Fabrication of Two-Dimensional Gradient Layer-by-Layer Films for Combinatorial Biosurface Studies
Bibliographic record
Abstract
We have developed a novel gradient fabrication method for combinatorial surface studies that provides the equivalent of 5000 individual polyelectrolyte multilayer (PEM) film physicochemical conditions in a single 7 cm square film. A simple, inexpensive, and versatile automated layering instrument was built, which can generate a gradient of physical properties on a film in 1 dimension laterally by simultaneously changing both the location of polyelectrolyte adsorption and the layering conditions, such as pH or salt concentration of the polyelectrolyte dipping solutions. By rotating the substrate 90° after each deposition cycle, full 2-dimensional gradient combinatorial films were fabricated over many layers, spanning virtually all previous combinations of stable deposition pH and salt conditions for both poly(allylamine hydrochloride) (PAH) and poly(acrylic acid) (PAA), a process which previously required more than 10 000 separate film samples. Surface spatial profiles of film thickness, surface energy (wettability), density (refractive index), and stiffness (modulus) were generated and correlated to assembly conditions. Additionally, step gradient films were generated first by varying the number of bilayers along one axis and pH along the other, which enabled us to measure their combined effect on thickness. To test for biocompatibility, we incubated HEK 293 cells on step gradient films and 2D combinatorial films for 48 h and determined that film assembly conditions played a major role, especially in controlling the stiffness and the density, which could be tailored with deposition pH over a wide range. Optimal growth conditions were discovered not at the extremes of fabrication pH, but instead near PAH pHs of 4–6 and PAA pH around 4, demonstrating that these PEM biosurfaces and this technique are suitable for optimizing high-throughput cellular screening.
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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 | 0.000 | 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) | 0.000 | 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".