Cell System Research Articles

Flexible and simultaneous multi-scale bioparticle manipulation to actuate cell and drug delivery utilizing AC electrokinetic -powered microswimmer

Bioparticle trapping and transporting are essential processes for developing an integrated cell and drug delivery system. Herein, we present a unique platform that effectively combines magnetic field and alternating current (AC) electrokinetics for the simultaneous manipulation of bioparticles across scales. The magnetic field is used to control the moving direction of Janus swimmers, and the AC electrokinetics incorporating dielectrophoresis (DEP) and induced charge electroosmosis (ICEO) flow are utilized to power Janus swimmers to manipulate bioparticles. Numerical characterization of the stagnant regions generated by DEP and ICEO elucidates the trapping mechanisms for both micro and nano bioparticles. We demonstrate that the Janus swimmers are capable of navigating along varied trajectories at speeds of up to hundreds of micrometers per second. Developed phase maps, derived from AC frequency and medium conductivity parameters, delineate three distinct bioparticle loading positions, offering a versatile guide for the manipulation of cells and nanodrugs. The platform’s adaptability is further highlighted by its success in performing mobile trapping of cells and nanodrugs along user-defined trajectories, targeted trapping of nanoparticle aggregations, and simultaneous multi-scale bioparticle capture. The presented multi-scale bioparticle trapping strategy, together with its unique features of simple geometric configuration, facile operation and broad applicability, can broaden utility in cellular and pharmaceutical research.

Experimental assessment of a heavy-duty fuel cell system in relevant operating conditions

During the past few years, the research interest in fuel cell systems (FCS) has increased significantly. Thus, this kind of powertrain is now at high Technology Readiness Levels (TRLs) and even starting to be commercialized. However, the currently available technology still has room for optimization. The existing bibliography shows studies mainly related to stack-level characterization or FCS studies with a poor level of detail. The present paper focuses on producing a detailed database about the performance of a specific FCS designed for heavy-duty vehicle (HDV) applications. The obtained dataset provides information about the influence of ambient conditions on the performance of the system, plus information about the anode and cathode circuits. These factors represent an important novelty with respect to other pieces of literature present in the actual research framework. Additionally, the obtained data regarding the performance of an FCS is highly significant for HDV manufacturers in the transportation sector and eases the pathway to the optimization of these technologies. The written piece of research proves the importance of studying the ambient conditions. The experimental campaign has shown that increasing relative humidity can improve the performance of the membrane up to a 2% for a 25% change in the humidity level. Additionally, the study shows the importance of implementing an appropriate supply gas strategy, both for anode and cathode, and designing the right purging mechanisms for H2 at the anode inlet. The obtained results show how there exist a relation between the optimal efficiency operating point and a H2 excess from the supply strategy.

Electrostatic ethanol fermentation: Experimental study and kinetic-based metabolic modeling

Due to the electrical nature of the cell, it is possible to modulate its behavior through the application of non-lethal external electric fields to improve fermentation processes. In this work, a microbial cell system with a chamber and two electrodes inside and connected to a voltage source was used. One of the electrodes was kept isolated to create an electric field without the flow of current. Cultures with two ethanol-producing microbial strains (Saccharomyces cerevisiae and Zymomonas mobilis) were conducted in this device. The application of voltages between 0 and 18 V was evaluated to determine the impact of the generated electric field on ethanol production. To analyze the possible effect of the field on the central carbon metabolism in each strain, biochemical-based kinetic models were formulated to describe the experimental fermentation kinetics obtained.It was found that low applied voltages did not have significant effects on growth rate in either strain, but all voltages evaluated increased substrate consumption and ethanol production rate in Z. mobilis, while only 18 V affected these rates in S. cerevisiae, indicating that Z. mobilis was the most sensitive to the electric field. At the end of the fermentation, significant increases in ethanol yields of 10.7% and 19.5% were detected for S. cerevisiae and Z. mobilis, respectively. The proposed mathematical models showed that substrate transport through the membrane catalyzed by the phosphotransferase system (PTS) for Z. mobilis and hexose transport proteins mechanism and hexokinase (HK) activity for S. cerevisiae and the transformation of pyruvate to ethanol, catalyzed by the decarboxylase (PDC) and alcohol dehydrogenase (ADH) enzymes, were the reactions most affected by the application of the external field.

