Enhanced Gold nanoparticle Optics for Nanophotonics, Photovoltaics and Green Photonics insights
Bibliographic record
Abstract
In this communication it was developed the basis of Nanotechnology and fundamental Research focused on Nanophotonics and Green Photonics for Biophotonics and Implantable devices with varied interest and applications. Thus, gold Nanoparticles as Optical active Nanomaterials presented different advantages in comparison to other materials due to their biocompatibility, low toxicity depending of chemistry of surfaces, and capability to generate high Electromagnetic fields known as Plasmonics properties. In addition gold Nano-surfaces are relatively easy to be conjugated with other materials due to their soft electronic bonding and polarizable surfaces. These characteristics provide to gold Nanoparticles interesting insights and future perspectives within Nanophotonics and Green Photonics developments. Thus, Nanophotonics based on gold Nanomaterials provide excellent light matter interactions accompanied with the generation of varied quantum and non-classical light properties. In this context, it should be noted recent trends of Green Photonics where materials and associated properties should be biocompatible contemplating their fabrication too. It is a great challenge to develop Green Nanomaterials with Enhanced conductive and luminescent properties; however gold based Nanomaterials are showing interesting insights within Nanotechnology. In these perspectives and looking for new Optical active materials it should be highlighted the design and fabrication of devices from the Nanoscale and beyond. And, it should be highlighted the particular interest on Implantable Optical active devices due to their potential perspectives within Life Sciences. Therefore, in this short Review it was intended to afford to discuss about how gold Nanomaterials could provide insights within Nanophotonics, Green Photonics, and Life Science applications.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".