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Record W4312115446 · doi:10.1002/bbb.2458

Wheat straw‐derived bio‐based hydroponic polyurethane foams for plant growth

2022· article· en· W4312115446 on OpenAlexafffund
Hongwei Li, Ali Zohaib, Zhongshun Yuan, Yulin Hu, Chunbao Xu

Bibliographic record

VenueBiofuels Bioproducts and Biorefining · 2022
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of Prince Edward IslandWestern University
FundersAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsPolyurethaneStrawRenewable resourceAbsorption of waterPolyolPulp and paper industryMaterials scienceChemistryAgronomyRenewable energyComposite materialBiology

Abstract

fetched live from OpenAlex

Abstract Agricultural waste is a renewable and sustainable resource that can be used as an alternative to petroleum for the production of chemicals and materials. In this study, liquefied wheat straw was used as bio‐polyols to prepare bio‐based hydroponic polyurethane (BHPU) foams. The effects of foam preparation variables, such as surfactants, catalysts, blowing agents and types of petroleum‐based polyols (PPG 400 and/or VORANOL 280) on the properties of BHPU foam were investigated through single‐factor experiments. Response surface methodology was applied to optimize the foam formula. The prepared BHPU foams with 40–50% (w/w) bio‐polyols had an open cell content of 70–96%, a water absorption capacity of 594–1085% (w/w) and a water absorption time of 6–140 s. The potential of using the prepared BHPU foam as a plant growing medium was evaluated via mechanical testing, cell morphology, thermal stability, UV weathering testing and Phaseolus vulgaris ( Linn. ) cultivar Prelude and Glycine max ( Linn. ) Merr. seed germination tests. The wheat straw‐derived BHPU foams demonstrated potential as a plant growth medium in horticulture. © 2022 Society of Chemical Industry and John Wiley & Sons, Ltd.

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 score0.020
Threshold uncertainty score0.941

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.016
GPT teacher head0.236
Teacher spread0.219 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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