From <scp>two‐dimensional</scp> materials to polymer nanocomposites with emerging multifunctional applications: A critical review
Bibliographic record
Abstract
Abstract 2D materials are a very up‐and‐coming class of additives in the field of polymer composites due to their versatility and exceptional intrinsic properties. This enables researchers to create a variety of nanocomposites that can be employed in a myriad of emerging multifunctional applications. The performance of such nanocomposites depends heavily on the quality of the 2D materials, their interactions with the polymer matrix, as well as on their dispersion and morphology when embedded in the polymer. In order to control these variables, one needs to choose wisely between the available synthesis techniques and mixing strategies, playing with the process‐structure–property relationships, while keeping in mind the compatibility with current industrial infrastructure. Therefore, this paper presents a brief review on the 2D materials most used in polymer nanocomposites, the main synthesis techniques and mixing routes developed, the state of the art on the most sought‐after properties in different systems, and what are the effects of the morphology evolution. In each section, the main challenges are highlighted, and possible strategies to overcome them are presented, for example, the advent of hybrid 2D nanostructures, which promote synergistic effects, enabling the combination of properties that were not previously achievable on the final material. Finally, the paper ends by presenting a perspective of the current state in the development of these emerging multifunctional nanocomposites and what are the most important steps that need to be taken, not only academically, but also industrially, in order for these materials to start being widely applied and become staples in the daily life of humanity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".