Photothermal polyurethane coatings with functionalized nanoparticles and quasi-liquid layer for enhanced anti-icing and solar-assisted de-icing
Bibliographic record
Abstract
• Surface-functionalized Fe 3 O 4 nanoparticles improve the photothermal and icephobic performance of polyurethane coatings. • Improved dispersion of surface-functionalized nanoparticles facilitates homogeneous photothermal conversion, accelerating the de-icing process. • Silicone functionalization reduces surface energy, whereas hydroxyl groups maintain a quasi-liquid layer at sub-zero temperatures, thereby reducing ice adhesion strength. • The synergy between photothermal effects and the quasi-liquid layer (QLL) enables robust passive–active anti-icing performance under extreme cold conditions. Polyurethane (PU) coatings effectively mitigate ice accumulation on surfaces in low-temperature conditions. Unlike traditional de-icing methods that can be ineffective, costly, or environmentally harmful, PU photothermal coatings offer environmental and economic benefits. They not only improve anti-icing properties and de-icing efficiency but also address critical operational and sustainability challenges associated with harsh winter environments. This study aims to develop a simple yet effective strategy for producing PU coatings with enhanced anti-icing and de-icing performance by methodically incorporating various forms of iron oxide nanoparticles—including Fe 3 O 4 (FPU), silicone oil–coated Fe 3 O 4 (SiFPU), and hydroxyl (OH)-functionalized Fe 3 O 4 (FOHPU)—at concentrations from 0.5 % to 10 % to investigate their influence on mechanical, photothermal, and icephobic behavior. PU coatings were fabricated and subjected to characterization using SEM, FTIR, UV–Vis spectroscopy, and tensile testing. IR thermography was used to evaluate photothermal performance under 1 sun xenon illumination. Icephobic properties was evaluated through push-off tests in a cold room under both with and without simulated sunlight. Photothermal de-icing was assessed using simulated sunlight and push-off tests (ice adhesion strength) in a cold room, both with and without simulated sunlight. The endurance of the coatings through repeated icing/de-icing cycles was assessed. UV–Vis spectroscopy revealed improved light absorption, with the band gap of Fe 3 O 4 nanoparticles being reduced by the silicone oil coating and hydroxyl functionalization (by 2.3 and 2.55 eV, respectively). The results indicate that the icephobic performance of PU coatings is considerably improved by using surface-functionalized nanoparticles. Also, 10FOHPU demonstrated a marked enhancement in mechanical properties, with a Young’s modulus of 140 ± 6.2 MPa and a tensile strength of 6.3 ± 0.2 MPa (compared to 106.1 ± 4.1 MPa and 6.1 ± 0.4 MPa for unmodified PU). In addition, the presence of a quasi-liquid layer on the FOHPU coatings was verified by ATR-FTIR spectroscopy conducted at sub-zero temperatures. Notably, 10SiFPU exhibited the lowest ice adhesion (40 ± 8 kPa) after 20 min of light exposure. These results highlight the potential of SiFPU and FOHPU coatings for sustainable and efficient anti-/de-icing applications. This optimized performance is facilitated by tailored nanoparticle surface chemistry.
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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".