Iron (Fe) is vital for human health, but imbalances can lead to diseases and environmental pollution, creating a need for effective detection methods.
A new dual-mode sensor called PAA@CDs was developed, combining sol-gel and fluorescence responses for more accurate Fe detection while reducing false positives.
This sensor operates by forming nonfluorescent complexes with Fe, leading to noticeable fluorescence changes and sol-gel phase transitions, with a detection range for fluorescence of 0.05-2.60 mM and sol-gel of 0.02-2.20 mM, proving effective for analyzing Fe in real water samples.
Construction waste sorting (CWS) is crucial for managing construction waste, but traditional manual sorting poses safety risks for workers.
Robotic sorting, powered by AI and automation, faces challenges in efficiently identifying materials in complex waste mixtures.
This research proposes a human-robot collaboration (HRC) system using augmented reality (AR) to enhance CWS accuracy by 10-15% while improving occupational safety and health (OSH) by reducing risks associated with contamination and machinery.