustive semantics and assuming synchronized input, independent of operand values. Addition and subtraction are executed intrinsically upon spike arrival, requiring no internal computation steps, while multiplication and division are completed in a single simulation step by one neuron. Furthermore, we introduce a modular-reduction kernel that operates in two simulation steps with a single neuron, and leverage its modular structure to construct modular multiplication and division through composition with non-modular arithmetic modules. Comparative evaluations against representative SNP and SNQ arithmetic designs demonstrate that HMN kernels achieve operand-independent execution time while requiring fewer neurons. Distinct from most existing approaches, the HMN framework natively supports signed operands through a dual-spike representation, thereby eliminating the need for auxiliary sign-handling mechanisms. Asynchronous spike arrivals can be managed by an optional synchronization membrane; since this mechanism is decoupled from the arithmetic kernels, its overhead is excluded from kernel performance and reported separately. Collectively, these results establish HMN systems as an efficient and modular platform for constant-time arithmetic computation, offering reusable arithmetic kernels that serve as a foundation for higher-level constructions, including those arising in elliptic-curve and modular arithmetic. Full article

The utilization of waste and renewable oils as asphalt modifiers is a crucial strategy for achieving sustainable development in pavement engineering. However, the different physicochemical effects exerted by oil sources (bio-based versus petroleum-based) on the asphalt–aggregate interface remain insufficiently understood. This study aims to elucidate the influence mechanism of two bio-based oils and two petroleum-based oils on asphalt adhesion and the pavement performance of mixtures. A quantitative evaluation method combining the boiling test with digital image processing (DIP) technology was developed to assess the anti-stripping performance of modified asphalt on different lithological aggregates (acidic granite and alkaline limestone). Additionally, Fourier transform infrared spectroscopy (FTIR) was employed to reveal the chemical evolution of the modified asphalt. The results indicated that, although all oil-based modifiers demonstrated excellent compatibility and storage stability with the base asphalt (segregation ratio < 5%), their adhesion properties were significantly influenced by aggregate lithology. The key finding was that, compared to petroleum-based oils, bio-based oils exhibited superior adhesion performance on acidic granite surfaces, markedly mitigating moisture-induced stripping. FTIR analysis confirmed that this enhancement was attributable to the aromatic and carbonyl functional groups introduced by bio-based oils, which effectively promoted the interfacial bonding. Furthermore, bio-oil-modified mixtures exhibited optimal low-temperature cracking resistance without compromising high-temperature stability. These findings elucidate the mechanism by which bio-oil enhances the water-damage resistance of acidic aggregate systems, providing a theoretical basis for the optimized selection of sustainable asphalt modifiers. Full article

Due to its relatively low apparent density, as well as high water absorption and crushing value, recycled aggregate (RA) is difficult to directly apply in practical concrete production. Existing studies have proposed a variety of pretreatment methods for RA. To further deepen the understanding of their effectiveness in improving the properties of RA and to provide guidance for its practical application, a comprehensive investigation is necessary. The pretreatment methods of RA can be classified into three main categories: physical strengthening, chemical enhancement, and bio-deposition. In this study, physical strengthening includes mechanical grinding and microwave heating, chemical strengthening includes acid treatment, polymer treatment, and carbonation curing, and biological strengthening refers to biological strengthening techniques. Different strengthening methods improve the properties of RA and the corresponding recycled aggregate concrete (RAC) to varying degrees. However, it is essential to control the external treatment conditions appropriately during the strengthening process. In addition, the source of RA and its inherent physical characteristics also have a significant influence on the treatment outcomes. Therefore, the optimal strengthening conditions should be determined based on the specific properties of each type of RA. This study systematically summarizes and discusses the influencing factors associated with each strengthening method, and the discussion section compares the pros and cons from different perspectives. Furthermore, within the context of engineering decision-making for sustainable and durable construction materials, the study also addresses the limitations of current strengthening technologies and proposes potential directions for future research. Full article

