Electrospun custom made flow battery electrodes are imaged in 3D using X-ray computed tomography. A variety of computational methods and simulations are applied to the images to determine properties including the porosity, fiber size, and pore size distributions as well as the material permeability and flow distributions. The simulations are performed on materials before and after carbonization to determine the effect it has in the internal microstructure and material properties. It is found that the deposited fiber size is constantly changing throughout the electrospinning process. The results also show that the surfaces of the fibrous material are the most severely altered during carbonization and that the rest of the material remained intact. Pressure driven flow is modeled using the lattice Boltzmann method and excellent agreement with experimental results is found. The simulations coupled with the material analysis also demonstrate the highly heterogeneous nature of the flow. Most of the flow is concentrated to regions with high porosity while regions with low porosity shield other pores and starve them of flow. The importance of imaging these materials in 3D is highlighted throughout.
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http://dx.doi.org/10.1002/smll.201703616 | DOI Listing |
Sci Rep
January 2025
Mechanical Engineering, Stanford University, Stanford, CA, 94305, USA.
Triply periodic minimal surface (TPMS) metamaterials show promise for thermal management systems but are challenging to integrate into existing packaging with strict mechanical requirements. Composite TPMS lattices may offer more control over thermal and mechanical properties through material and geometric tuning. Here, we fabricate copper-plated, 3D-printed triply periodic minimal surface primitive lattices and evaluate their suitability for battery thermal management systems.
View Article and Find Full Text PDFPolymers (Basel)
December 2024
Chemistry Department, Lomonosov Moscow State University, Moscow 119991, Russia.
Polymer-based aqueous redox flow batteries (RFBs) are attracting increasing attention as a promising next-generation energy storage technology due to their potential for low cost and environmental friendliness. The search for new redox-active organic compounds for incorporation into polymer materials is ongoing, with anolyte-type compounds in high demand. In response to this need, we have synthesized and tested a range of new water-soluble redox-active s-tetrazine derivatives, including both low molecular weight compounds and polymers with different architectures.
View Article and Find Full Text PDFMolecules
December 2024
Shandong Provincial Key Laboratory of Chemical Energy Storage and Novel Cell Technology, School of Chemistry and Chemical Engineering, Liaocheng University, Liaocheng 252059, China.
Hydrogen-bonded organic framework (HOF) materials are typically formed by the self-assembly of small organic units (synthons) with specific functional groups through hydrogen bonding or other interactions. HOF is commonly used as an electrolyte for batteries. Well-designed HOF materials can enhance the proton exchange rate, thereby boosting battery performance.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
Helmholtz-Zentrum Hereon, Institute of Membrane Research, Max Planck Str. 1, 21502, Geesthacht, Germany.
This work proposes a fuel cell power supply system for underwater applications (e.g., autonomous underwater vehicles), where artificial gills, based on a polymer membrane, harvest the required oxygen from the ambient water.
View Article and Find Full Text PDFISA Trans
December 2024
Group of Power Systems, Catalonia Institute for Energy Research (IREC), Jardins de les Dones de Negre, 1, 08930, Sant Adrià del Besòs, Spain. Electronic address:
This paper presents the design and implementation of a deep-learning-based observer for accurately estimating the State of Charge (SoC) of a vanadium flow battery. The novelty of the proposal lies in its direct use of terminal voltage and the application of a machine learning algorithm to model the battery's overpotentials, leading to greater accuracy and reduced complexity compared to classical models. The overpotentials model consists of a neural network trained using data generated by a classical observer that estimates species concentration using a physical electrochemical model and the open-circuit voltage measurement.
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