Comment on ar-2024-37 DOI Creative Commons

Published: Dec. 30, 2024

Abstract. Sulfuric acid, ammonia, and amines are believed to be key contributors the initial steps in new particle formation atmosphere. However, other compounds such as organic or nitric acid important for further growth at larger sizes. In this study, we investigate potential uptake of first-generation oxidation products from α-pinene (pinic pinonic acid), isoprene (trans-β-IEPOX, β4-ISPOOH, β1-ISOPOH), a highly oxidized molecule (HOM), formic acid. The is probed onto (SA)10(base)10 freshly nucleated particles (FNPs), where SA denotes sulfuric bases either ammonia (AM), methylamine (MA), dimethylamine (DMA), trimethylamine (TMA). addition free energies were calculated ωB97X-D3BJ/6-311++G(3df,3pd)//B97-3c level theory. We find favorable −8 −10 kcal/mol HOM, pinic on less sterically hindered (SA)10(AM)10 (SA)10(MA)10 FNPs. This suggests that do not contribute early FNPs, but do, accordance with their expected volatilities. Calculating second maintains its large energy decrease due two carboxylic groups interacting monomer well FNP. drops −3.9 weak interactions between FNP carbonyl group lack monomer–monomer interactions. high confirmed by calculating realistic atmospheric conditions. means has ∼2 nm implying dicarboxylic acids could potentially also aid growth.

Language: Английский

Quantum chemical modeling of atmospheric molecular clusters involving inorganic acids and methanesulfonic acid DOI Open Access
Morten Engsvang, Haide Wu, Yosef Knattrup

et al.

Chemical Physics Reviews, Journal Year: 2023, Volume and Issue: 4(3)

Published: Sept. 1, 2023

Atmospheric molecular cluster formation is the first stage toward aerosol particle formation. Despite intensive progress in recent years, relative role of different vapors and mechanisms for forming clusters still not well-understood. Quantum chemical (QC) methods can give insight into thereby yield information about potentially relevant compounds. Here, we summarize QC literature on clustering involving species such as sulfuric acid, methanesulfonic nitric acid. The importance iodine iodous acid (HIO2) iodic (HIO3) atmospheric an emerging topic, critically review our view how to future. We outline machine learning (ML) be used enhance configurational sampling, leading a massive increase compositions that modeled. In future, ML-boosted could allow us comprehensively understand complex with multiple pathways, one step closer implementing accurate models.

Language: Английский

Citations

16
Clusterome: A Comprehensive Data Set of Atmospheric Molecular Clusters for Machine Learning Applications DOI Creative Commons
Yosef Knattrup, Jakub Kubečka, Daniel Ayoubi

et al.

ACS Omega, Journal Year: 2023, Volume and Issue: 8(28), P. 25155 - 25164

Published: June 30, 2023

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

Language: Английский

Citations

13
Nitric Acid and Organic Acids Suppress the Role of Methanesulfonic Acid in Atmospheric New Particle Formation DOI
Yosef Knattrup, Jakub Kubečka, Jonas Elm

et al.

The Journal of Physical Chemistry A, Journal Year: 2023, Volume and Issue: 127(36), P. 7568 - 7578

Published: Aug. 31, 2023

Multicomponent atmospheric molecular clusters, typically comprising a combination of acids and bases, play pivotal role in our climate system contribute to the perplexing uncertainties embedded modern models. Our understanding cluster formation is limited by lack studies on complex mixed-acid-mixed-base systems. Here, we investigate multicomponent clusters consisting mixtures several acid base molecules: sulfuric (SA), methanesulfonic (MSA), nitric (NA), formic (FA), along with methylamine (MA), dimethylamine (DMA), trimethylamine (TMA). We calculated binding free energies comprehensive set 252 at DLPNO-CCSD(T0)/aug-cc-pVTZ//ωB97X-D/6-31++G(d,p) level theory. Combined existing datasets, simulated new particle (NPF) rates using Atmospheric Cluster Dynamics Code (ACDC). find that presence NA FA had substantial impact, increasing NPF rate 60% realistic conditions. Intriguingly, suppress MSA NPF. These findings suggest even high concentration has impact polluted regions NA. outline method for generating lookup table could potentially be used models sufficiently incorporates all required chemistry. By unraveling mechanisms get one step closer comprehending their implications global system.

Language: Английский

Citations

13
Current and future machine learning approaches for modeling atmospheric cluster formation DOI Open Access
Jakub Kubečka, Yosef Knattrup, Morten Engsvang

et al.

Nature Computational Science, Journal Year: 2023, Volume and Issue: 3(6), P. 495 - 503

Published: June 26, 2023

Language: Английский

Citations

12
A cluster-of-functional-groups approach for studying organic enhanced atmospheric cluster formation DOI Creative Commons
A. Pedersen, Yosef Knattrup, Jonas Elm

et al.

