A Comprehensive Review of Non-Fluorinated Durable Water-Repellent and Stain-Resistant Coatings
Bibliographic record
Abstract
For industries manufacturing textiles and functional surfaces that must resist water, stains, and environmental degradation, durable water-repellent (DWR) coatings are indispensable. Fluorinated DWRs have long dominated the market due to their excellent durability under adverse conditions and their combined hydrophobic, oleophobic, and stain-resistant properties. However, concerns over the toxicity of fluorinated compounds and harmful by-products generated during production and degradation have catalysed the search for sustainable, fluorine-free alternatives. This review begins with the fundamental principles of wettability, hydrophobicity, and stain resistance, drawing on classic theories, models, and biological inspirations such as lotus leaves, shark skin, insect ommatea, and spider silk. The discussion then shifts to non-fluorinated DWRs, organized by functional mechanism and fabrication strategy, with emphasis on the roles of surface energy reduction and surface roughness engineering. Four representative classes—hydrocarbon-based, silicon-containing, bio-derived, and nanoparticle-based coatings—are evaluated to assess the potential of current synthetic approaches in producing reliable and environmentally benign replacements. Finally, emerging applications across textiles and related hydrophobic materials are summarized, highlighting both the progress achieved and the challenges that remain for the development of next-generation sustainable DWRs.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".