Investigation of the energy dissipation mechanism for centrifugal air compressor used for fuel cell based on CFD coupling with CIT

Effective evaluation indicator is the focus of air compressor structural optimization and development of control strategies, leaving to be further investigated from physical occurrence mechanism. Considering air expansion and compression, on the basis of coupling computational fluid dynamics (CFD) with classical irreversible thermodynamics (CIT), an extended entropy generation rate was established. Accordingly, various entropy generation rates for air compressor under wide operating conditions was investigated, aiming to what, where and how much the energy was dissipated. Results show that entropy generation rate related to air expansion and compression is always the largest contributor to the total entropy generation of the system under all operating conditions. A new dimensionless number R is defined to distinguish the fluid-related entropy generation rate and mechanical-related entropy generation rate. Furthermore, physical-based empirical models concerning the relationship of entropy generation rates, motor speeds and loads are proposed in this study, whose usage for guiding structural design was discussed. The work done in this paper can not only provide a new method for guiding the structural design of air compressors, but also providing a new idea for air compressor-fuel cell system’s optimal control.

Analysis of the thermodynamic performance of the SOFC-GT system integrated solar energy based on reverse Brayton cycle

Efficient power generation systems are an essential way to reduce carbon emissions. To avoid the complex effects of high-pressure conditions on the solid oxide fuel cell (SOFC) and gas turbine (GT) hybrid systems, while further improving energy efficiency. A SOFC-GT hybrid system based on the reverse Brayton cycle (RBC) is proposed, in which the SOFC operates at near atmospheric pressure conditions and GT expands below atmospheric pressure. Solar energy is integrated to further enhance system performance. Rigorous assessments of the novel system and the pressurized system are conducted utilizing energy, exergy and economic analysis. The sensitivity analysis of environmental parameters on the novel system's performance is performed. The results show that the novel system generates power and efficiency better than the conventional system when the GT compression ratio is 3 and 6, respectively. The power generation and exergy efficiency of the novel system at the design point is 60.47 % and 58.57 %, respectively. The largest exergy destruction occurred in the SOFC. The lowest exergy efficient components are the parabolic dish collector (PDC) and heat exchanger 2. The novel system is more cost-effective due to the higher lifetime of the SOFC, with the levelized cost of energy (LCOE) is 0.1418 $/kWh.

Assessment of baseline CCL19+ dendritic cell infiltration for predicting responses to immunotherapy in lung adenocarcinoma patients

To explore the correlation of baseline CCL19+ dendritic cell (CCL19+ DC) infiltration in lung adenocarcinoma microenvironment with immunotherapy efficacy and CD8+ T cell infiltration. We retrospectively analyzed the data of patients with lung adenocarcinoma hospitalized at First Affiliated Hospital of Henan University of Science and Technology from January, 2020 to December, 2023, and collected tissue samples from 96 patients undergoing immunotherapy for assessing CCL19+ DC and CD8+ T cell infiltration using immunofluorescence assay. We evaluated the predictive value of baseline CCL19+ DCs for patient responses to immunotherapy using receiver-operating characteristics (ROC) curves and analyzed the correlations of baseline CCL19+ DC expression with immunotherapy efficacy and CD8+ T cell and cytotoxic T lymphocyte (CTL) infiltrations. In co-culture systems of lung adenocarcinoma PC9 cells, CD8+ T cells and DCs (overexpressing CCL19 with or without anti PD-1 antibody treatment), the expressions of granzyme B, perforin, IFN-γ, and Ki-67 in T cells were analyzed using flow cytometry. The patients with partial or complete remission following immunotherapy had a significantly higher baseline CCL19+ DC infiltration level in lung adenocarcinoma tissues than those with poor responses. CCL19+ DC infiltration had an area under ROC curve of 0.785, a sensitivity of 75.6%, and a specificity of 62.8% for predicting immunotherapy efficacy. The expression of CD8+ T cell surface molecules Granzyme B (P<0.01), Perforin (P<0.01), IFN-γ (P<0.01) and Ki-67 (P<0.001) in patients with high expression of CCL19+ DC were higher than those in patients with low expression of CCL19+ DC. The baseline CCL19+ DC infiltration level was positively correlated with immunotherapy efficacy (P=0.003), CTL infiltration of (r=0.6657, P<0.001) and CD8+ T cell infiltration (P=0.007). In the co-cultured cells, CCL19 overexpression combined with anti-PD1 treatment of the DCs more strongly enhanced cytotoxicity and proliferation of CD8+ T lymphocytes than either of the single treatments (P<0.01 or 0.001). The baseline CCL19+ DC infiltration level in lung adenocarcinoma microenvironment is positively correlated with immunotherapy efficacy and CTL infiltration and can thus predict the response to immunotherapy.

