Cationic silver hydride clusters (AgnH+) can be formed by a number of physical and chemical processes, holding great promise for a range of applications including photonics, catalysis, sensing, and biomedicine, among others. Here, we present a comprehensive theoretical investigation of AgnH+ clusters (n = 1-7) using highly accurate coupled-cluster (CC) theory. Multiple low-lying isomers are identified using CC theory with single and double excitations (CCSD), whereas their relative stabilities are determined with the more accurate CCSD(T) method. The CCSD(T) results predict a pronounced odd-even alternation in relative stabilities, with Ag2H+ being the most stable species, which is consistent with experimental mass spectrometry measurements. Ab initio molecular dynamics simulations show that all low-energy isomers remain structurally rigid at room temperature, whereas bonding analyses (frontier molecular orbitals, natural bond orbital, molecular electrostatic potential, quantum theory of atoms in molecules, non-covalent interaction) indicate strong ionic Ag-H interactions, weak non-covalent Ag-Ag interactions, and significant donor-acceptor stabilization in larger clusters. Electrical mobilities of these clusters, computed by the trajectory method, were labelled on experimental spectra, in order to contribute towards their interpretation. Overall, our results resolve inconsistencies from prior theoretical predictions, provide a rigorous description of cationic silver hydride clusters, and are used to improve the interpretation of earlier observations.

Abstract. The sizing of aerosol particles is commonly carried out by electrical techniques, requiring particles to reach a known charge distribution prior to measurement. This is typically achieved by passing the particles through bipolar diffusion chargers, where the resulting steady-state particle charge distribution depends on the properties of the ions therein. We present new measurements of the charge fractions of sub-20 nm particles after bipolar diffusion charging, along with measurements characterizing the properties of ions generated within the bipolar charger. Our results show that, under steady-state conditions, the particle charge distribution is primarily determined by charger ion properties, which can be influenced even by trace gas contamination. Specifically, the use of commonly employed conductive silicone tubing, which emits trace concentrations of volatile methyl siloxanes (VMS), changes the mean positive ion mobility by 20 %, leading to deviations of up to 25 % in the fraction of singly charged particles relative to measurements without the tubing. We further show that the use of conductive silicone tubing stabilizes the mean positive ion mobility to 1.05 ± 0.1 cm2 V−1 s−1associated with VMS. The mobility of the negative ions, on the other hand, remains highly dependent on gas composition. Building on the finding that positive ion properties can be readily stabilized to repeatable values, we show that negative ion properties can be approximated through a simple bipolar Mobility Particle Size Spectrometers (MPSS) measurement. Our measurements show good agreement with charge fractions predicted by Hoppel & Frick theory, with relative differences of up to −6.1 % and 0.6 % for positive and negative charge fractions, respectively, supporting the validity of classical charging theory for sub-20 nm particles. In contrast, significant deviations from the commonly used Wiedensohler (1988) approximation highlight the importance of accounting for environment-specific ion properties when predicting particle charge distributions.

Atmospheric new particle formation (NPF) is a major source of aerosol particles in the Earth's atmosphere. However, process-level understanding of the early stages of particle formation and growth remains poorly represented in climate models, limiting accurate estimates of aerosol effective radiative forcing. Here, we use comprehensive observations from the Spring Particles in Cyprus (SPICY) field campaign conducted at a rural background site in Cyprus. We report new observations of nanoparticle shrinkage (NPS), marked by the rapid decrease in size of sub-20 nm particles occurring in the absence of preceding NPF event. Thus, the particle size distributions exhibit a mirror image of the conventional “banana-shaped” NPF pattern, forming a distinctive “reverse-NPF” pattern. We identified three NPS events during the campaign and show that this phenomenon is not primarily driven by low concentrations of condensable vapours, their scavenging by pre-existing particles, or primary nanoparticle sources. Instead, it is associated with atmospheric dilution, as indicated by air-mass trajectory analysis. Furthermore, fast-moving air masses can enhance turbulent mixing, thereby altering particle size distributions. Together with volatility-resolved analysis, these results suggest that NPS is governed by atmospheric dilution, which reduces particle-phase organic mass and shift the gas-particle equilibrium toward evaporation, with contributions dominated by organic compounds of low and moderate volatility. Our results demonstrate that NPS events provide a previously unrecognised sink for nanoparticles, which are controlled by air-mass dynamics and organic vapour volatility.

