The Effect of Liquid Smoke Treatment on Sansevieria Trifasciata Laurentii Fibers on the Mechanical Properties of Composite Fiber Materials
Bibliographic record
Abstract
The growing demand for eco-friendly construction materials underscores the need for sustainable composite materials with enhanced mechanical and acoustic properties.However, natural fiber composites often suffer from limited mechanical strength and durability, necessitating appropriate treatments to improve their performance.This study examines the impact of liquid smoke treatment on Sansevieria trifasciata Laurentii fiber (STLF) reinforced composites, applying immersion durations of 1, 2, and 3 hours, followed by heat treatment at 40 for 30 minutes.The findings reveal that the 2-hour treatment (P2J) resulted in the highest tensile strength (90.10 MPa), flexural strength (42.78 MPa), and modulus of elasticity (33.746MPa), whereas the 3-hour treatment (P3J) achieved the highest impact strength (31.72 KJ/m) and optimal sound absorption coefficient (0.771 dB).However, extending the treatment to 3 hours led to a decline in tensile and flexural strength, indicating that 2 hours is the optimal treatment duration for maximizing mechanical performance.This research confirms that liquid smoke treatment significantly enhances the mechanical and acoustic properties of STLF composites, establishing them as a promising sustainable material for soundproof room partitions.
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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".