Nanoscale self-assembly: concepts, applications and challenges
Bibliographic record
Abstract
Self-assembly offers unique possibilities for fabricating nanostructures, with different morphologies and properties, typically from vapour or liquid phase precursors. Molecular units, nanoparticles, biological molecules and other discrete elements can spontaneously organise or form via interactions at the nanoscale. Currently, nanoscale self-assembly finds applications in a wide variety of areas including carbon nanomaterials and semiconductor nanowires, semiconductor heterojunctions and superlattices, the deposition of quantum dots, drug delivery, such as mRNA-based vaccines, and modern integrated circuits and nanoelectronics, to name a few. Recent advancements in drug delivery, silicon nanoelectronics, lasers and nanotechnology in general, owing to nanoscale self-assembly, coupled with its versatility, simplicity and scalability, have highlighted its importance and potential for fabricating more complex nanostructures with advanced functionalities in the future. This review aims to provide readers with concise information about the basic concepts of nanoscale self-assembly, its applications to date, and future outlook. First, an overview of various self-assembly techniques such as vapour deposition, colloidal growth, molecular self-assembly and directed self-assembly/hybrid approaches are discussed. Applications in diverse fields involving specific examples of nanoscale self-assembly then highlight the state of the art and finally, the future outlook for nanoscale self-assembly and potential for more complex nanomaterial assemblies in the future as technological functionality increases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".