Bi-coloured enhanced luminescence imaging by targeted switch on/off laser MEF coupling for synthetic biosensing of nanostructured human serum albumin
Bibliographic record
Abstract
In this communication luminescent bioconjugated human serum albumin nanostructures (HSA NPs) with tiny ultraluminescent gold core–shell silica nanoparticles (Au@SiO2-Fl) were designed with enhanced bi-coloured luminescence properties. The HSA NPs were obtained from Human Serum Albumin free (HSA free) through the desolvation method, and Au@SiO2-Fl, through modified Turkevich and Störber methods. In this manner, porous HSA Nanostructures of 150.0–200 nm and Au@SiO2-Fl 45.0 nm final diameters were obtained. Both methodologies and structures were conjugated to obtain modified Nanocomposites based on tiny gold cores of 15 nm surrounded with well spatial Nanostructured architectures of HSA (d15 Au@SiO2-Fl-HSA) that generated variable nanopatterns depending on the modified methodology of synthesis applied within colloidal dispersions. Therefore, three methodologies of non-covalent conjugation were developed. In optimal conditions, through Transmission Electronic Microscopy (TEM), well resolved multilayered nano-architectures with a size 190.0–200 nm in average with variable contrast depending of the focused nanomaterial within the nanocomposite were shown. Optimized nanoarchitecture was based on a template tiny gold core–shell surrounded by nanostructured HSA NPs (d15 Au@SiO2-Fl-HSA). In this manner, the NanoImaging generated by laser fluorescence microscopy permitted to record improved optical properties and functionalities, such as: (i) enhanced ultraluminescent d15 Au@SiO2-Fl-HSA composites in comparison to individual components based on Metal Enhanced Fluorescence (MEF); (ii) diminished photobleaching; (iii) higher dispersibility; (iv) higher resolution of single bright nano-emitters of 210.0 nm sizes; and (v) enhanced bi-coloured Bio-MEF coupling with potential non-classical light delivery towards other non-optical active biostructures for varied applications. The characterization of these nanocomposites allowed the comparison, evaluation and discussion focused on new properties generated and functionalities based on the incorporation of different types of tuneable materials. In this context, the biocompatibility, Cargo confined spaces, protein-based materials, optical transparent could be highlighted, as well as optical active materials. Thus, the potential applications of nanotechnology to both nanomedicine and nano-pharmaceutics were discussed. Graphical abstract
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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) | 0.000 | 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".