Optimization of Graphene Oxide’s Characteristics with TOPSIS Using an Automated Decision-Making Process
Bibliographic record
Abstract
The present study focuses on a new application of TOPSIS to predict and optimize graphene oxide’s characteristics. Although this carbon-based material has been investigated previously, its optimization with this method using an automated decision-making process has not been performed yet. The major problem in the design and analysis of this nanomaterial is the lack of information on comparing its characteristics, which has led to the use of diverse methods that have not been appropriately compared. Moreover, their advantages and inconveniences could be investigated better once this investigation provides information on optimizing its candidates. In the current research work, a novel automated decision-making process was used with the TOPSIS algorithm using the Łukasiewicz disjunction, which helped detect the confusion of properties and determine its impact on the rank of candidates. Several characteristics of graphene oxide, such as its antibiofilm activity, hemocompatibility, activity with ferrous ions in hydrogen peroxide, rheological properties, and the cost of its preparation, have been considered in its analysis with TOPSIS. The results of this study revealed that the consideration of the criteria of this nanomaterial as profit or cost criteria would impact the distances of candidates from the alternatives. Moreover, the ranks of the candidates changed when the rheological properties were considered differently in the data analysis. This investigation can help improve the use of this nanomaterial in academic and industrial investigations.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".