In this study, a co-catalytic route was explored to enhance the photo-ozone catalytic degradation of volatile organic compounds (VOCs). NiCo2O4 was loaded onto the surface of CeO2 nanoparticles to create a composite catalyst (10%NiCo2O4/CeO2). The integration of NiCo2O4 onto CeO2 enhanced the interaction between the catalyst and toluene, a representative VOC, resulting in significantly increased toluene adsorption without a corresponding increase in specific surface area. This integration also improved the utilization of charge carriers and conversion of ozone to O2-. Under visible light irradiation, H2O accumulated charge carriers at 10%NiCo2O4/CeO2\'s surface, facilitating both ozone utilization and toluene adsorption. Another benefit of NiCo2O4 loading was its ability to enhance the conversion efficiency of solar energy. Consequently, the toluene removal and mineralization efficiencies of 10%NiCo2O4/CeO2 were enhanced by 182% and 309% compared to CeO2, and by 201% and 357% compared to NiCo2O4, respectively. Overall, this study demonstrated a novel co-catalyst design strategy for enhancing the photo-ozone catalytic degradation of VOCs.

A promising pollution control technology is cold plasma driven chemical processing. The plasma is a pulsed electric gas discharge inside a near atmospheric-pressure-temperature reactor. The system is energized by a continuous stream of very short high-voltage pulses. The exhaust gas to be treated flows through the reactor. The methods applied involve the development of robust cold plasma systems, industrial applications and measuring technologies. Tests of the systems were performed at many industrial sites and involved control of airborne VOC (volatile organic compound) and odor. Electrical, chemical and odor measuring data were collected with state-of-the-art methods. To explain the test data an approximate solution of global reaction kinetics of pulsed plasma chemistry was developed. It involves the Lambert function and, for convenience, a simple approximation of it. The latter shows that the amount of removal, in good approximation, is a function of a single variable. This variable is electric plasma power divided by gas flow divided by input concentration. In the results sections we show that in some cases up to 99% of volatile pollution can be removed at an acceptable energy requirement. In the final sections we look into future efficiency enhancements by implementation of (sub)nanosecond pulsed plasma and solid state high-voltage technology and by integration with catalyst technology.

In the planar heterostructure of perovskite-based solar cells (PSCs), tin oxide (SnO2) is a material that is often used as the electron transport layer (ETL). SnO2 ETL exhibits favorable optical and electrical properties in the PSC structures. Nevertheless, the open circuit voltage (VOC) depletion occurs in PSCs due to the defects arising from the high oxygen vacancy on the SnO2 surface and the deeper conduction band (CB) energy level of SnO2. In this research, a cerium (Ce) dopant was introduced in SnO2 (Ce-SnO2) to suppress the VOC loss of the PSCs. The CB minimum of SnO2 was shifted closer to that of the perovskite after the Ce doping. Besides, the Ce doping effectively passivated the surface defects on SnO2 as well as improved the electron transport velocity by the Ce-SnO2. These results enabled the power conversion efficiency (PCE) to increase from 21.1% (SnO2) to 23.0% (Ce-SnO2) of the PSCs (0.09 cm2 active area) with around 100 mV of improved VOC and reduced hysteresis. Also, the Ce-SnO2 ETL-based large area (1.0 cm2) PSCs delivered the highest PCE of 22.9%. Furthermore, a VOC of 1.19 V with a PCE of 23.3% was demonstrated by Ce-SnO2 ETL-based PSCs (0.09 cm2 active area) that were treated with 2-phenethylamine hydroiodide on the perovskite top surface. Notably, the unencapsulated Ce-SnO2 ETL-based PSC was able to maintain above 90% of its initial PCE for around 2000 h which was stored under room temperature condition (23-25 °C) with a relative humidity of 40-50%.

This review is aimed at researchers in air pollution control seeking to understand the latest advancements in volatile organic compound (VOC) removal. Implementing of plasma-catalysis technology for the removal of volatile organic compounds (VOCs) led to a significant boost in terms of degradation yield and mineralization rate with low by-product formation. The plasma-catalysis combination can be used in two distinct ways: (I) the catalyst is positioned downstream of the plasma discharge, known as the \"post plasma catalysis configuration\" (PPC), and (II) the catalyst is located in the plasma zone and exposed directly to the discharge, called \"in plasma catalysis configuration\" (IPC). Coupling these two technologies, especially for VOCs elimination has attracted the interest of many researchers in recent years. The term \"synergy\" is widely reported in their works and associated with the positive effect of the plasma catalysis combination. This review paper investigates the state of the art of newly published papers about catalysis, photocatalysis, non-thermal plasma, and their combination for VOC removal application. The focus is on understanding different synergy sources operating mutually between plasma and catalysis discussed and classified into two main parts: the effect of the plasma discharge on the catalyst and the effect of the catalyst on plasma discharge. This approach has the potential for application in air purification systems for industrial processes or indoor environments.

