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Record W4229071598 · doi:10.3390/applbiosci1010001

Cellulose through the Lens of Microfluidics: A Review

2022· review· en· W4229071598 on OpenAlexaff
Aref Abbasi Moud

Bibliographic record

VenueApplied Biosciences · 2022
Typereview
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMicrofluidicsCelluloseNanotechnologyBiopolymerMaterials scienceLens (geology)PolymerEngineeringChemical engineeringComposite material

Abstract

fetched live from OpenAlex

Cellulose, a linear polysaccharide, is the most common and renewable biopolymer in nature. Because this natural polymer cannot be melted (heated) or dissolved (in typical organic solvents), making complicated structures from it necessitates specialized material processing design. In this review, we looked at the literature to see how cellulose in various shapes and forms has been utilized in conjunction with microfluidic chips, whether as a component of the chips, being processed by a chip, or providing characterization via chips. We utilized more than approximately 250 sources to compile this publication, and we sought to portray cellulose manufacturing utilizing a microfluidic system. The findings reveal that a variety of products, including elongated fibres, microcapsules, core–shell structures and particles, and 3D or 2D structured microfluidics-based devices, may be easily built utilizing the coupled topics of microfluidics and cellulose. This review is intended to provide a concise, visual, yet comprehensive depiction of current research on the topic of cellulose product design and understanding using microfluidics, including, but not limited to, paper-based microfluidics design and implications, and the emulsification/shape formation of cellulose inside the chips.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0030.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.112
GPT teacher head0.369
Teacher spread0.257 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations13
Published2022
Admission routes1
Has abstractyes

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