Comprehensive performance analysis and optimization for PEMFC-heat pump combined system considering dual heat source

Traditional proton exchange membrane fuel cell (PEMFC) systems face the challenges of strong thermal–electrical coupling and insufficient heating capacity. This study proposes a PEMFC–heat pump combined system considering dual heat source, which effectively utilizes fuel cell waste heat and environmental heat to achieve significant improvements in thermal–electrical decoupling and heating capacity. Energy, economic, and environmental analysis models were established, and the effects of key parameters on the combined system were systematically analyzed, followed by multi-objective optimization. Results show that the PEMFC current density has the largest effect on the heating capacity, while the heat flow ratio exerts the strongest influence on economic performance. The minimum pollutant emissions are achieved when wind power is adopted for hydrogen production. Under an ambient temperature of 248.15 K, the combined system increases the heating capacity by 12.8 % and reduces the levelized cost of energy and greenhouse gas emissions by 7.85 % and 15.9 %, respectively. The optimal solution presents a strong nonlinear relationship with the PEMFC current density and condenser undercooling, the heat flow ratio is close to the upper limit (0.99), and the operating temperature is close to the lower limit (333.4 K). These findings provide innovative thermoelectric decoupling and efficient energy supply solutions for PEMFC systems and have important implications for accelerating the low-carbon transition of regional energy systems.

An enhanced salp swarm algorithm with chaotic mapping and dynamic learning for optimizing purge process of proton exchange membrane fuel cell systems

Swarm intelligence algorithm is now a research focus for the energy optimization of fuel cell systems to improve hydrogen utilization efficiency. However, conventional algorithms exhibit poor robustness and low execution efficiency since complex constraints are involved in the fuel cell cathode purge process. Given that, this paper innovatively introduces chaotic mapping and dynamic learning mechanisms into the Salp swarm algorithm to enhance the algorithm's global search and local exploitation capabilities under complex constraints. First, we developed a three-stage nonlinear cathode purge model to describe the different water transfer behaviors in the three purging stages. Then, an optimizing function is created under the complex staged constraints to accurately reveal the energy consumption mechanism. Finally, the enhanced Salp swarm algorithm enables the rapid and accurate determination of the three-stage dynamic purge flows based on minimizing energy consumption. Compared with the fixed flow purge scheme, we found that energy consumption is reduced by 3.6 %–8.2 % under various constraints. The enhanced Salp swarm algorithm proposed in this work demonstrates an outstanding performance in the energy consumption optimization of fuel cell systems.

Development and Evaluation of a Shrimp Virus (IHHNV)-Mediated Gene Transfer and Expression System for Shrimps.

