Mengshi Lin

A multifunctional cellulose nanofibril-based aerogel incorporating waterborne polyurethane and folic acid- and silver nanoparticle-functionalized Cu-based metal–organic frameworks (HKUAg) was developed to integrate ethylene scavenging with mechanical cushioning for banana preservation. HKUAg exhibited a static ethylene uptake of 126.66 mL g⁻¹ STP at 298 K and a dynamic breakthrough capacity of 89.90 mL g⁻¹ STP under 80% relative humidity. The aerogel showed excellent resilience and prevented egg breakage in a 15-cm drop test. During 10 d of banana storage, it suppressed climacteric respiration and better maintained peel color, firmness, and weight than polyethylene foam. Multi-omics analysis indicated delayed senescence through improved membrane integrity and inhibition of browning-related metabolism. Life-cycle assessment showed 58.1% lower greenhouse gas emissions and 77.8% lower fossil resource demand than polyethylene foam, while the aerogel achieved >75% soil biodegradation within 42 d, demonstrating a sustainable strategy for active fruit packaging.

Micro- and nanoplastic particles (MNPs) are ubiquitous environmental contaminants, raising significant concerns due to their potential health risks. There is an urgent need for highly sensitive detection methods, especially for complex food samples. This study presents a novel surface-enhanced Raman spectroscopy (SERS) sensor, developed through in situ synthesis of gold-core silver-shell (Au@Ag) nanoparticles on bacterial cellulose (BC), for detecting MNPs in leafy vegetables. The BC serves as both a biocompatible scaffold and an eco-friendly reducing agent, thereby facilitating the green synthesis of nanoparticles. The BC@Au@Ag sensor enabled reliable detection of polyethylene (PE) and polystyrene (PS) micro- and nanoplastics in kale samples at 4 mg kg⁻¹, while the theoretically estimated limits of detection, calculated from weighted regression analysis, were as low as 1.22 mg kg⁻¹ for PS and 3.95 mg kg⁻¹ for PE. Combined recovery and precision analyses confirm the reproducibility and robustness of the BC@Au@Ag SERS platform, demonstrating its suitability for sensitive and reliable monitoring of MNPs in complex food matrices.

The widespread use of pesticides in agriculture poses food safety and environmental risks, highlighting the need for rapid detection techniques to mitigate contamination. Surface-enhanced Raman spectroscopy (SERS) coupled with machine learning provides a powerful approach for the detection and quantification of multiple pesticides in agricultural products. This study introduces the SERSFormer-2.0 model, which excels in both multilabel classification and multiregression tasks for pesticide analysis, leveraging the power of transformer-based machine learning architectures. SERSFormer-2.0 employs novel multitask learning approach with task specific feature representation layers, shared multihead attention transformer encoder, and task-specific output layers to detect pesticides and estimate the precise concentrations of each pesticide simultaneously. By utilizing core-shell gold-silver nanoparticles, the model achieves near-perfect performance in identifying and quantifying pesticide residues, with multilabel metrics and regression accuracy demonstrating exceptional reliability (accuracy = 0.999; F1 score = 0.992; precision = 0.990; recall = 0.996). A detailed examination of the Raman spectra reveals the predominant influence of certain pesticides, and the mechanisms behind spectral dominance were elucidated. Our findings underscore the SERSFormer-2.0 model 's robustness and its potential to detect mixed contaminants in agricultural products, enhancing food safety and regulatory practices.