Aerobic biodegradation of 3D printed biocomposites containing polylactic acid and industrial residual forest biomass
Bibliographic record
Abstract
This study investigates the biodegradation of 3D printed biocomposites under aerobic composting conditions. Biodegradable containers were prepared using forest biomass, wood ash (WA), wood sawdust (WS), and cellulose fiber (CF), as fillers and polylactic acid (PLA) as matrix and were processed via fused filament fabrication (FFF). Biodegradability tests were conducted in a laboratory-scale installation using the compost burial method for three months. Weight loss measurements were measured every 7 days throughout testing. The physicochemical and morphological properties of the samples were characterized. Of the biocomposites, PLA with 20 wt% wood sawdust showed the highest water absorption. The kinetic mechanisms followed typical Fickian diffusion behavior. The crystallinity improved with the addition of 20 wt% cellulose fibers. PLA degrades in a two-step process. Initially, temperature and moisture break down the PLA chains into lactic acid monomers. Subsequently, microorganisms in the compost convert these compounds into carbon dioxide, water, and biomass. A 97 % PLA weight loss was achieved after 3 months, with added fillers decreasing the biodegradability rate. Cracks on the surface and color changes were noted. Microorganisms were observed to settle in the spaces between the layers created by 3D printing. Fourier transform infrared spectra, scanning electron microscope micrographs, and synchrotron X-ray microtomographs revealed a microbial biofilm layer on the sample surfaces. After biodegradation, biocomposites can serve as soil fertilizer. Therefore, 3D printed biodegradable containers offer eco-friendly solutions that help minimize agricultural plastic waste accumulation and lower greenhouse gas emissions.
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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) | 0.000 | 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 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".