Print-and-plate architected electrodes for electrochemical transformations under flow
Bibliographic record
Abstract
Flow cell electrodes are typically composed of porous carbon materials, such as papers, felts, and cloths. However, their random architecture hinders fundamental characterization of electrode structure-performance relationships during in situ operation of porous electrochemical flow systems. Here, we report a “print-and-plate” method that uses high-resolution direct ink writing to produce periodic lattices followed by a two-step metal plating process to convert these lattices into highly conductive (sheet resistance 40 milli-Ohm per square) electrodes. We assessed their in operando performance in an anthraquinone disulfonic acid half-cell using electrochemical fluorescence microscopy, where output current and fluorescence intensity are in excellent agreement. We then compared the pressure drop of three electrode designs simulated with a high-fidelity numerical solution to the governing PDEs. The most efficient design was then fabricated via the print-and-plate method and confocal fluorescence microscopy was used to generate a 3D map of the state of charge (SOC) inside the working electrode. The experimental state of charge map is in good agreement with our simulations. By unlocking programmable architectures, print-and-plate electrodes offer new opportunities for fundamental investigations relating porous electrode microstructure to performance and direct replication of simulated structures.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".