Carbon Nanotube Assembly and Integration for Applications
Bibliographic record
Abstract
Carbon nanotubes (CNTs) have attracted significant interest due to their unique combination of properties including high mechanical strength, large aspect ratios, high surface area, distinct optical characteristics, high thermal and electrical conductivity, which make them suitable for a wide range of applications in areas from electronics (transistors, energy production and storage) to biotechnology (imaging, sensors, actuators and drug delivery) and other applications (displays, photonics, composites and multi-functional coatings/films). Controlled growth, assembly and integration of CNTs is essential for the practical realization of current and future nanotube applications. This review focuses on progress to date in the field of CNT assembly and integration for various applications. CNT synthesis based on arc-discharge, laser ablation and chemical vapor deposition (CVD) including details of tip-growth and base-growth models are first introduced. Advances in CNT structural control (chirality, diameter and junctions) using methods such as catalyst conditioning, cloning, seed-, and template-based growth are then explored in detail, followed by post-growth CNT purification techniques using selective surface chemistry, gel chromatography and density gradient centrifugation. Various assembly and integration techniques for multiple CNTs based on catalyst patterning, forest growth and composites are considered along with their alignment/placement onto different substrates using photolithography, transfer printing and different solution-based techniques such as inkjet printing, dielectrophoresis (DEP) and spin coating. Finally, some of the challenges in current and emerging applications of CNTs in fields such as energy storage, transistors, tissue engineering, drug delivery, electronic cryptographic keys and sensors are 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".