Simultaneous measurement of carbon emission and gas temperature via laser-induced breakdown spectroscopy coupled with machine learning.

A method, which can accurately measure carbon emission and gas temperature simultaneously in real-time from a laser-induced breakdown spectrum (LIBS) via machine learning, is proposed in this study. In typical, peak intensity ratios had been used to map species concentrations prior to plasma formation, after removing the broadband continuum of the spectrum; however, the dependence of these peak intensity ratios on the concentration changes with the change in gas density. Therefore, considering the fact that the strength and shape of this broadband continuum is a function of the gas density for a given optical setup, we attempted to collect a spectrum by shortening the time delay after the laser fire, such that the spectrum can contain some of the broadband continuum. Since the analytical quantification of this broadband continuum is not trivial, we employed a machine learning approach to acquire a model that simultaneously predicts the gas temperature and CO2 concentration. The predictive performance of the model trained with spectra that contain the broadband continuum was much better than that without it; the gradient-weighted regression activation mapping (Grad-RAM) analysis revealed that the model utilizes the broadband spectrum for temperature prediction and correction of changes in peak intensity due to temperature changes in the concentration prediction process.

Combined use of TDLAS and LIBS for reconstruction of temperature and concentration fields.

A new technique is developed for reconstructing the temperature and species-concentration fields by employing tunable diode laser absorption spectroscopy (TDLAS) and laser-induced breakdown spectroscopy (LIBS) on axisymmetric combustion fields. For two-line thermometry, the uncertainties in linestrengths of the absorption lines may cause systematic errors in temperature and species concentration estimations. Thus, the radial profiles of water vapor concentration are obtained first using the LIBS, assuming that the combustion is complete; then, the radial temperature profiles are estimated from the radial profiles of absorption coefficient, as reconstructed from the absorbance profiles obtained using the TDLAS. The spectral lines of water vapor at 7185.6 and 7444.36 cm-1 are selected as the linestrengths show monotonic decreases with the increase in temperature within the measuring temperature range. The radial profiles of temperature and water mole fraction are well-reconstructed, and the measurement error is found to be as low as 3%. The technique yielded higher temperatures compared to the thermocouple, possibly owing to the significant radiative heat loss in the thermocouple data.

Carbon Dioxide Concentration Estimation in Nonuniform Temperature Fields Based on Single-Pass Tunable Diode Laser Absorption Spectroscopy.

We propose a novel technique to accurately predict carbon dioxide (CO2) concentrations even in flow fields with temperature gradients based on a single laser path absorption spectrum measurement and machine learning. Concentration measurements in typical tunable diode laser absorption spectroscopy are based on a ratio of two integrated absorbances, each from a spectral line with different temperature dependence. However, the inferred concentrations can deviate significantly from the actual concentrations in the presence of temperature gradients. Furthermore, it is also difficult to find an analytical expression to compensate for the effect of nonuniform temperature profiles on concentration measurements. In this study, the entire absorption feature was considered since its shape and peak intensities vary with temperature and concentration. Specifically, a predictive model is obtained in a data-driven manner that can identify and compensate for the effect of a nonuniform temperature field on the spectrum. Despite a very detailed understanding of the CO2 absorption spectrum, it is nearly impossible to collect sufficient spectra for model acquisition by varying all temperature gradient conditions. Therefore, the model was obtained using only simulated data, much like the concept of a "digital twin". Finally, the predictive performance of the acquired model was verified using experimental data. In all test cases, the predictive performance of the model was superior to that of the two-line method. Additionally, a gradient-weighted regression activation mapping analysis confirmed that the model utilizes both the peak intensities as well as the change in the shape of absorption lines for prediction.

Effectiveness of ozone generated by a dielectric barrier discharge plasma reactor against multidrug-resistant pathogens and Clostridioides difficile spores.

The contaminated healthcare environment plays an important role in the spread of multidrug-resistant organisms (MDROs) and Clostridioides difficile. This study aimed to evaluate the antimicrobial effects of ozone generated by a dielectric barrier discharge (DBD) plasma reactor on various materials that were contaminated by vancomycin-resistant Enterococcus faecium (VRE), carbapenem-resistant Klebsiella pneumoniae (CRE), carbapenem-resistant Pseudomonas aeruginosa (CRPA), carbapenem-resistant Acinetobacter baumannii (CRAB) and C. difficile spores. Various materials contaminated by VRE, CRE, CRPA, CRAB and C. difficile spores were treated with different ozone concentrations and exposure times. Atomic force microscopy (AFM) demonstrated bacterial surface modifications following ozone treatment. When an ozone dosage of 500 ppm for 15 min was applied to VRE and CRAB, about 2 or more log10 reduction was observed in stainless steel, fabric and wood, and a 1-2 log10 reduction in glass and plastic. Spores of C. difficile were more resistant to ozone than were all other tested organisms. On AFM, the bacterial cells, following ozone treatment, were swollen and distorted. The ozone generated by the DBD plasma reactor provided a simple and valuable decontamination tool for the MDROs and C. difficile spores, which are known as common pathogens in healthcare-associated infections.

Fast and Easy Disinfection of Coronavirus-Contaminated Face Masks Using Ozone Gas Produced by a Dielectric Barrier Discharge Plasma Generator.

During the COVID-19 pandemic, face masks have become limited in stock. Most of sterilization methods are not applicable for eliminating virus from face masks without compromising the filtration efficiency of the masks. In this study, using a human coronavirus (HCoV-229E) as a surrogate for SARS-CoV-2 contamination on KF94 face masks, we show that the virus loses its infectivity with a 4 log reduction when exposed for 10 s to 120 ppm ozone gas produced by a dielectric barrier discharge plasma generator. Scanning electron microscopy, particulate filtration efficiency (PFE), and inhalation resistance tests revealed that there was no detectable structural or functional deterioration observed in the electrocharged filter layer of Korea Filter (KF) 94 masks even after their excessive exposure to ozone. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) showed decreases in amplification efficiency of HCoV-229E RNA recovered from masks exposed to ozone, indicating the damage to the RNA by the ozone treatment. Our results demonstrate that the plasma generator rapidly disinfects contaminated face masks at least five times without compromising filtration efficiency.