An efficient gene transfer and expression tool is lacking for shrimps and shrimp cells. To solve this, this study has developed a shrimp DNA virus-mediated gene transfer and expression system, consisting of insect Sf9 cells for viral packaging, the shrimp viral vector of pUC19-IHHNV-PH-GUS and the baculoviral vector of Bacmid or Bacmid-VP28 encoding the shrimp WSSV envelope protein VP28. The pUC19-IHHNV-PH-GUS vector was constructed by assembling the genomic DNA of shrimp infectious hypodermal and hematopoietic necrosis virus (IHHNV), which has shortened inverted terminal repeats, into a pUC19 backbone, and then an expression cassette of baculoviral polyhedron (PH) promoter-driven GUS (β-glucuronidase) reporter gene was inserted immediately downstream of IHHNV for proof-of-concept. It was found that the viral vector of pUC19-IHHNV-PH-GUS could be successfully packaged into IHHNV-like infective virions in the Sf9 cells, and the gene transfer efficiency of this system was evaluated and verified in three systems of Sf9 cells, shrimp hemolymph cells and tissues of infected shrimps, but the GUS expression could only be detected in cases where the viral vector was co-transfected or co-infected with a baculovirus of Bacmid or Bacmid-VP28 due to the Bacmid-dependence of the PH promoter. Moreover, the packaging and infection efficiencies could be significantly improved when Bacmid-VP28 was used instead of Bacmid.

Reversible solid oxide cells-based hydrogen energy storage system for renewable solar power plants

The power-H2-power system based on reversible solid oxide cell is a promising pathway for large-scale renewable energy storage but not well understood due to the absence of comprehensive system analyses. In this study, a reversible solid oxide cell-based H2 energy storage system for a 100 % renewable solar power plant is proposed and analyzed through detailed modeling approach and optimization framework. The detailed parametric analyses and economic evaluations are performed in electrolysis (12:00 pm, sufficient solar energy) and power generation (6:30 am, insufficient solar energy) conditions. Notably, it is found that larger stack capacity (Ncell) and higher operating temperature (TReSOC) of the cell enhances system net H2 production and system economics despite additional investment cost. Afterwards, the optimal system performance is obtained (VH2,produce = 498.40 Nm3·h−1, Z=263.59 $·h−1 for electrolysis, and VH2,consume = 380.33 Nm3·h−1 for power generation) through multi-objective optimization by fully considering cell internal operating characteristics and system energy-exergy-economic factors. Besides, it is also found that the performance of power-H2-power system is still limited by remarkable fuel (H2) costs for nighttime power generation (daytime H2 production is smaller than nighttime H2 consumption when Wtot = 1 MW). Therefore, the critical power capacity (Pcritical) is obtained, offering a convenient approach to determine the maximum output capacity (Pcritical = 880 kW) to make the power-H2-power system profitable at specific solar energy input. This study provides valuable insights between system capacity, economics, and cell operating features in solar power plants, which are useful for the design and optimization of practical power-H2-power systems for renewable energy storage.

Stack performance classification and fault diagnosis optimization of solid oxide fuel cell system based on bayesian artificial neural network and feature selection

Solid oxide fuel cell (SOFC) has attracted much attention because of its high efficiency and silent power generation, and can be applied to the field of fuel cell-powered unmanned boats. However, a fuel deficit failure in the system can lead to reduced stack performance, reducing the reliability of SOFC applications in the field of unmanned boats. Therefore, this paper proposes an efficient breakdown diagnosis model for SOFC system fault diagnosis. Firstly, Bayesian artificial neural network is constructed by using the best feature data subset after Relief-F combined with minimum Redundancy Maximum Relevance algorithm feature selecting. Secondly, the correct diagnosis rate under different number of optimal features is calculated, and the best feature combinations are combined to verify. Finally, the particle swarm algorithm selects the optimal hidden neuron count to optimization model. Experimental findings suggest that for correct diagnosis rate, the breakdown diagnosis model based on the optimal number of 3 features can reduce the training time and maintain high accuracy compared with the use of all 34 features. This research result provides a feasible way for the fault diagnosis application of SOFC system power generation in the field of unmanned boats.

ATP, the 31P Spectral Modulus, and Metabolism.

