MétaCan
Menu
Back to cohort
Record W2969412338 · doi:10.3390/biom9080383

Preparation and Coagulation Performance of Carboxypropylated and Carboxypentylated Lignosulfonates for Dye Removal

2019· article· en· W2969412338 on OpenAlexafffund
Khatereh Bahrpaima, Pedram Fatehi

Bibliographic record

VenueBiomolecules · 2019
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsLignosulfonatesChemistryCarboxylateFourier transform infrared spectroscopySN2 reactionProton NMRTitrationNuclear chemistryCarbon-13 NMRAnalytical Chemistry (journal)ChromatographyOrganic chemistryLigninChemical engineering

Abstract

fetched live from OpenAlex

In this work, 1-carboxypropyled (1-CPRLS) and 5-carboxypentyled lignosulfonates (5-CPELS) were synthesized using 2-chlorobutanoic acid and 6-chlorohexanoic acid as carboxylate group donors via SN1 and SN2 mechanisms, respectively. 1-Carboxypropyl and 5-carboxypentyl lignosulfonates with the charge densities of −3.45 and −2.94 meq g−1 and molecular weights of 87,900 and 42,400 g·mol−1 were produced, respectively, under mild conditions. The carboxylate content and degree of substitution (DS) of the 1-CPRLS product were 2.37 mmol·g−1 and 0.70 mol·mol−1, while those of 5-CPELS products were 2.13 mmol·g−1 and 0.66 mol·mol−1, respectively. The grafting of carboxypropyl and carboxypentyl groups to lignosulfonate was confirmed by Fourier transform infrared (FT-IR) and nuclear magnetic resonance (1H-NMR and 13C-NMR) spectroscopies. In addition, 1-CPRLS and 5-CPELS were applied as coagulants for removing ethyl violet (EV) dye from a simulated solution, and their performance was related to their charge densities and molecular weights. Furthermore, fundamental discussion is provided on the advantages of (1) producing 1-CPRLS and (2) the superior properties and performance of 1-CPRLS to carboxyethylated lignosulfonate.

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 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 score0.079
Threshold uncertainty score0.306

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 head0.004
GPT teacher head0.207
Teacher spread0.202 · 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

Citations18
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueBiomoleculesSame topicLignin and Wood ChemistryFrench-language works237,207