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Record W2243164026 · doi:10.1149/ma2015-02/45/1784

In-Situ Characterization of Microfluidic Redox Battery with Dual-Pass Architecture

2015· article· en· W2243164026 on OpenAlexaffabout
Omar Ibrahim, Marc‐Antoni Goulet, Erik Kjeang

Bibliographic record

VenueECS Meeting Abstracts · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMaterials scienceMicrofluidicsLaminar flowElectrodeElectrolyteNanotechnologyFabricationFlow batterySoft lithographyOptoelectronicsChemistry

Abstract

fetched live from OpenAlex

Microfluidic co-laminar flow cells is a growing field of energy research due to their inherent simplicity and low cost. In these cells, the two reactant containing electrolytes flow in a co-laminar manner that is dominated by slow diffusive mixing, which in turn forms an interface that provides the necessary separation of the reactant streams whilst allowing ion transfer between the electrodes [1]. Introducing the flow-through porous electrodes allowed higher performance in terms of power output and fuel utilization [2]. Furthermore, a unique dual-pass architecture was recently demonstrated which enabled in-situ recharging, fuel recirculation, and significant performance improvements [3-4]. The dual-pass cell architecture was further modified by splitting the electrodes, thus forming two symmetric cell portions, in order to experimentally measure and analyze the parasitic shunt current that appears in microfluidic electrochemical cell arrays with a shared electrolyte manifold [5-6]. In the present work, the same cell array design is used as an analytical device in order to analyze the performance characteristics of the dual-pass architecture and its application in co-laminar flow cells. The cell is fabricated using soft lithography techniques in polydimethylsiloxane (PDMS) using an SU-8 master and then bonded to a glass substrate. Porous carbon paper is cut into rectangular strips and placed as the electrodes for the device. Details about the vanadium redox electrolytes preparation and the device fabrication are described elsewhere [6]. Each cell portion is tested individually with vanadium redox species delivered at low and high flow rates and the results are used to quantify the species crossover losses, which result in a mixed potential and thus causes a drop in open circuit potential and overall performance. The crossover losses at the downstream portion are reduced from 41 mV at 10 μL/min to 13 mV at 100 μL/min. The upstream cell portion demonstrates maximum power density of 744 mW/cm 2 at a high current density around 1000 mA/cm 2 . This compares favorably to all previously reported conventional counterparts. Moreover, the two cell portions are connected in parallel to resemble the original cell with dual-pass architecture [3] which allows assessing the contribution of the inlet and outlet passes of the dual-pass architecture in-situ. The fuel utilization is estimated from the current outputs of the two portions at low and high flow rates, and the contribution of the downstream cell portion is found to be on the same order as that of the upstream portion. Overall, the results of this study are expected to provide a deeper understanding of the reactant conversion and reactant crossover phenomena in co-laminar flow cells which will be useful for future device optimization. Acknowledgements Funding for this research provided by the Natural Sciences and Engineering Research Council of Canada (NSERC), Canada Foundation for Innovation (CFI), and British Columbia Knowledge Development Fund is highly appreciated. References: [1] E. Kjeang, Microfluidic Fuel Cells and Batteries, Springer (2014). [2] E. Kjeang, R. Michel, D. A. Harrington, N. Djilali, and D. Sinton, J. Am. Chem. Soc., 130 , 4000-4006 (2008). [3] J. W. Lee, M.-A. Goulet and E. Kjeang, Lab chip, 13 , 2504-2507 (2013) [4] M.-A. Goulet and E. Kjeang, Electrochim. Acta., 140 , 217-224 (2014). [5] O. Ibrahim, M.-A. Goulet and E. Kjeang, In the 226 th Meeting of the Electrochemical society, Cancun, Mexico, October 2014. [6] O. Ibrahim, M.-A. Goulet and E. Kjeang, J. Electrochem. Soc., 162 , F639-F644 (2015).

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.236
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2015
Admission routes2
Has abstractyes

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