Functional Polymeric Membranes with Antioxidant Properties for the Colorimetric Detection of Amines.

Herein, the ability of highly porous colorimetric indicators to sense volatile and biogenic amine vapors in real time is presented. Curcumin-loaded polycaprolactone porous fiber mats are exposed to various concentrations of off-flavor compounds such as the volatile amine trimethylamine, and the biogenic amines cadaverine, putrescine, spermidine, and histamine, in order to investigate their colorimetric response. CIELAB color space analysis demonstrates that the porous fiber mats can detect the amine vapors, showing a distinct color change in the presence of down to 2.1 ppm of trimethylamine and ca. 11.0 ppm of biogenic amines, surpassing the limit of visual perception in just a few seconds. Moreover, the color changes are reversible either spontaneously, in the case of the volatile amines, or in an assisted way, through interactions with an acidic environment, in the case of the biogenic amines, enabling the use of the same indicator several times. Finally, yet importantly, the strong antioxidant activity of the curcumin-loaded fibers is successfully demonstrated through DPPH● and ABTS● radical scavenging assays. Through such a detailed study, we prove that the developed porous mats can be successfully established as a reusable smart system in applications where the rapid detection of alkaline vapors and/or the antioxidant activity are essential, such as food packaging, biomedicine, and environmental protection.

An online yarn spinning dataset.

This data article presents an online yarn spinning dataset for evaluation and benchmarking of a variety of image processing algorithms and computer vision models for imaging based testing of textile yarn quality. The dataset comprises of continuous yarn spinning videos of 59.05 tex, 29.5 tex and 14.76 tex cotton yarns. These videos were recorded during yarn production on a ring spinning frame using a customised image acquisition system. Three videos of 250 meters yarn length each were recorded for all three yarn varieties. Each yarn spinning video was 29.26 gigabytes in size and contained 20200 image frames. After image acquisition, each yarn sample was physically tested on an industrial yarn quality tester to generate ground truth labels for various yarn quality parameters. The online yarn spinning dataset was recently used to validate computer vision models for online detection of nep like defects in yarn spinning process through a comparison of defect count with ground truth labels [1]. Similarly, in the future, this dataset can be used to evaluate performance of a variety of other imaging based online and offline yarn quality testing and defect detection systems.

Nanocatalyst-Enabled Physically Unclonable Functions as Smart Anticounterfeiting Tags with AI-Aided Smartphone Authentication.

Counterfeiting is a worldwide issue affecting many industrial sectors, ranging from specialized technologies to retail market, such as fashion brands, pharmaceutical products, and consumer electronics. Counterfeiting is not only a huge economic burden (>$ 1 trillion losses/year), but it also represents a serious risk to human health, for example, due to the exponential increase of fake drugs and food products invading the market. Considering such a global problem, numerous anticounterfeit technologies have been recently proposed, mostly based on tags. The most advanced category, based on encryption and cryptography, is represented by physically unclonable functions (PUFs). A PUF tag is based on a unique physical object generated through chemical methods with virtually endless possible combinations, providing remarkable encoding capability. However, most methods adopted nowadays are based on expensive and complex technologies, relying on instrumental readouts, which make them not effective in real-world applications. To achieve a simple yet cryptography-based anticounterfeit method, herein we exploit a combination of nanotechnology, chemistry, and artificial intelligence (AI). Notably, we developed platinum nanocatalyst-enabled visual tags, exhibiting the properties of PUFs (encoding capability >10300) along with fast (1 min) ON/OFF readout and full reversibility, enabling multiple onsite authentication cycles. The development of an accurate AI-aided algorithm powers the system, allowing for smartphone-based PUF authentications.

Light Responsive Silk Nanofibers: An Optochemical Platform for Environmental Applications.

Photochromic spiropyran-doped silk fibroin poly(ethylene oxide) nanofibers which combine the attractive properties and biocompatibility of silk with the photocontrollable and reversible optical, mechanical, and chemical response of the spiropyran dopants are herein presented. As proved, the reversible variation of the absorption and emission signals of the mats and of their Young's modulus upon alternate UV and visible light irradiation is ascribed to the reversible photoconversion of the spiropyran form to its polar merocyanine counterpart. Most importantly, the interactions of the merocyanine molecules with acidic vapors as well as with heavy metal ions dispersed in solution produce analyte-specific spectral changes in the emission profile of the composite, accompanied by a characteristic chromic variation. Because of the high surface-to-volume ratio of the nanofibrous network, such interactions are fast, thus enabling both an optical and a visual detection in a 30-60 s time scale. The sensing platform can be easily regenerated for more than 20 and 3 cycles upon acid or ion depletion, respectively. Overall, the photocontrolled properties of the silk composites combined with a straightforward preparation method render them suitable as porous materials and scaffolds with tunable compliance and reusable nanoprobes for real time optical detection in biomedical, environmental, and industrial applications.