The development of aluminum-air batteries provide a promising solution for stabilizing intermittent renewable energy sources, such as solar and wind. A significant challenge to their large-scale deployment is the stability on aluminum electrodes in the electrolyte, which presents both economic and corrosion-related challenges. Issues related to corrosion, cost and electrode thinning compromises the performance of aluminum electrodes. Nonetheless, aluminum alloys are emerging as a compelling alternative for aluminium electrodes due to their high electrochemical activity and ease of processing. Herein, an integrated framework that combines Pourbaix diagrams (PD) with finite element modeling (FEM) is proposed to systematically investigate the stability of aluminum-based electrode materials. Within this framework, PD predict the stability regions of key electro active species of aluminum and its alloys, such as Al (OH)3, AlO2-and Al (OH)4-, under various pH, concentration and temperature. PD offer a comprehensive assessment of material behavior in corrosive environments. A FEM model incorporated in the framework illustrates thinning of the electrode-electrolyte interface due to electrode corrosion. The model predictions are validated against experimental data in an inbuilt cell, showing good agreement in electrode thickness reduction and corrosion rate predictions under acidic conditions. The model shows that pure Al obtained a pit with a depth of 1.31 mm at overpotential of 1.93 V, while Al 7075 eroded into a 1.9 mm pit at 1.87 V, thus having a larger corroded area. These findings provide a foundation for screening aluminum-based electrodes based on corrosion rates and thermodynamics parameters for batteries used in energy storage systems.
Deep electrification is reshaping distribution networks and driving unprecedented demand growth and resource-integration complexity. Traditional planning emphasizes short horizons and incremental reinforcement, and it inadequately captures flexible resources and their potential to compete with conventional investment. This paper proposes a competitive distribution network expansion planning framework from the independent distribution system operator (IDSO) perspective. Wire solutions, namely network reinforcement, and non-wire solutions, namely distributed energy resources, energy storage, and other flexible assets, compete on cost and flexibility to serve projected demand growth. Long-term investment and short-term operation are co-optimized within a single mixed-integer formulation. Embedding hourly operation in the planning problem verifies the feasibility of every investment against real operating conditions, so the resulting plans are both cost-optimal and operationally realistic. Capital-expenditure limits represent utility budget constraints, and a sensitivity study quantifies the effect of interest- and inflation-rate uncertainty. Two case studies, a simplified 4-node feeder and a modified IEEE 123-node network, demonstrate substantial savings, obtained mainly by deploying energy storage and shunt capacitors rather than costly infrastructure upgrades or renewable generation. A budget robust optimization of the same mixed-integer class hedges demand uncertainty, and an out-of-sample evaluation shows that the deterministic optimum is fragile to forecast error whereas a tunable hedge restores reliability at a modest, quantified cost premium. The framework equips the IDSO with a decision-support tool for cost-effective and operationally viable expansion aligned with deep-electrification and net-zero objectives.
Sintering of metal oxides typically requires long thermal exposure, which can cause grain coarsening and phase instability. Here, femtosecond laser pre-treatment introduces a high concentration of oxygen vacancies into TiO2 nanoparticles, shifting mass transport from slow classical lattice diffusion to rapid vacancy-mediated diffusion. Once the vacancy concentration exceeds a critical threshold, near-complete densification is achieved within only 10 min at 650 degrees C. Interrupted high-resolution transmission electron microscope reveals that oxygen vacancies first form nanoscale loops on {0 01}/{10 0} planes, then coalesce into one-dimensional vacancy arrays along low-migration-barrier {101} planes. These vacancy arrays interact with pre-existing dislocation cores, activating pipe-diffusion channels that direct fast mass transport toward the particle interface. Curvature-induced chemical-potential gradients further drive vacancy migration toward the high-curvature neck, where compensating Ti-O atom flux fills the neck and accelerates densification. Continued vacancy-atom exchange stabilizes a thin, coherent, oxygen-deficient cubic TiO interfacial layer, thereby improving the junction quality. Diffusion modeling confirms that the effective diffusivity exceeds classical lattice diffusivity beyond the vacancy threshold, validating a non-classical pipe-diffusion mechanism for rapid low-temperature oxide sintering. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
Flooding poses a substantial threat to suburban infrastructure, motivating the need for scalable approaches for flood-risk assessment. A key parameter in evaluating a building’s susceptibility to flood damage is its first-floor height (FFH), an engineering decision variable typically obtained through labour-intensive surveying. Traditional land-surveying methods are precise but labour-intensive and impractical at scale. Existing computer-vision approaches show promise, but many rely on single-view cues and high-quality imagery, making them sensitive to occlusion and sparse viewpoints in residential scenes. This paper presents First-Floor-Finder (F3), a multi-view, multi-stage framework for automated FFH estimation that integrates 2-D imagery with 3-D LiDAR geometry. F3 detects and refines three façade cues—front door, stoop, and basement window—across multiple viewpoints, then combines the resulting candidate height measurements using a Deep Feature Fusion Network (DFFNet) that assigns adaptive weights based on per-candidate reliability. Experiments on an independently collected, occlusion-prone dataset show that F3 achieves 16.3cm Root Mean Squared Error (RMSE), 13.4cm Mean Absolute Error (MAE), and 94.1% availability. Ablation studies indicate that multi-cue fusion and learned weighting improve both accuracy and coverage (availability) under foreground occlusion and variable façade configurations. These results support the use of F3 as a scalable approach for suburban FFH estimation in flood-risk analytics, by formalizing FFH inference as a reliability-aware fusion of heterogeneous visual and geometric evidence.
Conventional all-air cooling systems struggle with high latent loads and increased energy demand in tropical buildings, where humidity and solar radiation intensify the design challenge. This study systematically reviews radiant cooling system (RCS) research in tropical and hot, humid climates, synthesizing evidence from 152 peerreviewed studies published between 2013 and 2025. The review is structured around four dimensions: operating principles and system types, energy efficiency, thermal comfort, and advanced control strategies. Key quantitative findings include: properly designed RCSs reduce energy consumption by 4-43% relative to conventional air systems, with peak savings of up to 56% when integrated with phase change materials or geothermal heat pumps; RCSs maintain comfort at air temperatures 1-2 K higher than traditional systems, with a predicted mean vote within the acceptable range (-0.5 to +0.5) for over 81% of occupied hours; model predictive control maintains EN 15251 Category II standards for more than 95% of occupied hours while reducing cooling-tower energy use by up to 55%, with membrane-assisted sub-dew-point panels reducing annual discomfort hours by 3-6% versus conventional RCS. The primary contribution is a structured, evidence-based framework that: (1) taxonomizes hybrid RCS configurations by tropical microclimate severity; (2) synthesizes condensation-risk thresholds and control design criteria; and (3) delineates a technology-readiness roadmap for net-zero buildings in humid environments. Research gaps include the absence of in situ validation of membrane-assisted RCS in operational tropical buildings, the lack of standardized condensation-risk thresholds, and the need for machine learning controllers validated across diverse tropical microclimates.
