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Record W2964446415 · doi:10.1021/acssuschemeng.9b01928

“Cellulose Spacer” Strategy: Anti-Aggregation-Caused Quenching Membrane for Mercury Ion Detection and Removal

2019· article· en· W2964446415 on OpenAlexaff
Meng Li, Xuefei An, Mingyue Jiang, Shujun Li, Shouxin Liu, Zhijun Chen, Huining Xiao

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2019
Typearticle
Languageen
FieldChemistry
TopicMolecular Sensors and Ion Detection
Canadian institutionsUniversity of New Brunswick
FundersNatural Science Foundation of Hebei ProvinceMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of ChinaChina Association for Science and Technology
KeywordsMembraneCelluloseNanocelluloseFluorescenceChemistryStackingQuenching (fluorescence)Chemical engineeringAdsorptionOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

A membrane decorated with fluorescent dyes has a great potential in detection and removal of contaminant from wastewater. However, traditional fluorescent dyes suffer from the aggregation-caused quenching effect, which could compromise their sensing efficiency. Here, a new “cellulose spacer” strategy is developed to conquer this challenge. Specifically, the nanocellulose has a hydrogen bond interaction with hydroxyl-containing coumarin, which serves as a spacer that prevented the π–π stacking of coumarin. In such a manner, a fluorescent cellulose membrane with anti-aggregation-caused quenching is obtained. As a demonstration of as-developed materials, the fluorescent cellulose membrane is used for mercury ion recognition and removal, and the membrane shows great sensing and adsorption performance. Moreover, excellent cytocompatibility of the membrane is verified by cell proliferation of live/dead viability assays. This fabrication method is expected to provide a new concept for the construction of fluorescent and biocompatible membranes for a large variety of relevant applications.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.004
GPT teacher head0.190
Teacher spread0.186 · 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

Citations33
Published2019
Admission routes1
Has abstractyes

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