Bibliographic record
Abstract
In recent years, rare earth doped nanoparticles have been proposed for a number of exciting applications in a wide-range of fields including nanomedicine, nanoelectronics, biosensing, bioimaging, photovoltaics, photocatalysis, etc. This is due, primarily, to their interesting and versatile optical properties including their inherent ability to convert low-energy near-infrared (NIR) light to higher energies spanning the UV, visible, and NIR regions of the spectrum via a process known as upconversion. Upconversion, inherent to the rare earths, results from the multitude of 4 f electronic energy states, many of which are spaced equally and long-lived, that facilitate the absorption of multiple low energy photons to populate the higher energy emitting states. Thus, upconversion is a multiphoton process, but unlike other two-photon excited materials, the need for expensive ultrafast lasers is eliminated since the simultaneous absorption of multiple photons is not required; due to the long lifetimes of the rare earth ion excited states, sequential absorption occurs efficiently. Moreover, following NIR excitation, these nanoparticles can also undergo conventional luminescence and emit in the three NIR biological windows where tissues are optically transparent. Here, we will discuss the luminescence properties of rare earth doped nanoparticles and present a perspective on both their applicability as well as drawbacks for use in various applications. Finally, we will demonstrate that the intelligent combination of diverse materials, with different properties, will allow for the engineering of novel multifunctional nanostructures, which can usher in a new era for rare earth doped nanoparticles.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".