Optimization of Comfort and Saturated Magnetic and Electromagnetic Properties of Polyester Fabric Impregnated with Silica/Kaolinite/Silver In-Situ to Protect the Human Body
Bibliographic record
Abstract
In this study, silica/Kaolinite/silver nanocomposites were synthesized according to experimental design results, using the central composite design (CCD) method.Samples were synthesized by impregnation on the polyester fabric, to get an in-situ approach to make a new performance of the polyester fabric to protect the human body from dangerous magnetic waves.Initially, magnetic saturation of the designed specimens was tested and its optimum values were measured with a Vibrating Sample Magnetometer (VSM) device.Mechanical properties including tensile strength, friction, abrasion, hydrophobicity (drop absorption), bending, thickness, and Crease Recovery Angle (CRA) of polyester fabrics impregnated with different amounts of nano-composite components were investigated using Response Surface Methodology (RSM) and PLS statistical methods which can help to show the effect of variables on each other.FESEM, EDX, and FTIR analyses were conducted for raw polyester-impregnated nanocomposites using an in-situ method under optimum conditions.The results confirm that the polyester fabric impregnated with threecomponent nanocomposite by varying concentrations of silica, Kaolinite, and silver, can significantly enhance the properties of saturation magnetic, strength, abrasion, friction, hydrophobicity, bending, thickness, air permeability, and CRA.
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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) | 0.001 | 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".