Adenosine triphosphate (ATP) has a high intracellular millimolar concentration (ca. 2.4 mM) throughout the phylogenetic spectrum of eukaryotes, archaea, and prokaryotes. In addition, the function of ATP as a hydrotrope in the prevention of protein aggregation and maintenance of protein solubilization is essential to cellular, tissue, and organ homeostasis. The 31P spectral modulus (PSM) is a measure of the health status of cell, tissue, and organ systems, as well as of ATP, and it is based on in vivo 31P nuclear magnetic resonance (31P NMR) spectra. The PSM is calculated by dividing the area of the 31P NMR integral curve representing the high-energy phosphates by that of the low-energy phosphates. Unlike the difficulties encountered in measuring organophosphates such as ATP or any other phosphorylated metabolites in a conventional 31P NMR spectrum or in processed tissue samples, in vivo PSM measurements are possible with NMR surface-coil technology. The PSM does not rely on the resolution of individual metabolite signals but uses the total area derived from each of the NMR integral curves of the above-described spectral regions. Calculation is based on a simple ratio of the high- and low-energy phosphate bands, which are conveniently arranged in the high- and low-field portions of the 31P NMR spectrum. In practice, there is essentially no signal overlap between these two regions, with the dividing point being ca. -3 δ. ATP is the principal contributor to the maintenance of an elevated PSM that is typically observed in healthy systems. The purpose of this study is to demonstrate that (1) in general, the higher the metabolic activity, the higher the 31P spectral modulus, and (2) the modulus calculation does not require highly resolved 31P spectral signals and thus can even be used with reduced signal-to-noise spectra such as those detected as a result of in vivo analyses or those that may be obtained during a clinical MRI examination. With increasing metabolic stress or maturation of metabolic disease in cells, tissues, or organ systems, the PSM index declines; alternatively, with decreasing stress or resolution of disease states, the PSM increases. The PSM can serve to monitor normal homeostasis as a diagnostic tool and may be used to monitor disease processes with and without interventional treatment.

Performance analysis and optimization of a zero-emission solar-driven hydrogen production system based on solar power tower plant and protonic ceramic electrolysis cells

The solar-driven high-temperature steam electrolysis is promising for efficient large-scale H2 production. In this study, a comprehensive component-to-system model and optimization framework is developed to investigate the performance of a zero-emission H2 production system based on solar power plant and protonic ceramic electrolysis cell. Compared to previous system studies, the detailed description of cell internal operating characteristics is realized by integrating multi-physics simulation and artificial neural network. After parametric analyses, it is found that the system energy/exergy efficiency and co-generation performance are complicated by each subsystem. And the optimal system performance (ηth = 50.63 %, Z = 179.63 $·h−1 and ηex = 33.03 %, Z = 178.94 $·h−1, with LCOE = 0.172 $·kWh−1 and ZH2 = 6.497 $·kg−1) is obtained considering cell operating features and system energy-exergy-economic factors through multi-objective optimizations. Besides, the tradeoff between system maximum H2 production capacity and cell internal thermal conditions is revealed. This study can facilitate the development of zero-emission green H2 production driven by renewable energy.

Adeno-Associated Virus (AAV)-Delivered Exosomal TAT and BiTE Molecule CD4-αCD3 Facilitate the Elimination of CD4 T Cells Harboring Latent HIV-1.

Combinatorial antiretroviral therapy (cART) has transformed HIV infection from a death sentence to a controllable chronic disease, but cannot eliminate the virus. Latent HIV-1 reservoirs are the major obstacles to cure HIV-1 infection. Previously, we engineered exosomal Tat (Exo-Tat) to reactivate latent HIV-1 from the reservoir of resting CD4+ T cells. Here, we present an HIV-1 eradication platform, which uses our previously described Exo-Tat to activate latent virus from resting CD4+ T cells guided by the specific binding domain of CD4 in interleukin 16 (IL16), attached to the N-terminus of exosome surface protein lysosome-associated membrane protein 2 variant B (Lamp2B). Cells with HIV-1 surface protein gp120 expressed on the cell membranes are then targeted for immune cytolysis by a BiTE molecule CD4-αCD3, which colocalizes the gp120 surface protein of HIV-1 and the CD3 of cytotoxic T lymphocytes. Using primary blood cells obtained from antiretroviral treated individuals, we find that this combined approach led to a significant reduction in replication-competent HIV-1 in infected CD4+ T cells in a clonal in vitro cell system. Furthermore, adeno-associated virus serotype DJ (AAV-DJ) was used to deliver Exo-Tat, IL16lamp2b and CD4-αCD3 genes by constructing them in one AAV-DJ vector (the plasmid was named pEliminator). The coculture of T cells from HIV-1 patients with Huh-7 cells infected with AAV-Eliminator viruses led to the clearance of HIV-1 reservoir cells in the in vitro experiment, which could have implications for reducing the viral reservoir in vivo, indicating that Eliminator AAV viruses have the potential to be developed into therapeutic biologics to cure HIV-1 infection.

