Precision Engineering of Co-Laminar Flow Cells for Electrochemical Energy Conversion
Bibliographic record
Abstract
Most electrochemical cells used for energy conversion utilize a physical separator such as an ionomer membrane to keep reactants separated and prevent a mixed potential from reducing energy efficiency. A new class of electrochemical cell achieves this separation by design of channel dimensions such that reactant flows remain laminar. These co-laminar flow cells (CLFC)1,2, which include both fuel cells and flow batteries, are generally microfluidic and constrained in operation by the diffusion time of reactants across the channel. Due to their small scale and lack of a membrane, these co-laminar flow cells are both simpler and lower cost devices which make them well suited as an experimental platform for understanding electrochemical energy conversion. The cells in this study are based on flow-through porous carbon electrodes with vanadium redox reactants dissolved in sulfuric acid as both fuel and oxidant. This presentation reveals some of the advancements made with these cells over the past year and the lessons learned which may be applied to larger scale conventional flow batteries and fuel cells. Some of the topics to be discussed include electrode enhancement to improve the limiting reaction rate, supported by both ex situ and in situ characterization of the flow-through porous electrodes. Reaction kinetics are studied using a custom-developed three-electrode analytical cell for electrochemical analysis of flow-through porous electrodes. Multiphysics modeling work based on this characterization provides insight into some of the design principles which can be used to maximize the power density of these devices. 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. Goulet, M.-A. & Kjeang, E. Co-laminar flow cells for electrochemical energy conversion. J. Power Sources 260,186–196 (2014). 2. Kjeang, E. Microfluidic Fuel Cells and Batteries. (Springer, 2014).
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".