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Record W2735765166 · doi:10.1002/adv.21856

Application of tree biochar in PDMS pervaporation membranes

2017· article· en· W2735765166 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in Polymer Technology · 2017
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsUniversity of Toronto
FundersMinistry of Education of the People's Republic of China
KeywordsMaterials sciencePervaporationBiocharMembranePolymer scienceTree (set theory)Chemical engineeringComposite materialPyrolysisEngineering

Abstract

fetched live from OpenAlex

Abstract In this study, three kinds of biomass materials (lodgepole pine bark, larch wood, larch bark) were used to prepare biochar (BB), and the prepared products as fillers were mixed with polydimethylsiloxane (PDMS) for preparing composite membranes which were used for separating ethanol from water by pervaporation (PV). In accordance with the experiment results, the lodgepole pine bark BB was the best filler for the selective membrane to ethanol. The silane coupling agents NH 2 (CH 2 ) 3 Si(OC 2 H 5 ) 3 (KH‐550) and CH 2 =CH‐Si(OCH 3 ) 3 (YDH‐171) were applied in the modification of lodgepole pine bark BB. And the separation performances of the BB/PDMS composite membranes were researched in detail. The results showed that the permeability (flux and separation factor) of composite membranes has been significantly improved with the addition of modified BB. YDH‐171 was more effective than KH‐550. The optimum PV performances (the separation factor 11.3 and the corresponding flux 227.25 g m −2 h −1 ) were obtained by adding 3 wt. % modified BB for a 10 wt. % and 40°C ethanol solution. This study indicated the potential application of BB nanoparticles in preparing pervaporation separation membrane.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.389

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 head0.006
GPT teacher head0.256
Teacher spread0.250 · 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