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Record W4403866275 · doi:10.3390/molecules29215108

Role of Clay Minerals in Natural Media Self-Regeneration from Organic Pollution-Prospects for Nature-Inspired Water Treatments

2024· review· en· W4403866275 on OpenAlexaff
Abdelkrim Azzouz, David Dewez, Amina Benghaffour, Robert Hausler, René Roy

Bibliographic record

VenueMolecules · 2024
Typereview
Languageen
FieldEngineering
TopicEnvironmental remediation with nanomaterials
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsClay mineralsNatural (archaeology)PollutionRegeneration (biology)Environmental scienceNatural organic matterEnvironmental chemistryWater pollutionChemistryWater treatmentEnvironmental protectionEnvironmental engineeringGeologyMineralogyEcologyBiologyCell biology

Abstract

fetched live from OpenAlex

Pollution from organic molecules is a major environmental issue that needs to be addressed because of the negative impacts of both the harmfulness of the molecule structures and the toxicity that can spread through natural media. This is mainly due to their unavoidable partial oxidation under exposure to air and solar radiation into diverse derivatives. Even when insoluble, the latter can be dispersed in aqueous media through solvatation and/or complexation with soluble species. Coagulation-flocculation, biological water treatments or adsorption on solids cannot result in a total elimination of organic pollutants. Chemical degradation by chlorine and/or oxygen-based oxidizing agents is not a viable approach due to incomplete mineralization into carbon dioxide and other oxides. A more judicious strategy resides in mimicking natural oxidation under ambient conditions. Soils and aqueous clay suspensions are known to display adsorptive and catalytic properties, and slow and complete self-regeneration can be achieved in an optimum time frame with a much slower pollution throughput. A deep knowledge of the behavior of aluminosilicates and of oxidizing species in soils and aquatic media allows us to gain an understanding of their roles in natural oxidative processes. Their individual and combined contributions will be discussed in the present critical analysis of the reported literature.

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: Review · Consensus signal: Review
Teacher disagreement score0.135
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.007
GPT teacher head0.233
Teacher spread0.226 · 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
GenreReview

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

Citations3
Published2024
Admission routes1
Has abstractyes

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