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Record W2890210456 · doi:10.1039/c8ra05939j

Decontamination of radionuclides by functionalized mesoporous silica under gamma irradiation

2018· article· en· W2890210456 on OpenAlexaff
Sajid Iqbal, Jong-Il Yun

Bibliographic record

VenueRSC Advances · 2018
Typearticle
Languageen
FieldChemistry
TopicZeolite Catalysis and Synthesis
Canadian institutionsKootenay Association for Science & Technology
FundersNational Research Foundation of KoreaMinistry of Science and ICT, South KoreaNuclear Safety and Security CommissionNational Research Foundation
KeywordsHuman decontaminationRadionuclideRadiochemistryMesoporous materialMesoporous silicaIrradiationGamma irradiationNuclear chemistryChemistryEnvironmental scienceWaste managementCatalysisOrganic chemistryEngineeringPhysics

Abstract

fetched live from OpenAlex

Schiff base functionalized mesoporous silica (SA-SBA-15) was synthesized by the co-condensation method to remove the radioactive corrosion products from contaminated water coming from nuclear installations. SA-SBA-15 nanoparticles were first irradiated in a solid powder form and then applied to remove Cu(ii), Ni(ii), and Co(ii) from their aqueous mixture in the range from 0 to 1000 Gy of gamma irradiation. The FTIR, TGA, zeta potential, XRD, BET, TEM, and CHN analysis results revealed the stability of a ligand support material, mesopore ordering and the functional groups. The structural and functional group endurance under our studied gamma irradiation makes SA-SBA-15 a potential adsorbent for routine decontamination and decommissioning activities as well as in radioactively contaminated emergency scenarios. Furthermore, the adsorbent-metal complexation followed the Irving-Williams order (Cu(ii) > Ni(ii) > Co(ii)) and showed a higher selectivity for Cu(ii) with removal capacity up to 99.76 ± 0.01%, thus suggesting its applicability to separate Cu(ii) from other metal ions.

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 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 score0.333
Threshold uncertainty score0.997

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)0.0030.000

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 head0.010
GPT teacher head0.249
Teacher spread0.240 · 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.

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

Citations13
Published2018
Admission routes1
Has abstractyes

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