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Record W4389785324 · doi:10.26434/chemrxiv-2023-t7c5s

Biosourced Spherical Microbeads from Brewer’s Spent Grain for Sustainable Personal Hygiene Products

2023· preprint· en· W4389785324 on OpenAlexafffund
Amy McMackin, Vincent Banville, Sébastien Cardinal

Bibliographic record

VenueChemRxiv · 2023
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicBioeconomy and Sustainability Development
Canadian institutionsUniversité du Québec à Rimouski
FundersMitacsMinistère de l'Agriculture, des Pêcheries et de l'Alimentation
KeywordsHomogeneousRaw materialPetrochemicalPulp and paper industryMaterials sciencePersonal hygieneChemical engineeringChemistryOrganic chemistryMathematicsEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.892

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.232
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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