Fabrication of TiO<sub>2</sub>‐coated epoxy replicas with identical dual‐type surface topographies used in cell culture assays
Bibliographic record
Abstract
The goal of this study was to reproducibly generate samples with complex surface topographies and chemistries identical to a "master surface" and to test their response in cell culture using rat calvarial cells. Negative replicas of dual-type topography were fabricated using dental impression material with half of the surface exhibiting smooth and rough topography, respectively. Positive epoxy resin replicas were cast from the same negative replica eight times consecutively and coated with a 60-nm thin film of titanium dioxide using a vapor deposition technique. Atomic force microscopy, scanning electron microscopy, confocal white light microscopy, and X-ray photoelectron spectroscopy indicated that TiO(2)-coated epoxy replicas had surface topographical features and surface compositions nearly indistinguishable from the original titanium master surfaces. The described technique showed high reproducibility over at least eight generations of replication using the same negative replica. Rat calvarial osteoblasts proliferated just as well on dual topography surfaces as on single topography surfaces. The advantage of the dual-type substrates is that they facilitate comparison within a single culture dish, thus eliminating dish-to-dish variation as well as saving material, time and costs compared to the usual method of evaluating surfaces in separate dishes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".