Robust Optical Data Encryption by Projection-Photoaligned Polymer-Stabilized-Liquid-Crystals

The emerging Internet of Things (IoTs) invokes increasing security demands that require robust encryption or anti-counterfeiting technologies. Albeit being acknowledged as efficacious solutions in processing elaborate graphical information via multiple degrees of freedom, optical data encryption and anti-counterfeiting techniques are typically inept in delivering satisfactory performance without compromising the desired ease-of-processibility or compatibility, thus leading to the exploration of novel materials and devices that are competent. Here, a robust optical data encryption technique is demonstrated utilizing polymer-stabilized-liquid-crystals (PSLCs) combined with projection photoalignment and photopatterning methods. The PSLCs possess implicit optical patterns encoded via photoalignment, as well as explicit geometries produced via photopatterning. Furthermore, the PSLCs demonstrate improved robustness against harsh chemical environments and thermal stability, and can be directly deployed onto various rigid and flexible substrates. Based on this, it is demonstrated that single PSLC is apt to carry intricate information, or serve as exclusive watermark with both implicit features and explicit geometries. Moreover, a novel, generalized design strategy is developed, for the first time, to encode intricate and exclusive information with enhanced security by spatially programming the photoalignment patterns of a pair of cascade PSLCs, which further illustrates the promising capabilies of PSLCs in optical data encryption and anti-counterfeiting

Genome Characterization and Phylogenetic Analysis of Bovine Hepacivirus in Inner Mongolia, Northeastern China

Bovine hepacivirus (BovHepV) is a new member of the genus Hepacivirus in the family Flaviviridae , which has been detected in cattle in more than seven countries. The purpose of this study was to identify and genetically characterize BovHepV in cattle in Inner Mongolia, northeastern (NE) China. A total of 116 serum samples from cattle were collected from HulunBuir in Inner Mongolia from April to May, 2021, and were divided into three pools for metagenomic sequencing. The samples were verified with semi-nested RT-PCR with primers based on the BovHepV sequences obtained from metagenomic sequencing. The complete genomes of BovHepV were amplified, and were used for genome characterization and phylogenetic analysis. BovHepV was detected in two pools through metagenomic sequencing. Five BovHepV positive samples were identified in Yakeshi of HulunBuir, thus indicating a prevalence of 8.8% (5/57). Two 8840 nucleotide long BovHepV strains YKS01/02 were amplified from the positive samples and showed 79.3%–91.9% nucleotide sequence identity with the discovered BovHepV strains. Phylogenetic analysis classified the YKS01/02 strains into BovHepV subtype G group. This study reports the first identification of BovHepV in cattle in northeastern China, and expands the known geographical distribution and genetic diversity of BovHepV in the country

Vacancy-Mediated Magnetism in Pure Copper Oxide Nanoparticles

Room temperature ferromagnetism (RTF) is observed in pure copper oxide (CuO) nanoparticles which were prepared by precipitation method with the post-annealing in air without any ferromagnetic dopant. X-ray photoelectron spectroscopy (XPS) result indicates that the mixture valence states of Cu1+ and Cu2+ ions exist at the surface of the particles. Vacuum annealing enhances the ferromagnetism (FM) of CuO nanoparticles, while oxygen atmosphere annealing reduces it. The origin of FM is suggested to the oxygen vacancies at the surface/or interface of the particles. Such a ferromagnet without the presence of any transition metal could be a very good option for a class of spintronics

Performance and NOx Emissions Control for Modern Diesel Engine and SCR Systems

High combustion efficiency and low emissions output are two important targets for modern diesel engine system designs and for their control systems. In this work, different control strategies are investigated to improve the combustion efficiency of engines and to reduce the nitrogen oxide (NOx) emissions of vehicles.There are three main contributions of this work. First, to address emissions concerns, neural network based control algorithms were applied to selective catalyst reduction (SCR) systems. Compared with conventional model-based control, the control strategy based on neural networks can reduce the amount of time and cost required for model identification for these complex systems. The neural network controllers are developed and tested in simulations at different operating conditions for the Fe-zeolite SCR system first. In addition, methods for Jacobian information prediction are also discussed. According to the simulation results, the control strategy based on neural networks can track the desired reference and have reasonable NOx reduction efficiencies in most operating conditions. However, the NOx reduction efficiencies are poor at the low temperature situations in Fe-zeolite SCR systems. To improve this issue, the neural network control strategy was applied to a Cu-zeolite SCR and an improvement in the NOx reduction efficiencies was observed with reductions over 98% at different operating conditions. Second, to address efficiency concerns, a nonlinear model-based combustion control approach was investigated. This control approach aims to track a desired optimal combustion timing and leverages a combustion phasing model for a diesel engine that was developed and validated as part of this work. An intake gas properties model is also developed to capture the cylinder-to-cylinder difference of the temperature and pressure at intake valve closing (IVC). An adaptive controller and model-based controller were then designed for the diesel engine. These control strategies are evaluated in simulations and results show that the combustion phasing control system can track the optimal CA50 (crank angle at 50% mass of fuel burned). The combustion phasing control strategies were also expanded for use on dual-fuel compression ignition engines. The dual-fuel compression ignition engine is being considered as one of the candidates for the next generation of the modern diesel engines due to its ability to achieve high combustion efficiency and low emissions. To track the optimal combustion phasing in a dual-fuel engine, a non-linear combustion phasing model for this application was also developed and calibrated based on simulations. With the control-oriented model, controllers based on an adaptive control strategy and a feedforward control strategy are designed. The controllers are evaluated and shown to track the reference CA50s at varied operating conditions