Surfactant Selective Synthesis of Gold Nanowires by Using a DPPC−Surfactant Mixture as a Capping Agent at Ambient Conditions
Bibliographic record
Abstract
Gold (Au) nanowires (NW) have been synthesized by using mixtures of l -α-dipalmitoylphosphatidylcholine (DPPC) and various conventional surfactants (CS) of different polarities. The capping ability of the surfactant mixture has been quantitatively evaluated by using UV−vis, TEM, XPS, FTIR, and XRD studies. The synthesis has been carried out by using a total [DPPC+CS] in the range of 0.125 to 0.5 mM. The results clearly demonstrate a surfactant selective synthesis that significantly depends on the polarity of the CS head group. An anionic surfactant like sodium dodecylsulfate (SDS) along with DPPC leads to a network of NW formation with ≈50 nm thickness at different total surfactant concentrations while cationic surfactant, cetyltrimethylammonium bromide (CTAB), predominantly controls the spherical Au nanoparticle (NP) formation at high concentration. At low [DPPC+CTAB], the NW is achieved by end-to-end fusion of spindle shaped Au NP with an aspect ratio of ≈2.15. The chemical composition of the adsorbed capping surfactant on the Au surface and the alignment of the molecules were confirmed from XPS and FTIR studies. XPS results indicate that a small amount of DPPC+SDS caps the Au NW in comparison to that of DPPC+CTAB. FTIR results further support this and indicate even a smaller amount of DPPC is adsorbed in comparison to that of SDS, which is attributed to a stable micellar phase formation in the case of the DPPC+SDS mixture in comparison to DPPC+CTAB. This leaves a small amount of surfactant for capping action in the former case and causes anisotropic growth of Au NP to NW formation. Interestingly, no Au NW formation is observed if no DPPC is used. All pure CS lead to the formation of spherical Au NP of different dimensions. This fact suggests that one can control the morphology of Au NP by using DPPC along with different CS and thus one can design biofriendly nanomaterials for bioengineering applications.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".