A Real-Time Detection Algorithm of Flame Target Image

In many research tasks, the speed and accuracy of flame detection using supply chain have always been a challenging task for many researchers, especially for flame detection of small objects in supply chain. In view of this, we propose a new real-time target detection algorithm. The first step is to enhance the flame recognition of small objects by strengthening the feature extraction ability of multi-scale fusion. The second step is to introduce the K-means clustering method into the prior bounding box of the algorithm to improve the accuracy of the algorithm. The third step is to use the flame characteristics in YOLO+ algorithm to reject the wrong detection results and increase the detection effect of the algorithm. Compared with the YOLO series algorithms, the accuracy of YOLO+ algorithm is 99.5%, the omission rate is 1.3%, and the detection speed is 72 frames/SEC. It has good performance and is suitable for flame detection tasks.

Effect of high purity molybdenum oxide(vi) on crystal growth and OLED technology

The strong influence of MoO3 chemical purity from 99.99 wt% to 99.999 wt% on lithium triborate (LBO) and NaLa(MoO4)2 crystal growth as well as on OLED technologies has been demonstrated.

Molybdenum Trioxide (α-MoO3) Nanoribbons for Ultrasensitive Ammonia (NH3) Gas Detection: Integrated Experimental and Density Functional Theory Simulation Studies

A highly-sensitive ammonia (NH₃) gas sensor based on molybdenum trioxide nanoribbons was developed in this study. α-MoO₃ nanoribbons (MoO₃ NRs) were successfully synthesized via a hydrothermal method and systematically characterized using various advanced technologies. Following a simple drop-cast process, a high-performance chemiresistive NH₃ sensor was fabricated through the deposition of a MoO₃ NR sensing film onto Au interdigitated electrodes. At an optimal operation temperature of 450 °C, the MoO₃ nanoribbon-based sensor exhibited an excellent sensitivity (0.72) at NH₃ concentration as low as 50 ppb, a fast response time of 21 s, good stability and reproducibility, and impressive selectivity against the interfering gases such as H₂, NO₂, and O₂. More importantly, the sensor represents a remarkable limit of detection of 280 ppt (calculated based on a signal-to-noise ratio of 3), which makes the as-prepared MoO₃ NR sensor the most sensitive NH₃ sensor in the literature. Moreover, density functional theory (DFT) simulations were employed to understand the adsorption energetics and electronic structures and thus shed light on the fundamentals of sensing performance. The enhanced sensitivity for NH₃ is explicitly discussed and explained by the remarkable band structure modification because of the NH₃ adsorption at the oxygen vacancy site on α-MoO₃ nanoribbons. These results verify that hydrothermally grown MoO₃ nanoribbons are a promising sensing material for enhanced NH₃ gas monitoring.