Production of Silver Nano-Inks and Surface Coatings for Anti-Microbial Food Packaging and Its Ecological Impact.

Food spoilage is an ongoing global issue that contributes to rising carbon dioxide emissions and increased demand for food processing. This work developed anti-bacterial coatings utilising inkjet printing of silver nano-inks onto food-grade polymer packaging, with the potential to enhance food safety and reduce food spoilage. Silver nano-inks were synthesised via laser ablation synthesis in solution (LaSiS) and ultrasound pyrolysis (USP). The silver nanoparticles (AgNPs) produced using LaSiS and USP were characterised using transmission electron microscopy (TEM), Fourier transform infrared (FTIR) spectroscopy, UV-Vis spectrophotometry and dynamic light scattering (DLS) analysis. The laser ablation technique, operated under recirculation mode, produced nanoparticles with a small size distribution with an average diameter ranging from 7-30 nm. Silver nano-ink was synthesised by blending isopropanol with nanoparticles dispersed in deionised water. The silver nano-inks were printed on plasma-cleaned cyclo-olefin polymer. Irrespective of the production methods, all silver nanoparticles exhibited strong antibacterial activity against E. coli with a zone of inhibition exceeding 6 mm. Furthermore, silver nano-inks printed cyclo-olefin polymer reduced the bacterial cell population from 1235 (±45) × 106 cell/mL to 960 (±110) × 106 cell/mL. The bactericidal performance of silver-coated polymer was comparable to that of the penicillin-coated polymer, wherein a reduction in bacterial population from 1235 (±45) × 106 cell/mL to 830 (±70) × 106 cell/mL was observed. Finally, the ecotoxicity of the silver nano-ink printed cyclo-olefin polymer was tested with daphniids, a species of water flea, to simulate the release of coated packaging into a freshwater environment.

Abundance-based classifier for the prediction of mass spectrometric peptide detectability upon enrichment (PPA).

The function of a large percentage of proteins is modulated by post-translational modifications (PTMs). Currently, mass spectrometry (MS) is the only proteome-wide technology that can identify PTMs. Unfortunately, the inability to detect a PTM by MS is not proof that the modification is not present. The detectability of peptides varies significantly making MS potentially blind to a large fraction of peptides. Learning from published algorithms that generally focus on predicting the most detectable peptides we developed a tool that incorporates protein abundance into the peptide prediction algorithm with the aim to determine the detectability of every peptide within a protein. We tested our tool, "Peptide Prediction with Abundance" (PPA), on in-house acquired as well as published data sets from other groups acquired on different instrument platforms. Incorporation of protein abundance into the prediction allows us to assess not only the detectability of all peptides but also whether a peptide of interest is likely to become detectable upon enrichment. We validated the ability of our tool to predict changes in protein detectability with a dilution series of 31 purified proteins at several different concentrations. PPA predicted the concentration dependent peptide detectability in 78% of the cases correctly, demonstrating its utility for predicting the protein enrichment needed to observe a peptide of interest in targeted experiments. This is especially important in the analysis of PTMs. PPA is available as a web-based or executable package that can work with generally applicable defaults or retrained from a pilot MS data set.