3D Hierarchical Porous Carbon Aerogel Electrocatalysts Based on Cellulose/Aramid Nanofibers and Application in High-Performance Zn–Air Batteries
Bibliographic record
Abstract
Three-dimensional (3D) carbon aerogels (CAs) are composed of interconnected networks and emerge as appealing platforms for the combination of heteroatom dopants, defective sites, and hierarchical porous structures. Here, we propose an effective and sustainable strategy to construct TOCNF/ANF-Cd hydrogels/aerogels using TEMPO-oxidized cellulose nanofibers (TOCNFs), thermally stabilized aramid nanofibers (ANFs), and low-boiling-point Cd 2+ . Hierarchical porous N-doped aerogel catalysts (N/CA-Cd) with TOCNF/ANF synergistic cross-linking were obtained by moderate temperature pyrolysis. Apart from serving as a source of N, ANF also improves carbon retention and graphitization, increasing the electrical conductivity of carbon aerogels. Due to the spatially continuous structure and multiscale porous structure and abundant N dopants and edges/defects, the as-obtained N/CA 0.5 -Cd carbonaceous catalysts manifest an impressive electrocatalytic efficacy with a positive half-wave potential (0.86 V). In particular, the Zn–air batteries assembled with N/CA 0.5 -Cd as the air cathode catalyst have an excellent peak power density of 186 mW cm –2 and a splendid specific capacity of 730 mA h g –1 .
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 | 0.000 | 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) | 0.000 | 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".