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Record W4372352991 · doi:10.18280/ijdne.180218

Production and Characterization of Bio-Briquettes from the Cassava Stems and Bamboo Charcoal Bonded with Organic Adhesive

2023· article· en· W4372352991 on OpenAlexvenueno aff
Sandi Asmara, Fijriani Juli Kartika Purba, Intan Fajar Suri, Wahyu Hidayat

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBamboo properties and applications
Canadian institutionsnot available
FundersUniversitas Lampung
KeywordsBriquetteBamboo charcoalBambooAdhesiveCharcoalPulp and paper industryMaterials scienceComposite materialWaste managementEngineeringMetallurgy

Abstract

fetched live from OpenAlex

This study aimed to determine the effects of a materials combination used by the waste biomass of bamboo and cassava stem mixed with a tapioca adhesive on the quality of charcoal briquettes.The briquettes were made with a combination of the raw materials between the cassava stem and bamboo of 75%:25%, 50%:50%, and 25%:75%.Then, the used charcoal materials were mixed with three tapioca concentrations of 8%, 10%, and 12%.The characteristics of the charcoal briquettes, such as density, moisture content, shatter resistance index, calorific value, combustion rate, and compressive strength were observed.The charcoal briquettes with a high percentage of bamboo combination showed to increase the calorific value and the compressive strength but decreased the rate of combustion.In contrast, the low concentration of tapioca glue increased the density, compressive strength, and shatter resistance index but decreased the combustion rate.It was revealed that the used material combination and the adhesive content affected various properties of charcoal briquettes.Therefore, it can be suggested that the materials combination of bamboo and cassava stems waste can be utilized for making briquettes with a low percentage of tapioca adhesive.

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 score0.950
Threshold uncertainty score0.101

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 science0.0000.000
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 head0.015
GPT teacher head0.210
Teacher spread0.195 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

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