Recent Advances in Directed Assembly of Nanowires or Nanotubes
Bibliographic record
Abstract
Abstract Nanowires and nanotubes of diverse material compositions, properties and/or functions have been produced or fabricated through various bottom-up or top-down approaches. These nanowires or nanotubes have also been utilized as potential building blocks for functional nanodevices. The key for the integration of those nanowire or nanotube based devices is to assemble these one dimensional nanomaterials to specific locations using techniques that are highly controllable and scalable. Ideally such techniques should enable assembly of highly uniform nanowire/nanotube arrays with precise control of density, location, dimension or even material type of nanowire/nanotube. Numerous assembly techniques are being developed that can quickly align and assemble large quantities of one type or multiple types of nanowires through parallel processes, including flow-assisted alignment, Langmuir-Blodgett assembly, bubble-blown technique, electric/magnetic- field directed assembly, contact/roll printing, knocking-down, etc.. With these assembling techniques, applications of nanowire/nanotube based devices such as flexible electronics and sensors have been demonstrated. This paper delivers an overall review of directed nanowire assembling approaches and analyzes advantages and limitations of each method. The future research directions have also been discussed.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".