Influence of the Alkaline Treatment of Eucalyptus Fibres on the Mechanical Behaviour of Low-Density Polyethylene Composites
Bibliographic record
Abstract
The use of plant fibers in the formulation of polymer matrix composites requires prior treatment of the fibers. This treatment can be chemical, mechanical or the use of a coupling agent (usually copolymers). In the present study, the aim is to chemically treat the fibers. This involves treatment with 6% (w/v) sodium hydroxide. One of the aims of this study is to examine the influence of this treatment on the maximum loadings of the LDPE/eucalyptus fiber (EF) material. The LDPE matrix is readily available as virgin or recyclable waste, its low melting point between 105°C and 115°C makes it suitable for handling over a reasonable temperature range, and its mechanical strength holds up well before dropping between 75°C and 90°C. At the end of its useful life, this polymer is generally abandoned and becomes a source of environmental pollution, requiring recycling. All these advantages motivated the choice of LDPE in this study, the second aim of which is to produce environmentally friendly pavers. This choice also helps to reduce the pollution associated with LDPE polymers. The availability, fast growth, low density, and low cost of eucalyptus motivated the choice of eucalyptus species. 0%, 15% and 25% treated and untreated short fiber were used to formulate the composites. However, treated fiber-reinforced composites showed an improvement in ultimate tensile strength over untreated fiber composites. Microscopic (SEM-EDX) and FTIR-ATR analyses were carried out on the composites to determine their topology, as well as on the treated and untreated fibers to determine the change in functional groups after treatment. Fiber roughness is improved after treatment with NaOH.
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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".