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Record W2791390985 · doi:10.1002/cjce.23153

Characteristics of CO<sub>2</sub> adsorption on biochar derived from biomass pyrolysis in molten salt

2018· article· en· W2791390985 on OpenAlexaffvenue
Tianxiang Guo, Nan Ma, Yuanfeng Pan, Alemayehu H. Bedane, Huining Xiao, Mladen Eić, Yarong Du

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of New Brunswick
FundersBeijing Municipal Natural Science FoundationChina Scholarship Council
KeywordsBiocharAdsorptionMicroporous materialPyrolysisSelectivityFourier transform infrared spectroscopyChemistryChemical engineeringMolten saltAmbient pressureInorganic chemistryCatalysisMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract This work focused on the preparation and characterization of a promising biochar as a novel solid adsorbent towards CO 2. The biochar was prepared by catalytic pyrolysis of waste roasted peanut shell in molten salt; it was characterized by means of SEM‐EDS, BET, FTIR, and TGA, followed by determining the adsorption characteristics, such as adsorption capacity, isosteric heat of adsorption, uptake rate, and selectivity via adsorption temperature and gas pressure. The results indicated that the as‐prepared biochar had a rich microporous structure with a peak pore size in the range of 0.69–1.3 nm, and exhibited a good performance of CO 2 adsorption with a capacity of 3.8 mmol/g at 273 K and 100 kPa. Moreover, the adsorption selectivity of CO 2 over N 2 , O 2 , CO, and CH 4 was found to be above 12, 11, 8, and 7, respectively. In addition, an interesting phenomenon of an initial increase and then a decrease in the selectivity of CO 2 /N 2 adsorption with increasing gas pressure was experimentally revealed.

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 score0.007
Threshold uncertainty score0.630

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 head0.007
GPT teacher head0.179
Teacher spread0.172 · 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

Citations57
Published2018
Admission routes2
Has abstractyes

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