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Additive manufacturing (AM) also commonly known as 3D-printing is a manufacturing method which creates parts from adding layer into another. In the field of printed electronics Inkjet printing (IJP) and Aerosol Jet printing (AJP) are the most common AM techniques. IJP and AJP are non-contact-based printing techniques where ink is deposited on a surface with droplets. AJP aerosolizes the ink into a mist which is deposited on a surface according to the predetermined pattern. IJP instead produces singular droplets when printing. These printing methods have been used for manufacturing various printed electronics such as strain gauges which has been the focus of this project.  The purpose of this thesis was to investigate the feasibility of printing strain gauges. Through a literature study the overall function and use for strain gauges and various printing methods were investigated, as well as previous studies related to printed strain gauges using AJP and IJP. To further investigate one of these techniques, strain gauges were printed using Inkjet printing. The sensors were printed using two different inks, one containing silver particles and the other containing constantan particles. The strain gauges were also printed on various substrates such as Polyimide (PI) and Polyetheretherketone (PEEK), to determine the best material combination. The silver strain gauges were then sintered in an oven while the constantan sensors were sintered using photonic sintering. To evaluate each ink-substrate combination several tests was performed throughout the printing and sintering process. A tape test was used to determine adhesion, SEM analysis was performed to study the effect of the sintering process and the resistance was measured to calculate the conductivity and study the printability on different substrates. To characterise the printed strain gauges a bending test was performed where the change in resistance was measured with changing strain. The output was also studied over time to determine the stability of the printed sensors.  The silver ink showed overall better properties compared to the constantan ink, which could be due to that the silver ink has been more developed than the constantan ink. The resistivity of the silver ink was calculated to 7.0E-07 Ωm and the constantan ink to 2.23E-05 Ωm. The average gauge factor for the silver ink printed on PI was calculated to GFavg~1.6 at low strain and GFavg~2.1 at high strain, the silver samples printed on PEEK was GFavg~2.4 at low strain and GFavg~2.3 at higher strain, and the constantan samples was determined to GFavg~2.7 during loading at low strain and GFavg~17 at high strain due to deformation. Some of the samples printed with silver ink showed quite linear behaviour while the samples printed with constantan deformed when applying high stress. The silver samples printed on PEEK showed more hysteresis compared to the silver samples printed on PI, but the PEEK samples showed a better stability over time compared to PI.  The thesis shows that it is possible to manufacture strain gauges, but the result depends a lot on the ink and substrate material chosen. Silver inks has been developed over a long period and thus making it easier to handle and the result is better compared to newer inks such as constantan.

This paper aims to study how varying crossflow burning temperatures from 1100 C to 1800 C affect the liquid droplet breakup, size distribution, and atomization of a liquid water jet injected into a vitiated crossflow. The LJIC injection mechanism was implemented using the high-pressure axially staged combustion facility at the University of Central Florida. The measurement devices used to gather particle data from the exhaust plume were the TSI Aerodynamic Particle Sizer (APS), which measures particles between 0.523 µm and 20 µm, and the Sensirion SPS30 (SPS30), which measures particles between 0.3 µm and 10 µm. Both measurement devices were placed 3 ft away from the choked exit. Table 3 shows that the 1800 C crossflow temperature behaved as predicted by having the largest particle distribution of 67.97% and the largest particle count of 19,301 at 0.523 µm. The 1100 C crossflow produced the second-largest normalized particle count of 66.69% and raw particle count of 20,209 at 0.523 µm. This result is contrary to the original hypothesis because it shows that the relationship between temperature and particle count is non-linear and that many other factors must be at play in the atomization process, such as the droplet distribution at the nano level. The SPS30 was used to compare the particle size distributions between a 1500 C and 1800 C crossflow. Acquiring number concentration data for particles up to 10 µm in size, the 1800 C crossflow had a distribution peak at 802.76416 N/cm3, and the 1500 C crossflow had a peak of 867.28272 N/cm3. For the 0.5 µm peak, The 1800 C had a 10 µm particle size distribution peak at 674.27.76416 N/cm3, and the 1500C crossflow had a peak of 730.501 N/cm3. The decreased number concentration from 1500 C to 1800 C case grants the water particles in the 1800 C crossflow increased surface area, which allows for increased heat exposure from the vitiated crossflow [7]. Despite some nonlinear particle count results, the highest crossflow temperature of 1800 C produces the best atomization results by reducing the total particle count and having the largest collection of particles at the lowest detectable particle size of 0.523 µm.