Lignin, an abundant and renewable biopolymer, holds significant potential for asphalt modification owing to its unique aromatic structure and reactive functional groups. This review summarizes the main lignin preparation routes and key physicochemical attributes and assesses its applicability for enhancing asphalt performance. The physical incorporation of lignin strengthens the asphalt matrix, improving its viscoelastic properties and resistance to oxidative degradation. These enhancements are mainly attributed to the cross-linking effect of lignin’s polymer chains and the antioxidant capacity of its phenolic hydroxyl groups, which act as free-radical scavengers. At the mixture level, lignin-modified asphalt (LMA) exhibits improved aggregate bonding, leading to enhanced dynamic stability, fatigue resistance, and moisture resilience. Nevertheless, excessive lignin content can have a negative impact on low-temperature ductility and fatigue resistance at intermediate temperatures. This necessitates careful dosage optimization or composite modification with softeners or flexible fibers. Mechanistically, lignin disperses within the asphalt, where its polar groups adsorb onto lighter components to boost high-temperature performance, while its strong interaction with asphaltenes alleviates water-induced damage. Furthermore, life cycle assessment (LCA) studies indicate that lignin integration can substantially reduce or even offset greenhouse gas emissions through bio-based carbon storage. However, the magnitude of the benefit is highly sensitive to lignin production routes, allocation rules, and recycling scenarios. Although the laboratory research results are encouraging, there is a lack of large-scale road tests on LMA. There is also a lack of systematic research on the specific mechanism of how it interacts with asphalt components and changes the asphalt structure at the molecular level. In the future, long-term service-road engineering tests can be designed and implemented to verify the comprehensive performance of LMA under different climates and traffic grades. By using molecular dynamics simulation technology, a complex molecular model containing the four major components of asphalt and lignin can be constructed to study their interaction mechanism at the microscopic level. Full article

To overcome the high cost, marine ecological risks of traditional coral sand reinforcement, and the insufficient mechanical performance of standalone Enzyme-Induced Carbonate Precipitation (EICP), this study proposes a novel soil improvement method integrating EICP with aluminum chloride hexahydrate (AlCl₃·6H₂O). The objectives are to identify optimal EICP curing parameters, evaluate AlCl₃·6H₂O’s enhancement effect, and reveal the synergistic micro-mechanism. Through aqueous solution, unconfined compressive strength, permeability, X-ray diffraction (XRD), nuclear magnetic resonance (NMR), and Scanning Electron Microscope (SEM) tests, this study systematically investigated the reaction conditions, mechanical properties, anti-seepage performance, mineral composition, and pore structure. The results demonstrate that EICP achieves the best curing effect under specific conditions: temperature of 30 °C, pH of 8, and cementing solution concentration of 1 mol/L. Under these optimal conditions, the unconfined compressive strength of EICP-solidified coral sand columns reaches 761.6 kPa, and the permeability coefficient is reduced by one order of magnitude compared to unsolidified samples. Notably, AlCl₃·6H₂O incorporation yields a significant synergistic effect, boosting the UCS to 2389.1 kPa (3.14 times standalone EICP) and further reducing permeability by 26%. Micro-mechanism analysis reveals that AlCl₃·6H₂O acts both by generating cementitious aggregates that provide nucleation sites for uniform calcite deposition and by accelerating the transformation of metastable aragonite and vaterite to stable calcite, thereby enhancing cementation stability. This study delivers a cost-effective, eco-friendly solution for coral sand reinforcement, providing practical technical support for marine engineering in environments like the South China Sea. By addressing the core limitations of conventional bio-cementation, it opens new avenues for advancing soil improvement science and applications. Full article

