Biosourced Spherical Microbeads from Brewer’s Spent Grain for Sustainable Personal Hygiene Products
Bibliographic record
Abstract
Many countries have recently banned the production and importation of petrochemical plastic mi- crobeads for use as exfoliating agents in personal care products. Plastic particles in products of this nature are too small to be retrieved during wastewater treatment and they accumulate in the environment, negatively impacting living organisms and ecosystems. Sustainable alternatives that offer comparable mechanical properties to synthetic plastic microbeads could be developed using biowaste material. Brewer’s spent grain (BSG), the primary residue of the brewery industry, is shown herein to be a promising starting material in the development of biodegradable, non-toxic microbeads. After dilute acid hydrolysis, pretreated lignocellulosic pulp from BSG is solubilized using an aqueous system of NaOH and ZnO. Solid microbeads may then be formed by dropping the resulting solution into an acid bath, filtering, and drying. The conditions of each step required opti- mization to successfully produce spherical microbeads with a mean diameter as small as 1.25 mm, a homogeneous size distribution, and an average hardness of 199 MPa. The beads also demonstrated superior cleansing abilities to commercially available natural exfoliating particles. BSG microbeads are therefore a promising option for use as a physical exfoliating agent in various personal hygiene products.
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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) | 0.002 | 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".