Bovine serum albumin – persistent nanoparticle interactions: Luminescence and Raman data
Bibliographic record
Abstract
• Cr-doped ZGO particles (10–20 nm) were synthesized via the hydrothermal method. • Oleic acid enables a negative surface charge on NPs, reaching up to −50 mV. • Adding NPs to the BSA solution has minimal influence on NPs' optical properties. • Raman shows that BSA is less folded after interaction with negatively charged NPs. Persistent luminescence nanoparticles (PersL NPs) are of considerable interest for their use in the visualization of biological molecules. Meanwhile, the interaction of the PersL NPs with biomolecules, particularly proteins, remains poorly investigated. In the present work, ZnGa₂O₄:Cr³⁺ (ZGO:Cr³⁺) PersL NPs, including surface modifications with oleic acid (OA) and their interactions with the model protein bovine serum albumin (BSA) were investigated. Transmission electron microscopy revealed nanoparticles with sizes ranging from 10 to 20 nm, with OA-functionalized particles exhibiting a characteristic shell-like halo. Modification with OA resulted in a significant increase in surface charge from approximately –2 mV to –50 mV. Fluorescence lifetime measurements of BSA showed only a slight increase upon interaction with the nanoparticles, indicating negligible energy transfer. Raman spectroscopy, focusing on the amide I region, revealed small conformational changes in BSA following interaction with the nanoparticles. Notably, the alpha-helix content increased when interacting with ZGO:Cr³⁺ nanoparticles calcined at 650 °C, suggesting protein stabilization. In contrast, a reduction in alpha-helix content was observed with OA-modified nanoparticles, indicating partial protein unfolding. These findings provide valuable insights into the structural integrity, surface properties, and bio-interactions of ZGO:Cr³⁺ PersL NPs, with implications for bioimaging and nanobiotechnology applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".