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Record W4387417678 · doi:10.5267/j.ccl.2023.9.002

Nanoparticle synthesis advancements and their application in wastewater treatment: A comprehensive review

2023· review· en· W4387417678 on OpenAlexvenueno aff
Shubhangini Kumari, Vandna Singh, Deepali Singh

Bibliographic record

VenueCurrent Chemistry Letters · 2023
Typereview
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPhotocatalysisNanocompositeWastewaterNanoparticleNanotechnologySewage treatmentCarbon fibersCarbon nanotubeChemistryDegradation (telecommunications)Chemical engineeringMaterials scienceCatalysisEnvironmental scienceComposite numberEnvironmental engineeringOrganic chemistryComposite materialComputer science

Abstract

fetched live from OpenAlex

The global water crisis requires effective wastewater treatment methods where conventional approaches often face challenges related to cost, recyclability, efficiency, and overall effectiveness. In this regard, the significantly small size, large surface area, and enhanced photocatalytic properties of recently developed nanoparticles have opened new avenues for wastewater treatment. This comprehensive review focuses on recent advancements in the synthesis methods for different types of nanoparticles and nanocomposites based on metals, carbon, polymers, waste materials, and zeolites which have highly sustainable and innovative results in wastewater treatment. The introduction of silver and gold nanoparticles has enhanced photocatalytic and biological activities. Similarly, zeolite and seaweed composites have exhibited efficient dye degradation capabilities. Eco-friendly carbon soot nanoparticles have shown promising biological activities, while nitrogen-doped carbon quantum dots have exhibited long-term stability for various applications. Additionally, waste material-based calcium oxide nanoparticles and carbon quantum dot-carbon nanotube nanocomposites have also shown enhanced dye degradation activities.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.928
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
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 head0.059
GPT teacher head0.347
Teacher spread0.288 · 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 designNot applicable
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

Citations14
Published2023
Admission routes1
Has abstractyes

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