Tunneling nanotubes (TNTs) are dynamic cell surface conduits that enable direct transfer of ions, signaling molecules, and organelles. They have emerged as a key mechanism of intercellular communication, complementing classical pathways such as synapses and paracrine signaling. In the central nervous system (CNS), TNTs exhibit a functional duality, particularly under aging and stress, where TNT-mediated exchange may shift from protective to maladaptive. On one hand, TNTs support homeostatic functions, ranging from mitochondrial transfer to stem cell-mediated rescue and astrocyte–neuron metabolic support. On the other hand, they facilitate the spread of prions and neurodegenerative protein aggregates, such as Tau and α-synuclein, with astrocytes playing a regulatory role. Despite rapid advances, TNT research faces challenges from conceptual heterogeneity and experimental standardization, especially in complex tissues such as the CNS. Recent mechanobiological and bio-inspired approaches, including force-based assays and three-dimensional culture models, provide new insights into TNT formation, stability, and cargo transport, extending beyond neural systems. This review offers an integrative synthesis of molecular, structural, and mechanobiological principles underlying TNT-mediated communication, emphasizing astrocyte–neuron crosstalk, while proposing validation criteria to support rigor, reproducibility, and cross-study comparability. TNTs thus emerge as dynamic, context-dependent interfaces with broad relevance to neurodegeneration, cancer, and biomedical applications. Full article

Industrial gateways serve as critical data aggregation points within the Industrial Internet of Things (IIoT), enabling seamless data interoperability that empowers enterprises to extract value from equipment data more efficiently. However, their role exposes a fundamental trade-off between computational efficiency and prediction accuracy—a contradiction yet to be fully resolved by existing approaches. The rapid proliferation of IoT devices has led to a corresponding surge in network traffic, posing significant challenges for traffic forecasting methods, while deep learning models like Transformers and GNNs demonstrate high accuracy in traffic prediction, their substantial computational and memory demands hinder effective deployment on resource-constrained industrial gateways, while simple linear models offer relative simplicity, they struggle to effectively capture the complex characteristics of IIoT traffic—which often exhibits high nonlinearity, significant burstiness, and a wide distribution of time scales. The inherent time-varying nature of traffic data further complicates achieving high prediction accuracy. To address these interrelated challenges, we propose the lightweight and theoretically grounded DOA-MSDI-CrossLinear framework, redefining traffic forecasting as a hierarchical decomposition–interaction problem. Unlike existing approaches that simply combine components, we recognize that industrial traffic inherently exhibits scale-dependent temporal correlations requiring explicit decomposition prior to interaction modeling. The Multi-Scale Decomposable Mixing (MDM) module implements this concept through adaptive sequence decomposition, while the Dual Dependency Interaction (DDI) module simultaneously captures dependencies across time and channels. Ultimately, decomposed patterns are fed into an enhanced CrossLinear model to predict flow values for specific future time periods. The Dream Optimization Algorithm (DOA) provides bio-inspired hyperparameter tuning that balances exploration and exploitation—particularly suited for the non-convex optimization scenarios typical in industrial forecasting tasks. Extensive experiments on real industrial IoT datasets thoroughly validate the effectiveness of this approach. Full article

The accelerating global demand for sustainable energy, driven by population growth, industrialization, and environmental concerns, has intensified the search for renewable alternatives to fossil fuels. Biofuels, including bioethanol, biodiesel, biogas, and biohydrogen, offer a viable and practical pathway to reducing net carbon dioxide (CO₂) emissions. Yet, their large-scale production remains constrained by biomass recalcitrance, high pretreatment costs, and the enzyme-intensive nature of conversion processes. Recent advances in enzyme immobilization using magnetic nanoparticles (MNPs), covalent organic frameworks, metal–organic frameworks, and biochar have significantly improved enzyme stability, recyclability, and catalytic efficiency. Complementary strategies such as cross-linked enzyme aggregates, carrier-free immobilization, and site-specific attachment further reduce enzyme leaching and operational costs, particularly in lipase-mediated biodiesel synthesis. In addition to biocatalysis, nanozymes—nanomaterials exhibiting enzyme-like activity—are emerging as robust co-catalysts for biomass degradation and upgrading, although challenges in selectivity and environmental safety persist. Green synthesis approaches employing plant extracts, microbes, and agro-industrial wastes are increasingly adopted to produce eco-friendly nanomaterials and bio-derived supports aligned with circular economy principles. These functionalized materials have demonstrated promising performance in esterification, transesterification, and catalytic routes for biohydrogen generation. Technoeconomic and lifecycle assessments emphasize the need to balance catalyst complexity with environmental and economic sustainability. Multifunctional catalysts, process intensification strategies, and engineered thermostable enzymes are improving productivity. Looking forward, pilot-scale validation of green-synthesized nano- and biomaterials, coupled with appropriate regulatory frameworks, will be critical for real-world deployment. Full article

