Eco-Friendly Wall Cladding Panels from Recycled Fishing Gear and Clamshell Waste
Bibliographic record
Abstract
Eco-friendly wall cladding panels were developed from fishing industry waste by incorporating discarded ropes, wood fibers from lobster cages, and clamshell powder. Four panel formulations were investigated using MAPP and MAPE coupling agents: FRW-M (97% fishing rope), 30WF-M (67% rope with 30% wood fibers), 30CS-M (67% rope with 30% clamshell powder), and a hybrid 15CS15WF-M (67% rope with 15% each of wood fibers and clamshell powder). A DSC analysis revealed that clamshell powder addition reduced melting temperatures and crystallinity, while wood fiber incorporation led to slight increases in melting temperatures. The hybrid formulation exhibited enhanced crystallization temperatures despite lower overall crystallinity. A dynamic mechanical analysis showed an 85% improvement in storage modulus for the hybrid panel, with flexural testing demonstrating a 202% increase in modulus and 20% increase in strength. SEM-EDS analysis confirmed improved filler dispersion and interfacial adhesion in the hybrid formulation. Water absorption was lowest in FRW-M and highest in 30WF-M, while burning rate tests showed 30CS-M and 30WF-M as the best and worst performers, respectively. The hybrid formulation emerged as the optimal solution, combining enhanced mechanical properties with improved water resistance and fire retardancy, presenting a viable sustainable alternative for wall cladding 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".