Potential Controlled Electrochemical Conversion of AgCN and Cu(OH)<sub>2</sub> Nanofibers into Metal Nanoparticles, Nanoprisms, Nanofibers, and Porous Networks
Bibliographic record
Abstract
Nanowires are expected to provide considerable advances in the use of smaller and more efficient sensing, electronic, and photovoltaic devices. Good electrical connections of the nanowires within devices can, however, be problematic. We present here a new method that takes advantage of the available large-scale and reproducible wet-chemical syntheses of non-zero-valent anisotropic nanomaterials. The electrochemical reduction of preformed solid AgCN and Cu(OH)2 nanofibers (NFs) on surfaces allows one to form metallic nanostructures that are integrated in electrical junctions with excellent electrical contacts. Some fundamental aspects of the electrochemical reduction of AgCN NF are presented, including their redox potential and propagation of the metal boundary formed during the electrochemical reduction process. The clear connection between native (unreduced) AgCN NF and reduced Ag0 nanostructures is shown. The reduction potential, the nature of the supporting substrate (conductive vs insulating), and the size of the original fibers strongly influence the morphology and dimensions of the Ag0 nanostructures thus produced. A number of different Ag0 nanostructures are electrosynthesized, including nanoprisms, nanoparticles (NPs), and NFs, made from the aggregation of nanoprisms and NPs, and continuous fibers, whose width is tunable between 90 and 500 nm. We report the formation of excellent electrical contact via the electrochemical reduction of metal/Mz+ NF/metal junctions. This technique is simple, fast, and applicable to other materials such as Cu(OH)2 NF. It allows for the formation of electrically connected metallic networks with new interesting geometries, which could be applied to a form of electrochemical welding.
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 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".