2020.4.13 Given the COVID-19 situation and the urgent need for medical masks, we will test a wide range of household materials and common commercial fabrics that can be used to manufacture masks. The results will be updated daily on google sheet:
Please feel free to request access or send an email to firstname.lastname@example.org to access data.
Hello! Welcome to Particle Measurement & Technology Laboratory (PMTL) led by Professor Yang Wang at Missouri S&T. At PMTL, we conduct particle measurement, particle synthesis and develop particle characterization techniques for efficient environmental and energy applications.
Our research focus on understanding the nanoparticle “Structure-Property-Impact” relationship, to optimize the functionality of materials in nano-enabled devices, and to minimize the impact of atmospheric particulate matter on human health. These research projects are enabled or assisted by the real-time characterization of particles in the sub-micron and sub-nanometer range, an important frontier in aerosol science and technology. Novel techniques are being deployed in our lab, to study the incipient particle formation with a high size resolution, to examine the evolution of transient particles with a high time resolution, and to conduct air quality monitoring with a stronger network.
1. Early stages of particle formation in high temperature aerosol reactors
Aerosol science and technology enable continual advances in material synthesis and atmospheric pollutant control. Among these advances, one important frontier is characterizing the initial stages of particle formation by real time measurement of particles below 2 nm in size. Sub 2 nm particles play important roles by acting as seeds for particle growth, ultimately determining the final properties of the generated particles. Tailoring nanoparticle properties requires a thorough understanding and precise control of the particle formation processes, which in turn requires characterizing nanoparticle formation from the initial stages. The knowledge on particle formation in early stages can also be applied in quantum dot synthesis and material doping. This project pursued two approaches in investigating incipient particle characterization in systems with aerosol formation and growth: (1) using a high-resolution differential mobility analyzer (DMA) to measure the size distributions of sub 2 nm particles generated from high-temperature aerosol reactors, and (2) analyzing the physical and chemical pathways of aerosol formation during combustion.
2. Combustion synthesis of functional nanoparticles for energy and environmental applications
Compared to conventional approaches for the manufacture of nanoparticles, gas-phase synthesis offers the advantages of high-throughput production, fast processing, and simplicity. Wide applications of these nanomaterials can be found in energy and environmental engineering, and new fields of nanomedicine and nanorobotics have emerged and are expected to flourish. Recent applications of gas-phase synthesized nanomaterials in energy conversion include solar cells, lithium batteries, CO2 photo-reduction, and catalytic combustion of volatile organic compounds. In these applications, the nanomaterials act as a medium for the transport of electrons and ions or as a catalyst promoting the reaction rates. Their properties are a strong function of their structures, and the successful application of gas-phase synthesis requires an adequate degree of tailoring and control of these structures. By controlling the synthesis method, temperature, composition, and reaction time, the structure of the nanomaterials can be fine tuned for better performances in energy conversion applications. Modeling approaches are also developed to validate the synthesis techniques.
3. Laboratory evaluation of low-cost particle sensors for PM measurement, PM sensor network deployment, and data analysis
Particulate matter (PM) is a crucial factor of air quality affecting visibility, human health, and global climate.PM is quantified using PM10, PM2.5, or PM1, according to inhalation and deposition properties in the human respiratory system, representing the mass concentration of particles below 10 μm, 2.5 μm, and 1 μm in aerodynamic size, respectively. Temporal and spatial PM index may differ from each other significantly due to the limited transport coefficient of particles. In order to obtain accurate local PM indices down to streets or blocks, a higher density of measurement sites mapping out the entire area is required, but the expense of the complicated instruments frustrates the plan. With prototypes first assembled in smoke detectors and air purifiers decades ago, particle sensors become popular in recent years with the usage of portable PM monitors, due to the remarkably low price of around $ 10 USD and the merging need for “big data”. These low-cost sensors can be used in locating pollution hotspots or generating coarse 3-D map of PM concentrations for individuals, industries, and environmental agencies. Due to the huge amount of dataset generated by these sensors, a machine learning algorithm is needed to smartly select useful data and generalize the trend of the data. The accurate mapping of the PM information in indoor, local, and regional scales can be used to study the health and climate effects of PM.