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Record W2900691324 · doi:10.1039/c8ra08936a

Anomalous effect of the aging degree on the ionic permeability of silica shells

2018· article· en· W2900691324 on OpenAlexaff
Shenghua Wang, Chaoran Li, Zhijie Chen, Zhijie Zhu, Qishan Zhu, Ruijun Tang, Wei Sun, Le He, Xiaohong Zhang

Bibliographic record

VenueRSC Advances · 2018
Typearticle
Languageen
FieldMaterials Science
TopicMesoporous Materials and Catalysis
Canadian institutionsUniversity of TorontoToronto Public Health
FundersQinglan Project of Jiangsu Province of ChinaHigher Education Discipline Innovation ProjectNatural Science Foundation of Jiangsu ProvincePriority Academic Program Development of Jiangsu Higher Education InstitutionsGovernment of Jiangsu ProvinceChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsIonic bondingChemical engineeringWettingPermeability (electromagnetism)Materials scienceNanoparticleIonIonic strengthOxideChemistryNanotechnologyComposite materialOrganic chemistryMetallurgyMembrane

Abstract

fetched live from OpenAlex

We present a systematic study on the ionic permeability and protective ability of silica shells with different aging degrees by using the acid etching of silica-coated iron oxide nanoparticles as the model reaction. Contradictory to common impressions, we found that the ionic permeability of silica shells increased rather than decreased with the increasing aging degree. This trend may be explained by the chemical nature of the sol-gel silica shell that affects the wettability and, thereby, the transportation of water molecules and hydrated ions. Our study provides novel insights into the protective ability of sol-gel derived silica, which enables us to design thin but low-permeability shells for the stability of inner nanoparticles under harsh conditions without scarifying the performance of core-shell nanostructures.

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 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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.256
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.

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 routes1
Has abstractyes

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