Tribological Characterization and Hardness Analysis of Acrylonitrile Butadiene Styrene Composites Reinforced with Titanium Dioxide and Tungsten (ABS/TiO2W)
Bibliographic record
Abstract
In pursuit of materials that contribute to the reduction of power consumption and carbon emissions, the tribological properties of composites in automotive, aerospace, and power generation applications have become increasingly critical.This research examines the tribology of a novel Acrylonitrile Butadiene Styrene (ABS) composite, synthesized through an innovative solvent-assisted fluidization process.This technique involved swelling polymer pellets in acetone, followed by reinforcement with dual additives, Titanium Dioxide and Tungsten (TiO2W), in varying weight percentages (2.5%, 5%, 7.5%, and 10%).Experimental analysis revealed that the incorporation of these additives enhanced the material's Shore D hardness, with a peak value of 78 achieved at a 5% reinforcement level.Tribological assessments conducted with a tribometer under a constant sliding velocity of 0.5 m/s and a normal load of 10 Newtons over a 500-meter distance indicated that the addition of 2.5% TiO2W to the ABS matrix resulted in the lowest mass wear rate (210 -7 g/Nm) and a coefficient of friction (COF) of 0.191.These findings suggest that the strategic inclusion of TiO2W additives into ABS composites can significantly improve their wear resistance and hardness, which are essential for their performance in demanding environments.The research offers a foundation for the development of ABS-based materials with superior tribological properties for critical applications, potentially leading to enhanced energy efficiency and reduced environmental impact.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".