Search Results (7,490)

The development of hydrogen fueling processes is an essential infrastructure component needed for the adoption of hydrogen-fueled vehicles as a transportation technology. This study provides techno-economic analysis (TEA) for two hydrogen fueling pathways (Case A, Case B), one of which (Case A) does not employ hydrogen liquefaction, while the other one (Case B) does. Both cases consider the same conditions as one another, of gaseous hydrogen inlet availability and gaseous hydrogen outlet dispensing. The TEA analysis carried out is based on data supported from the literature and process flowsheet UNISIM^® software simulations. The obtained TEA results indicate that the levelized cost of hydrogen (LCOH) of the gaseous hydrogen Case A is USD 4.20/kg H₂, which is lower than the LCOH of the liquefied hydrogen Case B, which is USD 10.14/kg H₂. Given the energy equivalence of a gallon of gasoline to kgH₂, and the higher efficiencies of hydrogen fuel cell vehicles over gasoline vehicles, the above conditions suggest that Case B fueling (with hydrogen liquefaction) involves high energy consumption and may delay the growth of hydrogen-fuel-based transportation technology, while Case A fueling (no hydrogen liquefaction) will likely become preferrable over both Case B hydrogen fueling and gasoline fueling, thus accelerating the growth of hydrogen-fuel-based transportation technology. Full article

The pearlitic microstructure, comprising alternating lamellae of ferrite and cementite, provides a favorable combination of strength, toughness, and wear resistance. Consequently, pearlitic steels have been widely utilized in pipeline systems due to their advantageous mechanical properties and cost-effectiveness. These characteristics also render pearlitic steel pipelines promising candidates for hydrogen transport infrastructure, particularly in the context of repurposing existing natural gas networks. However, interactions between hydrogen and the pearlitic microstructure raise significant concerns regarding hydrogen embrittlement, a phenomenon that can substantially degrade mechanical performance and compromise long-term structural integrity. Experimental observations indicate that pearlitic microstructures are particularly susceptible to hydrogen embrittlement, largely due to the high density of ferrite–cementite interfaces, which act as effective hydrogen trapping sites. These detrimental effects motivate the present study, which aims to develop a deeper understanding of nanoscale mechanisms of hydrogen-assisted crack initiation and propagation in pearlitic microstructures. In this work, molecular dynamics simulations are employed to investigate the initiation and propagation of hydrogen-affected cracks in pearlitic microstructures, considering lamellar orientations both parallel and perpendicular to the applied tensile loading direction. The analysis focuses on the synergistic interaction between hydrogen-enhanced decohesion (HEDE), which promotes interfacial separation due to hydrogen segregation, and hydrogen-enhanced localized plasticity (HELP). Full article

The prevalence of diabetes and heart failure rises sharply with age, and their coexistence amplifies cardiovascular and renal risk. Elderly patients display unique clinical and biological profiles characterised by frailty, multimorbidity, and pharmacodynamic variability that challenge conventional treatment strategies. Sodium–glucose co-transporter-2 inhibitors (SGLT2i) have emerged as a cornerstone of cardio–renal–metabolic protection, with the most consistent cardiovascular benefit being the reduction in heart failure hospitalisation, whereas effects on cardiovascular death and major adverse cardiovascular events vary according to baseline cardiovascular risk, heart failure phenotype, diabetic status, and trial design. However, real-world use among the elderly remains limited due to concerns about tolerability, polypharmacy, and cost. This review analyses the pharmacological rationale and evidence base for SGLT2i therapy in older adults, highlighting mechanisms beyond glucose control, quantitative data from pivotal trials, and practical issues for geriatric implementation. Full article

The location and capacity of electric vehicle charging stations (EVCSs) directly determine the capital invested and construction costs while also affecting the travelling convenience and economy of electric vehicle (EV) users. Furthermore, the siting and sizing of EVCSs has an impact on distribution network flexibility. Therefore, a method for the siting and sizing of EVCSs that takes into account distribution network flexibility is proposed. Firstly, based on the definition of distribution network flexibility, the flexibility deficit is analyzed, and five flexibility assessment indicators are established. Secondly, the travel characteristics of EVs are simulated based on urban road topology and a trip probability matrix, and a model incorporating users’ bounded rationality is adopted to predict the temporal and spatial distribution of EV charging requirements. Furthermore, based on charging requirements and distribution network flexibility deficit, this paper establishes a model for the siting and sizing of EVCSs considering distribution network flexibility. Finally, case studies are conducted with a 29-node transportation network and a 33-node distribution network. The results show that the proposed method can formulate a more reasonable siting and sizing scheme for EVCSs, decrease the flexibility deficit of the distribution network, and reduce the annual comprehensive cost by 11.96%. Full article

