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Record W4220914960 · doi:10.3390/ijms23063391

Comparative Analysis of Bacterial Cellulose Membranes Synthesized by Chosen Komagataeibacter Strains and Their Application Potential

2022· article· en· W4220914960 on OpenAlexfundno aff
Monika Kaczmarek, Marzena Jędrzejczak‐Krzepkowska, Karolina Ludwicka

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsnot available
FundersPolitechnika LódzkaBeef Cattle Research Council
KeywordsCelluloseBacterial cellulosePolyvinyl alcoholChemistryCellulosic ethanolMembranePectinNanocompositeNanocelluloseChemical engineeringMaterials scienceStarchPulp and paper industryFood scienceOrganic chemistryComposite materialBiochemistry

Abstract

fetched live from OpenAlex

This article presents a comparative analysis of bacterial cellulose membranes synthesized by several strains of the Komagataeibacter genus in terms of their specific physical, physico-chemical, and mechanical properties. Herein, the aim was to choose the most suitable microorganisms producing cellulosic materials with the greatest potential for the fabrication of bio-inspired nanocomposites. The selection was based on three main steps, starting from the evaluation of BNC biosynthetic efficiency with and without the addition of ethanol, followed by the assessment of mechanical breaking strength, and the physical parameters (compactness, structural integrity, appearance, and thickness) of the obtained biological materials. Ultimately, based on the performed screening procedure, three efficiently growing strains (K. hansenii H3 (6Et), K. rhaeticus K4 (8Et), and Komagataeibacter sp. isolated from balsamic vinegar (12Et)) were chosen for further modifications, enabling additional cellulose functionalization. Here, supplementation of the growth medium with five representative polymeric compounds (citrus/apple pectin, wheat starch, polyvinyl alcohol, polyethylene glycol) led to significant changes in BNC properties, especially dye loading abilities, mechanical strength, and water adsorption/retention capacities. The resulting nanocomposites can be potentially useful in various fields of medicine and industry, and in the future, they may become a practical and cost-effective competitor against commercial biomaterials currently available on the market.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.302
Teacher spread0.286 · 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 designBench or experimental
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

Citations43
Published2022
Admission routes1
Has abstractyes

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