Macromolecular Design Strategies for Long-Lived and Energy Efficient All-Organic Redox-Flow Batteries

2017 ◽  
Keyword(s):  
Energy Efficient ◽  
Design Strategies ◽  
Redox Flow Batteries ◽  
Flow Batteries

Highly Energy-Efficient Vanadium Redox Flow Batteries with Thermo-Chemically Activated Graphite Felt Electrodes

2020 ◽  
Vol MA2020-02 (2) ◽  
pp. 208-208
Author(s):  
Vladimir Neburchilov ◽  
Ken Tsay ◽  
Khalid Fatih ◽  
Roberto Neagu ◽  
Oltion Kodra ◽  
...  
Keyword(s):  
Energy Efficient ◽  
Graphite Felt ◽  
Redox Flow Batteries ◽  
Flow Batteries ◽  
Vanadium Redox ◽  
Felt Electrodes

Binder-free thin graphite fiber mat sandwich electrode architectures for energy-efficient vanadium redox flow batteries

2021 ◽  
Author(s):  
M. Raja ◽  
Harun Khan ◽  
Shrihari Sankarasubramanian ◽  
Deepak Sonawat ◽  
Vijay Ramani ◽  
...  
Keyword(s):  
Energy Efficient ◽  
Graphite Fiber ◽  
Redox Flow Batteries ◽  
Flow Batteries ◽  
Binder Free ◽  
Vanadium Redox

scholarly journals Macromolecular Design Strategies for Preventing Active‐Material Crossover in Non‐Aqueous All‐Organic Redox‐Flow Batteries

2017 ◽  
Vol 56 (6) ◽  
pp. 1595-1599 ◽  
Author(s):  
Sean E. Doris ◽  
Ashleigh L. Ward ◽  
Artem Baskin ◽  
Peter D. Frischmann ◽  
Nagarjuna Gavvalapalli ◽  
...  
Keyword(s):  
Active Material ◽  
Design Strategies ◽  
Redox Flow Batteries ◽  
Flow Batteries

scholarly journals Leveraging Neural Networks and Genetic Algorithms to Refine Electrode Properties in Redox Flow Batteries

2021 ◽  
Author(s):  
Kevin Tenny ◽  
Richard Braatz ◽  
Yet- Ming Chiang ◽  
Fikile Brushett
Keyword(s):  
Neural Network ◽  
Structural Diversity ◽  
Cell Polarization ◽  
Data Sets ◽  
Design Strategies ◽  
Kinetic Rate Constant ◽  
Individual Parameter ◽  
Redox Flow Batteries ◽  
Flow Batteries ◽  
Testing Error

Redox flow batteries are a nascent, yet promising, energy storage technology for which widespread deployment is hampered by technical and economic challenges. A performance-determining component in the reactor, present-day electrodes are often borrowed from adjacent electrochemical technologies rather than specifically designed for use in flow batteries. A lack of structural diversity in commercial offerings, coupled with the time constraints of wet-lab experiments, render broad electrode screening infeasible without a modeling complement. Herein, an experimentally validated model of a vanadium redox flow cell is used to generate polarization data for electrodes with different macrohomogeneous properties (thickness, porosity, volumetric surface area, and kinetic rate constant). Using these data sets, we then build and train a neural network with minimal average root-mean squared testing error (17.9 ± 1.8 mA cm<sup>−2</sup>) to compute individual parameter sweeps along the cell polarization curve. Finally, we employ a genetic algorithm with the neural network to ascertain electrode property values for improving cell power density. While the developed framework does not supplant experimentation, it is generalizable to different redox chemistries and may inform future electrode design strategies.

scholarly journals Leveraging Neural Networks and Genetic Algorithms to Refine Electrode Properties in Redox Flow Batteries

2021 ◽  
Author(s):  
Kevin Tenny ◽  
Richard Braatz ◽  
Yet- Ming Chiang ◽  
Fikile Brushett
Keyword(s):  
Neural Network ◽  
Structural Diversity ◽  
Cell Polarization ◽  
Data Sets ◽  
Design Strategies ◽  
Kinetic Rate Constant ◽  
Individual Parameter ◽  
Redox Flow Batteries ◽  
Flow Batteries ◽  
Testing Error

Redox flow batteries are a nascent, yet promising, energy storage technology for which widespread deployment is hampered by technical and economic challenges. A performance-determining component in the reactor, present-day electrodes are often borrowed from adjacent electrochemical technologies rather than specifically designed for use in flow batteries. A lack of structural diversity in commercial offerings, coupled with the time constraints of wet-lab experiments, render broad electrode screening infeasible without a modeling complement. Herein, an experimentally validated model of a vanadium redox flow cell is used to generate polarization data for electrodes with different macrohomogeneous properties (thickness, porosity, volumetric surface area, and kinetic rate constant). Using these data sets, we then build and train a neural network with minimal average root-mean squared testing error (17.9 ± 1.8 mA cm<sup>−2</sup>) to compute individual parameter sweeps along the cell polarization curve. Finally, we employ a genetic algorithm with the neural network to ascertain electrode property values for improving cell power density. While the developed framework does not supplant experimentation, it is generalizable to different redox chemistries and may inform future electrode design strategies.

scholarly journals Macromolecular Design Strategies for Preventing Active-Material Crossover in Non-Aqueous All-Organic Redox-Flow Batteries

2017 ◽  
Vol 129 (6) ◽  
pp. 1617-1621 ◽  
Author(s):  
Sean E. Doris ◽  
Ashleigh L. Ward ◽  
Artem Baskin ◽  
Peter D. Frischmann ◽  
Nagarjuna Gavvalapalli ◽  
...  
Keyword(s):  
Active Material ◽  
Design Strategies ◽  
Redox Flow Batteries ◽  
Flow Batteries

Heterometal functionalization yields improved energy density for charge carriers in nonaqueous redox flow batteries

2018 ◽  
Vol 6 (28) ◽  
pp. 13874-13882 ◽  
Author(s):  
Lauren E. VanGelder ◽  
Ellen M. Matson
Keyword(s):  
Energy Density ◽  
Cell Voltage ◽  
Charge Carriers ◽  
Design Strategies ◽  
Redox Flow Batteries ◽  
Flow Batteries

Heterometal functionalization within a polyoxovanadate-alkoxide cluster significantly increases the solubility and cell voltage, highlighting design strategies for nonaqueous, energy dense charge carriers.

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