This paper presents an integrated assessment of liquid hydrogen as an aviation energy carrier, covering fuel production, aircraft performance, and fleet-level climate impacts. The results, based on the H2Avia research project, indicate substantial potential for reducing life-cycle global warming impacts compared to conventional kerosene. The analyses conducted for the interdisciplinary assessment are presented. The analysis shows that the use of liquid hydrogen eliminates CO₂ emissions during fuel burn, resulting in a significant reduction in global warming potential compared to conventional kerosene, despite remaining upstream emissions from production and transport. The aircraft application cases and the applied technologies assessment scenario are described. The modeled technologies essential for the hydrogen aircraft are discussed, and exemplary values are given. Integrated overall aircraft performance results are given and discussed. At the aircraft level, hydrogen-based aircraft require an 8–18% increase in design mission block energy compared to a 2040 kerosene baseline yet still achieve a reduction in effective global warming potential of 55–86% comparing a representative pair route between Europe and North America (6730 km). An overview of the fleet modeling approach and the applied scenarios is given. For a scenario with energy cost and climate impact as equally weighted minimization goals, the global fleet analysis yields a global warming potential reduction of 60% compared to the non-liquid hydrogen baseline scenario. Overall, the results suggest that liquid hydrogen-powered aircraft can deliver significant mission- and fleet-level reductions in global warming potential and thus represent a promising pathway for achieving long-term aviation climate targets. Full article

Green hydrogen is increasingly discussed as an energy carrier that can link electricity, gas, heat, and transport sectors. However, many existing reviews address this topic from separate viewpoints, such as hydrogen production technologies, Artificial Intelligence (AI) applications, or system integration, with less attention to how policy and market conditions affect deployment. This review brings these related aspects together in one structured discussion. The paper first reviews the hydrogen supply chain, including production, storage, transport, and utilization. It then discusses an integrated multi-energy architecture in which hydrogen interacts with electricity, natural gas, heat, and cooling networks. Policy instruments in five major economies, including the European Union, the United States, China, Japan, and India, are compared. The review also summarizes the main barriers to large-scale deployment, including high production costs, limited infrastructure, technological challenges, regulatory uncertainty, and supply-chain constraints. In addition, the current market structure and selected large-scale hydrogen projects planned in the United States are reviewed. The paper also examines the role of artificial intelligence in green hydrogen systems. AI applications are grouped into four main stages of the hydrogen value chain: forecasting renewable energy generation, improving electrolyzer design and operation, optimizing storage and distribution, and supporting system-level techno-economic assessment. Recent Machine Learning (ML) studies are compared based on their methods and their contributions to operation and planning. Overall, this review highlights the role of AI in enabling green hydrogen integration within multi-energy systems. Full article

With the global advancement of carbon peaking and carbon neutrality goals, the importance of carbon capture, utilization, and storage (CCUS) technologies has become increasingly prominent. As a critical component of CCUS systems, gaseous CO₂ pipeline transportation has emerged as a research hotspot due to its efficiency and cost effectiveness. However, there are invariably corrosion problems when it comes to gaseous CO₂ pipeline transportation. These issues pose a significant threat to both the safety and economic viability of pipeline operations. Therefore, it is of importance to investigate gaseous CO₂ corrosion during pipeline transportation. In this work, based on recent domestic and international research achievements, research progress in the field of gaseous CO₂ corrosion during pipeline transportation is systematically reviewed. First, the corrosion mechanisms and corrosion characteristics during gaseous CO₂ pipeline transportation are studied, and the synergistic mechanisms by which key parameters such as impurities, temperature, pressure, flow velocity, and water content jointly influence pipeline wall corrosion behavior are elucidated. Then, corrosion products in CO₂ transportation pipelines are analyzed, and protective measures applicable to gaseous CO₂ pipeline systems are synthesized. Finally, future research goals are proposed to promote research on gaseous CO₂ corrosion during pipeline transportation: the impact of interactions among multiple impurities on corrosion behavior should be clarified; the inhibitory effects of the dynamic evolution of product films on mass transfer processes should be considered in corrosion rate calculation models; and more economical and efficient anti-corrosion technologies should be developed to meet diverse operational requirements. This work can provide guidance for the corrosion protection of gaseous CO₂ pipeline transportation. Full article

