Tunable photoluminescence properties of selenium nanoparticles: biogenic versus chemogenic synthesis
Bibliographic record
Abstract
Abstract Various technological and biomedical applications rely on the ability of materials to emit light (photoluminescence [PL]), and, among them, metal nanoparticles (NPs) and semi‐conductor Quantum Dots (QDs) represent ideal candidates as sensing probes and imaging tools, portraying better PL features than conventional organic dyes. However, the knowledge of PL behavior of semiconductor NPs – i.e., selenium; SeNPs – is still in its infancy, especially for those synthesized by microorganisms. Considering the essential role played by biogenic SeNPs as antimicrobial, anticancer, and antioxidant agents, or food supplements, their PL properties must be explored to take full advantage of them as eco‐friendly and versatile tools. Here, PL features of SeNPs produced by the Se‐tolerant Stenotrophomonas maltophilia SeITE02 strain, compared with chemogenic ones, are investigated, highlighting the PL dependency on the NP size. Indeed, PL emission shifted from indigo‐blue (emission wavelength λ em 400–450 nm) to green‐yellow ( λ em 480–570 nm) and orange‐red ( λ em 580–700 nm) for small (ca. 50 nm) and big (ca. 100 nm) SeNPs respectively, revealing the versatility of an environmental bacterial isolate to synthesize diverse PL probes. Besides, biogenic SeNPs show PL lifetime comparable to those of the most used fluorophores, supporting their potential application as markers for (bio)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) | 0.001 | 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".