Investigation of mechanical and physicochemical properties of additively manufactured underutilized wood‐<scp>PLA</scp> biocomposites
Bibliographic record
Abstract
Abstract Despite Canada's abundant biomass resources, a significant portion remains underutilized due to a lack of large‐scale industrial applications. This research explores the utilization of low‐value biomass, specifically aspen fiber, in fused filament fabrication (FFF) to develop biocomposites. Various chemical treatments (NaOH, silane, and maleic anhydride (MA)) were applied to improve fiber compatibility with polylactic acid (PLA). Both untreated and treated fibers at 10 wt% loadings were blended with PLA and extruded into 3D printable filaments. Results showed that MA‐treated fiber‐based composites had around 15% higher tensile strength and modulus along with a 30% enhancement in storage modulus than untreated ones. Additionally, a 25% reduction in water uptake was witnessed in MA‐treated aspen‐derived composites. Furthermore, the effect of fiber loading on the mechanical performance of the composites was explored by producing aspen‐PLA composites with higher fiber weight percentages (10, 20, and 30). Despite successfully 3D‐printing biocomposites with up to 30 wt% fiber content without nozzle clogging, the mechanical properties deteriorated with higher fiber loading. All the findings highlight the untapped potential of underutilized biomass in the development of value‐added composites. Highlights Underutilized Aspen fiber was combined with PLA to develop biocomposites. Successfully 3D‐printed biocomposites with up to 30 wt% wood content The optimal printing temperature for FFF was found to be 220°C NaOH, silane, and maleic anhydride (MA) fiber treatments were applied. MA treatment was the most effective treatment for Aspen‐PLA biocomposite.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".