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Record W4393197585 · doi:10.3389/fchem.2024.1382547

Advances in the production of biosurfactants as green ingredients in home and personal care products

2024· article· en· W4393197585 on OpenAlexafffund
Makary Nasser, Malvika Sharma, Guneet Kaur

Bibliographic record

VenueFrontiers in Chemistry · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPersonal careBusinessCommercializationProduction (economics)Biochemical engineeringMarketingEngineeringEconomics

Abstract

fetched live from OpenAlex

Home and personal care industry is currently witnessing a growing demand for sustainable and eco-friendly alternatives to synthetic surfactants. This increase is fueled by concerns over the delayed degradation and environmental impact of the latter. To this, biosurfactants possess important properties such as biodegradability, low toxicity, and renewable sourcing. These qualities position them as compelling replacements of traditional synthetic surfactants. Their diverse attributes including emulsification, antimicrobial efficacy, surface tension reduction, and foaming capability, make them well-suited choices for home and personal care products. Biosurfactants can be produced through several inexpensive and renewable sources which contributes to their commercialization potential. This article discusses various microbial derived biosurfactants including rhamnolipids, sophorolipids, mannosyl-erythritol lipids, trehalolipids and lipopeptides, unraveling and comparing their distinctive roles and advantages in the home and personal care industry. It also focuses on the recent patent innovations in the production of biosurfactants which have aimed at improving their economic viability and performance attributes. Finally, the article sheds light on the challenges and future trajectories for better integration of these sustainable biosurfactants into mainstream consumer 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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.004
GPT teacher head0.208
Teacher spread0.204 · 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 designObservational
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

Citations25
Published2024
Admission routes2
Has abstractyes

Explore more

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