News — Laboratory of Polymer Composites

Program-Targeted Funding (PTF)

Program Objective

To modify the surface of inorganic minerals (such as basalt ocher and diorite) and evaluate their application as flame-retardant additives for producing high-performance composites based on vinyl ester resin.

Program Tasks

  1. Investigate the morphology and structure of modified inorganic minerals using liquid-phase exfoliation, hydrothermal synthesis, mechanical milling, and related methods.
  2. Study the dispersion state of modified minerals within the vinyl ester resin matrix.
  3. Examine the influence of modified minerals on the rheological properties of vinyl-ester-based composites during processing.
  4. Evaluate the effects of modified minerals on thermal, flame-retardant, and mechanical properties of vinyl-ester composites.
  5. Clarify correlations between mineral morphology/structure and composite performance (thermal stability, flame retardancy, mechanical strength).
  6. Study thermal decomposition products to understand flame-retardant mechanisms in both the condensed and gas phases.
  7. Develop a machine-learning–based predictive model of thermal stability and flame-retardant behavior for vinyl-ester composites containing modified minerals.
  8. Validate the predictive model against experimental data to optimize composite formulations with modified minerals.

Explanation of Tasks

Task 1. Determines morphology and structure of modified minerals, ensuring uniform composite properties.

Task 2. Analyzes particle distribution uniformity and level of agglomeration within the polymer matrix.

Task 3. Examines viscosity, flow behavior, and processing parameters of resin systems.

Task 4. Evaluates thermal stability and mechanical strength of the materials.

Task 5. Studies structure–property relationships between fillers and composite performance.

Task 6. Analyzes gas-phase and condensed-phase processes during thermal degradation.

Task 7. Applies machine learning to predict material properties.

Task 8. Compares model predictions with experimental data to refine composite composition.