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Record W2885032507 · doi:10.1016/j.jhazmat.2018.08.025

Micro-nanostructured δ-Bi2O3 with surface oxygen vacancies as superior adsorbents for SeOx2− ions

2018· article· en· W2885032507 on OpenAlex
Long Liu, Ning Chen, Yong Lei, Xuyan Xue, Lina Li, Jiancheng Wang, Sridhar Komarneni, Huaiyong Zhu, Dongjiang Yang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Hazardous Materials · 2018
Typearticle
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsCanadian Light Source (Canada)
FundersNational Natural Science Foundation of China
KeywordsSelenateAdsorptionSelectivityAqueous solutionIonChemistryOxygenVacancy defectSeleniumInorganic chemistryChemical engineeringMaterials scienceOrganic chemistryCrystallographyCatalysis

Abstract

fetched live from OpenAlex

Removal of the toxic selenium compounds, selenite (SeO32−) and selenate (SeO42−), from contaminated water is imperative for environmental protection in both developing and industrialized countries. Providing high selectivity adsorbents to the target ions is a big challenge. Here we report that micro sphere-like δ-Bi2O3 (MS-δ-Bi2O3) with surface oxygen vacancy defects can capture hypertoxic SeOx2− anions from aqueous solutions with superior capacity and fast uptake rate. High capture selectivity to SeO32− anions is observed, since the O atoms of SeO32− anions fill the oxygen vacancies on the (111) facet of δ-Bi2O3 forming a stable complex structure. This mechanism is distinctly different from other known mechanisms for anion removal, and implies that we may utilize surface defects as highly efficient and selective sites to capture specific toxic species. Thus, we present a new route here to design superior adsorbents for toxic 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.

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 categoriesnone
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 score0.032
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.015
GPT teacher head0.271
Teacher spread0.256 · 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