Polyaniline-Based Ink for Inkjet Printing for Supercapacitors, Sensors, and Electrochromic Devices
Bibliographic record
Abstract
In recent years, there has been a huge surge in interest in improving the efficiency of smart electronic and optoelectronic devices via the development of novel materials and printing technologies. Inkjet printing, known to deposit ‘ink on demand’, helps to reduce the consumption of materials. Printing inks on various substrates like paper, glass, and fabric is possible, generating flexible devices that include supercapacitors, sensors, and electrochromic devices. Newer inks being tested and used include formulations of carbon nanoparticles, photochromic dyes, conducting polymers, etc. Among the conducting polymers, PANI has been well researched. It can be synthesized and doped easily and allows for the easy formation of composite conductive inks. Doping and the addition of additives like metal salts, oxidants, and halide ions tune its electrical properties. PANI has a large specific capacitance and has been researched for its applications in supercapacitors. It has been used as a sensor for pH and humidity as well as a biosensor for sweat, blood, etc. The response is generated by a change in its electrical conductivity. This review paper presents an overview of the investigations on the formulation of the inks based on conductive polymers, mainly centered around PANI, and inkjet printing of its formulations for a variety of devices, including supercapacitors, sensors, electrochromic devices, and patterning on flexible substrates. It covers their performance characteristics and also presents a future perspective on inkjet printing technology for advanced electronic, optoelectronic, and other conductive-polymer-based devices. We believe this review provides a new direction for next-generation conductive-polymer-based devices for various applications.
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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 | 0.000 | 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) | 0.000 | 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".