Skin‐Inspired Nanofluid‐Filled Surfaces with Tunable Icephobic, Photothermal, and Energy Absorption Properties
Bibliographic record
Abstract
Abstract Ice buildup can significantly and negatively impact system performance in various industrial sectors, and has remained a persistent challenge for decades. Many compliant materials exhibit excellent de‐icing performance but are easily eroded by impacts from supercooled water droplets, sand, dust, and debris. A composite panel inspired by animal skin, consisting of a facesheet protecting a nanofluid layer beneath, which exhibits durable anti‐icing and tunable photothermal properties is proposed. The viscous liquid layer beneath the facesheet increases flexural rigidity, preventing large deflections and increasing deformation resistance, which alters ice's adhesion to the surface. The non‐uniform fluid pressure exerted by the viscous nanofluid‐filled composite panels facilitates ice detachment, resulting in ice adhesion strengths as low as τ ice ≈ 10 kPa. Further, by altering the fluid properties, different additional functionalities can be endowed to the system. Incorporating fumed silica in a fluid‐filled composite panel results in rheopectic behavior, and this doubles their impact resistance when the shear thickening properties are properly tuned. Additionally, the combination of a transparent facesheet and a solar light absorbent nanofluid allows for tunable photothermal properties, further enhancing the anti‐icing performance of the system. This durable and tunable nanofluid‐filled composite panel shows great promise as a multifunctional de‐icing material.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".