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Record W1500627386 · doi:10.5539/mas.v9n7p74

The Pyrolysis of Glycerol Using Microwave for the Production of Hydrogen

2015· article· en· W1500627386 on OpenAlexvenueno aff
Lailatul Qadariyah, Mahfud Mahfud, Pantjawarni Prihatini, Sofyan Hadi, Yuni Kurniati

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsnot available
Fundersnot available
KeywordsCatalysisPyrolysisMethanolHydrogenGlycerolMicrowaveActivated carbonCarbon fibersMaterials scienceHydrogen productionChemical engineeringNuclear chemistryChemistryInorganic chemistryOrganic chemistryComposite materialAdsorption

Abstract

fetched live from OpenAlex

The purpose of this research was to study pyrolysis of glycerol to produce hydrogen using microwave. The useof microwave aimed to produce high temperatures, because pyrolysis require high temperature.The effect of kindof catalyst and microwave power were studied. The catalyst was activated carbon and Ni/HZSM-5.The catalystof activated carbon was ready to use, whereas Ni/HZSM-5 catalyst was obtained by ion exchange fromNa-ZSM-5 with NH4Cl and then HZSM-5 was impregnated with metal solution of Ni (NO3)2.6 H2O.Experiments were conducted by mixing catalyst in the reactor together with glycerol solution of 10% (weightpercent) as much as 100 ml. Reactor was made from pyrex and mounted on microwave equipped with athermocouple. And then, reactor was heated on power of 400-700 Watt during thirty minutes. The reactionproduced gases and liquid to be analyzed by chromatography gas.The conclusion stated that microwave couldpyrolysis glycerol into hydrogen. By product of this reaction were methanol, allyl alcohol, acrolein andunidentified products. The difference of catalyst produced different product as well. The pyrolysis of glycerolusing activated carbon produced conversion of 60 %, while using catalysts Ni/HZSM-5 obtained conversion of87 %. The reaction produced hydrogen gases was relatively small for both of catalysts that is minimum of 0,59%and maximum of 0,88%.

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 score0.278
Threshold uncertainty score0.183

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 head0.037
GPT teacher head0.266
Teacher spread0.229 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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