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Record W2740369022 · doi:10.3311/ppch.10689

Novel Solid Base Catalyst Derived from Drinking Water Defluoridation for Biodiesel Synthesis

2017· article· en· W2740369022 on OpenAlexaff
Jharna Gupta, Madhu Agarwal, Ajay K. Dalai

Bibliographic record

VenuePeriodica Polytechnica Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsUniversity of Saskatchewan
FundersMalaviya National Institute of Technology, Jaipur
KeywordsCatalysisThermogravimetric analysisBiodieselBiodiesel productionCalcinationTransesterificationMethanolFourier transform infrared spectroscopyNuclear chemistryTitrationChemistryBase (topology)Yield (engineering)Materials scienceChemical engineeringInorganic chemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

In this study, a novel heterogeneous catalyst was synthesized from drinking water treatment sludge obtained during defluoridation in biodiesel production by transesterification. More specifically, the sludge was converted into an effective catalyst by calcination at 950 ºC for 3 h. The catalyst was characterized using X-ray diffraction, Fourier transform infrared spectroscopy, Thermogravimetric analysis, Scanning electron microscopy, Hammett titration method, and ion exchange method. The catalyst had a basicity of 12.57 mmol/g and a basic strength of 9.8 < H <17.2. It showed good catalytic activity in biodiesel synthesis. The maximum biodiesel yield obtained was 89% for the following reaction conditions: catalyst loading of 4 wt%, a reaction temperature of 65 ºC, the methanol-to-oil molar ratio of 12:1, and reaction time of 3 h. Thus, it was found that harmful waste can be used as an effective solid base heterogeneous catalyst.

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 categoriesMeta-epidemiology (narrow)
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 score0.411
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.017
GPT teacher head0.229
Teacher spread0.212 · 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.

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

Citations9
Published2017
Admission routes1
Has abstractyes

Explore more

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