Advanced 1D heterostructures based on nanotube templates and molecules
Bibliographic record
Abstract
Recent advancements in materials science have shed light on the potential of exploring hierarchical assemblies of molecules on surfaces, driven by both fundamental and applicative challenges. This field encompasses diverse areas including molecular storage, drug delivery, catalysis, and nanoscale chemical reactions. In this context, the utilization of nanotube templates (NTs) has emerged as promising platforms for achieving advanced one-dimensional (1D) molecular assemblies. NTs offer cylindrical, crystalline structures with high aspect ratios, capable of hosting molecules both externally and internally (Mol@NT). Furthermore, NTs possess a wide array of available diameters, providing tunability for tailored assembly. This review underscores recent breakthroughs in the field of Mol@NT. The first part focuses on the diverse panorama of structural properties in Mol@NT synthesized in the last decade. The advances in understanding encapsulation, adsorption, and ordering mechanisms are detailed. In a second part, the review highlights the physical interactions and photophysics properties of Mol@NT obtained by the confinement of molecules and nanotubes in the van der Waals distance regime. The last part of the review describes potential applicative fields of these 1D heterostructures, providing specific examples in photovoltaics, luminescent materials, and bio-imaging. A conclusion gathers current challenges and perspectives of the field to foster discussion in related communities.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".