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Record W1970494541 · doi:10.1080/02773813.2011.607535

Surface Characterization and Classification of Slow and Fast Pyrolyzed Biochar Using Novel Methods of Pycnometry and Hyperspectral Imaging

2012· article· en· W1970494541 on OpenAlexaff
B. Dutta, Vijaya Raghavan, Michael Ngadi

Bibliographic record

VenueJournal of Wood Chemistry and Technology · 2012
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsMcGill UniversitySte. Anne's Hospital
Fundersnot available
KeywordsBiocharPyrolysisPorosityHyperspectral imagingCharChemistryWavelengthReflectivityDiffuse reflectionDiffuse reflectance infrared fourier transformMineralogyMaterials scienceRemote sensingOpticsOrganic chemistryGeologyPhotocatalysis

Abstract

fetched live from OpenAlex

Abstract The birchwood biochar produced by slow as well as fast pyrolysis was analyzed and compared according to physical characteristics of porosity and reflectance. A relation between char porosity and the reflectance of the biochar structure was found wherein porosity was found to be inversely proportional to reflectance. The wavelengths providing images with maximum clarity were established for classification through hyperspectral imaging technique. The wavelengths that were found to be optimum for both slow and fast pyrolysis biochars were 947 nm and 1685 nm, which fall in the near-IR and short-IR wavelength ranges, respectively. The results of the hyperspectral imaging support the findings of the porosity evaluations from pycnometric analysis, which showed that the biochar sample treated at 350°C for slow pyrolysis and 400°C for fast pyrolysis for a holding time of 20 min had the highest porosity and in turn showed the lowest reflectance mean values. The theory that reflectance of biochar samples decrease with increasing porosity of the char structure was substantiated through this qualitative study.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.323

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 head0.012
GPT teacher head0.248
Teacher spread0.236 · 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

Citations21
Published2012
Admission routes1
Has abstractyes

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