Abstract. Low cost sensors (LCSs) for measuring the concentrations of gaseous pollutants hold great promises for air quality monitoring (AQM) as they can improve the spatio-temporal resolution of observational networks. However, the performance of LCSs is affected by a number of factors including temperature and relative humidity of ambient air, as well as cross-sensitivities with gaseous species other than the target gas, thereby deteriorating the quality of their measurements. To address these issues, data from LCSs can be calibrated against reference instruments using machine learning (ML) algorithms. Here, we have evaluated the performance of a number of ML algorithms for calibrating measurements from CO, NO2, O3 and SO2 LCSs against respective reference measurements. The best model is then used to determine (1) the influence of temporal resolution of the measurements to the calibration performance, (2) the minimum fraction of data needed for model training while maintaining the quality of calibrated measurements within acceptable levels, and (3) the ideal calibration frequency with collocated reference measurements. We found that the quality of LCS measurements improve significantly for all sensors after ML calibration, with Random Forest (RF) being the best performing algorithm, corroborating previous works. By varying the temporal resolution of the training data from 1 h to 2 min, the performance of the RF model in terms of the normalized root mean squared error and the relative expanded uncertainty calculated at maximum observed concentration improves by 11–21 %. The results also suggest that the minimum fraction of data required for training the ML models depends on the frequency of carrying out collocated measurements with reference instruments and using the resulting datasets for training the calibration model. If the calibrations are carried out on a monthly basis, ca. 50 % of the period is needed for collecting data to train the RF algorithm and qualify the LCSs for indicative measurements as defined by the EU directive (2008/50/EC). If the training is carried out every 3 or 6 months by sampling the training data continuously, then ca. 60 % of the measuring period is required for collecting training data. In those cases, if the sampling of the training data is made over specific periods every month, but the entire training dataset is used to calibrate the measurements over 3 or 6 months, the amount of data required for qualifying the LCSs for indicative measurements can significantly reduce to 22 %. However, this would require that the measurements from the LCSs be calibrated retrospectively, which for specific applications is not such of a problem.

Post-winter haze events in Delhi, India, comprise great air quality challenges, yet remain poorly understood due to limited measurements of vertical profiles of particulate matter (PM) concentrations. This study employs a drone-mounted PM low-cost sensor (PM-LCS) with an optimized sampling system to capture vertical PM2.5 profiles during March 2021. Elevated PM2.5 concentrations (160 µg/m3) were observed at an altitude of 100 m, being 60% higher than ground level. Vertical profiles of the PM1/PM2.5 ratio under humid conditions (RH > 70%), showed that haze formation is likely driven by hygroscopic inorganic aerosols. Comparison with model simulations showed significant underestimation of PM2.5 (−52.6 ± 5.5%) during morning haze episodes, coinciding with a dry bias in modeled RH (−30.1 ± 8.3%). During non-hazy episodes, PM2.5 underestimation decreased to 10.8 ± 1.2% with a minimal RH bias. This suggests that the dry bias of the model limits its ability to simulate aerosol hygroscopic growth. Overall, our findings demonstrate that drone-mounted PM-LCS provides a valuable vertical air quality assessment tool.

Nickel clusters have drawn considerable interest because of their distinctive structural and electronic characteristics, which differ significantly from those of their bulk-material counterparts. In this work, we investigate the structures of Ni metal atomic clusters (Nin, n = 1-20) in neutral charge state. We also explore how these clusters interact with a range of gases such as CO, CO2, CH4, NO, NO2, NH3, H2, H2O, N2, O2, and SO2, and compute the adsorption energies, in an effort to assess their potential exploitation in sensing materials. The geometries of the clusters are optimized, and the adsorption energies are calculated using the Density Functional Theory (DFT) method at the B3LYP-GD3BJ/LANl2DZ level of theory. Indicators, including cohesive energy, HOMO-LUMO energy gap, dissociation energy, and conceptual DFT analysis descriptors, show that the stability of these clusters increases with increasing size. Ni19 was found to be the most stable cluster among those that we studied, having the highest binding energy and a compact icosahedral geometry. As the size of the clusters increased, the cohesive energy increased, while the HOMO-LUMO gap decreased, indicating a transition from molecular to metallic behavior. The calculated adsorption energies revealed weak physisorption (0 to -1 eV) for CH4, H2, H2O, and N2, and strong chemisorption (-4 to -20 eV) for O2, NO, NO2, and SO2, with NO and NO2 binding most strongly on Nin, for n = 16-19. Charge analysis indicates greater electron transfer and partial covalent bonding for the strongly adsorbed gases.

