Dual-Function Metal–Phenolic Networks-Capped Starch Nanoparticles for Postharvest Pesticide Removal and Produce Preservation
Bibliographic record
Abstract
The safety and quality of fresh produce are increasingly challenged by pesticide residues and postharvest losses. Traditional postharvest treatments often show limited effectiveness in removing pesticide residues and may introduce micro/nanoplastics contaminations, leading to the need for innovative solutions that both enhance pesticide removal and preserve produce quality. Herein, we developed a novel dual-function postharvest wash based on metal–phenolic networks capped-starch nanoparticles (FTN@SNPs), designed to synergistically remove pesticide residues and reduce postharvest deterioration. The wash formulation integrates the nutritive polyphenol tannic acid and iron ions into a metal–phenolic networks (MPNs), which is further capped onto starch nanoparticles to create a multifunctional solution with effective washing capability, antimicrobial and antioxidant coating properties. Comprehensive evaluations on fresh produce demonstrated that the FTN@SNPs wash reduced diverse types of surface pesticide residues by over 86% as quantified by surface-enhanced Raman spectroscopy, outperforming conventional wash solutions. Molecular dynamics simulations reveal that the interactions between pesticide residues and MPNs are primarily formed through π-π interactions, van der Waals forces, and hydrogen bonds. In addition, detailed characterization of the coating's impact on quality parameters, including visual appearance, weight loss, titratable acidity, and total soluble solids, demonstrated its substantial benefits in maintaining postharvest freshness in both whole and fresh-cut produce. This dual-action approach highlights the potential of the FTN@SNPs wash as a sustainable, biodegradable, and scalable strategy to improve food safety and minimize postharvest losses in fresh produce supply chains.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".