Bibliographic record
Abstract
Nanotechnology is the understanding and control of matter at the diemnsions ranging between 1-100 nm. One nanometer is one billionth of a meter. Nanotechnology involves manipulation of atoms, imaging, measuring and modelling at nano scale. Its potentials were first highlighted by Richard Feynman in the American Physical Scociety meeting in 1959. Though, he did not coin the world nanotechnology himself but he explored the possiblities of functional materials at the bottom of the scale. In last two decades this technology has been commercialized to great extent and gaining importance day by day influencing the economies of different countries and henceforth enforcing the policy makers to address the issues like environment, health and safety. Governments are regularisaing and monitoring its research, uses, applications and technology transfer which includes intelluctaul property rights. This paper addresses the dimensions and trends of nanotechnology covering economic aspects. The paper is focussed on the changes in the functional properties of nanomaterials as physical, chemical, optical, electronic, electrical, magnetic etc. in comparision to those of the bulk of material. It has been discussed how the basic and advance research in nanoscience could be explotiedfor making technologies for its commercial and industrial applications for the benefit and safety of the soceity. Thin film magnetism is demonstrated using Monte Carlo simulation method. Experimental synthesisof some of thenanorods and qunatum dots are also 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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".