Research on Hydrogen Production from Ammonia Decomposition by Pulsed Plasma Catalysis.

Driven by dual-carbon targets, marine engines are accelerating their transition towards low-carbon and zero-carbon. Ammonium-hydrogen fusion fuel is considered to be one of the most promising fuels for ship decarbonization. Using non-thermal plasma (NTP) catalytic ammonia on-line hydrogen production technology to achieve hydrogen supply is one of the most important means to guarantee the safety and effectiveness of hydrogen energy in the storage and transportation process.

Opportunistic use of chest low-dose computed tomography (LDCT) imaging for low bone mineral density and osteoporosis screening: cutoff thresholds for the attenuation values of the lower thoracic and upper lumbar vertebrae.

Background: Osteoporosis remains substantially underdiagnosed and undertreated worldwide. Chest low-dose computed tomography (LDCT) may provide a valuable and popular opportunity for osteoporosis screening. This study sought to evaluate the feasibility of the screening of low bone mineral density (BMD) and osteoporosis with mean attenuation values of the lower thoracic compared to upper lumbar vertebrae.

The Removal of CH and NO from Marine LNG Engine Exhaust by NTP Combined with Catalyst: A Review.

Compared to diesel, liquefied natural gas (LNG), often used as an alternative fuel for marine engines, comes with significant advantages in reducing emissions of particulate matter (PM), SO, CO, and other pollutants. Promoting the use of LNG is of great significance for achieving carbon peaking and neutrality worldwide, as well as improving the energy structure. However, compared to diesel engines, medium- and high-speed marine LNG engines may produce higher methane (CH) emissions and also have nitrogen oxide (NO) emission issues.

Prediction of black carbon in marine engines and correlation analysis of model characteristics based on multiple machine learning algorithms.

Ship black carbon emissions have caused great harm to ecological environment. In order to estimate the black carbon emissions, thereby reducing the cost of black carbon experiments, here, we introduced four machine learning algorithms which are lasso regression, support vector machine, extreme gradient boosting, and artificial neural network to predict ship black carbon emissions. The prediction models were established with using the datasets acquired from similar marine engines under various steady-state conditions.