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Cyanobacteria plays an important role in other ecological processes in paddy soils, particularly in terms of nitrogen input to the ecosystem. Organic fertilizer and biochar are common soil amendment materials used to preserve soil health in agricultural intensification background. However, the consequent increase in soil nutrition may inhibit soil cyanobacteria, therefore decreasing nitrogen fixation and changes other soil processes. To test this hypothesis, we established a 2 × 2 full factorial experiment in a paddy field in South China, which included four treatments: Ctr (control, receiving no organic fertilization or biochar addition), +OF (organic fertilizer application only), +BC (biochar application only), and +Mix (organic fertilizer and biochar applications). The soil cyanobacterial community was analyzed using metagenomics technology, and 14 soil property variables were measured. The results suggested that organic fertilizer was effective in enhancing nutrient levels, leading to a significant increase in extractable and soluble nitrogen, phosphorus, and potassium. In contrast, biochar application had a stronger effect on total soil carbon, potassium, and soil pH. However, both organic fertilizer and biochar applications induced significant decreases in overall cyanobacterial abundance and species number. Dominant cyanobacterial organisms, particularly the two most abundant genera, Leptolyngbya and Phormidium, experienced a greater decrease compared to others. Canonical correlation analyses and structural equation models indicated that organic fertilizer and biochar applications affected soil cyanobacterial community mainly through soil available nitrogen and pH. In total, the present study highlighted that both organic fertilizer and biochar applications in paddy soils notably change soil physicochemical traits, inhibiting rather than benefiting cyanobacterial microorganisms, especially the dominant ones, and potentially reducing nitrogen input. Our study reveals the impacts of oragnic fertilizer and biochar applications in paddies on soil cyanobacteria and how the consequent changes in soil properties mediate this impact, thereby enhancing our understanding of the responses of different soil microbial groups to soil improvement measures.

Solid Oxide Fuel Cells (SOFCs) are emerging as a leading solution in sustainable power generation, boasting high power-to-energy density and minimal emissions. With efficiencies potentially exceeding 60% for electricity generation alone and up to 85% when in cogeneration applications, SOFCs significantly outperform traditional combustion-based technologies, which typically achieve efficiencies of around 35–40%. Operating effectively at elevated temperatures (600 °C to 1000 °C), SOFCs not only offer superior efficiency but also generate high-grade waste heat, making them ideal for cogeneration applications. However, these high operational temperatures pose significant thermal management challenges, necessitating innovative solutions to maintain system stability and longevity. This review aims to address these challenges by offering an exhaustive analysis of the latest advancements in SOFC thermal management. We begin by contextualizing the significance of thermal management in SOFC performance, focusing on its role in enhancing operational stability and minimizing thermal stresses. The core of this review delves into various thermal management subsystems such as afterburners, heat exchangers, and advanced thermal regulation strategies. A comprehensive examination of the recent literature is presented, highlighting innovations in subsystem design, fuel management, flow channel configuration, heat pipe integration, and efficient waste heat recovery techniques. In conclusion, we provide a forward-looking perspective on the state of research in SOFC thermal management, identifying potential avenues for future advancements and their implications for the broader field of sustainable energy technologies.

