Modification strategies of conductive polymers with advanced carbon materials for energy and environmental solutions
Bibliographic record
Abstract
• CP–CNS nanocomposites offer enhanced pollutant removal and water purification. • Photocatalytic H₂ production shows high efficiency with tailored nanostructures. • Functionalized composites show improved CO₂ capture and conversion to fuels. • CP–CNS hybrids achieve over 90 % degradation for dyes, metals, and drugs. Environmental remediation has become an urgent necessity, with water security emerging as a primary global concern. To address this challenge, the development of affordable and sustainable materials is essential, particularly for improving accessibility in remote regions. Among emerging candidates, Conductive Polymers (CPs) have gained significant attention due to their sustainable synthesis using mild chemicals and their ability to bypass energy-intensive processing routes. The delocalization of π-electrons along their conjugated backbones enables efficient charge transport, allowing them to function as photocatalysts while simultaneously adsorbing pollutants. This dual property not only supports environmental remediation but also extends their application to energy generation and storage technologies. Another promising class of materials, Carbon Nanostructures (CNS), offers excellent charge transfer rates and structural tunability. However, their high production cost often limits large-scale applications. To overcome these limitations, recent studies have explored the synergy between CPs and CNS, leading to the design of advanced composites through methods such as in situ polymerization, electrodeposition, and aerogelization. These hybrid materials have demonstrated superior photocatalytic performance, making them attractive for fabricating electrodes used in various remediation strategies. Such electrodes have been successfully applied in hydrogen and oxygen evolution reactions, carbon dioxide capture, dye degradation, and the removal of heavy metals and pharmaceuticals. Beyond environmental cleanup, processes like hydrogen and oxygen evolution also provide alternative energy pathways, thereby linking remediation with sustainable energy production. This review highlights recent developments, along with other innovative materials and waste valorization strategies, underscoring their potential in fostering a greener and more resilient ecology.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".