Nanomaterial advanced smart coatings: Emerging trends shaping the future
Bibliographic record
Abstract
Advancements in nanotechnology have positioned coatings as a pivotal field with the potential to significantly impact both industry and society. This review delves into nanomaterials and their potential to create smart coatings capable of real-time monitoring and flexible electronics applications. The mechanisms of conductivity and sensing capabilities within these coatings are emphasized to highlight their importance in the context of artificial intelligence. Furthermore, the current trends shaping the coatings industry are summarized, such as the concept of electronic skin (E-skin) and increasing focus on sustainability. In the digital era, the integration of the Internet of Things (IoT) is set to transform the future of coatings, enhancing their intelligence and environmental interactivity. Smart coatings are poised to revolutionize our interaction with the environment, spanning applications from consumer goods to robotics and sensors. The ongoing development of these materials and technologies promises to unlock new and exciting possibilities. By discussing the above aspects in detail, this review positions itself as a forward-looking contribution that summarizes the state-of-the-art and anticipates future directions for smart coatings, offering insights into how ongoing advancements can unlock new possibilities for both industrial applications and societal impact.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 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".