Soil cyanobacteria in tropical forests is understudied despite its important role in soil biochemical process and plant growth. Under a nitrogen (N) deposition background in tropical forests, it is important to learn how soil cyanobacterial communities respond to N deposition and whether phosphorus (P) mediated this response. A fully two-factor (N and P additions) factorial design with four blocks (replicates), each including a 12 × 12 m plot per treatment (Control, +N, +P, and +NP) were established in a tropical secondary forest in 2009. In July of 2022, soil cyanobacteria at 0–10 cm and 10–20 cm depths in the experimental site were collected and analyzed using a metagenomic method. The impact of N and P additions on soil cyanobacteria remained consistent across the different soil depths, even though there was a significant contrast between the two layers. The effect of N addition on soil cyanobacteria did not significantly interact with P addition. N addition increased soil N availability and decreased soil pH but did not significantly affect the soil cyanobacterial community. In contrast, P addition increased soil P availability and soil pH, but decreased soil N availability and substantially changed the soil cyanobacterial community. P addition significantly decreased the abundance of soil cyanobacteria, especially abundant ones. P addition also increased cyanobacterial species richness and Shannon’s diversity, which might be explained by the decline in dominant species and the emergence of new species as nestedness and indicator species analyses suggest. We concluded that (1) soil cyanobacteria in tropical forests exhibits a greater sensitivity to elevated P availability compared to N; (2) an increase in soil P supply may mitigate the advantage held by dominant species, thus facilitating the growth of other species and leading to alterations in the soil cyanobacterial community. This study improves our understanding on how soil cyanobacterial communities in tropical forest responds to N and P addition.

The Solid Oxide Fuel Cell (SOFC) system is a highly intricate system characterized by multiple variables and couplings. Developing an accurate model for the SOFC independent power generation system is of paramount importance. Conducting experimental studies on the SOFC system is costly, and it carries certain risks due to the requirements for pure hydrogen, high-temperature environments, and other factors. To address these challenges, a high-performing model that precisely reflects the inherent characteristics of the SOFC is essential for dynamic static analysis and the identification of optimal operating points. This paper presents a SOFC system model based on current controls, which was implemented in the MATLAB/Simulink environment, and it utilized a nodal approach for modeling. The model incorporated a cold air bypass, which enabled the more precise control of the SOFC reactor’s inlet and outlet temperatures. Furthermore, a 3D test and verification method are proposed in order to focus on the influence of input parameters on the four electrical characteristics, and four thermal characteristics, of output parameters. By conducting one-dimensional, two-dimensional, and three-dimensional studies of these output parameters, a more intuitive understanding of the system’s response to changes in input parameters was obtained. Under conditions wherein all other variables were kept constant, the entire system attained its maximum efficiency at approximately FU = 0.8, BP = 0, and AR = 6. The outcomes of this study have significant implications for exploring the optimal operating point in the SOFC independent power generation system in an in-depth manner. It provides valuable insights for enhancing the system’s efficiency and performance.

Direct ethanol solid oxide fuel cells (SOFCs) are the most energy-efficient and low-carbon technology for renewable power generation from biomass fuels, but they are hindered by carbon deposition on the Ni-based cermet anode. In this work, excessive Na+ dopant into La0.75Sr0.25Cr0.5Fe0.4Cu0.1O3-δ (LSCFC) perovskite was used as an additional internal reforming catalyst for direct carbon dioxide-ethanol SOFCs. Excessive Na+-doped LSCFC (N-LSCFC) demonstrated great potential in promoting electrochemical performance and internal reforming process fueled by carbon dioxide-ethanol mixture, because more oxygen vacancies and the precipitated Cu nano catalyst were helpful for the improvement of internal reforming and carbon tolerance. Electrochemical investigations proved that the vertical-microchannel anode supported the single cells using the N-LSCFC-Gd0.1Ce0.9O2-δ (GDC) internal reforming catalyst, showing a peak power density of 1044.41 and 855.56 mW/cm2 at 800 °C fueled by H2 and 50% CO2-50% C2H5OH, respectively. The preceding results indicate that excessive Na+ doping strategy into LSCFC as the additional internal reforming catalyst can improve the electrochemical performance and internal reforming process of direct carbon dioxide-ethanol SOFCs.

