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Scalable membrane-less microbial electrolysis cell with multiple compact electrode assemblies for high performance hydrogen production
Bioelectrochemical hydrogen production via microbial electrolysis cells (MECs) is a promising method for sustainable energy production and decarbonization of energy systems. However, the application of MECs is limited by the electrochemical performance, scalability, and the cost associated with expensive materials. Here, in this study, a scalable MEC (500 mL) with novel compact electrode assemblies and high electrode surface area to volume ratio (160 m 2 /m 3 ) was designed and constructed. The use of membranes, precious metal catalyst, and current collectors with high costs was avoided. A high current density at the steady state of 49.5 ± 5.3 A/m 2 was achieved using acetate as the substrate with phosphate buffer under the applied voltage of 1.01 V. The corresponding volumetric current density was 3948 ± 422 A/m 3 . The compact electrode assembly design limited methane production rate to 3.9 ± 0.2 L/L/D, while achieving a hydrogen production rate of 33.7 ± 1.7 L/L/D. With the suppression of microbial hydrogen consumption, the hydrogen production rate was 39.8 ± 1.9 L/L/D, higher by almost one order of magnitude than those of MECs with scaling up attempts. The compact electrode configuration reduced internal resistance to 88.5 ± 4.4 Ω cm 2 . The energy efficiency based on input electricity was 146 ± 7 % to 189 ± 9 % within the applied voltage range of 0.71 to 1.05 V. The results in this study demonstrated successful scaling up of high performance small MECs and offered a new possible approach of scaling up MECs by stacking high-performance subunits, with no trade-offs on electrochemical performance.
Symbiotic bacteriophages exhibit multiple adaptive strategies in activated sludge flocs and contribute to floc stability
Despite the importance of phages for the dynamics and functions of microbial communities, it remains largely unexplored how symbiotic phages adapt in activated sludge systems and influence microbial aggregate stability, which is critical for clarification and reliable performance. Here, based on 12,127 phage contigs recovered from 12 activated sludge microbiomes, the symbiotic phages exhibited broad host ranges and infected dominant prokaryotes in activated sludge. Moreover, these phage communities exhibited high lysogenicity (49% to 66% lysogenic phages) and harbored diverse auxiliary metabolic genes (AMGs) that could enhance microbial aggregate stability, such as genes coding for lipopolysaccharide, peptidoglycan, and extracellular polysaccharide biosynthesis. Additionally, prokaryotic antiviral systems were widely distributed in dominant prokaryotes, particularly the Restriction-Modification (RM) and CRISPR-Cas systems, which could mitigate potential harmful phage infections. Overall, this study reveals the contemporary beneficial relationship between phages and prokaryotic hosts in activated sludge microbiomes and the potential benefits of symbiotic phages on floc stability.
Impact of multiple drying and rewetting events on biochar amendments for Hg stabilization in floodplain soil from South River, VA
Abstract not provided
Higher-order LaSDI: Reduced order modeling with multiple time derivatives
Solving complex partial differential equations (PDEs) is essential across scientific disciplines but often requires numerical models that can be prohibitively expensive in time-sensitive applications. Reduced-order models (ROMs) address this challenge by exploiting low-dimensional structure to create fast approximations. The Latent Space Dynamics Identification (LaSDI) framework has demonstrated success in learning ROMs for parameterized PDE families, but remains limited to first-order systems. Here, in this paper, we propose Higher-Order LaSDI (HLaSDI), which extends the LaSDI framework to PDEs with arbitrary order of time derivatives. This generalization significantly expands the applicability of LaSDI-based methods to systems previously outside their scope, including hyperbolic PDEs. We demonstrate HLaSDI’s accuracy and efficiency on several linear and nonlinear benchmark problems.