Metal nitride and metal oxide nanoparticles (NPs) provide key material components for a number of applications due to their unique properties. Here we demonstrate that spark ablation of metallic electrodes, quenched with a pure N2 flow at atmospheric pressure, can be used as a reactive generator to synthesize metal nitride, metal oxide or pure metallic NPs depending on the material. The composition of the synthesized NPs was determined through their crystal structure using X-ray diffraction and transmission electron microscopy (TEM). Our results show that the composition of the resulting NPs strongly depends on the electrode material: Ti and Al form mixtures of metal nitride and oxide NPs, whereas Mg and Pd produce respectively only oxide and pure metallic NPs. Repeated XRD measurements of the samples after exposing them to ambient air over periods of several months showed that the stability of TiN was higher compared to that of the AIN NPs, with the first being converted to TiNyOx and the latter to gamma-Al2O3 after 9 months.

New particle formation (NPF) and subsequent growth are key processes controlling cloud condensation nuclei (CCN) number concentrations, as newly formed particles can grow into the CCN size range and thereby influence cloud properties and climate. In this study, we investigate particle number size distributions, CCN activity, and hygroscopicity during the Cloud–Aerosol Interactions in a Nitrogen Dominated Atmosphere (CAINA) campaign conducted in spring 2025 at a coastal site in the northern Netherlands, using a combination of a Scanning Mobility Particle Sizer (SMPS), a Particle Size Magnifier (PSM), and size-resolved CCN measurements. SMPS measurements covering the size range 6.7–969 nm were conducted between 29 March and 13 May 2025, while PSM measurements (1.19–12.0 nm) were available from 4 April to 9 May 2025. Based on visual classification of particle size distribution evolution, 19 NPF events were identified during the 46-day period (41%), 5 days were classified as undefined (11%), and the remaining 22 days as non-event days (48%). In addition, size-resolved CCN measurements were performed between 12 and 23 April 2025 to investigate in more detail the processes governing new particle formation and their growth towards CCN-relevant sizes. The measurements were carried out using a CCN counter operating at supersaturations (SS) of 0.3% and 1% downstream of a Differential Mobility Analyzer (DMA), covering particle diameters between 40 and 140 nm. The data were used to derive CCN activation fractions, characteristic activation diameters (D50), and the apparent hygroscopicity parameter kappa for the two different supersaturations. Our results show a clear size dependence of particle hygroscopicity, with particles activated at 0.3% SS generally exhibiting higher kappa values than particles activated at 1% SS. Average kappa values are around 0.1–0.2 for larger particles and 0.3–0.4 for smaller particles. A detailed case study of a NPF event shows a higher particle hygroscopocity before and during the start of the event, while the hygroscopicity decreases when the particles grow. These findings provide new insights into the link between NPF, particle chemical properties, and their ability to act as CCN.

The contribution of non-exhaust emissions (NEEs) to particle number concentration (PNC) remains insufficiently quantified, particularly across different urban environments. In this study, we address this gap by quantifying the contribution of NEEs to airborne nanoparticles in urban areas. Using positive matrix factorisation (PMF), conditional probability function analysis, Pearson correlation, and source identification, we identified five source factors contributing to PNC at two sites in London: a traffic site and a background site. Five source factors were resolved at both sites: Aitken-mode traffic exhaust particles, nucleation-mode exhaust emission, secondary aerosol, non-exhaust emission, and regional background accumulation. Interestingly, the contribution of NEEs differed between the two sites. At the traffic site, NEEs contributed 14.9%, while at the background site, their contribution was higher at 28.5%, likely due to the favourable summer dispersion conditions. However, the contribution of nucleation-mode exhaust emission also showed significant differences: 26.6% at the traffic site and only 9.9% at the background site. Based on these findings, we propose that air quality policies should integrate NEEs into regulations, improve road maintenance, and use PNC-based along with metal tracers to identify and control PNC. This study offers valuable insights for developing strategies to manage urban nanoparticle pollution.

