Synthesis of praseodymium-and molybdenum- sulfide nanoparticles for dye-photodegradation and near-infrared deep-tissue imaging
Bibliographic record
Abstract
Abstract Development of nanoparticles with multi-functionalities is of great importance. In this study, praseodymium sulfide (Pr 2 S 3 ) and molybdenum sulfide (MoS 2 ) nanoparticles were synthesized. The structural, morphological and optical properties of the as-obtained products were investigated by XRD, XPS, TEM, UV–vis-NIR spectroscopy, and photoluminescence spectroscopy. Pr 2 S 3 is found to be used in selective photodegradation of fluorescein sodium salt. MoS 2 can be utilized for selective photodegradation of rhodamine B. In the mixture of rhodamine B, fluorescein sodium salt and rhodamine 6 G, most of rhodamine B and part of fluorescein sodium salt are optically degraded by Pr 2 S 3 . In the mixture of rhodamine B, fluorescein sodium salt and rhodamine 6 G, part of fluorescein sodium salt and most of rhodamine B is degraded by MoS 2 . Moreover, they emit near-infrared fluorescence (800–1100 nm) when excited by the 785 nm light. Deep tissues imaging with high-contrast is shown, utilizing a nanoparticle-filled centrifuge tube covered with animal tissues (pig Bacon meat). Maximum imaging depth below the tissue surface of 1 cm is achieved. Our work provides a rapid yet efficient procedure to make nanoparticles for dual-application-potential in dye-photodegradation and near-infrared deep tissue imaging.
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) | 0.000 | 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 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".