Multi-parameter and multi-objective optimization of dual-fuel cell system heavy-duty vehicles: Sizing for serial development

Dual-fuel cell hybrid system provides an attractive propulsion option in transportation, especially for heavy-duty vehicles. However, the larger vehicle weight improves the sensitivity of power demand to road conditions and vehicle handling, making it a challenge to realize reasonable sizing. The scope of this work is to demonstrate a multi-objective and multi-parameter optimization for the serial development of the heavy-duty vehicle, powered by a dual-fuel cell hybrid system. Toward this end, a comprehensive modeling is presented combining the degradation model of the FC system and the battery system. The Pareto theory is introduced to evaluate the three-dimensional objectives involving the equivalent hydrogen consumption, the mass goal, and the vehicle dynamic, which is derived from different six-dimensional parameters under a dual-layer optimization approach. The brute force approach is not applicable in the presence of the curse of dimensionality arising from multi-parameter optimization. The proposed methodology offers a viable approach to acquiring rational sets of sizing solutions in the optimization space with high-dimensional parameters. Considering the serialization of products, the improved solution and the corresponding performance upper limit have be determined according to the proposed methodology under different weight levels as well.

Numerical analysis and parametric optimization of a direct ammonia solid oxide fuel cell system integrated with organic Rankine cycle

In this study, to maximize the electrical efficiency of the direct ammonia solid oxide fuel cell (SOFC) system, anode-off gas is recirculated for enhancing the system fuel utility and the system exhaust heat is utilized for the organic Rankine cycle (ORC) generating additional electricity. To confirm the superiority of the anode-off gas recirculation (AOGR) SOFC–ORC hybrid system, its electrical efficiency has been compared with two reference systems such as the stand-alone SOFC system without AOGR and the stand-alone AOGR SOFC system. A numerical model on three systems has been developed using Aspen Plus® and validated. Parametric analysis of the three systems has been conducted by varying the operating parameters such as the pump pressure, current density, AOGR ratio, condensation ratio, and fuel utilization. The AOGR SOFC-ORC hybrid system has the highest electrical efficiency among the three types of systems, 70.92 % under nominal conditions. The optimal operational range for the AOGR SOFC-ORC hybrid system has been revealed. As an ammonia-fueled distributed power generation, the proposed system can contribute to achieving net zero in countries where renewable energy is not abundant. This study can provide fundamental insights for establishing an optimization of direct ammonia SOFC systems.

Application of hybrid chaotic particle swarm optimization and slime mould algorithm to optimally estimate the parameter of fuel cell and solar PV system

Through the use of fuel cells and photovoltaic (PV) solar cells, green energy has become one of the most popular developments in electricity generation in recent years. Accurate modeling of fuel cell and PV cell is critical in the simulation and analysis of energy systems that use both sources. Due to the complexity and nonlinearity of these hybrid systems, the model must be optimized under different operating conditions. In this article, a novel metaheuristic efficient hybrid algorithm is proposed for this purpose. A Hybrid Chaotic Particle Swarm Optimization and Slime Mould Algorithm (HCPSOSMA) has been proposed to estimate the PV/fuel cell system parameters. The hybrid HCPSOSMA algorithm is superior to other algorithms in terms of higher accuracy in searching for optimal solutions and better explorative capability. The problem formulation is based on minimizing the Root Mean Square Error (RMSE) between the calculated values and measured values for both the triple-diode PV model and the proton exchange membrane (PEM) fuel cell. The results show that the hybrid algorithm outperforms other algorithms in determining the best PV/PEMFC parameters that produce accurate V–I and P–I characteristic curves and the RMSE is about 4 × 10–12. Finally, non-parametric tests based on Friedman's ranking, Wilcoxon's rank sum test and Kruskal-Wallis test are implemented to prove the superiority of the proposed hybrid algorithm in the parameter estimation.

