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Record W2994162648 · doi:10.1039/c9ra07836c

A facile fabrication of sepiolite mineral nanofibers with excellent adsorption performance for Cd<sup>2+</sup> ions

2019· article· en· W2994162648 on OpenAlexaff
Fei Wang, Ming Hao, Jinsheng Liang, Peizhang Gao, Maomao Zhu, Baizeng Fang, Hui Zhang, Zengyao Shang

Bibliographic record

VenueRSC Advances · 2019
Typearticle
Languageen
FieldMaterials Science
TopicClay minerals and soil interactions
Canadian institutionsVancouver Biotech (Canada)University of British Columbia
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsSepioliteAdsorptionFabricationIonNanofiberMineralChemical engineeringMaterials scienceNanotechnologyChemistryMetallurgyPhysical chemistryOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

under the microwave power of 400 W, while the zeta potential reaches a maximum when the pH is 5.0. The adsorption efficiency of sepiolite mineral nanofibers for cadmium ions can reach 68.6% as the optimal value. The as-fabricated sepiolite nanofibers can be regarded as a low-cost and environmentally friendly material which is a promising candidate for heavy metal ion removal from industrial wastewater.

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 score0.379
Threshold uncertainty score0.410

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 communication0.0000.001
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 head0.013
GPT teacher head0.258
Teacher spread0.244 · 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

Citations20
Published2019
Admission routes1
Has abstractyes

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