Low-cost fabrication technologies for nanostructures: state-of-the-art and potential
Bibliographic record
Abstract
In the last decade, some low-cost nanofabrication technologies used in several disciplines of nanotechnology have demonstrated promising results in terms of versatility and scalability for producing innovative nanostructures. While conventional nanofabrication technologies such as photolithography are and will be an important part of nanofabrication, some low-cost nanofabrication technologies have demonstrated outstanding capabilities for large-scale production, providing high throughputs with acceptable resolution and broad versatility. Some of these nanotechnological approaches are reviewed in this article, providing information about the fundamentals, limitations and potential future developments towards nanofabrication processes capable of producing a broad range of nanostructures. Furthermore, in many cases, these low-cost nanofabrication approaches can be combined with traditional nanofabrication technologies. This combination is considered a promising way of generating innovative nanostructures suitable for a broad range of applications such as in opto-electronics, nano-electronics, photonics, sensing, biotechnology or medicine.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".