Fluid Transport Properties from 3D Tomographic Images of Electrospun Carbon Electrodes for Flow Batteries
Bibliographic record
Abstract
The flow battery remains a cost challenged technology, significant effort has been made into improving performance by way of advanced redox couples, different chemistries, and different electrode materials.[1] One aspect of flow cell performance that has remained relatively overlooked is the effect of different transport properties in the electrode itself. The structure and morphology of the porous media plays an essential role in the electrolyte dispersion and the access the fibers have to the active species. Custom made porous media was electrospun in house and carbonized for use in as a flow battery electrode. 3D tomography images were taken of both the carbonized and ‘as-spun’ polyacrylonitrile (PAN) mats at a variety of different spinning conditions. Lattice-Boltzmann simulations were performed on the tomographic images to determine the flow distribution through the material as well as the permeability of the electrospun mats. The findings were consistent with previous experimentally measured permeabilities of electrospun PAN materials.[2] While the results indicated permeabilities that would be acceptable or even beneficial for a flow battery, the modelling and analysis showed that material inconsistencies in the fibrous mat led to a large variability in local material and transport properties that could have a very negative effect on flow battery performance. There was a small variation in local fiber sizes and a much more significant variation in material porosity. Because these variations only existed in one axis, specifically through the thickness of the material, we were able to determine that the cause must be changing electrospinning conditions as the material was deposited. The findings of this study are being used to develop more optimized flow battery electrodes from electrospun materials. While there is tradeoff in terms of ease of production when utilizing electrospun electrodes versus traditional graphite fibrous electrodes, electrospun materials allow for a greater degree of control of fiber size and general morphology allowing the optimization of permeability, surface area as well as mass transfer properties. The attached figure demonstrates the detail and the accuracy generated through the Lattice-Boltzmann simulations. The fibers and streamlines shown are actual electrospun carbonized PAN with fibers that have a diameter of approximately 750 nm. [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]M. D. R. Kok and J. T. Gostick, “Transport properties of electrospun fibrous membranes with controlled anisotropy,” J. Membr. Sci., vol. 473, pp. 237–244, Jan. 2015. Figure 1
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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 | 0.001 | 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".