Polyaniline and its composites engineering: A class of multifunctional smart energy materials
Bibliographic record
Abstract
Polymer material provides significant advantages over the conventional inorganic material-based electronics due to its attractive features including miniaturized dimension and feasible improvisations in physical properties through molecular design and chemical synthesis. In particular, conjugate polymers are of great interest because of their ability to control the energy gap and electronegativity through molecular design that has made possible the synthesis of conducting polymers with a range of ionization potentials and electron affinities. Polyaniline (PANI) is one of the most popular conjugated polymers that has been widely explored so far for its multifunctionality in diverse potential applications. This review is focusing on the recent advances of PANI for smart energy applications including supercapacitors, batteries, solar cells and nanogenerators and the development in its synthesis, design, and fabrication processes. A details investigation on the different types of chemical process has been discussed to fabricate PANI in nanostructures, film, and composites form. The paper includes several studies which are advantageous for understanding: the unique chemical and physical properties of this polymer; and the easily tunable electrical properties along with its redox behavior; and different processes to develop nanostructures, film, or bulk form of PANI that are useful to derive its applicability in smart objects or devices.
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 | 0.001 |
| 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".