Engineering Science & Technology

Design of Asymmetric Janus Aerogel Toward High-Performance Solar Evaporation

Liumin Luo — 2026-07-31

Interfacial solar evaporation technology enables high-efficiency evaporation with low energy consumption through solar-driven localized heating and has broad application prospects in seawater desalination and industrial wastewater treatment. As novel functional materials with asymmetric physicochemical characteristics, Janus aerogels provide a new approach for overcoming the performance limitations of traditional evaporators. In this work, a Janus aerogel was fabricated using Polyvinyl Alcohol/Cellulose Nanofiber (PVA/CNF) as the substrate and Carbon Nanotubes (CNT) as the photothermal layer. Owing to its asymmetric structure, which integrates hydrophilic water transport and hydrophobic photothermal characteristics, the resulting aerogel achieves efficient photothermal conversion and directional water delivery. Its maximum surface temperature reaches 64.4 °C with a light absorption efficiency of 99%, consistent with the finite element simulation results. Under 1 sun, the evaporation rate is 1.32 kg·m^-2·h^-1 and the corresponding evaporation efficiency is 88.37%, and the aerogel maintains stable evaporation performance over 7 cycles, with only minor salt precipitation on the surface after 2 h of irradiation. This developed material shows promising potential in seawater desalination and wastewater purification, offering a novel strategy for the rational design of multifunctional aerogel evaporators.

Numerical Investigation of Seismic Demands in Floor Diaphragms and Collectors

Alireza Sheykhaleslami — 2026-08-05

This study investigates the seismic demand in diaphragms, chords, and collectors in typical floor systems used in steel structures using nonlinear Finite Element (FE) analyses. These systems include prefabricated one-way steel joist floor systems, open-web steel joists, and composite steel decks. The Lateral-Force-Resisting Systems (LFRSs) considered were Special Concentrically Braced Frames (SCBFs) and Special Moment Frames (SMFs) of varying heights. For each system, the effects of using shear connectors for collector beams, joist direction, geometric configuration, and number of stories on lateral force transfer were examined. Analyses were conducted on selected models. The study shows, in steel joist floor systems without shear connectors on collector beams, the diaphragm force transfer path is significantly influenced by joist orientation. Furthermore, using shear connectors on collectors bypasse joist participation in lateral load transfer, allowing the floor slab to transmit forces directly to the LFRS like a rigid diaphragm through the collectors. In composite steel decks, using shear connectors on collector beams was found to improve diaphragm ductility and cyclic performance. Overall, the study clarifies the true structural role of diaphragms, demonstrating that without proper analysis models, seismic load paths can become disrupted and traditional design assumptions may fail to ensure structural safety. The findings indicate that the introduction of diaphragm-specific response modification factors of one or less (Rs ≤ 1) results in more realistic diaphragm forces. Applying the outcomes of this research will align diaphragm design practice more closely with the actual behavior.

Uncertainty-Aware Semantic Segmentation via Decision Boundary Modeling and Confusion Zone Refinement

Antoni Mestre — 2026-08-10

Deep learning has significantly improved the performance of semantic segmentation in high-resolution imagery and computer vision tasks. However, standard segmentation models treat all pixel-wise predictions equally, despite the fact that predictions close to the decision boundary are inherently uncertain. This limitation often leads to unstable classifications and increased false positives or false negatives in complex scenes. In this paper, we propose a practical uncertainty-aware semantic segmentation refinement framework that explicitly handles decisionboundary ambiguity through an empirical routing mechanism through a Confusion Zone mechanism. Instead of forcing a binary decision for all pixels, predictions whose probability scores fall within a predefined interval [τ_low, τ_high] are marked as uncertain and redirected to a secondary refinement module that exploits additional spatial features and local context before assigning a final label. The proposed two-stage architecture allows the segmentation model to treat ambiguous predictions differently from confident ones, improving robustness near object boundaries and visually complex regions. Experiments on benchmark remote sensing segmentation datasets demonstrate that incorporating the Confusion Zone mechanism improves segmentation quality, achieving up to 0.851 IoU with SegFormer and consistent gains of 2-3 percentage points across U-Net, DeepLabV3+, and SegFormer backbones. These results suggest that explicitly routing decision-boundary predictions for local refinement is a simple and practical strategy for improving the reliability of deep semantic segmentation systems.