Methane is considered one of the cleanest energy sources as it produces fewer pollutants upon burning compared to other fossil fuels. Its accidental release during extraction, transportation, and use, however, poses significant environmental and safety risks, warranting advanced sensing technologies to monitor its concentration in ambient air. Here, we prepare nanoparticle-based materials for sensing methane at concentrations that are highly relevant in the atmospheric environment. The nanoparticle (NP) building blocks of the sensing materials are produced by spark-ablating and simultaneously quenching Sn electrodes with a N2 flow at atmospheric pressure. The resulting Sn NPs are subsequently collected and oxidized to SnO2 by thermal annealing in ambient air before doctor blading them onto substrates with interdigitated electrodes. The synthesized materials were characterized by X-ray diffraction and photoelectron spectroscopy, Brunauer-Emmett-Teller analysis, as well as atomic force, transmission, and scanning electron microscopy. The results show that our sensing materials can quantify methane concentrations down to 0.2 ppm, having a signal-to-noise ratio of 58 and a theoretical limit of detection of ca. 7 ppb. What is more, they maintain excellent robustness across a relative humidity range of 20-80% and exhibit a high cycling stability and repeatability; features that render them superior compared to other metal oxide semiconducting materials reported in the literature so far. Based on our measurements, we also offer new insight into how the NP synthesis process can affect sensor sensitivity, demonstrating a correlation between spark-ablation energy and NP size, which in turn determines the crystal size, the specific surface area, as well as the fraction of adsorbed oxygen on the surface of the sensing material, and consequently its interaction with the target gas. Combined with the simplicity of their preparation, these sensing materials hold great potential for a wide range of environmental and industrial applications.

We propose an active learning (AL) framework to develop classical force fields (FFs) that accurately model the potential energy surfaces (PES) of gas/solid atomic-scale complexes. A central challenge is integrating AL with flexible, computationally efficient physics-aware potentials to achieve quantum-level accuracy for complex interfacial systems. Our approach trains physics-aware potentials, with incorporated flexibility and smoothness, on actively sampled density functional theory (DFT) data to describe interactions between undercoordinated atomic silver (Ag) clusters and gaseous pollutants (CO2, CO, SO2), relevant for environmental applications like sensing. The AL process follows three stages: (1) FFs are trained using adaptable physics aware potentials of semiempirical descriptors, optimized via a Pareto analysis scheme; (2) new candidate structures are generated through the use of the refined FFs in Metropolis Hastings Monte Carlo (MHMC) or stochastic molecular dynamics (sMD) simulations; (3) a subset of candidates is selected for DFT computations based on an outlier score (OS), which utilizes the existing data descriptor distributions, ensuring diverse PES exploration. This framework produces FFs capable of capturing cohesive, physisorption, and chemisorption interactions with admirable accuracy, close to ab initio methods, while retaining the efficiency of semiempirical potentials. To demonstrate, produced FFs are utilized in molecular dynamics (MD) simulations of single Ag clusters embedded in bulk gas phases, examining condensation characteristics. Our methodology is highly versatile, easily accommodating various choices of descriptors, model basis sets, and sampling techniques.

Aircraft emissions of (ultra)fine particles during landing and take-off operations pose increasing human health hazards for airport employees and near-airport communities. Measurements of in-operation aircraft are therefore crucial for characterizing real-world aircraft emissions, and their variability. In this work, we develop an approach that enables the gathering of large quantities of data on real-world aircraft-specific emissions. We use three types of portable PM sensors located ca. 200 m downwind of an operational runway at Amsterdam Airport Schiphol, over different seasons, to characterize the plumes from ca. 500 specific operations covering most aircraft types of the global flying fleet. High concentration peaks (in the order of 106 particles/cm3) of sub-25 nm particles are observed in the near field. While departure plumes exhibit higher particle number concentrations than arrival plumes, the values do not necessarily scale with aircraft size or engine thrust rating. We find large variability among aircraft types and engine models, highlighting the importance of incorporating real-world observations when assessing the impacts of aviation on the atmospheric composition and human health.

