This study examines the influence of nanofillers on the ultraviolet (UV) penetration depth of photopolymer resins used in stereolithography (SLA) 3D printing, and their impact on printability. Three nanofillers, multiwalled carbon nanotubes (MWCNT), graphene nanoplatelets (xGNP), and boron nitride nanoparticles (BNNP), were incorporated into a commercially available photopolymer resin to prepare nanocomposite formulations. The UV penetration depth (Dp) was assessed using the Windowpane method, revealing a significant reduction with the addition of nanofillers. At a concentration of 0.25 wt.%, MWCNT showed the highest reduction in Dp (90%), followed by xGNP (65%) and BNNP (33%). SLA 3D printing was performed at varying nanofiller concentrations to evaluate printability. The findings highlight a strong correlation between Dp and the maximum printable nanofiller concentration, with MWCNT limiting printability to 0.05 wt.% due to its low Dp, while BNNP allowed printing up to 1.5 wt.%. Mechanical testing showed substantial improvements in hardness and elastic modulus, even at low nanofiller concentrations, with BNNP outperforming other fillers. Compared to a clear photopolymer, the elastic modulus for 3D printed nanocomposite samples with 0.05 wt.% nanofiller compositions showed an improvement of 43% for MWCNT, 63% for xGNP, and 104% for BNNP. The hardness results showed an improvement of 86% for MWCNT, 103% for xGNP, and 179% for BNNP. These results underscore the importance of Dp in determining the layer thickness and print success in SLA 3D printing. Practical applications include the design of advanced photopolymer nanocomposites for biomedical devices, electronics, and lightweight structural components. This research provides valuable insights for tailoring material properties to meet the demands of high-performance additive manufacturing.

Coal dust explosion constitutes a significant hazard in underground coal mines, coal power plants and other industries utilising coal as fuel. Knowledge of the explosion mechanism and the factors causing coal explosions is essential to investigate for the identification of the controlling factors for preventing coal dust explosions and improving safety conditions. However, the underlying mechanism involved in coal dust explosions is rarely studied under Artificial Intelligence (AI) based modelling. Coal from three different regions of Khyber Pakhtunkhwa, Pakistan, was tested for explosibility in 1.2 L Hartmann apparatus under various particle sizes and dust concentrations. First, a random forest algorithm was used to model the relationship between inputs (coal dust particle size, coal concentration and gross calorific value (GCV)), outputs (maximum pressure (P_max) and the deflagration index (K_st)). The model reported an R² value of 0.75 and 0.89 for P_max and K_st. To further understand the impact of each feature causing explosibility, the random forest AI model was further analysed for sensitivity analysis by SHAP (Shapley Additive exPlanations). The study revealed that the most critical parameter affecting the explosibility of coal dust were particle size > GCV > concentration for P_max and GCV > Particle size > Concentration for Kst. Mutual interaction SHAP plots of two variables at a time revealed that with 400 gm/L), explosibility is relatively lower irrespective of GCV and particle sizes.

Reducing energy consumption in buildings has received intensified research impetus since the introduction of the decarbonization goals set in the Paris agreement. Many domestic and specialized applications require clean rooms (indoor built environments) for safe and clean operation. Energy efficiency in clean room spaces depends on maintaining livable or required conditions such as temperature, humidity, and particle concentration with minimal use of energy and new carbon dioxide (CO₂) emissions. In the literature, parameters such as temperature, relative humidity, particle concentrations, and CO₂ emissions are not able to be properly controlled in clean room systems. The designed system in the literature involves high energy consumption and high economic costs. All these factors add novelty to this research, which was a significant research gap in previous studies. This clean room is directly linked to environmental parameters such as ambient temperature, relative humidity, etc. The clean room is also related directly to the building and infrastructure in such a way that there are certain regulatory requirements for designing a clean room. For designing and constructing the controlled environment in a clean room, the English (EN) documents, ISO 9000, and various other standards allow for clean rooms for different types of products. In this research, the designed control configurations properly control the system. Additionally, this system is energy efficient, with positive environmental aspects regarding CO₂ emissions. Three control configurations were designed in this research, option A, option B, and option C, and three parameters are controlled in the study. These parameters are room temperature, relative humidity, and CO₂ emissions (outside the room). CO₂ emissions are controlled outside the room (in the environment). In the last research phase, a comparative analysis of these three control configurations was performed to find an energy-efficient system with fewer CO₂ emissions. Control configuration B (option B) provides reliable results regarding an energy-efficient system and fewer CO₂ emissions emitted to the environment. In this study, an optimized configuration for the air conditioning system was developed for a clean room (volume 185.6 m³) with a required temperature of 23 °C, relative humidity of 40%, and a particle size of less than 0.3 μm. Three different design configurations were analyzed using TRNSYS simulation software. The minimization of energy use and CO₂ emissions were the objective functions. Energy loads were calculated for each of the configurations by varying the fixed air change per hour and the minimum outdoor air flow rate. The results of a whole year simulation run for control configurations A, B, and show that, on the one hand, the ambient weather conditions of temperature and relative humidity (RH) is varied throughout year and, on the other hand, the clean room temperature was maintain at exactly 23 °C, which is the required set point temperature, for all the three configurations (A, B, and C). Furthermore, the clean room relative humidity was maintained at 36% for configuration A, below the 40% which was the set point for clean room relative humidity, and at 40% for configurations B and C. Configuration B exhibited the minimum energy use (7300 kWh), at a fixed air change per hour value of 20 and a minimum outdoor air flow rate of 150 L/s, with the least amount of CO₂ emissions, offering an overall 25% improvement over configurations A and C.