Foreign-exchange decisions rest on hierarchically organized evidence whose latent structure is inadequately captured by Euclidean representations. Reinforcement-learning agents trained on flat embeddings inherit stability guarantees that do not transfer to the manifold supporting the latent state. We address both limitations through a hybrid architecture in which a schema-constrained structured chain-of-thought is embedded into a Poincaré ball, transported to a qubit register via angle encoding, and processed by an L-layer hardware-efficient variational ansatz on a state-vector backend. The circuit exposes two read-outs to the policy, namely, a scalar Pauli-Z observable and a projected quantum kernel inducing a fidelity-based similarity over magnet-price attractors, the latter identified via kernel-weighted recurrence density and finite-time Lyapunov statistics. The Lipschitz constraint on the action-value function is lifted from the hyperbolic geodesic distance to a joint metric on ${\mathbb{B}}_{\kappa }^n\times \mathbb{P}\left(\mathcal{H}\right)$. A stability theorem yields an explicit bound depending on the read-out operator norm, on the depth–width product of the ansatz, and on the curvature–Hilbert balance. The pipeline is evaluated on nine major FX crosses over a 2015–2025 out-of-sample window, with rolling-origin walk-forward retraining and broker-published transaction costs. The system attains $2.55\%$ pair-averaged non-compounded monthly P&L and $8.83\%$ maximum drawdown, with Sharpe $1.78$, Calmar $3.43$, and Probabilistic Sharpe Ratio exceeding $0.95$ on every cross. The gain remains significant under a deflated-Sharpe-ratio test with $N_{\mathrm{trials}}=42$ correction. Block-wise ablations exhibit strictly monotone degradation: removing the projected kernel costs $-4.15$ p.p. on annualized P&L, the joint Lipschitz penalty $-6.42$ p.p., the attractor module $-7.64$ p.p., and the hyperbolic embedding $-8.40$ p.p. The quantum block thereby instantiates a structurally non-classical, geometry-aware regularizer identifiable through ablation rather than asymptotically advantageous. Full article

The rapid electrification of urban freight transport requires new optimization approaches that jointly consider logistics operations and energy system constraints. The problem is formulated as a mixed-integer linear programming (MILP) model that captures the interdependencies between vehicle operations, battery constraints, charging infrastructure availability and the temporal variability of photovoltaic energy. A multi-objective structure is adopted to minimize total energy costs and CO₂ emissions while maximizing the utilization of locally generated renewable energy. The model is evaluated using scenario-based simulations under three solar integration levels (0%, 30% and 60%). The results demonstrate that integrating solar energy into routing and charging decisions significantly reduces grid dependency, lowers emissions and improves overall system efficiency. Three types of charging stations are considered in the study (S1, S2, and S3), differing in photovoltaic (PV) energy penetration levels, ranging from conventional grid-based charging (S1) to high renewable integration stations (S3). The quantitative analysis reveals a clear resource and emission structure across the simulated scenarios. Incorporating charging stops grid-wide increases the total distance from theoretical routes to real tracks with stops to overcome the 120 kW battery limit. However, the integration of solar energy significantly alters the system’s environmental costs: total CO₂ emissions drop non-linearly by 33.4%, decreasing from 364.64 kg in the ‘Low Sun’ scenario to 243 kg in the ‘High Sun’ scenario. Furthermore, the localized impact shows that utilizing pure grid energy (S1) results in 405 kg of CO₂, while maximizing solar integration up to 60% (S3) reduces emissions to 162 kg. The sensitivity analysis showed how varying the share of solar energy at the two main stations (S2 and S3) affects the total CO₂ emissions, while maintaining the same routes. Three scenarios were examined: low (10% and 30%), base (30% and 60%) and high (50% and 90%) solar energy shares. As the share of solar energy in the system increases, a clear effect of emission reduction and energy cost optimization is observed. Full article

