Numerical Simulation of Paper-Based Flow Cells during Dynamic Infiltration Phase
Notice bibliographique
Résumé
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
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».