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Record W3030881060 · doi:10.1515/ijcre-2019-0202

Bisphenol S adsorption with activated carbon prepared from corncob: optimization using response surface methodology

2020· article· en· W3030881060 on OpenAlexaff
Xiangyang Zhang, Xiuli Han, Chun Chang, Pan Li, Hongwei Li, Chunbao Xu

Bibliographic record

VenueInternational Journal of Chemical Reactor Engineering · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsWestern University
FundersScience and Technology Department of Henan Province
KeywordsAdsorptionActivated carbonAqueous solutionCorncobLangmuir adsorption modelFourier transform infrared spectroscopyMonolayerChemistryExothermic reactionChemical engineeringResponse surface methodologyBisphenol ALangmuirNuclear chemistryMaterials scienceChromatographyOrganic chemistryRaw material

Abstract

fetched live from OpenAlex

Abstract Activated carbon derived from raw corncob (CCAC), which prepared with steam as the activating agent, was used to adsorb bisphenol S (BPS) from aqueous solution. Characterizations of CCAC were measured by using the Brunauer-Emmett-Teller, scanning electron microscopy, and Fourier transform infrared spectroscopy. Adsorption conditions including initial BPS concentration, contact time, adsorbent dosage and pH were optimized by response surface methodology (RSM). The results show that adsorption equilibrium was well described by the Langmuir and Koble–Corrigan models. The maximum monolayer adsorption capacity of BPS was found to be 617.29 mg g −1 at 298 K. Based on the thermodynamic parameters analysis, the BPS adsorption process was turned out to be spontaneous and exothermic. The adsorption process of BPS was well described by the pseudo-second-order kinetic model. It also found that H-bonding, π–π interaction, and electrostatic interaction were the main mechanisms in the process of BPS adsorption onto the CCAC.

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 score0.093
Threshold uncertainty score0.502

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 head0.031
GPT teacher head0.253
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; 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

Citations2
Published2020
Admission routes1
Has abstractyes

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Same venueInternational Journal of Chemical Reactor EngineeringSame topicAdsorption and biosorption for pollutant removalFrench-language works237,207