Synthesis of Dispersed and Self-Assembled Metal Particles in Epoxy via Aqueous to Organic Phase Transfer Technique
Bibliographic record
Abstract
A convenient and effective method of dispersing gold (Au), silver (Ag) and copper (Cu) particles in epoxy is described.Particles were synthesized in aqueous phase and subsequently dispersed in toluene-epoxy with or without the presence of curing agent.Sodium borohydride and 2-propanol were used as reducing and phase transfer agents respectively.The surface plasmon resonance (SPR) at ~ 540-620 nm, ~ 525 nm and ~450 nm confirms the presence of Cu, Au and Ag nanoparticles in the composites.No chemical interaction occurred between the metal particles and the epoxy or cured epoxy.Increase in epoxy content caused the size of the particles to decrease for all metals.The average particle size for Au, Ag and Cu in 1% and 10% epoxy are 3.9 nm and 2.5 nm, 10.1 nm and 3.2 nm as well as 8.1 nm and 5.9 nm respectively.Similar decreasing trend was also observed upon addition of curing agent.In all cases, metal nanoparticles exhibit self-assembly with inter-particle spacing of <10 nm.The absence of any curing peak in the DSC thermograms suggests that the composites are well cured before any of the DSC analyses were performed.The Tg of the metal-cured epoxy is higher than neat epoxy, that is 71.3 °C, 69.8 °C and 62.0 °C for Cu, Au and Ag respectively.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".