Bibliographic record
Abstract
Since approximately 2000, lanthanide doped nanoparticles have received significant attention due to their interesting luminescent properties. At the core of this interest is their ability to convert near-infrared excitation light (typically 980 or 800 nm) to higher energies spanning the ultraviolet, visible and near-infrared regions of the spectrum through a multiphoton process known as upconversion. Upconversion differs from conventional multiphoton excitation in other materials where no real intermediate states are present necessitating the use of ultrafast lasers (in the femtosecond regime) for simultaneous excitation to the upper emitting state. The lanthanide ions possess a multitude of 4 f electronic states that have long lifetimes (micro- to millisecond) thus act as population reservoirs in the upconversion process. Hence, upconversion occurs through real, long-lived intermediate states through a sequential photon absorption process. This eliminates the need for ultrafast excitation and as a result, upconversion can be observed using inexpensive, continuous wave diode lasers. Upconversion luminescence can be exploited for a number of applications in nanomedicine, theranostics, photovoltaics, photocatalysis, as well as many others. While their upconversion luminescence has been studied in great detail, lanthanide doped nanoparticles can also emit in the near-infrared through a direct Stokes (down-shifted) luminescence process. Of particular importance is that these near-infrared emissions lie within the biological windows where biological tissues are optically transparent. For obvious reasons, a great deal of the work on the nanomedicine and theranostics applications of lanthanide doped nanoparticles has shifted to their near-infrared luminescence properties. In this presentation, we will demonstrate the synthesis of these nanoparticles, establish how changing their nanoscale architecture can affect their luminescence properties (both upconversion and near-infrared), and discuss their potential applications, particularly in nanomedicine.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".