Advances in the Fabrication of Superhydrophobic Polymeric Surfaces by Polymer Molding Processes
Bibliographic record
Abstract
Superhydrophobic materials are found in a suite of scientific and industrial applications, and given their broad potential use, there is great interest in facilitating their mass production. Although numerous methods have been used to produce superhydrophobic materials, only a few are capable of fabricating superhydrophobic surfaces and materials at an industrial scale. Techniques such as injection molding, compression molding, hot embossing, and polymer casting play an important role in the mass production of superhydrophobic polymer surfaces. This technical literature review summarizes recent advances in the polymer molding processes used to fabricate superhydrophobic materials. Here, we review replication methods and the materials that can be used by these approaches. We also evaluate the advantages and disadvantages of these methods and discuss the challenges of molding and demolding single-level structures (e.g., microstructures and nanostructures) and multilevel structures (e.g., micro-nanostructures, micro-microstructures, and micromicro-nanostructures), with a focus on superhydrophobic surfaces. We evaluate the relationship between structure geometry and the wettability of a surface, highlighting the effect of structure type and size in achieving the desired wettability. We then offer perspectives, discuss current limitations, and suggest required studies. This review aims to assist researchers in understanding the fundamentals related to the fabrication of patterned surfaces via polymer molding processes and offer avenues for the successful creation of superhydrophobic polymeric surfaces.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".