Synthesis of Highly Conductive, Uniformly Silver-Coated Carbon Nanofibers by Electroless Deposition
Bibliographic record
Abstract
Noble-metal-coated carbon-based nanoparticles, when used as electrically conductive fillers, have the potential to provide excellent conductivity without the high weight and cost normally associated with metals such as silver and gold. To this effect, many attempts were made to deposit uniform metallic layers on core nanoparticles with an emphasis on silver for its high conductivity. The results so far were disheartening with the metal morphology being better described as a decoration than a coating with small effects on the electrical conductivity of the bulk particles. We tackled in this work the specific problem of electroless deposition of silver on carbon nanofibers (CNFs) with the investigation of every step of the process. We performed X-ray photoelectron spectroscopy (XPS), transmission and scanning electron microscopy (TEM, SEM), zeta potential, and electrical conductivity measurements to identify a repeatable, reliable set of parameters allowing for a uniform and fully connected silver deposition on the surface of the CNFs. The bulk particles’ specific electrical conductivity (conductivity per unit mass) undergoes a more than 10-fold increase during the deposition, reaching 2500 S·cm 2 /g, which indicates that the added metal mass participates efficiently to the conduction network. The particles keep their high aspect ratio through the process, which enables a percolated conduction network at very low volume loadings in a composite. No byproducts are produced during the reaction so the particles do not have to be sorted or purified and can be used as produced after the short ∼15 min reaction time. The particles might be an interesting replacement to conventional fillers in isotropic conductive adhesives, as a conductive network is obtained at a much lower loading. They might also serve as electrically conductive fillers in composites where a high conductivity is needed, such as lightning strike protection systems, or as high surface area silver electrodes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".