The lacking of laboratory black carbon (BC) samples have long challenged the corresponding toxicological research; furthermore, the toxicity tests of engineered carbon nanoparticles were unable to reflect atmospheric BC. As a simplified approach, we have synthesized artificial BC (aBC) for the purpose of representing atmospheric BC. Surface chemical properties of aBC were controlled by thermal treatment, without transforming its physical characteristics; thus, we were able to examine the toxicological effects on A549 human lung cells arising from aBC with varying oxidation surface properties. X-ray photoelectron spectroscopy, as well as Raman and Fourier transform infrared spectroscopy, verified the presence of increased amounts of oxygenated functional groups on the surface of thermally-treated aBC, indicating aBC oxidization at elevated temperatures; aBC with increased oxygen functional group content displayed increased toxicity to A549 cells, specifically by decreasing cell viability to 45% and elevating reactive oxygen species levels up to 294% for samples treated at 800 °C.

Exposure to particulate matter (PM) is related to various respiratory diseases, and this affects the respiratory immune system. Alveolar macrophages (AMs), which are defenders against pathogens, play a key role in respiratory inflammation through cytokine production and cellular interactions. Coconut oil demonstrates antioxidant and anti-inflammatory properties, and it is consumed worldwide for improved health. However, reports on the protective effects of coconut oil on the PM-induced respiratory immune system, especially in AMs, are limited. In this study, we generated artificial PM (APM) with a diameter approximately of 30 nm by controlling the temperature, and compared its cytotoxicity with diesel exhaust particles (DEP). We also investigated the antioxidant and anti-inflammatory effects of coconut oil in APM– and DEP–stimulated AMs, and the underlying molecular mechanisms. Our results showed that APM and DEP had high cytotoxicity in a dose-dependent manner in AMs. In particular, APM or DEP at 100 μg/mL significantly decreased cell viability (p < 0.05) and significantly increased oxidative stress markers such as reactive oxygen species (p < 0.01); the GSSH/GSH ratio (p < 0.01); and cytokine production, such as tumor necrosis factor-α (p < 0.001), interleukin (IL)-1β (p < 0.001), and IL-6 (p < 0.001). The expression of the genes for chemokine (C-X-C motif) ligand-1 (p < 0.05) and monocyte chemoattractant protein-1 (p < 0.001); and the proteins toll-like receptor (TLR) 4 (p < 0.01), mitogen-activated protein kinase (MAPK), and c-Jun N-terminal kinase (p < 0.001), p38 (p < 0.001); and extracellular receptor-activated kinase (p < 0.001), were also upregulated by PM. These parameters were reversed upon treatment with coconut oil in APM– or DEP–stimulated AMs. In conclusion, coconut oil can reduce APM– or DEP–induced inflammation by regulating the TLR4/MAPK pathway in AMs, and it may protect against adverse respiratory effects caused by PM exposure.

A fully integrated sensor array assisted by pattern recognition algorithm has been a primary candidate for the assessment of complex vapor mixtures based on their chemical fingerprints. Diverse prototypes of electronic nose systems consisting of a multisensory device and a post processing engine have been developed. However, their precision and validity in recognizing chemical vapors are often limited by the collected database and applied classifiers. Here, we present a novel way of preparing the database and distinguishing chemical vapor mixtures with small data acquisition for chemical vapors and their mixtures of interest. The database for individual vapor analytes is expanded and the one for their mixtures is prepared in the first-order approximation. Recognition of individual target vapors of NO₂, HCHO, and NH₃ and their mixtures was evaluated by applying the support vector machine (SVM) classifier in different conditions of temperature and humidity. The suggested method demonstrated the recognition accuracy of 95.24%. The suggested method can pave a way to analyze gas mixtures in a variety of industrial and safety applications.

The demand for high-performance two-dimensional gas barrier materials is increasing owing to their potential for application in optoelectronic devices. These materials can help the devices maintain their properties over a long period. Therefore, in this study, we investigated the gas barrier performance of hexagonal boron nitride (h-BN) monolayers grown on copper foils via electrochemical polishing (ECP). The ECP treatment helped reduce the surface roughness of the copper foils. As a result, the nucleation density was reduced and highly crystalline h-BN monolayers were produced. The gas barrier performance of h-BN monolayers on copper foils with ECP was comparable to that of graphene. Our finding demonstrates the potential of monolayer h-BN as a high-performance and economical gas barrier material for organic-based optoelectronic devices.