(Invited) Small Yet Bright Lanthanide-Based Nanoparticles Via Rapid Microwave-Assisted Synthesis
Bibliographic record
Abstract
Based on their outstanding optical properties, lanthanide-based compounds have been suggested for a wide range of applications including the fields of biomedicine, optoelectronics, and solar energy conversion. For instance, the capability of lanthanide-based materials to emit visible and near-infrared (NIR) light under NIR excitation is highly sought after when aiming for biomedical applications. This is as NIR light penetrates deeper into biological tissue and is less phototoxic than UV light commonly used for optical bioprobes. Our favorite nanomaterials are lanthanide-based fluorides (MLnF 4 , M = alkali metal, Ln = lanthanides and Yttrium), and our research addresses challenges in their synthesis as well as the establishment of structure-property relationships. The growing attention toward such optically active materials has prompted the development of novel synthesis methods for a more reliable and efficient access to these systems. In this regard, microwave-assisted approaches provide unique advantages over traditional solvothermal methods reliant on convectional heating: namely, significantly shorter reaction durations, more rigid reaction conditions, and thus a higher degree of reproducibility. Upon control of multiple physico-chemical reaction parameters – including the choice of precursors, the reaction temperature/time profile, and the metal ion to Ln 3+ ion ratio – MLnF 4 materials of tailored crystalline phase and size at the sub-10 nm realm can be obtained. Such control is crucial for the understanding of fundamental structure-property relationships and when aiming for the design of design of next-generation bioprobes and energy converters. For instance, the upconverting properties of the obtained nanomaterials render them suitable energy donors when combined with lanthanide-based complexes acting as energy acceptors. Key features of the resulting hybrid system are the simultaneous UV-blue and NIR light harvesting capabilities. These traits render the presented hybrid system a formidable candidate for the development of photoactivated devices capable of operating under multiple excitation wavelengths and to transduce the absorbed light into narrow, well-defined spectral regions.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".