Solution-Processable and Eco-Friendly Functionalization of Conductive Silver Nanoparticles Inks for Printable Electronics
Bibliographic record
Abstract
The functionalization of conductive inks has been carried out through the decomposition of hydrogen peroxide (H2O2) onto the surface of silver nanoparticles (AgNPs). The ink prepared using this eco-friendly chemical reagent has been characterized structurally, chemically, and morphologically, showing the presence of stable AgNPs with suitable properties as well as the absence of residual contamination. The electrical conductivity of such a solution-processable ink is evidenced for patterns designed on flexible photographic paper substrates, using a refillable fountain pen that is implemented as a printing mechanism for the fabrication of simple printed circuit boards (PCBs). The functionality and durability of the tested systems are demonstrated under various mechanical constraints, aiming to basically reproduce the normal operation conditions of flexible electronic devices. The obtained results indicate that the implementation of these AgNP-based inks is relevant for direct applications in inkjet printing technology, thus paving the way for the use of greener chemicals in ink preparation.
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) | 0.001 | 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 | 0.000 | 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".