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Record W2320259907 · doi:10.1021/sc3001367

Green Strategy Guided by Raman Spectroscopy for the Synthesis of Ammonium Carboxylated Nanocrystalline Cellulose and the Recovery of Byproducts

2012· article· en· W2320259907 on OpenAlexaff
Edmond Lam, Alfred C. W. Leung, Yali Liu, Ehsan Majid, Sabahudin Hrapovic, Keith B. Male, John H. T. Luong

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2012
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMicrocrystalline celluloseCelluloseNanocrystalline materialNanomaterialsRaman spectroscopyChemistryCellulosic ethanolNanocelluloseAmmonium persulfateGreen chemistryPersulfateChemical engineeringSulfateAmmoniumMicrocrystallineMaterials scienceCatalysisOrganic chemistryNanotechnologyReaction mechanism

Abstract

fetched live from OpenAlex

A one-pot green procedure for the treatment of cellulosic biomass with ammonium persulfate (APS) was developed for the synthesis of highly crystalline carboxylated nanocrystalline cellulose (NCC–COOH), an emerging nanomaterial with a plethora of diversified applications. Raman spectroscopy proved applicable for monitoring the fate of APS and its two byproducts during the production of NCC–COOH from microcrystalline cellulose (MCC). The two main byproducts were then identified and quantified as ammonium sulfate (AS) and H 2 SO 4 with the latter accounted for 60% of the total sulfate ions in solution. On the basis of such findings, one-step neutralization of H 2 SO 4 and NCC with NH 4 OH immediately after reaction was implemented for the formation of additional AS, followed by its quantitative recovery by precipitation. This was a very effective and critical step in waste stream management and cost reduction for the large scale production of NCC. In addition, the process resulted in highly crystalline NCC with COO – NH 4 + groups, a nanomaterial with improved dispersion and thermal characteristics over NCC with COOH and COO – Na + groups.

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)0.001
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 score0.012
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.008
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.

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

Citations72
Published2012
Admission routes1
Has abstractyes

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