Hard Templating Pathways for the Synthesis of Nanostructured Porous Co<sub>3</sub>O<sub>4</sub>
Bibliographic record
Abstract
The present study is concerned with the nanocasting preparation of Co 3 O 4 nanostructures employing two-dimensional (2D) hexagonal SBA-15 and three-dimensional (3D) cubic KIT-6 as hard templates. The influence of framework connectivity of the parent silica and loading of the cobalt source are studied in detail. Structures can be tailored as isolated or randomly organized Co 3 O 4 nanowires or as highly ordered mesoporous Co 3 O 4 networks retaining the symmetry of the silica parent using 2D hexagonal parent materials. Applying cubic KIT-6 silica with suitable wall thickness and degree of framework interconnectivity as a template, we can vary the pore size of mesostructured Co 3 O 4 from 3 nm up to values as high as 10 nm. To verify the influence of surface properties and texture, we employed different mesoporous silicas that were conventionally calcined at 550 °C and equivalent silica materials microwave treated in the presence of a concentrated H 2 O 2 /HNO 3 mixture. The parent silica materials and the resulting Co 3 O 4 were characterized in detail at different steps during the templating route by nitrogen physisorption measurements and powder X-ray diffraction. Transmission electron microscopy and scanning electron microscopy investigations were performed to visualize the structure and morphology of the nanocast materials, and thermogravimetry−differential thermal analyses (TG−DTA) were done to follow the formation of Co 3 O 4 . With our method, preparation of nanocast Co 3 O 4 is highly reproducible, regardless of template shapes and sizes, which makes the pathway much more versatile for a great variety of templates.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".