MétaCan
Menu
Back to cohort

Self-nitrogen-doped biochar derived from soybean cake for rhodamine B removal prepared via simple carbonization

2023· article· en· W4386823960 on OpenAlexaff
Xinyu Zhang, Chao Yang, Tingwei Zhang, Jiaqi Guo, Yusheng Gan, Haibiao Wu, Mohammad Rizwan Khan, Huining Xiao, Junlong Song

Bibliographic record

VenueBioResources · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsUniversity of New Brunswick
FundersPriority Academic Program Development of Jiangsu Higher Education InstitutionsGovernment of Jiangsu ProvinceKing Saud UniversityNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsBiocharCarbonizationRhodamine BAdsorptionMaterials scienceSpecific surface areaEndothermic processLigninNitrogenCelluloseChemical engineeringNuclear chemistryOrganic chemistryChemistryPyrolysisCatalysis

Abstract

fetched live from OpenAlex

Nitrogen-doped biochar performs much better in dye adsorption due to its rich functional groups. Soybean cake, a by-product of soybean oil production, comprises rich contents of cellulose, lignin, and protein. Therein, simple direct carbonization was utilized to prepare self-nitrogen-doped biochar (SCB). The results showed that the N content of SCB was 6.81 wt%, and its specific surface area was 18.8 m2/g. X-ray photoelectron spectroscopic results confirmed that the surface of SCB was rich in pyridine-N, pyrrole-N, graphite-N, and oxidized-N functional groups. The adsorption capacity of SCB for Rhodamine B was 17.2 mg/g, which is higher compared with other unactivated biochars. The results of thermodynamic parameters indicate that Rhodamine B adsorption on SCB is an endothermic, entropy-increasing, and spontaneous process.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.014
GPT teacher head0.231
Teacher spread0.217 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueBioResourcesSame topicAdsorption and biosorption for pollutant removalFrench-language works237,207