Application of TOPSIS algorithm in describing bacterial cellulose-based composite hydrogel performance in incorporating methylene blue as a model drug
Bibliographic record
Abstract
A multi-component hydrogel was developed using bacterial cellulose, alginate, and gelatin with the aid of glycerol as trihydric alcohol which participates in re-distribution of hydrogen bonds in the test system. FTIR, XRD, SEM, and TGA as instrumental techniques were used to structurally characterize the physical/chemical properties of the formed composite hydrogel. By using an exponential equation, swelling behavior of the hydrogel was evaluated. By incorporating a model drug (methylene blue-MB) in the formed hydrogel, experiments were directed to study release characteristics of the MB where the medium solution for the release was prepared at four different pHs. The maximum cumulative drug release at pH 2.8, 6, 7.4, and 9 were 42.8, 63, 80, and 84.5%, respectively. Data fitting process was carried out using five kinetic models (Korsmeyer-Peppas, Higuchi, Hopfenberg, zero-order, and first-order equations) and the preferred kinetic model at each pH was estimated by applying TOPSIS algorithmic technique. The adsorption capacity of the hydrogel in relation to MB was determined while thermodynamic properties of this relationship were quantified ([Formula: see text] and [Formula: see text]). The results of the present study were in favor of the potential usage of the developed composite hydrogel in drug delivery systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".