Cities preparing Sustainable Urban Mobility Plans (SUMPs) increasingly require practical tools capable of merging diverse mobility datasets and transforming them into planning-relevant indicators. This article introduces PRIORITY (Platform for the tRansition to sustaInable zerO-caRbon mobilITY), a prototype platform designed to support mobility analysis and decision-making in urban contexts. The platform integrates Floating Car Data, GTFS feeds describing public transport supply, and detailed land-use and zoning information. By relying on these heterogeneous data streams, PRIORITY generates indicators such as travel and stop times, trip distances, trip volumes, energy consumption, pollutant emissions, external costs, and electric-vehicle charging behavior. The platform is organized into two main components: a back end and a front end. The back end, which constitutes the operational core, manages all collected data and ensures their structured storage in a shared database capable of handling large volumes of information on urban form, individual mobility patterns, public transport services, and modeling outcomes. The front end provides an intuitive and versatile interface that dynamically presents the outputs generated by the platform’s analytical and modeling processes. A case application for the Metropolitan City of Rome (Italy) illustrates the operational use of the prototype and shows how PRIORITY can support transparent and reproducible evaluations during the preparation and monitoring of SUMPs. The demonstrated workflow highlights the prototype’s value for public authorities and planners seeking data-informed approaches to urban mobility assessment and decarbonization strategies. Full article

Accurate quantification of riprap volume is critical for cost control, quality assurance, and navigation safety in inland waterway maintenance projects. Conventional methods, such as draft mark reading and RTK-based point surveying, are constrained by limited accuracy, low efficiency, and operational risk. To address these limitations, this study proposes a fully automated riprap volume quantification method based on portable LiDAR simultaneous localization and mapping. The proposed framework establishes a seamless, intervention-free workflow. This automated process sequentially integrates real-time scan monitoring, target vessel extraction, riprap segmentation, deck baseline reconstruction, and 3D volume estimation. Specifically, riprap-laden transport vessels are automatically identified using density-based clustering and trajectory information. Subsequently, deck-loaded riprap piles are extracted through point-cloud geometric analysis and quantified via deck fitting and mesh reconstruction. The method was validated through ten field experiments in the Jingjiang reach of the middle Yangtze River, China. Compared to benchmark volumes established via standard point-cloud processing software, the proposed method achieved an average relative error of 1.37% and a maximum error strictly below 5%. Furthermore, the system proved highly efficient, requiring an average processing time of only 392.1 s per dataset. The results demonstrate that the proposed method is accurate, efficient, and robust, and has strong potential for intelligent riprap quantification in inland waterway engineering. Full article

The volatility of renewable energy generation and nodal electricity prices provides an arbitrage opportunity for Mobile Battery Energy Storage Systems (MBESS) leveraging both temporal and spatial advantages. However, the inherent high complexity and strong randomness of both power and transportation systems lead to complex risks for MBESS control. Existing works mainly consider the market price risk and ignore the transportation system risk caused by traffic congestion. Specifically, they are constrained by two critical limitations: (1) decisions can only be made upon arrival at a destination, making the agent unresponsive on the road, and (2) traffic congestion risk is neither quantified nor controlled, leading to suboptimal routing strategies. To address these limitations, the MBESS needs more flexible “on the road” decision making and multiple risk management capabilities. Guided by this objective, a flexible deep reinforcement learning-based MBESS control framework is proposed, considering both market and traffic congestion risk. First, dynamic routing ability is integrated with the MBESS agent to provide more flexibility in making decisions, regardless of whether the agent has reached the designated location or not. Second, two risk metrics are proposed to quantitatively assess the traffic congestion risk based on moving time, and then the agent can make decisions considering both market and traffic congestion risk. Finally, considering the inefficiency of learning caused by introducing multiple risks, a risk curriculum learning method is proposed to improve the training efficiency and reduce learning costs. These components are unified in the Multiple Risk Estimation SDDPG (MRE-SDDPG) algorithm, which jointly maximizes profitability while controlling electricity price and traffic congestion risk. Simulations in the IEEE 30 bus environment show that the proposed framework can increase profit by 8.6% while reducing the traffic time by 15.8% on average, demonstrating the superiority of our design in considering traffic congestion risk. Full article

The increasing global demand for renewable energy has intensified the search for high-efficiency and cost-effective solar cell technologies. Quantum dot-sensitized solar cells (QDSSCs) have emerged as promising candidates due to their tunable optoelectronic properties and enhanced light absorption. In this study, SnS quantum dots were synthesized from dithiocarbamate complexes using different ligands, namely m-toluidine (SnS1), aniline (SnS2), and p-toluidine (SnS3), to investigate the influence of precursor chemistry on material properties and device performance. Structural analysis confirmed the formation of an orthorhombic phase for all samples, while morphological studies revealed well-dispersed nanocrystals for SnS1 (5.93 nm), increased aggregation for SnS2 (8.57 nm), and partially fused domains with an intermediate size for SnS3 (6.67 nm). Optical measurements showed bandgap energies of 2.8, 2.2, and 2.7 eV for SnS1, SnS2, and SnS3, respectively, with SnS3 exhibiting reduced charge-recombination behaviour. Photovoltaic devices fabricated using these materials yielded power conversion efficiencies of 3.40, 2.03, and 7.63% for SnS1, SnS2, and SnS3, respectively, with no significant improvement observed for bifacial configurations. The superior performance of SnS3 is attributed to an optimal balance between light absorption, morphology, and charge transport properties, highlighting the critical role of precursor ligand selection in tuning quantum dot characteristics for improved QDSSC performance. Full article

