Unlocking the Potential of Polydopamine-Mediated Hybrid MXene and hBN 2D Nanosheets for Improved Thermal Energy Storage and Management
Bibliographic record
Abstract
One challenge in phase change materials (PCM) is boosting thermal conductivity without compromising latent heat, which is essential for storing thermal energy via phase changes, such as melting and solidification. This study focuses on developing a hybrid nanoenhanced plant-based paraffin wax, by incorporating surface-modified hexagonal boron nitride (hBN) and MXene. Here, polydopamine (PDA) served as a surface and chemical modifier to enhance the compatibility and long-term stability of MXene and hBN nanosheets within the NE-PCM, which are characterized through FTIR, XPS, XRD, and TEM. The surface modification enabled the nanosheets to disperse uniformly in the PCM without needing a surfactant, and they remained stable even after 1 h of centrifugation. The results indicate that the addition of 1% of PDA@hBN/MXene to PCM led to enhancements in all thermo-physical properties, including a 25.5% increase in the latent heat of melting, a 79% increase (at 15 °C) and a 59% increase (at 40 °C) in thermal conductivity, and a 32.5% increase (in the liquid state) and a 17.8% increase (in the solid state) in specific heat capacity. These findings underscore the significant advantages of hybrid NE-PCM in enhancing the performance of thermal energy management applications such as building energy management with pipe-encapsulated methods.
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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) | 0.000 | 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 | 0.000 |
| 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".