Tunable photoluminescence properties of selenium nanoparticles: biogenic versus chemogenic synthesis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it