Bio-friendly multi-stimuli responsive α-CD polymer-gated mesoporous carbon nanoherbicides for enhanced paraquat delivery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Weeds seriously affect crop yield in global agricultural production. Paraquat (PQ), as one of low cost and highly effective herbicide, is forbidden or severely restricted in production and sales owing to its lethal toxicity to humans. Creating an efficient and bio-friendly PQ formulation is crucial to facilitate the open use of PQ in world's agriculture. OBJECTIVES: This study aims to construct one intelligent and bio-friendly mesoporous carbon nanoparticles (MCN) nanoherbicides coated with α-CD polymer (CDP) gatekeepers. METHODS: MCN was prepared through the low-concentration hydrothermal way, calcined and carbonized. PEG stalks were immobilized on MCN surface by amidation reaction. The PQ was trapped in the MCN pores via physical diffusion adsorption and the robust π-π effects between electron-deficient PQ and electron-rich MCN. CDP gatekeepers were fastened via host-guest effects between the chamber of α-CD units and PEG stalks. RESULTS: The PQ-loaded MCN-PEG@CDP nanoherbicides integrated with multi-stimuli responses to amylase, elevated temperature under sunlight, and competitors at leaf interface to control the PQ release for efficient weed control, while appeared low PQ leakage under the simulated human gastric or intestinal conditions, low cytotoxicity to human normal cells in vitro, and high mouse survival rate in vivo. Even through the nanoherbicides inevitably contact with water or intake by beneficial insects, they appear good biosafety on zebrafish (D. rerio) and honeybees (Apis mellifera L.). CONCLUSION: The as-prepared nanoherbicides have high herbicidal efficacy and low risks to non-target species, and could promote the open use of PQ in agriculture.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it