We present a new method for investigating the oxidation and emission behavior of air-permeable materials. Employing this method, a differentiated statement can be made about the extent to which critical volatile organic compounds (VOCs) such as formaldehyde, acetaldehyde, and acrolein are contained in the material as impurities or formed by thermo-oxidative degradation of the polymer matrix in the use phase. The parameters affecting methods of VOC analysis are reviewed and considered for the developed method. The molecular mechanisms of VOC formation are discussed. Toxicological implications of the reaction kinetics are put into context with international guidelines and threshold levels. This new method enables manufacturers of cellular materials not only to determine the oxidative stability of their products but also to optimize them specifically for higher durability. ENVIRONMENTAL IMPLICATION: Cellular materials are ubiquitous in the technosphere. They play a crucial role in various microenvironments such as automotive interiors, building insulation, and cushioning. These materials are susceptible to oxidative breakdown, leading to the release of formaldehyde, acetaldehyde, and acrolein. The ecotoxicological profiles of these compounds necessitate monitoring and regulation. The absence of reproducible and reliable analytical methods restricts research and development aimed at risk assessment and mitigation. This work significantly enhances the toolbox for optimizing the oxidative stability of any open-cell cellular material and evaluating these materials in terms of their temperature-dependent oxidation and emission behavior.

Room-temperature catalytic oxidation of formaldehyde (HCHO) has been extensively investigated due to its high efficiency, convenience, and environmental friendliness. Herein, nickel-iron layered double hydroxide (NiFe LDH) nanosheets were synthesized in-situ on a nickel foil (NF) using a facile one-step hydrothermal method, followed by the deposition of ultra-low content (0.069 wt%) of Pt nanoparticles through NaBH4 reduction. The resulting three-dimensional (3D) hierarchical Pt/NiFe-NF catalyst exhibited exceptional activity for the complete decomposition of formaldehyde to carbon dioxide (CO2) at room temperature (∼95 % conversion within 1 h), as well as remarkable cycling stability. The 3D porous structure of Pt/NiFe-NF provides fast transport channels for the diffusion of gas molecules, making the active catalyst surfaces more accessible. Moreover, abundant hydroxyl groups in NiFe LDH serve as adsorption centers for HCHO molecules to form dioxymethylene (DOM) and formate intermediates. Furthermore, electronic interactions between NiFe LDH and Pt enhance the adsorption and activation of O2 on Pt surfaces, leading to the complete decomposition of intermediates into non-toxic products. This work presents new insights into the design and preparation of Pt-based 3D hierarchical catalysts with surface-rich hydroxyl groups for the efficient removal of indoor HCHO.

Plants emit a range of volatile organic compounds (VOCs) as a way of interacting with their biotic and abiotic surroundings. These VOCs can have various ecological functions, such as attracting pollinators, repelling herbivores, or may be emitted in response to abiotic stress. For the present dataset, we used gas chromatography coupled ion mobility spectrometry (GC-IMS) to analyse the VOCs emitted by different plant species under controlled conditions. GC-IMS is a rapid and sensitive technique for gas phase analysis, that separates VOCs based on their retention time and drift time, resulting in characteristic heatmaps where the xy-position of a signal corresponds to compound identity, while signal intensity reflects its abundance. In this dataset, rapid analysis by GC-IMS was used to record emission pattern of 140 plant species from different taxonomic groups. This includes both floral volatiles and emission from leaves after induced damage. The data was pre-evaluated and listed in one table, containing information on the plant material used, as well as information on the respective emission patterns (including already identified compounds). Thus, this dataset provides a broad overview over plant VOC emissions. These can be used to either check the distribution of knowns substances, or the specific emissions of plants for functional, ecological or physiological studies or as the starting point for chemotaxonomic studies. The extraordinary ease with which these data can be generated - with the suitable set-up - lends itself to larger scale systematic or ecological studies across plant (or animal) groups and even ecosystems.

