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Use of Cerium Nitrate Nanoparticles in Biological Environment Along with the Factors Effecting It: An Empirical Overview

2021· preprint· en· W3197666860 on OpenAlexaff
Zarish Fatima, Sameer Quazi, Shreelaxmi Gavas, Tomasz M. Karpiński

Bibliographic record

VenuePreprints.org · 2021
Typepreprint
Languageen
FieldMaterials Science
TopicAdvanced Nanomaterials in Catalysis
Canadian institutionsEmergent BioSolutions (Canada)
Fundersnot available
KeywordsCerium oxideNanoparticleCeriumStandardizationNanotechnologyNanomedicineEnvironmentally friendlyBiochemical engineeringMaterials scienceChemistryComputer scienceCatalysisEngineeringOrganic chemistryEcologyBiology

Abstract

fetched live from OpenAlex

The advancement in the production and usage of the cerium oxide nanoparticles have diverted the attention of scientists towards their usage in medical field and therapeutic usage. The clinical usage of these Nano ceria is based on their ability to moderate the oxidative stress and this is only because of their ability to change their valent state from +3 to +4 which makes them ideal for scavenging radicals for use in a number of systemic and neurodegenerative disorders. This review aims to synthesize the basic methods used for the synthesis of nanoparticles along with the use of ligand, stabilizing agent and other components. This review also concludes that how various physical and chemical properties of nanoparticles effect the basic biological activities such as antimicrobial activity, cytotoxicity and many others. However, during the standardization, some of the physiochemical properties, methods used for preparation and catalytic abilities must be taken into account.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.293
GPT teacher head0.382
Teacher spread0.089 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations3
Published2021
Admission routes1
Has abstractyes

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Same venuePreprints.orgSame topicAdvanced Nanomaterials in CatalysisFrench-language works237,207