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Record W1569948253 · doi:10.5539/mas.v9n7p69

The Effect of Decomposition Time on Cellulose Degradation in Ionic Liquid/Acid with Pressurized CO2

2015· article· en· W1569948253 on OpenAlexvenueno aff
Sumarno Sumarno, Yeni Rahmawati, P. N. T Risanti, Novi Eka Mayangsari

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsnot available
FundersDirektorat Jenderal Pendidikan Tinggi
KeywordsCelluloseIonic liquidDecompositionOxalic acidSolventHydrolysisChemistryMaterials scienceDegradation (telecommunications)Chemical engineeringNuclear chemistryChromatographyCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Cellulose material is the most abundant carbohydrate that has a simple polymer structure, but it forms ofcrystalline micro-fibrils lead it insoluble in various solvent and highly resistant for hydrolysis process. Thedegradation of cellulose into glucose will increase the raw material for production of ethanol, isopropanol orbutanol. The conversion into oligomer can be applied for pharmaceutical, food additives, etc. There are manytechnologies for conversion of cellulose such as degradation with ionic liquids, acid, enzymatic/fermentation,and hydrothermal. In this work, we studied cellulose decomposition by hydrothermal process, and a combinationwith ionic liquids. We used NaCl as a simple ionic liquid, oxalic acid as a catalyst, and CO2 as pressurizing gasin order to enhance the degradability of cellulose in water. Cellulose and NaCl/oxalic acid solution (20 gr L-1)was conducted under 70 bar of subcritical CO2 in 125ºC and various decomposition times (1 to 5 h). Afterdecomposition time was achieved, the sample was separated between liquid and solid. For liquid product wereanalyzed by Dinitrosalicylic acid method (DNS method) using spectrophotometry UV-Vis and LiquidChromatography – Mass Spectrometry (LC-MS). And solid products were analyzed by using X-Ray Diffraction(XRD) and Scanning Electron Microscopy (SEM). The result shows that the glucose concentrations was increasewith an increasing decomposition time and reach a maximum at 4 hour. SEM and XRD showed the changes inthe morphology and the structure of cellulose.

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 score0.339
Threshold uncertainty score0.270

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 studies0.0000.000
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 head0.005
GPT teacher head0.203
Teacher spread0.198 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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