Fabrication and Characterization of Electrospun Electrodes for Flow Battery Applications
Bibliographic record
Abstract
Flow batteries are a promising candidate for grid-level energy storage, due to their decoupled power generation and energy storage [1]. Since they were first proposed by NASA in the 1970’s [2], numerous prototypes have been shown, but commercialization has been hindered by their suboptimal design and high cost. Most research in this field has been focused on adjusting redox chemistry or improving catalytic properties of the electrode. Less effort has been made in addressing the mass transport within the electrochemical cell. The electrode is typically a porous carbon material which supports redox reactions on its surface [1]. Optimizing the hydrodynamic conditions and transport phenomena via structural modifications of the electrode is a promising option to improve cell performance. Fibrous materials are of special interest due to their high surface area, which leads to a higher reaction rate, while at the same time being highly porous to provide good permeability and diffusivity. In this work, a range of fibrous electrodes were produced by electrospinning of polyacrylicnitrile (PAN) followed by carbonization. Electrospinning is a convenient method for making prototype materials since it allows tremendous flexibility in the final product by simply varying processing parameters [3]. The use of novel electrodes to improve mass transport and reactive surface area in flow batteries has started to be the focus of investigations [4], but recent modeling work [5] has shown that significant improvements can be made. Materials were produced with a range of fiber morphology, porosity, pore sizes, and thickness. SEM images of 1 specific electrospun material before and after carbonization are shown in figure 1. In addition to these structural measurements, key transport properties such as diffusivity and permeability were also measured and found to vary widely between materials. It is expected that these properties will correlate closely with cell performance, hence proper characterization of transport parameters will be essential to the further optimization of high performance materials. [1] A. Z. Weber, M. M. Mench, J. P. Meyers, P. N. Ross, J. T. Gostick, and Q. Liu, “Redox flow batteries: a review,” J. Appl. Electrochem. , vol. 41, no. 10, pp. 1137–1164, Sep. 2011. [2] L. H. Thaller, “Electrically rechargeable REDOX flow cell,” US3996064 A, 07-Dec-1976. [3] B. Zhang, F. Kang, J.-M. Tarascon, and J.-K. Kim, “Recent advances in electrospun carbon nanofibers and their application in electrochemical energy storage,” Prog. Mater. Sci. , vol. 76, pp. 319–380, Mar. 2016. [4] G. Lin, P. Y. Chong, V. Yarlagadda, T. V. Nguyen, R. J. Wycisk, P. N. Pintauro, M. Bates, S. Mukerjee, M. C. Tucker, and A. Z. Weber, “Advanced Hydrogen-Bromine Flow Batteries with Improved Efficiency, Durability and Cost,” J. Electrochem. Soc. , vol. 163, no. 1, pp. A5049–A5056, Jan. 2016. [5] M. D. R. Kok and J. T. Gostick, “Multiphysics Simulation of the Bromine Cathode: Cell Architecture and Electrode Optimization,” ECS Trans. , vol. 69, no. 1, pp. 21–35, Sep. 2015. Figure 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".