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Record W2977553203 · doi:10.29173/aar50

Production of Biochar from Biomass

2019· article· en· W2977553203 on OpenAlexaffvenue
Iqmat Iyiola, Deepak Pudasainee, Monir Khan, Rajender Gupta

Bibliographic record

VenueAlberta Academic Review · 2019
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBiocharSlash-and-charBiomass (ecology)PyrolysisCharcoalCarbon sequestrationEnvironmental scienceNitrogenRaw materialThermogravimetric analysisCombustionCarbon fibersPulp and paper industryEnvironmental chemistryChemistryAgronomyMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Biochar is a kind of charcoal that’s produced from biomass using pyrolysis technology. As climate change continues to be a growing concern, biochar has been sought for its environmental applications. It is both cost effective and environmentally sound in terms of being a soil additive and renewable fuel. When used as a soil amendment, biochar has been shown to improve water holding capacity and absorb more nutrients. Biochar also sequesters carbon dioxide when applied to soil, and can also be used as a replacement for activated carbon that is prepared from coal. The objectives of this research was to produce biochar from biomass and study biochar’s properties. A thermogravimetric analysis (TGA) was used to measure the weight loss behavior of the wood sample (biomass) as the temperature increased. The biomass was heated to 500 °C in a nitrogen atmosphere and then cooled in nitrogen to prevent combustion of biochar. The overall yield of biochar was 15%. Elemental analysis of biomass shows the composition of the sample to be mostly carbon and oxygen with fewer amounts of hydrogen and nitrogen. Surface area of the prepared biochar was 305 m2/g, which is approximately 100 times the surface area of raw biomass. Biochar’s higher porosity will allow for greater absorption of nutrients when applied to soil.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.135
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.011
GPT teacher head0.233
Teacher spread0.222 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2019
Admission routes2
Has abstractyes

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