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Low swelling Alginate/Lignin network gels with redox responsiveness for sustained release of agricultural fungicide and Pb2+ complexation

2024· article· en· W4391152037 on OpenAlexafffund
Lin Zheng, Farzad Seidi, Hongmiao Wu, Yang Huang, Weibing Wu, Huining Xiao

Bibliographic record

VenueEuropean Polymer Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicPolymer-Based Agricultural Enhancements
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsFungicideChemistryRedoxLigninSwellingChelationTebuconazoleMetal ions in aqueous solutionMetalOrganic chemistryAgronomyMaterials scienceBiology

Abstract

fetched live from OpenAlex

Fungicides are among the most essential agrochemicals for controlling crop diseases. However, the increased consumption of fungicides has resulted in environmental pollution and adversely affected human health. The slow-release technology can improve the efficacy and reduce the required amount of fungicide. Here, we developed a low-swelling redox-responsive alginate/lignin network bearing covalently conjugated fungicide thymol-Cl. Natural reductants in the environment caused the selective cleavage of disulfide bonds and release of thymol-Cl and thiazolidin-2-one (a by-product), which both could inhibit the phytopathogenic fungus ( F. oxysporum ). At the same time, metal chelating groups in the gel endow strong interactions with heavy metal ions for improving soil remediation and preventing the adsorption of toxic metals by the plant roots. The fungicide-loaded carrier could alleviate the poisonous effect of fungi on maize seeds. Indeed, the secretion of natural reducing agents by fungi and maize led to the cleavage of disulfide bonds and the release of fungicides.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.199
Teacher spread0.193 · 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

Citations13
Published2024
Admission routes2
Has abstractyes

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