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Record W74469902 · doi:10.1021/bk-2007-0954.ch029

Production of Activated Carbon from Biochar Using Chemical and Physical Activation: Mechanism and Modeling

2007· book-chapter· en· W74469902 on OpenAlex
Ajay K. Dalai, Ramin Azargohar

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACS symposium series · 2007
Typebook-chapter
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBiocharPotassium hydroxideActivated carbonPyrolysisBET theoryYield (engineering)Thermogravimetric analysisSpecific surface areaChemical engineeringBiomass (ecology)Carbon fibersChemistryMaterials scienceCatalysisOrganic chemistryAdsorptionComposite materialAgronomy

Abstract

fetched live from OpenAlex

Biochar, a solid product of fast pyrolysis of biomass, was converted to activated carbon by physical (steam) and chemical (potassium hydroxide) activation. The effects of operating conditions on the BET surface area and the reaction yield of physically and chemically activated carbons were investigated. Two models for BET surface area and reaction yield of each activated carbon were developed. Using these models, the optimum operating conditions for production of activated carbons with large surface area and high yield were determined. The BET surface area and yield of products predicted by models and from experiments at optimum operating conditions showed good agreement. The effects of activating agent on the chemical structure of biochar, duringchemical activation, were investigated by thermogravimetric method and infrared spectroscopy.

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.

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 score0.010
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)0.0000.000

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 head0.018
GPT teacher head0.210
Teacher spread0.192 · 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