To address the global water crisis, desalination technologies contribute about 1% of the global freshwater supply. Membrane-based desalination technologies offer high performance, operational ease, cost-effectiveness and high scalability compared to conventional thermal desalination modes. Among all membrane-based technologies, reverse osmosis is prevailing globally. However, the high energy demand of the reverse osmosis process and fouling in case of hypersaline feed streams motivate the exploration of alternative technologies, i.e., pervaporation. Pervaporation desalination involves dense hydrophilic polymer membranes to deal with high salt streams at low cost, along with less fouling than a few other membrane processes, i.e., reverse osmosis and membrane distillation. Mass transport through pervaporation desalination membranes is well-explained by solution-diffusion theory involving a tri-stage transfer, i.e., sorption, diffusion and evaporation. Since the last few decades, a green approach in all domains has offered chemical products and processes with the least hazards and minimal waste production. Application of biodegradable materials like poly(lactic acid) in combination with suitable green solvents, e.g., ethyl lactate, methyl lactate, cyrene, dimethyl isosorbide and gamma valerolactone for pervaporation desalination would be a good roadmap to meet the sustainability criterion. Some intrinsic features of poly(lactic acid) that make it a ‘material of choice’ for pervaporation desalination include hydrophilicity imparted by the presence of polar ester groups, high salt rejection, biodegradability with simple mineralization products, i.e., H₂O and CO₂, sustainable production, low toxicity, low carbon footprint, ease of processing and versatility. Poly(lactic acid) undergoes four interrelated degradation mechanisms: hydrolytic degradation, biodegradation, thermal degradation and photodegradation. The concern for poly(lactic acid) based pervaporation desalination is increased hydrolytic cleavage of poly(lactic acid) at high temperatures, which requires some modifications, e.g., nanoenhancement, additions of crosslinkers, surface modifications, addition of other polymers to prepare blends and post-treatments. These modifying strategies result in an increased stability and better performance of poly(lactic acid) films. However, optimization of various parameters relevant to such modifications leaves room for further research. This review offers a critical analysis of the need for biodegradable polymers with special focus on poly(lactic acid) rather than their fossil fuel-based alternatives, the environmental and health effects of all these polymers, cost estimation and possible performance-efficient, green and eco-friendly solutions. Full article

Conventional formwork systems are limited in their ability to efficiently realize complex and free-form concrete geometries, while additive manufacturing (AM)-based formwork faces constraints in casting-stage structural stability and cost-effectiveness, particularly at construction scale. To address these limitations, a hybrid formwork system integrating a structural steel frame with 3D-printed modules is proposed, in which the steel frame resists casting-induced lateral pressure while the printed components define complex mold geometries. The system was fabricated and validated through a full-scale case study structure measuring 3.0 m × 1.7 m × 2.2 m, produced using a large-scale fused deposition modeling (FDM) process with carbon-fiber-reinforced ABS (ABS-CF20). Geometric accuracy was evaluated by comparing design dimensions with as-built measurements across planar, edge, curved, and inclined regions. Construction efficiency and cost performance were assessed through process-based and cost-based comparisons with conventional steel formwork and fully 3D-printed formwork alternatives. The constructed structure reproduced the intended geometry with an average deviation of approximately 3.2 mm and a maximum deviation within ±4 mm, and no notable formwork deformation or damage was observed during concrete casting. Relative to conventional steel formwork, the hybrid system reduced total fabrication duration by about 50% and fabrication cost by about 60% based on a normalized cost index, while also outperforming fully 3D-printed formwork in cost efficiency by about 45%. The modular configuration and bolted connection system further improved transportability, on-site assembly efficiency, and component reusability. These findings demonstrate that the proposed hybrid formwork system provides a practical and resource-efficient pathway for fabricating complex concrete structures, supporting the broader adoption of digital fabrication in sustainable construction practice. Full article