(Invited) Exploring in the Near Infrared: Multifunctional Nanoplatforms for Biomedical Applications
Bibliographic record
Abstract
Near-infrared (NIR) absorbing and emitting nanomaterials attract significant attention in bioimaging, which shows great potential for disease detection due to its high sensitivity at the subcellular level and low cost of related imaging facilities. However, currently available optical probes are mainly based on visible-emitting materials. The tissue-induced optical extinction and autofluorescence in the visible range result in limited penetration depth and ambiguous photoluminescence signal, which restricts their in vivo use. To address this issue, photoluminescent probes, with both absorption and emission wavelengths operating in the biological windows in the NIR range, in which tissues are optically transparent, are highly desired. Their integration with superparamagnetic nanomaterials to make a multifunctional platform further opens a wide range of promising applications, including bimodal imaging (photoluminescence and magnetic resonance imaging), synergistic hyperthermia (magnetothermal and photothermal), magnetic confinement of trace amounts of biospecies for ultra high-sensitivity biodetection, etc. In this talk, I will present our most recent work on the synthesis of NIR-emitting water soluble, stable core/shell/shell quantum dots (QDs) and multifunctional (NIR photoluminescent and superparamagnetic) nanoparticles and their use in biomedicine. For instance, in one case, multifunctional particles contain single superparamagnetic nanoparticles as cores and NIR-luminescent nanomaterials as shells. In another case, the multifunctional nanoplatform is compose of multiple superparamagnetic nanoparticles and NIR quantum dots in single particles. These different types of multifunctional particles are designed for different biomedical applications. References: [1] ACS Nano 2019, 13, 408-420; [2] Adv. Funct. Mater. 2018, 1706235 (Inside Back Cover); [3] Chem. Mater. 2019, 31, 3201-3210.
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 | 0.000 |
| 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".