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Record W3177364146 · doi:10.3724/sp.j.1123.2021.02035

Advances in solid-phase extraction for bisphenols in environmental samples

2021· article· en· W3177364146 on OpenAlexaboutno aff
Hongyuan Liu, Jing Jin, Cuicui Guo, Jiping Chen, Chun Hu

Bibliographic record

VenueChinese Journal of Chromatography · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEffects and risks of endocrine disrupting chemicals
Canadian institutionsnot available
Fundersnot available
KeywordsBisphenol ABisphenol SChemistryExtraction (chemistry)BisphenolSolid phase extractionEnvironmental chemistryPollutantChromatographyEnvironmentally friendlyGenotoxicityEnvironmental pollutionSolventEnvironmental scienceOrganic chemistryEnvironmental protectionToxicity

Abstract

fetched live from OpenAlex

Owing to the strict restrictions on the production and use of bisphenol A (BPA), bisphenol analogs (e. g., bisphenol S and bisphenol F) are gradually coming to use in many fields. BPA and these bisphenol analogs are so-called bisphenols (BPs). BPs as a class of endocrine disrupters are widely distributed in the environment (water, sediments, sludge, and aquatic products). BPs enter the human body through various routes, leading to endocrine disruption, cytotoxicity, genotoxicity, reproductive toxicity, dioxin-like effects, and neurotoxicity. The Canadian government has identified BPs as substances for further scoping/problem formulation. Because of the widespread attention paid to BPs in the environmental field, research is being expanded to cover water, sediment, dust, and biological samples, and other media. Given the significant differences in the complexity and pollution concentration of environmental samples, the development of pretreatment methods that afford high extraction efficiency, good purification selectivity, strong universality, operational simplicity, and high-throughput extraction and purification, are necessary to realize the highly sensitive detection of BPs in environmental media. In recent years, solid-phase extraction (SPE), accelerated solvent extraction (ASE), microwave-assisted extraction (MAE), and dispersion liquid-liquid-microextraction (DLLME) as new pretreatment technologies have gradually replaced the traditional liquid-liquid extraction and Soxhlet extraction. SPE has seen rapid development for the extraction and purification of BPs in various environmental samples, overcoming the bottlenecks related to time, energy, and solvent consumption in traditional methods while extending technical support for the analysis of emerging pollutants. The physicochemical properties, usage, and environmental hazards of typical BPs were briefly reviewed, with emphasis on the application of SPE products, development of new adsorbents, and transformation of the SPE mode. Commercialized SPE products are universally applicable in the field of environmental monitoring, while products suitable for the pretreatment of BPs are limited. The development of new adsorbents mainly focused on their adsorption capacity and selectivity. For example, ordered mesoporous silicon, carbon nanomaterials, metal-organic frameworks, and cyclodextrins have large surface areas, good adsorption performance, and regular pore structures, which improve the adsorption capacity of BPs. Molecularly imprinted polymers (MIPs) and mixed-mode ion-exchange polymers are mainly used to improve the selectivity of BPs in the purification process. In addition, MIPs have high chemical, mechanical, and thermal stabilities, which ensures their widespread application in the extraction, preconcentration, and separation of BPs. A variety of new SPE adsorbents can partially meet the diverse needs for detection. There is a consensus that the current challenges in analytical chemistry include the determination of contaminants at low concentration levels, but at the same time, more efficient and environment-friendly methodologies are required. With the introduction of high-sensitivity instruments in the market, the SPE model is seeing gradual development in terms of miniaturization, automation, and simplification. This in turn has minimized solvent consumption, analysis time, and labor cost, resulting in more efficient and affordable analytical methods such as QuEChERS, solid-phase microextraction (SPME), and magnetic solid-phase extraction (MSPE) to adapt to the new development scenario.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.458

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 communication0.0000.001
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 head0.008
GPT teacher head0.380
Teacher spread0.372 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2021
Admission routes1
Has abstractyes

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