Monoacylglycerols (MAGs) are significant intermediate byproducts in the hydrolysis of oils and fats. The accumulation of MAGs not only reduces the quality and purity of the final products in biodiesel production and edible oil refining but also poses challenges for downstream separation processes. Therefore, the development of efficient biocatalysts for the specific MAG conversion is of great industrial importance. The lipase from Aspergillus oryzae (AOL) has shown potential for lipid modification; however, the wild-type enzyme (WT) suffers from poor solubility, tendency to aggregate, and low specific activity towards MAGs in aqueous systems, which severely restricts its practical application. In this study, a combinatorial protein engineering strategy was employed to overcome these limitations. We integrated fusion protein technology with rational design to enhance both the functional expression and catalytic efficiency of AOL. Firstly, the superfolder green fluorescent protein (sfGFP) was fused to the N-terminus of AOL. The results indicated that the sfGFP fusion tag significantly improved the solubility and stability of the enzyme, preventing the formation of inclusion bodies. The fusion protein sfGFP-AOL exhibited a MAG conversion rate of approximately 65%, confirming the positive impact of the fusion tag on enzyme developability. To further boost catalytic performance, site-directed mutagenesis was performed based on structural analysis. Among the variants, the mutant sfGFP-Y92Q emerged as the most potent candidate. In the MAG conversion, sfGFP-Y92Q achieved a conversion rate of 98%, which was not only significantly higher than that of sfGFP-AOL but also outperformed the widely used commercial immobilized lipase, Novozym 435 (~54%). Structural modeling and docking analysis revealed that the Y92Q mutation optimized the geometry of the active site. The substitution of Tyrosine with Glutamine at position 92 likely enlarged the substrate-binding pocket and altered the local electrostatic environment, thereby relieving steric hindrance and facilitating the access of the bulky MAG substrate to the catalytic center. In conclusion, this work demonstrates that the synergistic application of sfGFP fusion and rational point mutation (Y92Q) can dramatically transform the catalytic properties of AOL. The engineered sfGFP-Y92Q variant serves as a robust and highly efficient biocatalyst for MAG degradation. Its superior performance compared to commercial standards suggests immense potential for cost-effective applications in the bio-manufacturing of high-purity fatty acids and biodiesel, offering a greener alternative to traditional chemical processes. Full article

This study developed a bio-based fluid loss reducer based on itaconic acid-grafted corn starch (IACS) for water-based drilling fluid systems. The product was synthesized through free radical graft copolymerization and characterized by FTIR, TGA, and SEM. In bentonite-based mud systems, IACS demonstrated excellent filtration control performance significantly superior to that of conventional fluid loss reducers such as PAM, CMC, and PAC. IACS exhibited outstanding temperature resistance, salt tolerance, and calcium contamination resistance. Particle size analysis revealed that IACS effectively dispersed bentonite particles to the nanoscale at elevated temperatures, preventing thermal aggregation. Mechanistic studies indicated that carboxyl groups introduced by the grafting reaction endowed IACS with strong adsorption capacity and hydration ability, forming a dense polymer network layer on clay particle surfaces. Environmental evaluation confirmed that IACS possessed moderate biodegradability and extremely low toxicity, meeting green drilling fluid additive requirements. This study provides new insights for developing high-performance, environmentally friendly fluid loss reducers. Full article