Advancements in the Design and Fabrication of Membraneless Air-Breathing Microfluidic Fuel Cells for Electronic Applications
Bibliographic record
Abstract
The growing demand for portable energy sources necessitates advancements in power output, lifespan, and miniaturization, while still meeting the energy requirements of current and future portable electronic devices. Micro fuel cells present a promising solution, offering mobility, extended runtime, and environmental sustainability through the use of renewable organic fuels. Among various fuel cell types, mixed-reactant air-breathing micro fuel cells stand out for their innovative approach to electricity generation. In these systems, fuel and oxidant (oxygen from air) are combined in a single stream, utilizing a selective cathode that remains inactive towards the fuel. This design eliminates the need for a polymer membrane electrolyte, external pumps, and significantly reduces weight and volume, while simplifying manifolding and reducing sealing requirements. These advantages result in more compact, efficient, and cost-effective designs, with great potential for commercialization. In addition to fuel cell applications, microelectrochemical fluidic systems integrate microfluidic control with electrochemical processes, significantly enhancing the performance of fuel cells. By reducing reactant diffusion distances and optimizing mass transport, these systems improve efficiency and reaction kinetics. These systems' compact design makes them particularly suitable for portable electronics and biomedical implants, offering sustainable energy solutions where traditional power sources fall short. However, challenges such as the integration of complex components and scaling from prototypes to commercial products remain. Addressing these hurdles is crucial for unlocking the full potential of microelectrochemical fluidic systems in practical applications. This presentation will discuss our progress in the design and fabrication of mixed-reactant air-breathing microfluidic membraneless fuel cells as a viable power supply solution for portable electronics. We will present and discuss several designs through advanced computational modeling and rigorous experimental validation. These include air-breathing membraneless cells with separate reactant streams, air-breathing membraneless mixed-reactant microfluidic fuel cells, and filter paper-based electrolyte flow transport systems using vaporized methanol as fuel. Modeling of these systems utilize finite element analysis, incorporating mass transport equations, electrochemical reactions (fuel oxidation and oxygen reduction reaction), chemical species distribution, electrode porosity, and air breathing at the cathode, alongside a database of component properties (electrodes, fluid density, fluid viscosity, etc.). These models successfully predicted experimental trends within acceptable tolerance limits and provided accurate estimations of cell polarization, power output, and fuel utilization across a range of current densities and flow rates. Proof-of-concept demonstrations were conducted by stacking three U-shaped microfluidic fuel cells, each powered by a 0.5 M HCOOH solution. The fuel cells, with a footprint of 9.6 cm2 and a total volume of 10.6 cm3, were connected in series to power four green LEDs (each requiring 2.1–2.5 V and 4.2–5 mW) for 20 hours. This was achieved with a constant low flow rate of 16.7 μL/min (approximately 4.23 μL/min per cell). Another proof-of-concept was realized using a paper-based microfluidic fuel cell, which relies on the spontaneous capillary flow of reactant solutions in filter paper to passively transport the fuel and oxidant. This self-pumping device utilizes methanol vapor as the fuel and incorporates an air-breathing cathode for continuous oxygen supply from the atmosphere. Additionally, a stack of four microfluidic fuel cells was developed and tested in both series and parallel configurations. In the parallel configuration, the stack delivered a maximum open circuit potential (OCP) of 0.69 V, with a maximum current of 34.53 mA and a maximum power output of 4.14 mW. In contrast, the series configuration produced a total current of 7.35 mA, an OCP of 2.39 V, and a maximum power output of 3.57 mW. As a proof of concept, this stacked system successfully powered a 3-LED green array for a continuous duration of 3 hours. These results represent a significant step toward the development of micro-energy systems capable of long-term electricity generation for low-power electronic applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".