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Record W2588388642 · doi:10.1021/acs.inorgchem.6b02927

Interfacial Interaction of Titania Nanoparticles and Ligated Uranyl Species: A Relativistic DFT Investigation

2017· article· en· W2588388642 on OpenAlexafffund
Hong-Bo Zhao, Ming Zheng, Georg Schreckenbach, Qing‐Jiang Pan

Bibliographic record

VenueInorganic Chemistry · 2017
Typearticle
Languageen
FieldChemistry
TopicRadioactive element chemistry and processing
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsChemistryUranylNanoparticleChemical engineeringInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

To understand interfacial behavior of actinides adsorbed onto mineral surfaces and unravel their structure–property relationship, the structures, electronic properties, and energetics of various ligated uranyl species adsorbed onto TiO 2 surface nanoparticle clusters (SNCs) were examined using relativistic density functional theory. Rutile (110) and anatase (101) titania surfaces, experimentally known to be stable, were fully optimized. For the former, models studied include clean and water-free Ti 27 O 64 H 20 ( dry ), partially hydrated (Ti 27 O 64 H 20 )(H 2 O) 8 ( sol ) and proton-saturated [(Ti 27 O 64 H 20 )(H 2 O) 8 (H) 2 ] 2+ ( sat ), while defect-free and defected anatase SNCs involving more than 38 TiO 2 units were considered. The aquouranyl sorption onto rutile SNCs is energetically preferred, with interaction energies of −8.54, −10.36, and −2.39 eV, respectively. Energy decomposition demonstrates that the sorption is dominated by orbital attractive interactions and modified by steric effects. Greater hydrogen-bonding involvement leads to increased orbital interactions (i.e., more negative energy) from dry to sol/sat complexes, while much larger steric interaction in the sat complex significantly reduces the sorption interaction (i.e., more positive energy). For dry SNC, adsorbates were varied from aquo to aquo-carbonato, to carbonato, to hydroxo uranyl species. Longer U–O surf /U–Ti distances and more positive sorption energies were calculated upon introducing carbonato and hydroxo ligands, indicative of weaker uranyl sorption onto the substrate. This is consistent with experimental observations that the uranyl sorption rate decreases upon raising solution pH value or adding carbon dioxide. Anatase SNCs adsorbing aquouranyl are even more exothermic, because more bonds are formed than in the case of rutile. Moreover, the anatase sorption can be tuned by surface defects as well as its Ti and O stoichiometry. All the aquouranyl–SNC complexes show similar character of molecular orbitals and energetic order although differing in highest occupied molecular orbital (HOMO)–lowest unoccupied molecular orbital (LUMO) gaps and orbital energy levels, but changes can be accomplished by adding carbonato and hydroxo ligands.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
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 head0.023
GPT teacher head0.256
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations14
Published2017
Admission routes2
Has abstractyes

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