Conducting Polymer Based Ink for Inkjet Printing: Formulation, Fabrication and Application
Bibliographic record
Abstract
The recent years various, there huge interest to improve the efficiency of smart electronic and optoelectronic devices via developing emerging materials and printing technologies. However, printing technologies becomes one of the most interesting areas for researcher over a convenient coating method because it not only helps to improve the efficiency of device but also help for rapid, reproducible, and low-cost production which open as a new window for mass production for commercial use. Inkjet printing technology has become the most attractive among various printing technologies due to which it is acceptable in most of scientific and various of industrial applications. This review paper presents an overview of the investigations on formulation of the ink, fabrication, and the applications of conductive polymer via inkjet printing for variety of devices including supercapacitors, sensors, electrochromic devices, and patterning of conductive polymers on flexible substrates. The review covers the different conducting polymer explored for supercapacitors, sensors, electrochromic devices, and patterning of conductive polymers, their ink formulation, inkjet printing process and their keys features. This review also presented future perspectives for inkjet printing technology for advanced electronic, optoelectronic, and other conductive polymer-based devices. We believe this review provides new direction to next generation conductive polymer-based devices for various applications.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".