Vacuum-Assisted Layer-by-Layer Nanocomposites for Self-Standing 3D Mesoporous Electrodes
Bibliographic record
Abstract
Electrochemical energy storage devices will play a critical role for efficient storage and reliable on-demand supply to portable electronics, electric and/or plug-in-electric vehicles that entail rapid charging/discharging with long cycle life. The design and assembly of nanoscale materials is critical for developing high performance mesoporous electrodes for energy storage devices that can be scaled-up for manufacturing. To address the challenge of nanostructured electrode development, this work reports a layer-by-layer (LbL) fabrication technique based on electrostatic self-assembly coupled with vacuum assisted filtration. By combining electrostatic interactions with vacuum force, thick electrodes (4–50 μm) of electroactive polyaniline (PANi) nanofibers and oxygen functionalized multiwalled carbon nanotubes (MWNT) are assembled in tens of minutes. The electronic conductivity and mechanical stability are further improved through controlled heat treatment of these electrodes that shows high surface area with interpenetrating networks of nanofibers and nanotubes. Electrochemical measurements reveal high specific capacity of 147 mAh/g originating from the MWNTs and redox active PANi nanofibers that store charges through both electrical double layer and faradaic mechanism with excellent charge/discharge stability over 10,000 cycles. The precise control over the electrode thickness and rapid assembly from this VA-LbL technique show promise for the development of binder-free mesoporous electrodes for next generation electrochemical energy storage devices.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".