Evaluation of Conductive Porous Biobased Composites with Tunable Mechanical Properties for Potential Biological Applications
Bibliographic record
Abstract
In this work, starch-based porous cryogels with controlled mechanical and electrical properties were prepared for tissue engineering applications. The starch cryogels were formulated using κ-carrageenan, poly(vinyl alcohol) (PVA), and styrylpyridinium-substituted PVA (SbQ) into the composite. A conductive cryogel was polymerized by chemical oxidation of 3,4-ethylenedioxythiophene (EDOT) using iron(III) p-toluenesulfonate as a strategy to control the electrical properties. The physical, thermal, and mechanical properties were evaluated for the obtained composites. Macro- and nanoscale results confirmed the capability of tuning the mechanical properties of the material by the addition of biopolymers in different contents. The presence of κ-carrageenan significantly increased the storage modulus and decreased the damping effect in the formulations. The presence of PVA showed a plasticizing effect in the formulations, confirmed by the buffering effect and an increase in storage modulus. PVA-SBQ improved the mechanical properties by cross-linking. The addition of PEDOT increased the mechanical and electrical properties of the obtained materials.
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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) | 0.000 | 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 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".