Harnessing multifunctional electrospun nanofibers containing phase change materials for energy-efficient thermal management: A review on recent trends
Bibliographic record
Abstract
Energy efficient solutions that can effectively regulate the temperature of various systems are crucial across a wide array of applications, spanning from electronic circuits and battery technologies to textiles and biomedical dressings. Phase change materials (PCMs) offer an effective means to tackle pressing energy challenges through their ability to absorb or release significant amounts of so-called 'latent' thermal energy during a change in their physical state. Various techniques have been implemented to solve the fluidity and leakage challenges of PCMs in their melt state, which has proven to be a limiting factor in their application. Electrospinning stands out as an efficient technique for confining PCM into versatile form stable polymeric nanofibers. Despite the prevalence of electrospinning, there is a noticeable limitation in existing literature, particularly in comprehensive reviews focusing on multifunctional electrospun PCM fibers. Therefore, this review seeks to consolidate insights into the advancements associated with the utilization of electrospinning techniques for creating nanofibrous PCM composites. The review provides an overview of electrospinning technology, PCM fiber formation mechanisms, possible additives incorporation for the creation of multifunctionality, and their utilization in personal thermal management, electronics and electromagnetic shiels, as well as medical dressings. It delves into advancements in material selection, fusion enthalpies, and transformation temperatures, offering a comprehensive summary and discussion. The aim is to highlight advances and potentials while also assessing research gaps in electrospun nanofibrous PCMs, thereby offering guidance for new research directions and advancements in this promising area. • Review of electrospinning's role in creating form-stable nanofibrous phase change materials (PCMs) for thermal management. • Uniaxial electrospinning is common for nanofibrous PCMs, but coaxial technique shows promising new functionalities. • The review explores how integrating nano-additives can tailor properties and enhance multifunctionality. • Nanofibrous PCMs are trending in personal thermal management, medical supplies, electronics, and electromagnetic shielding. • Research gaps in nanofibrous PCMs offer a roadmap for future advancements, driving progress in this promising field.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".