Formation of Ordered Two‐Dimensional Polymer Latticeworks With Polygonal Meshes by Self‐Organized Anisotropic Mass Transfer
Bibliographic record
Abstract
Abstract This manuscript addresses the formation of self‐assembled two‐dimensional (2D) polymer latticeworks with multiple polygonal meshes packed in various ordered arrays. Firstly, ordered arrays of water droplets were formed in the hydrophilic regions of patterned self‐assembled monolayers (SAMs) consisting of isolated hydrophilic circles surrounded by a continuous hydrophobic region. After dip‐coating this water‐patterned surface into a polymer solution in chloroform, dewetting of the polymer solution led to the formation of a crater‐like porous polymer film. Next, the resulting polymer film with round pores arranged in a 2D ordered array was subjected to a thermal treatment carried out at a temperature higher than the glass‐transition temperature (Tg) of the polymer. The thermal annealing process resulted in a morphological transformation from circular pores into polygonal meshes packed in either a similar or different ordered array. This morphological transition is self‐organized, involving mass transfer, an anisotropic process, and is controlled by the minimization of the Gibbs free energy. An empirical equation was established to guide the experiments. Thus, the patterned features of the polymer meshes can be designed via the ordered arrays of the hydrophilic circles of the SAMs as well as by experimental parameters such as the concentration of the polymer solution. The formation of the polymer latticework with polygonal meshes reveals that self‐organized mass transfer can be applied in micropatterning by elaborate experimental design. magnified image
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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".