Over the last decade, the French ATR-42 research aircraft explored contrasting polluted plumes in the Paris megacity, the North-West Mediterranean Basin (WMB) and South West Africa (SWA) in the framework of the MEGAPOLI, ChArMEx/SAFMED and DACCIWA international projects, respectively. Major VOCs were measured by a high-sensitivity airborne Quadrupole Proton Transfer Reaction Mass Spectrometer (Q-PTR-MS), showing a robust and consistent response. Regardless of the location, the air mass composition is dominated by oxygenated VOC (OVOC: methanol, formaldehyde, acetaldehyde, acetone and isoprene oxidation products), which explain 70 % of the total VOC burden measured by the Q-PTR-MS. The distribution between OVOC, anthropogenic AVOC and biogenic BVOC is consistent between the three regions. The calculated OH loss rates (12 s-1) and ozone-forming potential (1200 OFP-relative ppb) are three times higher in the SWA plumes. These values are consistent with the calculated and measured reactivities at the ground. The reactivity of the plumes is by far dominated by biogenic BVOC. The chemical processing of VOC was examined by establishing various metrics linking Δ[O/VOC] (VOC or oxygenated VOC), plume dilution and the time processing of the plume (cumulative OH exposure Δt[OH] and the linear decay of primary AVOC and the production/decay of secondary OVOC). As expected, ∆[Ox]/∆[CO] increases with Δt[OH], with significant R2 (0.58 to 0.93). AVOC (aromatics) usually show a decay rate between -0.5 and -3.2 pptAVOC ppbCO-1 per hour, while OVOC either show an increase (secondary production) or a decrease. The production rate is by far the strongest, up to 18 pptOVOC ppbCO-1 per hour (acetaldehyde) during the eastern flight 33 in Paris. Our results set a benchmark for future photochemical studies to compare with. While the anthropogenic origin of some BVOC (terpenoids) and interferences are not excluded, it also emphasizes the importance of the VOC biogenic fraction in anthropogenically influenced environments, which is expected to increase in a warming climate.

BACKGROUND: Throughout the COVID-19 pandemic, virus evolution and large-scale vaccination programs have caused multiple exposures to SARS CoV-2 spike protein, resulting in complex antibody profiles. The binding of these to spike protein of \"future\" variants in the context of such heterogeneous exposure has not been studied.
METHODS: We tested archival sera (Delta and Omicron period) stratified by anti-spike antibody (including IgG) levels for reactivity to Omicron-subvariants(BA.1, BA.2,BA.2.12.1, BA.2.75, BA.4/5 and BF.7) spike protein. Assessed antigenic distance between groups using Antigenic Cartography and performed hierarchical clustering of antibody data in a Euclidean distance framework.
RESULTS: Antibody (including IgG) antibody reactivity to Wild-type (CLIA) and subvariants (ELISA) spike protein were similar between periods (p > 0.05). Both \'High S\' and \'Low S\' of Delta and Omicron periods were closely related to \"future\" subvariants by Antigenic Cartography. Sera from different S groups clustered together with \'Low S\' interspersed between \'High S\' on hierarchical clustering, suggesting common binding sites. Further, anti-spike antibodies (including IgG) to Wild-type (S1/S2 and Trimeric S) clustered with Omicron-subvariant binding antibodies.
CONCLUSIONS: Hybrid immunity caused by cumulative virus exposure in Delta or Omicron periods resulted in equivalent binding to \"future\" variants, which might be due to binding to conserved regions of spike protein of future variants. A prominent finding is that the \'Low S\' antibody demonstrates similar binding.

The accurate prediction of standard vaporization enthalpy (ΔvapHm°) for volatile organic compounds (VOCs) is of paramount importance in environmental chemistry, industrial applications and regulatory compliance. To overcome traditional experimental methods for predicting ΔvapHm° of VOCs, machine learning (ML) models enable a high-throughput, cost-effective property estimation. But despite a rising momentum, existing ML algorithms still present limitations in prediction accuracy and broad chemical applications. In this work, we present a data driven, explainable supervised ML model to predict ΔvapHm° of VOCs. The model was built on an established experimental database of 2410 unique molecules and 223 VOCs categorized by chemical groups. Using supervised ML regression algorithms, the Random Forest successfully predicted VOCs\' ΔvapHm° with a mean absolute error of 3.02 kJ mol-1 and a 95% test score. The model was successfully validated through the prediction of ΔvapHm° for a known database of VOCs and through molecular group hold-out tests. Through chemical feature importance analysis, this explainable model revealed that VOC polarizability, connectivity indexes and electrotopological state are key for the model\'s prediction accuracy. We thus present a replicable and explainable model, which can be further expanded towards the prediction of other thermodynamic properties of VOCs.