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Tubular solid oxide fuel cells (TSOFCs) generate high-grade waste heat during operation, but the existing waste heat recovery technologies designed for flat solid oxide fuel cells cannot be directly applied to TSOFC due to the geometry mismatch. To efficient harvest the waste heat, a new geometry-matching hybrid system including TSOFC and annular thermoelectric generator (ATEG) is synergistically integrated to evaluate the performance upper limit. A mathematical model is formulated and verified to describe the hybrid system by considering various thermodynamic-electrochemical irreversible effects. Key performance indicators are established to assess the potential performance. Calculations show that the peak power density and corresponding efficiency of the proposed system are enhanced by 20.39 % and 13.89 %, respectively, compared to a standalone TSOFC. Furthermore, the exergy destruction rate is reduced by 7.04 %. Extensive sensitivity analyses indicate that higher operating temperatures enhance the system’s performance, while larger electrode tortuosity negatively affects it. Additionally, various optimization paths of ATEG are explored to improve the system performance, including considerations such as the number of thermocouples, leg radial width, leg thickness, or annular shape parameter. The three-objective optimization yields an efficient design solution for the entire system, offering valuable insights for its design and operation. [ABSTRACT FROM AUTHOR]

Pure SnO2 and 1 at.% PdO–SnO2 materials were prepared using a simple hydrothermal method. The micromorphology and element valence state of the material were characterized using XRD, SEM, TEM, and XPS methods. The SEM results showed that the prepared material had a two-dimensional nanosheet morphology, and the formation of PdO and SnO2 heterostructures was validated through TEM. Due to the influence of the heterojunction, in the XPS test, the energy spectrum peaks of Sn and O in PdO–SnO2 were shifted by 0.2 eV compared with SnO2. The PdO–SnO2 sensor showed improved ethanol sensing performance compared to the pure SnO2 sensor, since it benefited from the large specific surface area of the nanosheet structure, the modulation effect of the PdO–SnO2 heterojunction on resistance, and the catalyst effect of PdO on the adsorption of oxygen. A DFT calculation study of the ethanol adsorption characteristics of the PdO–SnO2 surface was conducted to provide a detailed explanation of the gas-sensing mechanism. PdO was found to improve the reducibility of ethanol, enhance the adsorption of ethanol's methyl group, and increase the number of adsorption sites. A synergistic effect based on the continuous adsorption sites was also deduced. [ABSTRACT FROM AUTHOR]

Conventional disinfection techniques, relying on a single disinfection step, often fail to directly eliminate microorganisms, instead causing them to enter a viable but nonculturable (VBNC) state. However, microorganisms in the VBNC state retain metabolic activity and can reactivate under suitable conditions, representing a "hidden source of contamination" that threatens drinking water safety. This study fundamentally assessed the feasibility of combined disinfection methods by integrating UV254 with disinfectant (NaClO, PAA, and PDS) for inactivating Pseudomonas aeruginosa (P. aeruginosa), an opportunistic pathogen that has been widely detected in water supply systems. The number of culturable cells was determined using the heterotrophic plate counting (HPC) method, and the number of VBNC cells was quantified using our recently developed qPCR approach. Quantitative analyses showed that combined disinfection methods can effectively reduce both culturable and VBNC cells by several orders of magnitude compared to a single disinfection step. Notably, VBNC P. aeruginosa, after 30 min of UV/NaCIO treatment, was below the detection limit (3.191 log CFU/mL) of PMA-qPCR. The reactivation experiment also confirmed that VBNC P. aeruginosa did not reactivate for 16 h after 30 min of UV/NaClO treatment under controlled laboratory conditions. The higher disinfection capacity of combined methods can be attributed to the generation of reactive radicals. This study highlighted combined disinfection as a promising approach for the inactivation of bacteria in the VBNC state, yet further studies are needed before an application can be considered for minimizing VBNC reactivation in city utility water processing or high-risk building water distribution systems. [ABSTRACT FROM AUTHOR]

Anti-drift is a new and serious challenge in the field related to gas sensors. Gas sensor drift causes the probability distribution of the measured data to be inconsistent with the probability distribution of the calibrated data, which leads to the failure of the original classification algorithm. In order to make the probability distributions of the drifted data and the regular data consistent, we introduce the Conditional Adversarial Domain Adaptation Network (CDAN)+ Sharpness Aware Minimization (SAM) optimizer—a state-of-the-art deep transfer learning method.The core approach involves the construction of feature extractors and domain discriminators designed to extract shared features from both drift and clean data. These extracted features are subsequently input into a classifier, thereby amplifying the overall model's generalization capabilities. The method boasts three key advantages: (1) Implementation of semi-supervised learning, thereby negating the necessity for labels on drift data. (2) Unlike conventional deep transfer learning methods such as the Domain-adversarial Neural Network (DANN) and Wasserstein Domain-adversarial Neural Network (WDANN), it accommodates inter-class correlations. (3) It exhibits enhanced ease of training and convergence compared to traditional deep transfer learning networks. Through rigorous experimentation on two publicly available datasets, we substantiate the efficiency and effectiveness of our proposed anti-drift methodology when juxtaposed with state-of-the-art techniques. [ABSTRACT FROM AUTHOR]