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Record W4386854011 · doi:10.1149/ma2023-01282791mtgabs

Numerical Simulation of Paper-Based Flow Cells during Dynamic Infiltration Phase

2023· article· en· W4386854011 on OpenAlexaff
Pardis Sadeghi, Erik Kjeang

Bibliographic record

VenueECS Meeting Abstracts · 2023
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMicrofluidicsCapillary actionMicropumpMaterials scienceElectrodeElectrolyteVolumetric flow rateDrop (telecommunication)PorosityFlow (mathematics)DissolutionMechanicsComposite materialMechanical engineeringNanotechnologyChemical engineeringEngineeringChemistry

Abstract

fetched live from OpenAlex

Microfluidic electrochemical cells are increasingly being used as power sources for energizing portable electronic devices [1]. Of particular interest is the capillary-driven flow cells because they do not need any type of micropump to establish the flow. In a recent work [2], we developed a general, robust mathematical/numerical model for designing capillary-driven, paper-based, microfluidic flow cells. The model was validated against experimental data available for a novel single-use microfluidic flow cell of this nature called PowerPAD [3]. This flow cell is activated by a drop of water when poured on its sample pad. After dissolving the solid electrolytes stored below the sample pad, the liquid electrolytes produced this way infiltrate the porous electrodes before entering the cellulosic absorbent pad situated below the porous electrodes. Soon after entering the pad, they start flowing in the lateral direction until they are brought into direct contact with each other (at some point in time) so that the electrochemical reactions can take place at the electrodes. The two liquids then continue flowing co-laminarly until the pad becomes fully-saturated and the flow rate drops to zero. The experimental data reported by the inventors of the PowerPAD actually correspond to the fully-saturated case [3]. (Under these conditions the cell works like an ordinary battery when connected to an external load.) They demonstrated that, dependent on the thickness of the absorbent pad, the cell can generate electricity for roughly an hour. In [2], we showed that, for a given electrode, by modifying the microstructure of its absorbent pad (e.g., its porosity or pore-size) and/or its flow structure the runtime of the cell can be extended to roughly three hours so that it can be used for energizing certain portable electronic devices. However, there are other prospective applications in which the power might be needed for merely a few seconds [4]. Remote sensors used for measuring/reporting the pH of acid rains belong to this category. PowerPAD can be used for such short-lived applications, but the mathematical model presented in [2] has to be refined to simulate power generation during the dynamic infiltration process, which is the objective of the present work. As the first step, Darcy’s equation is solved numerically to find the bulk velocity from which the Reynolds number is obtained and used to calculate the mass-transfer coefficient. More importantly, the Richards equations is solved numerically to find the time-dependent saturation field, S(t), which is needed for calculating the mass-transfer coefficient during the infiltration process. Here, the empirical correlation proposed by Barton and Brushett [5] for the Sherwood number (Sh) is modified to incorporate a diffusion-limited term which varies linearly with the saturation field, S(x,y,t); that is: where Re is the Reynolds number and Sc is the Schmidt number. Figure 1a shows the two-dimensional model of PowerPAD used for the simulations, which were performed using the finite-element software package COMSOL; see [2] for the details. According to the imbibition results obtained for the 4h-PAD system [3], the cell is predicted to start generating electricity after t = 0.33 s; see Fig. 1b. The system, however, needs roughly t = 20 s to become fully-saturated. Figure 1c shows the polarization curves for the 4h-PAD system at discrete times, whereas Fig. 1d shows variation of the maximum power as a function of time, up to the fully-saturated time. In these figures the discrete times (5.2, 8, 12, and 20 s) correspond, respectively, to the flow rates 11, 4.5, 0.4, and 0.01 mm3/s. According to Fig. 1d, during the transient phase the maximum power is roughly 50% larger than that for the fully-saturated case. The higher power generation during this initial infiltration process is attributed to the bulk fluid flow through the porous electrodes implying that the mass-transfer coefficients are improved through Re and Sc. References: [1] O.A. Ibrahim, M. Navarro-Segarra, P. Sadeghi, N. Sabaté, J.P. Esquivel, and E. Kjeang, Chem. Rev., 122 (7) (2022) 7236–7266. [2] P. Sadeghi, and E. Kjeang, Computational modelling of paper-based capillary-driven microfluidic flow cells, J. of Power Sources, 548 (2022)232084. [3] J.P. Esquivel, P. Alday, O.A. Ibrahim, B. Fernández, E. Kjeang, and N. Sabaté, A metal-free and biotically degradable battery for portable single-use applications, Adv. Energy Mater., 7 (2017) 1700275-86. [4] C. Dincer, R. Bruch, E. Costa-Rama, M.T. Fernández-Abedul, A. Merkoçi, A. Manz, G.A. Urban, and F. Güder, Disposable sensors in diagnostics, food, and environmental, monitoring, Adv. Mater., 31 (2019)1-28. [5] J.L. Barton, and F.R. Brushett, A one-dimensional stack model for redox flow battery analysis and operation, Batteries, 5 (2019) 1-25. 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.245
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2023
Admission routes1
Has abstractyes

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