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Mechanical and sound absorption performance of cashew apple bagasse and mahogany fruit-based hybrid composites

2025· article· en· W4412476793 on OpenAlexaff
Murugesan Palaniappan, Sivasubramanian Palanisamy, T. Murugesan, Carlo Santulli, Srinivas Tadepalli

Bibliographic record

VenueBioResources · 2025
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsCarleton University
Fundersnot available
KeywordsMaterials scienceBagasseComposite materialHorticulturePulp and paper industryBiologyEngineering

Abstract

fetched live from OpenAlex

Mechanical and acoustic properties were studied for hybrid composites developed from Cashew Apple Bagasse (CAB) fibers and Mahogany tree Fruit Filler (MFF) in an epoxy matrix. The effect of alkali treatment (5% and 10% NaOH solutions) on CAB fiber composites at different weight fractions of MFF (0 to 25 wt%) was studied. The composites including 15 to 20 wt% MFF and treated with 5% NaOH offered the highest performance. Tensile, flexural, and impact strengths were improved by 40%, 50%, and 45.7%, respectively, when compared to untreated ones; also, a 56.7% increase in noise reduction coefficient (NRC) was measured. These enhancements can be attributed to the removal of surface impurities on the fiber surface, improving fiber-matrix bonding, and achieving even dispersion of filler in the matrix. Using alkali treatment with NaOH concentration exceeding 10% and with a higher filler content (> 20 wt%) embrittled the fibers, at the same time allowing them to clump together, which decreased both mechanical and acoustic performance. The best approach was to combine CAB and MFF in appropriate quantities into a hybrid composite, striking a good balance between reinforcement and load transfer efficiency. These composites are intended to be used in car interiors, soundproof panels, and lightweight structural parts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.227
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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