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Record W2888637744 · doi:10.1021/acsami.8b08496

Loading across the Periodic Table: Introducing 14 Different Metal Ions To Enhance Metal–Organic Framework Performance

2018· article· en· W2888637744 on OpenAlexafffund
Shoushun Chen, Bryan E. G. Lucier, Wilson Luo, Xinkai Xie, Kun Feng, Hendrick Chan, Victor V. Terskikh, Xuhui Sun, Tsun‐Kong Sham, Mark S. Workentin, Yining Huang

Bibliographic record

VenueACS Applied Materials & Interfaces · 2018
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsUniversity of OttawaWestern University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsMaterials scienceMetalIonMetal ions in aqueous solutionTable (database)Metal-organic frameworkPeriodic tableNanotechnologyInorganic chemistryMetallurgyPhysical chemistryOrganic chemistryComputer scienceData miningAdsorption

Abstract

fetched live from OpenAlex

Loading metal guests within metal–organic frameworks (MOFs) via secondary functional groups is a promising route for introducing or enhancing MOF performance in various applications. In this work, 14 metal ions (Li+, Na+, K+, Mg2+, Ca2+, Ba2+, Zn2+, Co2+, Mn2+, Ag+, Cd2+, La3+, In3+, and Pb2+) have been successfully introduced within the MIL-121 MOF using a cost-efficient route involving free carboxylic groups on the linker. The local and long-range structure of the metal-loaded MOFs is characterized using multinuclear solid-state NMR and X-ray diffraction methods. Li/Mg/Ca-loaded MIL-121 and Ag nanoparticle-loaded MIL-121 exhibit enhanced H2 and CO2 adsorption; Ag nanoparticle-loaded MIL-121 also demonstrates remarkable catalytic activity in the reduction of 4-nitrophenol.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.001

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 head0.011
GPT teacher head0.266
Teacher spread0.255 · 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

Citations28
Published2018
Admission routes2
Has abstractyes

Explore more

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