Multi-layer hierarchical cellulose nanofibers/carbon nanotubes/vinasse activated carbon composite materials for supercapacitors and electromagnetic interference shielding
Bibliographic record
Abstract
Developing porous self-supporting electrodes with excellent conductivity, good mechanical properties, and high electrochemical activity is crucial for constructing electrode materials with lightweight, ultra-thin, flexible, and high capacitance performance. In this work, we prepared a cellulose nanofibers (CNFs)/carbon nanotubes (CNTs)/vinasse activated carbon (VAC) (CCV) composite material with a multi-layer hierarchical conductive structure through simple vacuum filtration and freeze-drying. In this composite material, the self-assembly of CNF provides the main skeleton structure of a multi-layer hierarchical structure. CNT provides a fast path for the rapid transfer of electrons and is beneficial for the loss of electromagnetic waves. VAC provides sufficient double layer performance. The synergistic effect of the above three endows CCV composite materials with excellent energy storage performance and electromagnetic interference (EMI) shielding performance. In addition, we endowed the CCV composite with a certain shape and performance by introducing a vitrimer polymer with a dynamic cross-linked network structure. In summary, thanks to the synergistic effect of various components in the multi-layer hierarchical structure, CCV composite materials exhibit excellent integration performance, especially stable energy storage performance and EMI shielding performance. These significant properties make CCV composite materials have great application prospects in the fields of energy storage and intelligent EMI shielding.
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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".