Co-training of multiple neural networks for simultaneous optimization and training of physics-informed neural networks for composite curing
This paper introduces a Physics-Informed Neural Network (PINN) technique that co-trains neural networks (NNs) that represent each function in a system of equations to simultaneously solve equations representing an out-of-autoclave (OOA) cure process while conducting optimization in adherence to process requirements. Specifically, this co-training approach benefits from using NNs to represent OOA inputs (air temperature profile) and outputs (part and tool temperature profiles and degree of cure). Production requirements can then be levied on the inputs, such as maximum air temperature and minimum cure cycle, and simultaneously on the outputs, such as degree of cure, maximum part temperature, and part temperature rate limits. The technique is validated with finite element (FE) simulations and physical experiments for curing a Toray T830H-6 K/3900-2D composite panel. Furthermore, this novel approach efficiently models and optimizes the OOA cure process.
Multiple damaging and self-healing properties of cement paste incorporating microcapsules
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Comprehensive analysis of multiple cytokines and blood parameters for the diagnosis of bacterial infections in rheumatoid arthritis
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Multiple-effect desiccant-based zero liquid discharge desalination systems
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Design and performance assessment of a dual-mode latent thermal storage integrated heat pump across multiple climate zones
The electrification of space cooling and heating systems risks overloading the existing electrical grid during peak hours. Heat pumps integrated with thermal energy storage (HP-TES) offer a promising solution by shifting peak loads to off-peak hours, reducing grid strains. This work presents the design and assessment of a dual-mode HP-TES that uses a single 22°C phase-change material (PCM) as a heat source or sink in heating and cooling modes, respectively. Performance assessment was conducted using Modelica-based transient models, and full-year simulations were conducted across eight US climate zones using typical weather data. Two HP-TES performance metrics: peak energy reduction and recharge energy increases, were defined by comparing HP-TES energy consumption with base HP peak consumptions. Annual heating demand reductions (40–65%) exceeded cooling demand reductions (15–20%) due to the elimination of peak-hour backup heating. Cooling mode recharge energy requirements (0–10%) were reduced in locations with lower summer nighttime temperatures, while heating recharge requirements were lower than the high base-peak energy demand from backup heating. Simplified rapid methods that were 10 7 times faster than annual simulations were developed to predict seasonal cooling and heating energy reductions, and recharge energy requirements, with maximum deviations of ±2.5% and ±3.5% points. These methods enabled rapid parametric studies that identified optimal location-specific PCM temperatures between 22°C and 27°C, highlighting the need to consider both discharge and recharge energy requirements to achieve sustainable, energy-efficient peak load shifting across various climate zones.
Multiple metal exposure and platelet counts during pregnancy: A repeated measure study
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Measuring multiple cosmogenic nuclides in glacial cobbles sheds light on Greenland Ice Sheet processes
The behavior of the Greenland Ice Sheet during the Pleistocene remains uncertain due to the paucity of evidence predating the Last Glacial Maximum. In this study, we employ a novel approach, cosmogenic nuclide analysis of individual subglacially-derived cobbles, which allows us to make inferences about ice sheet processes and subglacial erosion. From three locations in western Greenland, we collected 86 cobbles from the current ice sheet margin and nine cobbles exposed on the modern proglacial land surface. We measured the concentration of in situ 10 Be in all cobbles (n = 95) and 26 Al and 14 C in a subset (n = 14). Cobbles deposited during Holocene retreat have 10 Be exposure ages generally consistent with the timing of ice retreat determined by other methods. Conversely, most of the 86 subglacial cobbles contain very low concentrations of 10 Be (median 1.0×10 3 atoms g –1 ), although several have ~10 4 and one has ~10 5 atoms g –1 . The low concentrations of 10 Be in most subglacial cobbles imply that their source areas under the Greenland Ice Sheet are deeply eroded, preserving minimal evidence of surface or near-surface exposure. The presence of measurable 14 C in ten of the cobbles requires that they experienced cosmogenic nuclide production within the past ~30 ka; however, 14 C/ 10 Be ratios of ~6 suggest that nuclide production occurred during shielding by overlying material. Only two of the 86 subglacial cobbles definitively have cosmogenic nuclide concentrations consistent with prior surface exposure. Overall, isotopic analysis of subglacial cobbles indicates that much of western Greenland's subglacial landscape is characterized by deep erosion and minimal subaerial exposure.
Multiple linear regression and thermodynamic fluctuations are equivalent for computing thermodynamic derivatives from molecular simulation
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