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Record W3040747189 · doi:10.1038/s41598-020-67909-3

Water purification ultrafiltration membranes using nanofibers from unbleached and bleached rice straw

2020· article· en· W3040747189 on OpenAlexaff
Mohammad L. Hassan, Shaimaa M. Fadel, Ragab Abouzeid, Wafaa S. Abou-Elseoud, Enas A. Hassan, Linn Berglund, Kristiina Oksman

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Toronto
FundersVetenskapsrådet
KeywordsCelluloseNanofiberMembranePulp (tooth)Chemical engineeringUltrafiltration (renal)StrawMaterials sciencePapermakingNanofiltrationPulp and paper industryUltrapure waterRegenerated celluloseLigninFiltration (mathematics)EffluentChemistryComposite materialChromatographyWaste managementOrganic chemistryNanotechnology

Abstract

fetched live from OpenAlex

There has been an increasing interest in recent years in isolating cellulose nanofibers from unbleached cellulose pulps for economic, environmental, and functional reasons. In the current work, cellulose nanofibers isolated from high-lignin unbleached neutral sulfite pulp were compared to those isolated from bleached rice straw pulp in making thin-film ultrafiltration membranes by vacuum filtration on hardened filter paper. The prepared membranes were characterized in terms of their microscopic structure, hydrophilicity, pure water flux, protein fouling, and ability to remove lime nanoparticles and purify papermaking wastewater effluent. Using cellulose nanofibers isolated from unbleached pulp facilitated the formation of a thin-film membrane (with a shorter filtration time for thin-film formation) and resulted in higher water flux than that obtained using nanofibers isolated from bleached fibers, without sacrificing its ability to remove the different pollutants.

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 score0.002
Threshold uncertainty score0.551

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 studies0.0010.000
Scholarly communication0.0010.001
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 head0.045
GPT teacher head0.283
Teacher spread0.239 · 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

Citations59
Published2020
Admission routes1
Has abstractyes

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