Fabrication of High Surface Area Carbon-Based Electrodes with Multiwalled Carbon Nanotubes
Bibliographic record
Abstract
Efficient energy storage systems are critical for addressing climate change and facilitating the transition to renewable energy sources. Although carbon-based electrodes are widely used in many electrochemical energy storage applications, their commercial forms as powders, papers, cloths and felts are often not optimized for the final application they are intended for. This study demonstrates high-performance carbon-based electrodes for flowing systems such as fuel cells and flow batteries, through the addition of carbon-based nanomaterials such as multiwalled carbon nanotube structures (MWCNTs). This study presents three main objectives. First, high-surface-area electrodes are fabricated through one-step deposition of nanomaterials onto the surface of commercially available carbon paper for flow-through porous electrodes. Surface and structural characterizations of the fabricated electrodes are investigated through scanning electron microscopy (SEM) and Brunauer-Emmett-Teller (BET) techniques. Second, the active electrochemical surface area (ESA) enhancement of these electrodes is evaluated in non-faradaic electrolyte (i.e. H2SO4). Finally, the electrochemical activity of the developed electrodes is tested in a redox-active FeCl2-FeCl3 electrolyte system, demonstrating enhanced reaction kinetics and their potential for redox flow battery applications. The results of this study indicate a promising avenue for increasing the performance of existing carbon electrodes for their future employment in advanced energy storage systems.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".