Modular Anti-Counterfeit Tags Formed by Template-Assisted Self-Assembly of Plasmonic Nanocrystals and Authenticated by Machine Learning

Multifunctional Hydrogen-Bonded Organic Frameworks for Intelligent Anti-Counterfeiting and Food Safety Monitoring

In an era of increasing digital threats and product counterfeiting, this study introduces MA-IPA@NPA, a groundbreaking hydrogen-bonded organic framework (HOF) material designed for advanced anticounterfeiting applications. This innovative material showcases distinctive optical properties, including dual fluorescence-phosphorescence emission and an ultralong phosphorescence lifetime, making it exceptionally difficult to replicate. Additionally, MA-IPA@NPA demonstrates efficacy as a fluorescence sensor for detecting tryptamine and gallic acid in food products, addressing both security and food safety concerns. Through electrospinning technology, we successfully developed a multifunctional composite nanofiber membrane by integrating MA-IPA@NPA with poly(vinyl alcohol) nanofibers, thereby combining the superior optical properties of the HOF with the flexibility and durability of polymer nanofibers. This advanced composite forms the foundation of our Multiple-Responsive Intelligent Anticounterfeiting label, which exhibits dynamic responses to diverse stimuli and incorporates multilevel verification through sophisticated machine learning methods, including binary image recognition and a Back-Propagation Neural Network. Our comprehensive research not only presents a novel strategy for developing advanced, multifunctional anticounterfeiting systems but also offers a timely solution to the escalating demand for enhanced security and authenticity verification in our increasingly digital world, paving the way for more robust and reliable anticounterfeiting measures across various industries.

Machine Learning in nanoarchitectonics

Perhaps no so visible and even difficult to notice at a quick glance, the links between nanoarchitectonics and machine learning are strong and profound both historically and thematically. From ancient times through middle-ages to modern digital world, mathematics has played an important role and made an impact on many areas, including what has emerged now as nanoscience and nanoarchitectonics. In this review, we analyze artificial intelligence, machine learning and deep learning for discovery, prediction, optimization, characterization and imaging in nanoarchitectonics. Although three more general parts are highlighted: (1) atomic and molecular sciences; (2) nanotechnology for colloids and nanofilms; (3) micro- and macro- technologies, application of machine learning in nanotechnology for colloids and nanofilms (2) is particularly relevant and important, because nanofabricated structures do not coincide with projected nano-designs. In machine learning, eXplainable Artificial Intelligence (XAI) is becoming an important area helping humans to understand why a machine would make such a decision - here, it is scrutinized through analyzing interpretability, time, accuracy, parameters (ITAP) matrix. Eventually, optimization of materials design and fabrication is linked with autonomous synthesis which is discussed in perspectives finalized with conclusions, which provides the summary and inherent links between these fields.

Stable versus Temporally Sensitive Optical Security Tags from Metal Nanoparticles

Counterfeit goods are ubiquitous, causing economic fallout, device reliability issues, and health and safety concerns. To address this challenge, design principles for stable as well as temporally sensitive optical security tags from metal nanoparticles are established by comparing tags created from Ag-based nanorods, polymer-coated Ag-based nanorods, and Au nanorods. Both physically unclonable functions and unique images were created with these nanoparticles, and their colorimetric dark-field scattering responses were tracked as a function of time. The tags created from Ag-based nanorods showed the largest change in color response and were the most responsive to an increased temperature, followed by the polymer-coated Ag nanorods. The tags created from Au nanorods were the most temporally and thermally stable. The different temporal responses arise from variable sensitivity to surface oxidation. These findings outline how the composition and surface modification of nanoparticles can be designed and serve as indicators of time, structural heath, and/or tampering.

Emerging Functional Porous Scaffolds for Liver Tissue Engineering

Liver tissue engineering holds promising in synthesizing or regenerating livers, while the design of functional scaffold remains a challenge. Owing to the intricate simulation of extracellular matrix structure and performance, porous scaffolds have demonstrated advantages in creating liver microstructures and sustaining liver functions. Currently, various methods and processes have been employed to fabricate porous scaffolds, manipulating the properties and morphologies of materials to confer them with unique supportive functions. Additionally, scaffolds must also facilitate tissue growth and deliver cells, possessing therapeutic or regenerative effects. In this review, it is initially outline typical procedures for fabricating porous scaffolds and showcase various morphologies of microstructures. Subsequently, it is delved into the forms of cell loading in porous scaffolds, including scaffold-based, scaffold-free, and synergetic or bioassembly approaches. Lastly, the utilization of porous scaffolds in liver diseases, offering significant insights and future implications for liver regeneration research in tissue engineering is explored.