Poly(dimethyl siloxane) bimodal brush: Simple method of preparation and performance enhancement of omniphobic coatings
Bibliographic record
Abstract
• PDMS bimodal brushes show improved dynamic dewetting over unimodal brushes. • Bimodal brushes reduce sliding angles, enhancing repellency, wear resistance, and flexoprinting. • Bimodal brush coatings are cost-effective to produce. End-tethered chains in a polymer brush usually have a unimodal length distribution and are commonly prepared using ’graft-from’ or ’graft-to’ methods. This paper introduces a novel self-assembly technique to create an epoxy-bearing ladder-like polysilsesquioxane (L) coating with a bimodal poly(dimethyl siloxane) (PDMS) surface brush. The process begins with synthesizing L#k, which consists of L with a minor fraction of a graft ( g ) copolymer (L- g -#k), where #k represents PDMS with a number-average molecular weight of 2.0, 5.0, or 10.0 kDa. The coating is made by casting a solution of two L#k samples mixed with a photoinitiator, allowing solvent evaporation, and then photocuring the film. During solvent evaporation, PDMS chains that are grafted to L and are of two different lengths migrate to the surface, forming a bimodal PDMS brush that reduces surface energy. PDMS layer thicknesses determined from atomic force microscopy (AFM) and cross-sectional analyses confirm brush formation from different polymer mixtures. The L coating with bimodal PDMS brushes shows reduced water and organic solvent sliding angles, enhanced graffiti paint repellency, and improved wear resistance compared to coatings bearing unimodal PDMS brushes. This study highlights a facile approach to creating bimodal polymer brushes with self-cleaning properties and potential applications in fields like flexographic printing.
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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 | 0.001 | 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) | 0.000 | 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 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".