Role of Different Phospholipids in the Synthesis of Pearl-Necklace-Type Gold−Silver Bimetallic Nanoparticles as Bioconjugate Materials
Bibliographic record
Abstract
A seed-growth (S-G) method has been used to synthesize gold (Au) and Au−silver (Ag) bimetallic pear-necklace-type nanoparticles (NP) as bioconjugate materials by using a series of phospho-glycerol (PG) and phospho-choline (PC) lipids as capping agents. All PG lipids produce a fine pear-necklace arrangement of Au NP with dimensions between 10 and 20 nm. Addition of Ag converts this arrangement into Au−Ag bimetallic NP with a diameter essentially close to that of Au NP. Use of PC lipids does not show this arrangement but promotes a significant anisotropic growth especially in the presence of Ag. This difference has been attributed to a difference in the capping ability of PG and PC lipids because of their anionic and zwitterionic nature, respectively. XPS results have demonstrated the presence of adsorbed PG and PC lipids on Au or Au−Ag bimetallic surfaces in their respective samples. The results also indicate a decrease in the capping amount of a lipid with an increase in the growth of Au−Ag bimetallic NP. The growth of Au−Ag bimetallic NP from Au NP has been ascribed to the nucleation of Ag atoms at the {111} facets of Au NP in the presence of PG lipids, while anisotropic growth is occurring mainly at all other planes of fcc crystal geometry of Au−Ag bimetallic NP in the presence of PC lipids. It has been concluded that in order to get fine Au−Ag bimetallic bioconjugate materials capped with PG lipids, one has to use ascorbic acid (AA) as a weak reducing agent at the end of the S-G reaction sequence so as to give the required time for lipid molecules to adsorb at the liquid−solid interface.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".