Co-Laminar Flow Cell with Power Density of 2 Wcm<sup>-2</sup>
Bibliographic record
Abstract
Early studies have demonstrated the feasibility of circulating liquid electrolytes for gaseous reactant separation such as in alkaline fuel cells [1,2]. More recently, a new class of electrochemical cell based on co-laminar flow to maintain separation of liquid reactants is gaining considerable interest among academic researchers [3]. Without any physical separators or costly membranes, some of these inexpensive co-laminar flow cells (CLFCs) are being developed for disposable applications such as point of care biomedical devices [4], while others are targeting higher power applications such as on-chip cooling of microprocessors [5]. This presentation demonstrates the effectiveness of co-laminar flow for reactant separation which also exploits the high ionic conductivity of sulfuric acid to minimize ohmic loss. The cell being showcased is based on a previous design relying on vanadium redox reactants and flow-through porous electrodes [6]. By optimization of electrolyte formulation and CLFC architecture and implementation of current collectors, the cell depicted in Fig. 1 achieves very low area specific resistance ASR = 0.12 Ω·cm 2 . In addition, these changes to the cell design reduce the overall device footprint by half, leading to a cross-sectional power density of 0.88 Wcm -2 . The optimized cell design is further enhanced with a novel in situ flowing deposition method for improving both the electrochemical surface area and mass transport properties of the porous carbon paper electrodes [7,8]. By dynamically depositing carbon nanotubes at the entrance of and within the carbon paper flow-through porous electrodes, the cross-sectional peak power density is increased to a record breaking 2.01 Wcm -2 , as shown in Fig. 2. When normalized by the volume of both electrodes and the center channel, this equates to a peak volumetric power density of 13.4 Wcm -3 . The CLFC performance demonstrated in this study provides a new benchmark for electrochemical cells based on co-laminar flow of reactants. In addition, the simple design principles and methods developed in this work are likely to be applicable to other electrochemical flow cells such as the larger scale flow batteries being developed for grid energy storage. Acknowledgements Funding for this research provided by the Natural Sciences and Engineering Research Council of Canada (NSERC), Canada Foundation for Innovation, and British Columbia Knowledge Development Fund is highly appreciated. References [1] G.F. Mclean, T. Niet, N. Djilali, An assessment of alkaline fuel cell technology, Int. J. Hydrogen Energy. 27 (2002) 507–526. [2] K. Kordesch, V. Hacker, J. Gsellmann, M. Cifrain, G. Faleschini, P. Enzinger, et al., Alkaline fuel cells applications, J. Power Sources. 86 (2000) 162–165. [3] M.-A. Goulet, E. Kjeang, Co-laminar flow cells for electrochemical energy conversion, J. Power Sources. 260 (2014) 186–196. [4] J.W. Lee, E. Kjeang, A perspective on microfluidic biofuel cells., Biomicrofluidics. 4 (2010) 41301. [5] M.M. Sabry, A. Sridhar, D. Atienza, P. Ruch, B. Michel, Integrated Microfluidic Power Generation and Cooling for Bright Silicon MPSoCs, in: Proc. IEEE/ACM 2014 Des. Autom. Test Eur. Conf., 2014: pp. 70–75. [6] M.-A. Goulet, E. Kjeang, Reactant recirculation in electrochemical co-laminar flow cells, Electrochim. Acta. 140 (2014) 217–224. [7] M.-A. Goulet, E. Kjeang, Process of increasing energy conversion and electrochemical efficiency of a scaffold using a deposition material, (2015) US Patent Application 14/842,812. [8] M.-A. Goulet, A. Habisch, E. Kjeang, In Situ Enhancement of Flow-through Porous Electrodes with Carbon Nanotubes via Flowing Deposition, Electrochim. Acta. PA-16-124 (2016) under review. 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 | 0.000 | 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".