MIL-53(Fe) was prepared and modified with benzoic acid to prepare MIL-53(Fe)-BA additive, which was used to improve the catalytic oxidation rate of sulfite, prevent the scaling of the desulfurization tower, and improve the desulfurization efficiency during the wet flue gas desulfurization (WFGD) process of power plants. MIL-53(Fe)-BA exhibits abundant Lewis acid sites because of the appearance of coordination unsaturated Fe atoms. Due to the excellent sorption capacity, Ca(OH)2 was used as the main SO2 desulfurizer. The composite desulfurizers were prepared by mixing MIL-53(Fe)-based additives and Ca(OH)2, and were characterized by SEM, XRD, and FT-IR. A desulfurization unit was set up at laboratory scale to study the effect of catalytic oxidation additives on sulfite oxidation and desulfurization efficiency. The results showed that the addition of MIL-53(Fe)-BA can increase the oxidation capacity of sulfite by 159%, and greatly improve the desulfurization efficiency. These composite desulfurizers broaden the adaptability of the desulfurizing system to high-sulfur coals, and provide support for improving the desulfurizing efficiency of power plants.

A solid oxide fuel cell (SOFC) is an innovative power generation system that is green, efficient, and promising for a wide range of applications. The prediction and evaluation of the operation state of a solid oxide fuel cell system is of great significance for the stable and long-term operation of the power generation system. Prognostics and Health Management (PHM) technology is widely used to perform preventive and predictive maintenance on equipment. Unlike prediction based on the SOFC mechanistic model, the combination of PHM and deep learning has shown wide application prospects. Therefore, this study first obtains an experimental dataset through short-term degradation experiments of a 1 kW SOFC system, and then proposes an encoder-decoder RNN-based SOFC state prediction model. Based on the experimental dataset, the model can accurately predict the voltage variation of the SOFC system. The prediction results of the four different prediction models developed are compared and analyzed, namely, long short-term memory (LSTM), gated recurrent unit (GRU), encoder–decoder LSTM, and encoder–decoder GRU. The results show that for the SOFC test set, the mean square error of encoder–decoder LSTM and encoder–decoder GRU are 0.015121 and 0.014966, respectively, whereas the corresponding error results of LSTM and GRU are 0.017050 and 0.017456, respectively. The encoder–decoder RNN model displays high prediction precision, which proves that it can improve the accuracy of prediction, which is expected to be combined with control strategies and further help the implementation of PHM in fuel cells.

A solid oxide fuel cell (SOFC) system is a kind of green chemical-energy–electric-energy conversion equipment with broad application prospects. In order to ensure the long-term stable operation of the SOFC power-generation system, prediction and evaluation of the system’s operating state are required. The mechanism of the SOFC system has not been fully revealed, and data-driven single-step prediction is of little value for practical applications. The state-prediction problem can be regarded as a time series prediction problem. Therefore, an innovative deep learning model for SOFC system state prediction is proposed in this study. The model uses a two-layer LSTM network structure that supports multiple sequence feature inputs and flexible multi-step prediction outputs, which allows multi-step prediction of system states using SOFC system experimental data. Comparing the proposed model with the traditional ARIMA model and LSTM recursive prediction model, it is shown that the multi-step LSTM prediction model performs better than the ARIMA and LSTM recursive prediction models in terms of two evaluation criteria: root mean square error and mean absolute error. Thus, the proposed multi-step LSTM prediction model can effectively and accurately predict and evaluate the SOFC system’s state.

In this study, a data-driven fault diagnosis method was developed for solid oxide fuel cell (SOFC) systems. First, the complete experimental data was obtained following the design of the SOFC system experiments. Then, principal component analysis (PCA) was performed to reduce the dimensionality of the obtained experimental data. Finally, the fault diagnosis algorithms were designed by support vector machine (SVM) and BP neural network to identify and prevent the reformer carbon deposition and heat exchanger rupture faults, respectively. The research results show that both SVM and BP fault diagnosis algorithms can achieve online fault identification. The PCA + SVM algorithm was compared with the SVM algorithm, BP algorithm, and PCA + BP algorithm, and the results show that the PCA + SVM algorithm is superior in terms of running time and accuracy, the diagnosis accuracy reached more than 99%, and the running time was within 20 s. The corresponding system optimization scheme is also proposed.