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Record W4416388786 · doi:10.1002/ceur.202500224

The Application of Carbon Dots in Crops for Sustainable Agriculture

2025· article· en· W4416388786 on OpenAlexaff
X. L. Li, Lei Jin, Gurpreet Singh Selopal, Federico Rosei, Jinhua Li

Bibliographic record

VenueChemistryEurope · 2025
Typearticle
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsDalhousie University
FundersSouthern University of Science and TechnologyChina Scholarship CouncilChina Postdoctoral Science Foundation
KeywordsAgricultureSustainable agricultureCropSustainabilitySustainable developmentPostharvest

Abstract

fetched live from OpenAlex

Sustainable agricultural systems face numerous challenges, including the need for sustainable resources, enhanced crop productivity, and improved postharvest management. Due to their unique physicochemical properties, such as tunable luminescence range, biocompatibility, ease of synthesis, facile surface functionality, water solubility, and low toxicity, carbon dots (CDs) have emerged as promising nanomaterials for sustainable agriculture. These features enable their broad application throughout the agricultural life cycle, from promoting plant growth and development to enhancing the preservation and detection of agricultural products. CDs can be synthesized from a wide variety of organic waste, including crop residues, facilitating their reintegration into a closed‐loop agricultural system. Here, recent progress in the use of CDs in key stages of the crop growth cycle, including seed germination, vegetative development, and crop protection, is summarized. In addition, their role in postharvest preservation, antimicrobial activity, and safety assessment of agricultural commodities is discussed. Finally, current limitations and future directions for the application of CDs in sustainable agriculture are critically evaluated.

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 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 score0.093
Threshold uncertainty score0.176

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.004
GPT teacher head0.244
Teacher spread0.240 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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