Intuitive Polydimethylsiloxane Deposition Relying on Resource Conservative, Large Scale Compatible Capillary Flow Dynamics
Bibliographic record
Abstract
With the increased demand for flexible and wearable electronics, the need for alternative and accessible large-scale compatible production methods for polymer thin films has never been more urgent. Polydimethylsiloxane (PDMS) has shown immense promise in various applications because of its versatile chemical and physical properties, though a waste free, industry scale, and highly controllable production technique has yet to be introduced. Herein, we propose an intuitive approach to PDMS film fabrication relying on capillary action to grow a ready-to-use large-scale layer from a relatively small volume of solution-processed precursor with zero waste. The technique, referred to as capillary crawl film formation, or CCFF, introduced in this work is simple, primarily focused on limiting consumable resources and manual processing steps and on enabling large monolithic structures. We demonstrate precise control over the dimensions and morphology of the cured PDMS films without discarding any residual unwanted material; thicknesses ranging from μm to mm, both small- and large-scale surfaces, and varying degrees of preliminary 2D and 3D microstructuring are effortlessly added to all polymer surfaces during concurrent layer formation, for an even wider range of applications. The different film properties formed by either horizontal or vertical CCFF are tuned by modifying the solution volume, the dimension, adhesion, and morphology of the substrates used. Finally, we use CCFF to demonstrate its applicability for sensory applications, healing torn polymer films, and as an excellent encapsulant for perovskite solar cells where no drop in their 21% efficiency achieved was recorded in over a 1300 h testing period under ambient and high humidity conditions, promoting great device stability and longevity.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".