Bibliographic record
Abstract
Moores law predicts the reduction of the device elements size and the advancement of physics with time for the next generation microelectronic industries. Materials and devices sizes and enriched physics are strongly correlated phenomena. Everyday physics moves a step forward from microscale classical physics toward nanoscale quantum phenomenon. Similarly, the vast micro/nanoelectronics needs advancement in growth and characterization techniques and unexplored physics to cope with the 21stcentury market demands. The continuous size reduction of devices stimulates the researchers and technocrats to work on nanomaterials and devices for the next generation technology. The semiconductor industry is also facing the problem of size limitation and has followed Moores law which predicts 16 nm nodes for next generation microelectronic industries. Nanometer is known as the 10 times of an Angstrom unit, where it is common consensus among the scientists that any materials and devices having physical dimensions less than 1000 times of an Angstrom will come under the umbrella of Nanotechnology. This review article focuses on the fundamental aspects of nanoscale materials and devices: (i) definitions and different categories of nanomaterials, (ii) quantum scale physics and technology, (iii) self-assembed nanostructures, (iv) growth conditions and techniques of 0D, 1D, 2D, and 3D dimensional materials, (v) understanding of the multifunctionalities of the nanomaterials, (vi) nanoscale devices for low energy consumption and fast response, (vii) integration of nanoscale materials with Si-based systems, and (viii) major technical challenges.
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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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".