(Invited) Redox Flow Battery Innovation
Bibliographic record
Abstract
Flow battery innovations should offer significant improvements in performance, without compromising the durability / lifetime, and be cost-effective and scalable. The presentation will review some of the progress that has been made to enhance flow battery performance, and will discuss a number of recent innovations that aim to deliver these characteristics. These will include: Magnetic flowable electrodes applied in a polysulfide-iodide flow battery. Using flow through the current feeder to enhance mass transport and enable dendrite free zinc deposition in the zinc-iodide flow battery. Graphene modified membrane for enhanced power density. Flowable electrodes have emerged as a novel concept for high energy density batteries. To date, in most cases the flowable solid phase includes a redox active energy storage material, for example in zinc-nickel, sodium-sulfur, and lithium-sulfur systems [1-3]. In contrast, we have demonstrated the use of a carbon – magnetite nanocomposite which acts as an electrocatalyst but is not redox active [4,5]. This nanomaterial can be dispersed in the electrolyte and circulated through the battery to enhance the performance of a conventional static electrode. The magnetic characteristics of the nanocomposite can also be exploited, by using a magnetic field to assemble a high surface area electrode comprising a percolating network of the nanomaterial on the current feeder. The electrode also can be removed by releasing the magnetic field at the current feeder, and after being washed out of the cell the nanocomposite can be separated in a magnetic field. This enables replacement of the active electrode without the need to dismantle the cell. Zinc-iodide flow batteries offer high energy density due to the high aqueous solubility of the ZnI2. However, the power density that can be achieved is limited by potential for the dendritic growth of zinc deposits, and as zinc metal builds up in the cell the areal capacity is limited. We have found that by drawing some of the electrolyte through the current feeder, improved performance can be obtained [6]. This enables operation at higher power density and the denser uniform deposit should enable increased areal capacity. We attempted to reduce crossover in the all-vanadium redox flow battery by using a graphene modified nafion membrane. However, we found that the addition of the graphene reduced the losses in the battery and enabling a significant increase in the power density and discharge capacity. Currently we are working to optimize and scale up the membrane modification process, and to explore the mechanism of performance enhancement. References G. Zhu et al. (2020) High-energy and high-power Zn–Ni flow batteries with semi-solid electrodes. Sustainable Energy Fuels, 4, 4076-4085. Yang et al. (2018) Sodium–Sulfur Flow Battery for Low-Cost Electrical Storage. Advanced Energy Materials, 11, 1711991. Suo et al. (2015) Carbon cage encapsulating nano-cluster Li2S by ionic liquid polymerization and pyrolysis for high performance Li–S batteries. Nano Energy, 13, 467-473. Rahimi, A.M. Dehkordi, E.P.L. Roberts (2021) Magnetic nanofluidic electrolyte for enhancing the performance of polysulfide/iodide redox flow batteries. Electrochimica Acta, 309, 137687. Rahimi, A.M. Dehkordi, H. Gharibi, E.P.L. Roberts (2021) Novel Magnetic Flowable Electrode for Redox Flow Batteries: A Polysulfide/Iodide Case Study. Ind. Eng. Chem. Res., 60, 824-841. F. ShakeriHosseinabad et al. (2021) Influence of Flow Field Design on Zinc Deposition and Performance in a Zinc-Iodide Flow Battery. ACS Applied Mat. & Interfa
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.110 | 0.067 |
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 source (direct Gemma or distilled Codex), 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".