Fabrication of Three-Dimensional Carbon Nanotube and Metal Oxide Hybrid Mesoporous Architectures
Bibliographic record
Abstract
Three-dimensional (3D) vertically aligned carbon nanotube (CNT) patterns were utilized as templates for fabricating mesoporous hybrid architectures composed of CNTs and various crystalline metal oxide (MO; M = Co, Zn, Mn) nanoparticles by a microwave-assisted chemical approach. Post-synthesis thermal treatment of the CNT/MO patterns culminated in structural reorganization, depending on the treatment conditions. In air, CNTs were removed by oxidation. The remaining MO architectures preserved the shape and alignment of the original 3D CNT patterns, but with different porosity characteristics and improved MO crystallinity. Elastocapillary condensation and bending were demonstrated to be useful tools for further architecture alternation. The mesoporous nature of the CNT/MO hybrids and the MO materials were confirmed by N2-BET measurements. CNT/Co3O4 aligned strips were used as an example to demonstrate the potential application of the CNT/MO architectures as electrode materials for supercapacitive storage. Galvanostatic measurements showed that the CNT/Co3O4 strips were stable up to 1000 charge-discharge cycles at a current density of 377 μA/cm(2) with a specific capacitance as high as 123.94 F/g.
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) | 0.001 | 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".