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Record W2290516521 · doi:10.1002/btpr.2251

<scp>PDADMAC</scp> as a flocculant for lignosulfonate of <scp>NSSC</scp> pulping process

2016· article· en· W2290516521 on OpenAlexafffund
Farshad Oveissi, Thomas Sitter, Pedram Fatehi

Bibliographic record

VenueBiotechnology Progress · 2016
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsChemistryFlocculationProcess (computing)Pulp and paper industryComputer scienceEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

The spent liquor (SL) of neutral sulfite semi-chemical (NSSC) pulping process contains about 8 wt% lignocelluloses that can be extracted and used in the production of value-added materials. In this work, a flocculation process followed by centrifugation was considered for isolating lignosulfonate and hemicelluloses from SL. It was observed that, by adding 20 mg/g of polydiallyldimethylammuniom chloride (PDADMAC) with 100,000-200,000 g/mol molecular weight to SL, 45% of lignosulfonate and 39% of hemicelluloses were removed at 30°C. The lignocellulose removal was more efficient for the dual flocculation system of low and high molecular weights PDADMAC than for individual PDADMAC systems. Overall, 49% of lignosulfonate, 47% of hemicelluloses and 97% of turbidity were removed from SL from the dual system when 10 mg/g low molecular weight PDADMAC and 10 mg/g high molecular weight PDADMAC were added to the SL at 30°C, subsequently. The thermogravimetric analysis (TGA) of generated flocs showed that all samples had similar thermal behaviour and 13-16 wt% of flocs remained as ash after burning at 700°C in nitrogen. As the flocs are made of lignocellulosic materials and they are thermally stable, they could be used as fillers in paper board production. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:686-691, 2016.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score1.000

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 science0.0010.000
Research integrity0.0010.000
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 head0.008
GPT teacher head0.234
Teacher spread0.226 · 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 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

Citations25
Published2016
Admission routes2
Has abstractyes

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