Simple Synthesis of Mesoporous Carbon Nanofibers with Hierarchical Nanostructure for Ultrahigh Lithium Storage
Bibliographic record
Abstract
In this study, a simple and reproducible synthesis strategy was developed to fabricate mesoporous carbon nanofibers (MCNFs) by using dual hard templates, a porous anodic aluminum oxide (AAO) membrane, and colloidal silica (Ludox TM-40). By using commercial templates, and removing AAO and the silica simultaneously, the synthesis procedures for MCNFs are greatly simplified without the need for separate preparation or the removal of templates in sequence. With phenol resin as a carbon precursor, the as-prepared MCNFs material reveals not only high surface area and mesoporous volume but also hierarchical nanostructure composed of hollow macrochannels derived from the AAO template, large mesopores (ca. 22 nm) from the removal of silica particles and micropores from the carbonization of phenol resin. Such unique surface and structural characteristics could provide a large quantity of active sites for Li storage and facilitate fast mass transport. Moreover, a one-dimensional (1D) carbon nanofiber (CNF) nanostructure favors fast electron transfer. The as-prepared MCNF anode demonstrates ultrahigh lithium storage capacity particularly at high rates, which is much higher than that reported for the commercial graphite and also significantly higher than other nanostructured carbon materials, such as ordered mesoporous carbon CMK-3 and ordered multimodal porous carbon (OMPC).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".