Clouds play an important role in the Earth’s climate through well-established mechanisms, such as their interactions with solar radiation and their role in precipitation. However, their influence on future climate projections remains highly uncertain. One of the key challenges is the understanding of aerosol-cloud interactions in studying clouds, as aerosols can serve as cloud condensation nuclei. Aerosols can have significant variability across both space and time. While in situ measurements provide precise data for a small atmospheric volume—just a few cubic centimetres—they may not accurately reflect the spatial (horizontal and vertical) variability of aerosol characteristics and therefore do not give accurate statistical information on the average cloud state and its variability.Airborne observations offer the capability of sampling a larger volume of the atmosphere and therefore give a more comprehensive understanding of clouds.This study highlights UAV-based observations of particle size distributions both inside and outside clouds, conducted during the #CHOPIN (CleanCloud Helmos Orographic Site Experiment) campaign. As part of this campaign, the Unmanned Systems Research Laboratory (USRL) of the Cyprus Institute deployed light Unmanned Aircraft Systems at Mt. Helmos, Greece, from October 11 to November 1, 2024, providing valuable data for the study of clouds and their interactions with aerosols. This is one of the few times USRL/CYI reported observations aerosol-cloud interaction flights.The #CHOPIN campaign, conducted in collaboration with NCSR Demokritos and FORTH/EPFL, was hosted at the Kalavryta Ski Center with a base altitude for the UAS takeoffs and landings of ~1.7 km ASL. The campaign aimed to improve the understanding of aerosol-cloud interactions and to evaluate remote sensing algorithms and models. Located in a rapidly changing "climate hotspot" at the intersection of various air masses, Mount Helmos is particularly sensitive to environmental changes, with interactions between wildfire smoke, pollution, sea salt, and Saharan dust. This unique setting provides an ideal location to study the dynamics of aerosol-cloud interactions.During the campaign, several flights were performed inside and outside clouds operating in a horizontal area of approximately ~16km² and providing vertical profiles of particle size distribution from the ground up to 3.5 km ASL. We will focus on the cloud observations and the derivation of particle and droplet size distributions from UAV-based optical particle counters. These observations provide a good dataset for improving cloud-resolving models and for comparison with fixed station observations.

This article describes a single-step method for synthesizing nanostructured materials using evaporation-condensation synthesis and inertial impaction of aerosol nanoparticles. The as-deposited films exhibit anisotropic vertical and horizontal sintering of their palladium nanoparticle building blocks, yielding vertical structures. The electrical conductivity of the films is stable and highly sensitive to the presence of hydrogen in the overlaying gas, at concentrations that range from a few hundreds parts per million to a few percent.

Optical Particle Sizers (OPSs) are widely used for measuring size distributions of particles larger than ca. 0.2 μm. To do so, they use mirrors or lenses to gather light scattered by particles passing through a focused beam, directing it to a photo-detector that produces electric pulses from the scattering events. Considering their ability to provide near real-time measurements with minimal attendance and maintenance, and to expand the networks of Particulate Matter (PM) monitoring, several manufacturers have developed low-cost and compact OPS systems. Despite that low-cost OPSs are already available in the market and employed for monitoring PM concentrations, their reported values typically deviate from those of high-end instruments, warranting further efforts to improve their performance. In this work, we designed and built a custom-made yet inexpensive OPS optical system, and studied its performance using a combination of computational and experimental methods at different flow conditions. Our results demonstrate the importance of the flow field within the OPS optical system, and how this can affect its counting and sizing ability. The overall performance of our OPS optical system is very similar to that of high-end instruments, exhibiting a counting efficiency of 50% for particles having a diameter of 320 nm, and a sizing resolution of below 15% for 500-nm particles, complying with the ISO 21501-1 and 21501-4 standards.