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Characterization and solubility effects of the distribution of carboxymethyl substituents along the carboxymethyl cellulose molecular chain

2019· article· en· W2979487155 on OpenAlexfundno aff
Pinyu Yu, Yufeng Hou, Hongjie Zhang, Wenhui Zhang, Shuo Yang, Yonghao Ni

Bibliographic record

VenueBioResources · 2019
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaCanada Research Chairs
KeywordsCarboxymethyl celluloseHydrolysisSolubilitySwellingEnzymatic hydrolysisChemistryCellulaseCelluloseDissolutionPolymer chemistryMaterials scienceNuclear chemistrySodiumOrganic chemistry

Abstract

fetched live from OpenAlex

Sodium carboxymethyl cellulose (CMC) is a major cellulosic derivative that has a wide variety of applications. The solubility of CMC is affected by the degree of substitution (DS) of carboxymethyl groups and their distribution along the CMC molecular chain. In this study, an enzymatic hydrolysis technique was used to determine the distribution of carboxymethyl substituents. Two key parameters, namely the average length of the molecular chain segments not susceptible to enzymatic hydrolysis by cellulases (L ̅Sn, including chains fully substituted or containing single unsubstituted unit) and the average length for the molecular chain segments susceptible to enzymatic hydrolysis (L ̅Gn, unsubstituted chains segments), were obtained. This approach was subsequently applied to characterize four CMC samples having similar DS values (~0.80). The distributions of carboxymethyl substituents along the CMC molecular chain were found to be drastically different. The L ̅Sn varied from 9.6 to 49.5, while the L ̅Gn was almost constant (from 4.5 to 5.4). This information was correlated to the CMC solubility. The swelling ratio and the dynamic contact angle revealed that the CMC samples with higher L ̅Sn exhibited stronger swelling and wettability than those with lower L ̅Sn. The dissolving time of the CMC molecule decreased substantially with the increase in L ̅Sn.

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 score0.127
Threshold uncertainty score0.336

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 studies0.0000.001
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 head0.225
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

Citations15
Published2019
Admission routes1
Has abstractyes

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