From raw microalgae to bioplastics: conversion of <i>Chlorella vulgaris</i> starch granules into thermoplastic starch
Bibliographic record
Abstract
Abstract Microalgae are emerging as a promising feedstock for bioplastics, with Chlorella vulgaris yielding significant amounts of starch. This polysaccharide is convertible into thermoplastic starch (TPS), a biodegradable plastic of industrial relevance. In this study, we developed a pilot-scale protocol for extracting and purifying starch from starch-enriched Chlorella vulgaris biomass. From 430.3 ± 0.5 g (dry weight - DW) of microalgae biomass containing 42.2 ± 3.4 % of starch, we successfully extracted 205.8 ± 1.2 g DW of purified starch extract containing 86.9 ± 3.0 % of starch, resulting in a final recovery yield of 98.5%. We have characterized this extracted starch and processed it into TPS using twin-screw extrusion and injection molding. Microalgal starch showed similar properties to those of native plant starch, but with smaller granules. We compared the mechanical properties of microalgal TPS with two controls, namely a commercial TPS and a TPS prepared from commercial potato starch granules. TPS prepared from microalgal starch showed a softer and more ductile behavior compared to the reference materials. This study demonstrates the feasibility of recovering high-purity microalgal starch on a pilot scale with high yields, and highlights the potential of microalgal starch for the production of TPS using industrially relevant processes. Graphical abstract
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".