The current and future trends of composite materials: an experimental study
Bibliographic record
Abstract
The usages of the composite materials range from simple household to light-to-heavy industrial purposes including oilfield applications. The objective of this study is to evaluate current and potential uses of composite materials for the petroleum industry. This article gathered all the available composite materials that are normally used specially in oilfield and surface pipeline applications. Out of those, four fiberglass-reinforced plastic materials (i.e., AR-glass, boron-free E-glass, C-glass, and E-glass) were selected to conduct an experiment in acidic and alkaline environments. The results show that AR-glass is corrosion resistant at high temperature and high acidic and alkaline environments. The weight loss due to corrosion is less than the other three materials. Boron-free E-glass is also better than C-glass and E-glass, especially in acidic environment. Another aspect of this research is to find out a research gateway toward the development of sustainable composites. When toxic components are used during the development of new materials, nowadays, this becomes an issue for environmental groups. Therefore, this study suggests the researchers to look for an environment-friendly, sustainable composite material that can be widely used in the petroleum industry. Finally, the trend of future research has been outlined and an indication of sustainable composite material choice has been proposed for oilfield applications.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".