Near-Infrared Light-Excited Core–Core–Shell UCNP@Au@CdS Upconversion Nanospheres for Ultrasensitive Photoelectrochemical Enzyme Immunoassay

A novel photoelectrochemical (PEC) enzyme immunoassay was designed for the ultrasensitive detection of alpha-fetoprotein (AFP) based on near-infrared (NIR) light-excited core–core–shell UCNP@Au@CdS upconversion nanospheres. Plasmonic gold (Au) between the sandwiched layers was not only utilized as an energy harvester for the collection of the incident light but also acted as an energy conveyor to transfer the energy from upconversion NaYF₄:Yb³⁺,Er³⁺ (UCNP) to semiconductor CdS, thus exciting the efficient separation of electron–hole pairs by the generated H₂O₂ of enzyme immunoreaction under the irradiation of a 980 nm laser. By virtue of high catalytic activity of natural enzymes, gold nanoparticles heavily functionalized with glucose oxidase (GOx) and polyclonal anti-AFP antibody were utilized to generate H₂O₂. A sandwiched immunoreaction was first carried out in a monoclonal anti-AFP antibody-coated microplate by using an antibody-labeled gold nanoparticle as secondary antibody. Accompanying the gold nanoparticle, the carried GOx oxidized glucose in H₂O₂, thereby resulting in the enhanced photocurrent via capturing holes on the valence band of CdS to promote the separation of electron–hole pairs. Under optimum conditions, the NIR light-based PEC immunosensing system exhibited good photocurrent responses toward target AFP within the dynamic working range of 0.01–40 ng mL^–1 at a detection limit of 5.3 pg mL^–1. Moreover, the NIR light-based sensing platform had good reproducibility and high selectivity. Importantly, good well-matched results obtained from NIR light-based PEC immunoassay were acquired for the analysis of human serum specimens by using AFP ELISA kit as the reference

DataSheet1_Application of a digital twin for highway tunnels based on multi-sensor and information fusion.docx

Due to the harsh environment of highway tunnels and frequent breakdowns of various detection sensors and surveillance devices, the operational management of highway tunnels lacks effective data support. This paper analyzes the characteristics of operational surveillance data in highway tunnels. It proposes a multimodal information fusion method based on CNN–LSTM–attention and designs and develops a digital twin for highway tunnel operations. The system addresses issues such as insufficient development and coordination of the technical architecture of operation control systems, weak information service capabilities, and insufficient data application capabilities. The system also lacks intelligent decision-making and control capabilities. The developed system achieves closed-loop management of “accurate perception–risk assessment–decision warning–emergency management” for highway tunnel operations based on data-driven approaches. The engineering demonstration application underscores the system’s capacity to enhance tunnel traffic safety, diminish tunnel management costs, and elevate tunnel driving comfort.</p