Simulation analysis of BOP thermal management system for hydrogen fuel cell bus

Hydrogen fuel cell bus has been rapidly developed. As an important part of the hydrogen fuel cell system, its BOP (Balance of plant) mainly includes air compressor, controller, and intercooler. In practical applications, the BOP thermal management system generally includes separated cooling circuits. This study proposes an integrated BOP thermal management system, in which the air compressor, the controller and the intercooler share the same cooling circuit. A 1-D numerical model is built in this paper. On the basis of the model, the 13 possible layout schemes of cooling circuit are investigated. Additionally, the BOP temperature variation characteristics under the UDDS (Urban Dynamometer Driving Schedule) condition are also analyzed. The results indicate that not all layouts of integrated scheme can meet the cooling requirements. To meet cooling requirements of the whole BOP, the coolant should pass through the intercooler first in the serial schemes. As for the parallel schemes, the air compressor should be in a single branch, and the controller and the intercooler should be in series successively at another branch. Under the same total flow rate, the average temperature of BOP in the series scheme can be lower than that in parallel scheme by 8 %. In the three components, the intercooler is most affected by the stack power. This study provides reference for the design of the BOP thermal management system.

Co/N-codoped carbon nanoplate array coated carbon fiber cathode for solar driven uranium extraction from complex radioactive wastewater

Organics bring great challenge on uranium reduction due to the formation of stubborn complexes between uranyl ions (UO22+) and organics. Herein, we developed an effective strategy to synchronously degrade organics and extract uranium from complicated radioactive wastewater, accompanied with electricity generation, by a novel self-driven photocatalytic fuel cell (NSPFC) system, in which a three-dimensional (3D) Co/N-doped carbon nanoplate array (Co-N-C/CF) decorated carbon fiber is employed as the cathode for highly efficient UO22+ reduction. Compared to pure CF, the optimized Co-N-C/CF cathode increases the rate constants (k) by 9 and 9.1 times for UO22+ and organic elimination, respectively, and improves electricity output by 2 times (Pmax, 1180 μW/cm2). This improvement should be ascribed to the 3D morphology of Co-N-C/CF that provided plenty of surface area for U(VI) reduction, and the doping of N and Co, which effectively increased the U(VI) adsorption sites and optimized the electron behavior on the cathode, respectively. These features endow the NSPFC system with excellent adaptability in treating complex radioactive wastewater with a wide range of pollutant concentration, pH, co-existed ions, pollutant organics and natural organic matters. Fascinating activity is also achieves in treating polluted seawater or under real sunlight illumination. This work not only provides a outstanding nanostructured cathode for uranium reduction but also suggests a charming system to resourcefully treatment of radioactive wastewater or polluted water bodies.

Next Generation Lab-Scale Bipolar Membrane Electrodialysis Test System

The world of electrochemical devices has witnessed remarkable progress in recent years, with particular emphasis on the development and advancement of bipolar membranes (BPMs). This progress has predominantly centered around BPM applications within membrane electrode assembly (MEA) systems, encompassing technologies such as fuel cells and electrolyzers. The exploration of BPM in these MEA systems has allowed for an almost complete elimination of the BPM overpotential for water dissociation and has dramatically changed the playing field and viability of BPM. This rapid advancement in understanding BPM fundamentals has in part come from the commercially available and universally utilized lab-scale electrolyzer/fuel cell systems. These devices have allowed labs around the world to focus on finite aspects of the BPM without being slowed by the debates of varying test stands.With the development of advanced, MEA optimized BPM and increased understanding of BPM fundamentals, the time is right to embark on the next phase of BPM research. This next phase looks to expand the use of BPM to non-MEA systems, including CO2 sequestration systems, electrodialysis, brine remediation, organic acid/base production, and more. These non-MEA systems present unique challenges and opportunities that demand innovation. To begin the expansion of advanced BPM to non-MEA applications we present here a lab-scale BPM electrodialysis system that allows for small membrane sizes and full system monitoring. This system is cheap to build, highly modular, easy to operate, and allows for a wide range of non-MEA systems to be explored. With this, we hope to speed up the translation of advanced, MEA optimized BPM to non-MEA applications.