Formic acid (FA) is a prominent candidate for organic enhanced nucleation due to its high abundance and stabilizing effect on smaller clusters. Its role in new particle formation is studied through the use of state-of-the-art quantum chemical methods on the cluster systems (acid)1–2(FA)1(base)1–2 with the acids being sulfuric acid (SA)/methanesulfonic acid (MSA) and the bases consisting of ammonia (A), methylamine (MA), dimethylamine (DMA), trimethylamine (TMA), and ethylenediamine (EDA). A funneling approach is used to determine the cluster structures with initial configurations generated through the ABCluster program, followed by semiempirical PM7 and ωB97X-D/6-31++G­(d,p) calculations. The final binding free energy is calculated at the DLPNO-CCSD­(T0)/aug-cc-pVTZ//ωB97X-D/6-31++G­(d,p) level of theory using the quasi-harmonic approximation. Cluster dynamics simulations show that FA has a minuscule or negligible effect on the MSA–FA–base systems as well as most of the SA–FA–base systems. The SA–FA–DMA cluster system shows the highest influence from FA with an enhancement of 21%, compared to its non-FA counterpart.

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Formation and growth of atmospheric molecular clusters into aerosol particles impact the global climate and contribute to the high uncertainty in modern climate models. Cluster formation is usually studied using quantum chemical methods, which quickly becomes computationally expensive when system sizes grow. In this work, we present a large database of ∼250k atmospheric relevant cluster structures, which can be applied for developing machine learning (ML) models. The database is used to train the ML model kernel ridge regression (KRR) with the FCHL19 representation. We test the ability of the model to extrapolate from smaller clusters to larger clusters, between different molecules, between equilibrium structures and out-of-equilibrium structures, and the transferability onto systems with new interactions. We show that KRR models can extrapolate to larger sizes and transfer acid and base interactions with mean absolute errors below 1 kcal/mol. We suggest introducing an iterative ML step in configurational sampling processes, which can reduce the computational expense. Such an approach would allow us to study significantly more cluster systems at higher accuracy than previously possible and thereby allow us to cover a much larger part of relevant atmospheric compounds.

This study examines doping products seized by the police in three regional police districts in Denmark from December 2019 to December 2020. The products, often referred to as performance and image-enhancing drugs (PIEDs), are described in relation to the country of origin, manufacturing company, and the active pharmaceutical ingredient (API) stated on the packaging versus the one identified by subsequent chemical analysis. The study also includes a description of the degree of professionalism by which the products appear according to EU requirements. A total of 764 products were seized during the study period. The products originate from 37 countries, mainly located in Asia (37%), Europe (23%), and North America (13%). One hundred ninety-three different manufacturing companies could be identified from the product packaging. The most frequent compound class was the androgenic anabolic steroids, found in 60% of the products. In 25%-34% of the products, either no or an incorrect API relative to the one stated on the product was found. However, only 7%-10% contain either no API or a compound from a different compound class than the one stated. Most products had a professional appearance fulfilling most EU requirements for packaging information. The study shows that many different companies supply PIEDs to the Danish market and that counterfeit and substandard products are widespread. Many products do, however, appear professional to the user giving an impression of a high-quality product. Although many products are substandard, they most often contain an API from the same compound class as the one labeled.