Multiscale Hierarchical Micro- and Nanostructures: Nanotubes and Micro-Assembly
Bibliographic record
Abstract
Nanostructured materials have attracted a great attention last years due to their unusual physico-chemical properties and potential use in many applications. As a result, our knowledge of synthetic routes and the methods for controlling morphology, shape and the geometry of individual nanostructures (including nanotubes, nanofibers, nanowires, nanosheets etc) has significantly improved. Early attempts to use nanostructured materials in many technological application has shown the shortage of the methods, which could allow facile packing of nanostructures into the various micrometer size structures with defined geometry and dimensions. Such control of the morphology simultaneously in both microand nanoscale, although very common in natural biological materials, is very challenging task for artificial synthetic materials. General overview of several approaches for crafting multiscale hierarchical microand nanostructures including both top down and bottom up methodologies as well as their combinations is to be discussed. Examples of simultaneous assembly of mictro and nanotubes of TiO2 [1], step by step crafting of titanate nanotubes into TiO2 nanotubes [2] or on the surface of ZnO nanorods [3] are to be considered.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".