Aerosol–cloud interaction is the most uncertain component of the overall anthropogenic forcing of the climate, in which cloud droplet number concentration (Nd) sensitivity to aerosol (S) is a key term for the overall estimation. However, satellite-based estimates of S are especially challenging, mainly due to the difficulty in disentangling aerosol effects on Nd from possible confounders. By combining multiple satellite observations and reanalysis, this study investigates the impacts of (a) updraft, (b) precipitation, (c) retrieval errors, and (d) vertical co-location between aerosol and cloud on the assessment of S in the context of marine warm (liquid) clouds. Our analysis suggests that S increases remarkably with both cloud-base height and cloud geometric thickness (proxies for vertical velocity at cloud base), consistent with stronger aerosol–cloud interactions at larger updraft velocity for midlatitude and low-latitude clouds. In turn, introducing the confounding effect of aerosol–precipitation interaction can artificially amplify S by an estimated 21 %, highlighting the necessity of removing precipitating clouds from analyses of S. It is noted that the retrieval biases in aerosol and cloud appear to underestimate S, in which cloud fraction acts as a key modulator, making it practically difficult to balance the accuracies of aerosol–cloud retrievals at aggregate scales (e.g., 1◦ × 1 ◦ grid). Moreover, we show that using column-integrated sulfate mass concentration (SO4C) to approximate sulfate concentration at cloud base (SO4B) can result in a degradation of correlation with Nd, along with a nearly twofold enhancement of S, mostly attributed to the inability of SO4C to capture the full spatiotemporal variability of SO4B. These findings point to several potential ways forward to practically account for the major influential factors by means of satellite observations and reanalysis, aiming at optimal observational estimates of global radiative forcings due to the Twomey effect and also cloud adjustments.

Fibrous air filters are commonly used to capture airborne particles due to their potential for a relatively high capture efficiency and low airflow resistance. Their performance characteristics make them ideal candidates in many instances, spanning a wide range from residential to sensitive industrial applications. However, as more particles are captured, the performance of the filter will evolve. This evolution of performance typically manifests as a higher capture efficiency and higher airflow resistance resulting from the additional particulate deposits. The prediction of fibrous filter performance has been the focus of research for many decades, resulting in numerous analytical, numerical, and empirical models. This work seeks to improve upon the state of aerosol filtration by investigating the process through which these models are developed and validated. To meet this objective, three major efforts are implemented: 1) a comprehensive literature review, 2) an aerosol and media measurement analysis focusing on instrumentation and Scanning Electron Microscope (SEM) imagery, and 3) the creation of a process to analyze and develop fibrous air filter models. A conceptual foundation is provided by the literature review, establishing the current state of fibrous filtration modeling of solid particles and identifying candidate models for implementation. The influence of data collection and reduction methodology for particle mass loading experiments is explored with an emphasis on the resulting effects towards filtration model development. Furthermore, an automated methodology to measure the physical characteristics of high efficiency particulate air (HEPA) filtration media is investigated, completing the set of variables necessary to predict filtration performance. Finally, an algorithm is proposed to optimize and correlate model variables to collected empirical data, allowing for the improvement of model predictions by investigating model functionality and identifying limitations. Altogether, the three efforts provide a framework through which fibrous aerosol filtration models of solid particles may be developed, validated, and systematically analyzed.

The satellite-based cloud condensation nuclei (CCN) proxies used to quantify the aerosolcloud interactions (ACIs) are column integrated and do not guarantee the vertical co-location of aerosols and clouds. This has encouraged the use of height-resolved measurements of spaceborne lidars for ACI studies and led to advancements in lidar-based CCN retrieval algorithms. In this study, we present a comparison between the number concentration of CCN (nCCN) derived from ground-based in situ and spaceborne lidar cloud-aerosol lidar with orthogonal polarization (CALIOP) measurements. On analysing their monthly time series, we found that about 88% of CALIOP nCCN estimates remained within a factor of 1.5 of the in situ measurements. Overall, the CALIOP estimates of monthly nCCN were in good agreement with the in situ measurements with a normalized mean error of 71%, normalized mean bias of 39% and correlation coefficient of 0.68. Based on our comparison results, we point out the necessary measures that should be considered for global nCCN retrieval. Our results show the competence of CALIOP in compiling a global height- and type-resolved nCCN dataset for use in ACI studies.

The main purpose of this thesis is to examine the mineral dust aerosol transport into the Arctic. Two three-dimensional reanalysis datasets of atmospheric composition, the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), are analyzed with regard to dust transport into the Arctic. The reanalyses agree on that the largest mass transport of dust into the Arctic occurs across western Russia during spring and early summer, although large dust transport events can occur across other geographical areas during all seasons. In several aspects, the reanalyses show substantial differences. The transport in CAMSRA is considerably smaller, more concentrated and occurs at lower altitudes. Furthermore, the transport in CAMSRA is to a larger extent than MERRA-2 driven by well-defined events of dust transport in space and time. The reanalysis data are compared with surface measurements of dust in the Arctic and dust extinction satellite retrievals from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The comparison indicates that CAMSRA underestimates the dust transport into the Arctic and that MERRA- 2 likely overestimates it. The discrepancy between CAMSRA and MERRA-2 can in part be explained by the assimilation process where too little dust is assimilated in CAMSRA while MERRA-2 overestimates the production of light particles, causing an excessive transport, and the assimilation process further increases the dust concentration in remote areas. Despite the clear differences between the reanalyses, this study provides new insights into the spatio-temporal distribution of the dust transport into the Arctic and the transported mass is estimated to be within the range 1.5–31 Tg yr-1. The thesis also briefly examines the aerosol transport of all five aerosol species carried by the reanalyses, that in addition to dust are black carbon, organic matter, sea-salt and sulfate. The annual aerosol mass transport to the Arctic in CAMSRA and MERRA-2 are 24 Tg and 50 Tg respectively. The reanalyses show substantial differences regarding the proportions of the different aerosol types — emphasizing that it is crucial that the aerosol module manages to simulate the correct aerosol mass fractions, as the assimilation of AOD alone cannot change the proportions between the aerosols.

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