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Record W2900070487 · doi:10.1021/acsanm.8b01457

MnO<sub>2</sub>/Fe<sub>2</sub>O<sub>3</sub> Nanocomposite Sorbent for Gas Capture

2018· article· en· W2900070487 on OpenAlexafffund
Xiaowei Ma, Rui Yang, Renny Doig, Aaron Liu, M.A. Rankin, Lisa M. Croll, Peng Zhang, J. R. Dahn

Bibliographic record

VenueACS Applied Nano Materials · 2018
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutions3M (Canada)Dalhousie University
FundersNatural Sciences and Engineering Research Council of Canada3M
KeywordsNanocompositeSorbentAdsorptionMaterials scienceMesoporous materialChemical engineeringAqueous solutionTransmission electron microscopyNanoparticleSpecific surface areaPorosityAbsorption (acoustics)Analytical Chemistry (journal)NanotechnologyChemistryComposite materialPhysical chemistryOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

A special type of hydrous MnO2/Fe2O3 nanocomposite was prepared using a two-step precipitation method. Fe2O3·xH2O nanoparticles, precipitated from Fe(NO3)3 and NH3·H2O, were proposed to be embedded into the mesoporous network of MnO2, which was synthesized by the aqueous reaction between KMnO4 and glucose. The nanocomposite with an equal Mn/Fe molar ratio shows strong synergy, with a specific surface area of 388 m2/g, much larger than those of individual MnO2 or Fe2O3·xH2O samples. As a result, this nanocomposite exhibited the highest adsorption capacity for NH3 and SO2. The isolation of Fe2O3·xH2O by MnO2, leading to mitigated aggregation of Fe2O3·xH2O nanoparticles, was characterized by transmission electron microscopy, powder X-ray diffraction, and vibrational spectra. X-ray absorption spectroscopy was used to study the interaction between Fe2O3·xH2O and MnO2 after the formation of composites. This typical method for the preparation of nanocomposites proved to be effective to improve porosity, as demonstrated by N2 adsorption isotherms and small-angle X-ray scattering.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
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 score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.004

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 head0.234
Teacher spread0.223 · 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

Citations4
Published2018
Admission routes2
Has abstractyes

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