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Record W4403850847 · doi:10.18280/acsm.480504

Biodiesel Synthesis from Calophyllum Inophyllum Utilizing Modified Zeolite and Bentonite Catalysts

2024· article· en· W4403850847 on OpenAlexvenueno aff
Herman Hindarso, Aning Ayucitra, Dian Retno Sari Dewi, Nyoman Puspa Asri

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2024
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsnot available
FundersDirektorat Riset dan Pengabdian MasyarakatDirektorat Jenderal Pendidikan Tinggi
KeywordsBentoniteCatalysisZeoliteBiodieselWaste managementMaterials scienceOrganic chemistryChemical engineeringChemistryEngineering

Abstract

fetched live from OpenAlex

Biodiesel serves as a renewable, eco-friendly alternative fuel.Its production relies on converting triglyceride sources into methyl esters through transesterification.These triglycerides can be sourced from vegetable oils and animal fats.Previous studies often relied on acids and bases as catalysts.However, there's a lack of research exploring the use of modified zeolite and bentonite as catalysts, which hold the promise of higher yields compared to previous studies.In this study, biodiesel was derived from Calophyllum inophyllum via esterification and transesterification processes.Characterization of these catalysts was done using XRD and FTIR analyses.Transesterification to produce biodiesel occurred at temperatures ranging from 50 to 70 over a reaction period of 5 hours.The largest yield of biodiesel was obtained at 60, with 88.19% using modified zeolite catalyst, 78.84% using modified bentonite catalyst, and 82.36% using a blend of modified zeolite and bentonite catalysts in a 1:1 ratio.Biodiesel resulting from the highest yield using modified zeolite catalyst underwent characteristic testing in accordance with SNI standards.The findings revealed a density of 887 kg/m 3 , viscosity of 5.633 mm 2 /s, cetane number of 53, flash point at 121, and methyl ester content of 94.55%.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.030
GPT teacher head0.253
Teacher spread0.223 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAnnales de Chimie Science des MatériauxSame topicBiodiesel Production and ApplicationsFrench-language works237,207