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Record W3184256682 · doi:10.3389/fvets.2021.689085

Copper/Carbon Core/Shell Nanoparticles: A Potential Material to Control the Fish Pathogen Saprolegnia parasitica

2021· article· en· W3184256682 on OpenAlexfundno aff
Ju Zhang, Juncai Chen, Qianjun Huang, Brett MacKinnon, Omid Nekouei, Hong Liu, Peng Jia, Jinjin Wang, Na Li, Liqing Huang, Ying Yang, Pok Him Ng, Sophie St‐Hilaire

Bibliographic record

VenueFrontiers in Veterinary Science · 2021
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsnot available
FundersDepartment of Health and Social CareInternational Development Research CentreGovernment of the United Kingdom
KeywordsCopperFungicideSaprolegniaAquacultureMyceliumTurbidityNanoparticleLeaching (pedology)Pulp and paper industryBiologyMaterials scienceBotanyFish <Actinopterygii>MetallurgyFisheryEcologyNanotechnologySoil water

Abstract

fetched live from OpenAlex

Copper-based fungicides have a long history of usage in agriculture and aquaculture. With the rapid development of metal-based nanoparticles, copper-based nanoparticles have attracted attention as a potential material for prevention and control of Saprolegnia parasitica . The present study investigated the effectiveness of copper/carbon core/shell nanoparticles (CCCSNs) and a commercial CCCSNs filter product (COPPERWARE ® ) against S. parasitica in a recirculating system. Results showed that the growth of agar plugs with mycelium was significantly suppressed after exposure to both CCCSNs powder and COPPERWARE ® filters. Even the lowest concentration of CCCSNs used in our study (i.e., 100 mg/mL) exhibited significant inhibitory effects on S. parasitica . The smallest quantity of the filter product COPPERWARE ® (3.75 × 3.7 × 1.2 cm, 2.58 g) used in our aquarium study also demonstrated significant inhibition compared with the control group. However, we observed leaching of copper into the water especially when larger quantities of COPPERWARE ® were used. Water turbidity issues were also observed in tanks with the filter material. Besides these issues, which should be further investigated if the product is to be used on aquatic species sensitive to copper, CCCSNs has promising potential for water disinfection.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.251
Teacher spread0.236 · 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 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

Citations8
Published2021
Admission routes1
Has abstractyes

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