Development of Bambusa tulda fiber-micro particle reinforced hybrid green composite: A sustainable solution for tomorrow's challenges in construction and building engineering
Bibliographic record
Abstract
Researchers are continually focusing on natural alternatives to synthetic materials due to the ongoing rise in global warming and sustainability concerns. Interest in natural fiber reinforced polymeric composite (NFRPC) is growing steadily due to their low cost, biodegradability, lightweight nature, and superior lifecycle. NFRPCs are used everywhere, from manufacturing automobile interior parts to constructing engineering projects. The current experimental investigation focuses on developing a hybrid composite reinforced with Bambusa tulda fiber and microparticles . Bamboo biomass, collected as waste from nearby industries, is converted into valuable bamboo micro-particles through chemical treatment. Hybrid composites have been developed with a 30 % bamboo fiber loading, varying the weight fraction of bamboo microparticles from 0 to 10 with intervals of 2.5 wt%. The experimental investigation revealed that adding micro particles to the bamboo fiber reinforced composite resulted in a 12.72 % maximum increase in tensile strength and a 19.79 % maximum increase in flexural strength . The addition of microparticles beyond 5 % resulted in agglomeration, leading to a decrease in properties. Based on the comparative analysis of the results, it can be concluded that the developed composite has the potential to be used in the construction and building engineering industries.
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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".