Aerosol Research, Journal Year: 2024, Volume and Issue: 2(1), P. 123 - 134

Published: June 4, 2024

Abstract. The role of organic compounds in atmospheric new particle formation is difficult to disentangle due the myriad potentially important oxygenated molecules (OOMs) present atmosphere. Using state-of-the-art quantum chemical methods, we here employ a novel approach, denoted “cluster-of-functional-groups” for studying involvement OOMs cluster formation. Instead usual “trial-and-error” approach testing ability experimentally identified form stable clusters with other nucleation precursors, study which, and how many, intermolecular interactions are required given OOM clusters. In this manner can reverse engineer elusive structure candidates that might be involved enhanced We calculated binding free energies all combinations donor acceptor functional groups investigate which most preferentially bind each precursors such as sulfuric acid bases (ammonia, methyl-, dimethyl- trimethylamine). find multiple carboxyl lead substantially more compared groups. Employing dynamics simulations, hypothetically composed stabilize acid–base provide recommendations potential multi-carboxylic tracer should explicitly studied future. presented generally applicable employed many applications, ion-induced elucidating structural patterns facilitate ice nucleation.

Language: Английский

Citations

5
Reparameterization of GFN1-xTB for atmospheric molecular clusters: applications to multi-acid–multi-base systems DOI Creative Commons
Yosef Knattrup, Jakub Kubečka, Haide Wu

et al.

RSC Advances, Journal Year: 2024, Volume and Issue: 14(28), P. 20048 - 20055

Published: Jan. 1, 2024

Reparameterization of GFN1-xTB for atmospheric molecular clusters leads to a massive decrease in energy errors and deviation.

Language: Английский

Citations

5
Atmospheric implications of fumaric acid - water binary clusters DOI

Neba Lovette Ambe,

Olivier Holtomo,

Ayiseh Frederick Tandong

et al.

Journal of Aerosol Science, Journal Year: 2025, Volume and Issue: unknown, P. 106524 - 106524

Published: Jan. 1, 2025

Language: Английский

Citations

0
Uptake of organic vapours and nitric acid on atmospheric freshly nucleated particles DOI Creative Commons
Yosef Knattrup, Jonas Elm

Aerosol Research, Journal Year: 2025, Volume and Issue: 3(1), P. 125 - 137

Published: Feb. 28, 2025

Abstract. Sulfuric acid, ammonia, and amines are believed to be key contributors the initial steps in new particle formation atmosphere. However, other compounds such as organic or nitric acid important for further growth at larger sizes. In this study, we investigate potential uptake of first-generation oxidation products from α-pinene (pinic pinonic acid) isoprene (trans-β-IEPOX, β4-ISPOOH, β1-ISOPOOH), a highly oxidised molecule (HOM), formic acid. The is probed onto (SA)10(base)10 freshly nucleated particles (FNPs), where SA denotes sulfuric bases ammonia (AM), methylamine (MA), dimethylamine (DMA), trimethylamine (TMA). addition free energies were calculated ωB97X-D3BJ/6-311++G(3df,3pd)//B97-3c level theory. We find favourable −8 −10 kcal mol−1 HOM, pinic on less sterically hindered (SA)10(AM)10 (SA)10(MA)10 FNPs. This suggests that do not contribute early FNPs, but do, accordance with their expected volatilities. Calculating second maintains its large energy decrease due two carboxylic groups interacting monomer, well FNP. pinonic-acid drops −3.9 weak interactions between FNP carbonyl group lack monomer–monomer interactions. under realistic atmospheric conditions, FNPs studied too small (1.4 nm) support monomers. accretion product pinyl diaterpenylic ester (PDPE; C17H26O8) yields an value −17.1 mol−1. PDPE can overcome strong Kelvin effect 1.4 nm lead spontaneous ambient conditions.

Language: Английский

Citations

0
The enhanced role of formic acid on sulfuric acid-ammonia-driven nucleation in forest regions and polluted city areas DOI

Shasha Chen,

Rongrong Li, Chengyan Zhang

et al.

Journal of Environmental Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: May 1, 2025

Language: Английский

Citations

0
Similarity-based analysis of atmospheric organic compounds for machine learning applications DOI Creative Commons
Hilda Sandström, Patrick Rinke

Geoscientific model development, Journal Year: 2025, Volume and Issue: 18(9), P. 2701 - 2724

Published: May 15, 2025

Abstract. The formation of aerosol particles in the atmosphere impacts air quality and climate change, but many organic molecules involved remain unknown. Machine learning could aid identifying these compounds through accelerated analysis molecular properties detection characteristics. However, such progress is hindered by current lack curated datasets for atmospheric their associated properties. To tackle this challenge, we propose a similarity that connects to existing large used machine development. We find small overlap between non-atmospheric using standard representations applications. identified out-of-domain character related distinct functional groups atomic composition. Our investigation underscores need collaborative efforts gather share more molecular-level chemistry data. presented similarity-based can be future dataset curation development sciences.

Language: Английский

Citations

0