Characterization of atmospheric-pressure spark
generated atomic silver and gold clusters by
time-of-flight mass spectrometry

A. Maisser, K. Barmpounis, S. Holm, M. Attoui, A. Schmidt-Ott, J.

Kangasluoma, G. Biskos

► To cite this version:

A. Maisser, K. Barmpounis, S. Holm, M. Attoui, A. Schmidt-Ott, et al. Characterization of atmospheric-pressure spark generated atomic silver and gold clusters by time-of-flight mass spectrometry. Journal of Aerosol Science, 2021, 156, pp.105780. 10.1016/j.jaerosci.2021.105780. hal-04379577

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# Characterization of atmospheric-pressure spark generated atomic silver and gold clusters by time-of-flight mass spectrometry

Maisser, A.

Elsevier Ltd.

2021-08

Maisser, A, Barmpounis, K, Holm, S, Attoui, M, Schmidt-Ott, A, Kangasluoma, J & Biskos, G
2021, 'Characterization of atmospheric-pressure spark generated atomic silver and gold
clusters by time-of-flight mass spectrometry', Journal of Aerosol Science, vol. 156, 105780. https://doi.org/10.1016/j.jaerosci.2021.105780

http://hdl.handle.net/10138/356528
10.1016/j.jaerosci.2021.105780

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Please cite the original version.
Characterization of Atmospheric-Pressure Spark Generated Atomic
Silver and Gold Clusters by Time-of-Flight Mass Spectrometry

A. Maisser¹*, K. Barmpounis², S. Holm³, M. Attoui, A⁴. Schmidt-Ott, J⁵. Kangasluoma³, G. Biskos¹,⁵,

¹Climate and Atmosphere Research Centre, The Cyprus Institute, Nicosia 2121, Cyprus

²LemonLabs, Nicosia 1035, Cyprus

³Institute for Atmospheric and Earth System Research, University of Helsinki, 00014 Helsinki, Finland

⁴LISA, UMR7583, Université Paris-Est-Créteil, Université de Paris, Institut Pierre Simon Laplace (IPSL), Créteil, France

⁵Faculty of Applied Sciences, Delft University of Technology, NL-2629 HZ Delft, The Netherlands

* To whom correspondence should be addressed:

a.maisser@cyi.ac.cy;

For submission to Journal of Aerosol Science
Abstract

The potential of using atmospheric-pressure spark ablation for production of atomic clusters has been shown in the recent past. In this study we used this method to produce atomic clusters in Argon and investigated their chemical composition with an atmospheric pressure inlet time-of-flight mass spectrometer (API-TOF-MS). Both anionic and cationic silver and gold clusters were generated and characterised. The carrier gas was used directly from the gas cylinder without the application of any additional gas purification. As a result, the carrier gas stream contained trace impurities of N₂, O₂, and H₂O, as well as radicals such as O, OH, and H and their ionized forms resulting from the plasma created by the sparks. The experimental results show that different reactivities towards oxygen and water traces can be observed depending on the number of metal atoms in the clusters, their polarity, as well as their compositions; e.g., Au⁻ shows less reactive behaviour towards oxygen and hydrogen compared to Agₙ. Our results show that certain cluster types can be generated at large enough quantities and at relatively low cost, paving the way towards its use for numerous applications.

**Keywords:** spark discharge, atomic clusters, cluster ions, mass spectrometry, silver clusters, gold clusters

1. Introduction

Clusters consisting from a few up to a hundred atoms have sizes that range from a fraction of a nanometer up to a few nanometers. In this size regime, several cluster properties including their ionization potential, thermodynamic properties, or their catalytic activity, are non-scalable; i.e., they do not scale continuously as a function of cluster size, but rather they show large variabilities when a single atom is removed or added (Aguado & Jarrold, 2011; Jackschath et al., 1992; Kroto, 1988; Luo et al., 2016; Neal et al., 2007; Yalamanchali et al., 2017; Zemski et al., 2002). Clusters comprised of a specific number of atoms, commonly referred to as magic numbers, exhibit a high stability that is influenced by their electronic structure (e.g., those of closed shells) and/or geometry (Jackschath et al., 1992; Rabin et al., 1991; Rao & Jena, 1999). Examples of highly stable clusters are Au₅₅ and a big family of fullerenes including C₆₀ (Boyen et al., 2002; Kroto, 1988).

Stable atomic clusters are of particular interest as they form the basis of novel materials, typically referred to as cluster-assembled materials (Castleman Jr & Khanna, 2009; Claridge et al., 2009; Jena & Sun, 2018; Schmidt-Ott, 2020), which can exhibit properties of interest for use in solar cells (Di Vece, 2019; He et al., 2012), photodetectors (Gong et al., 2009), high-capacity hydrogen storage media (Yoon et al., 2007), antioxidants and neuroprotective agents for medical applications (Georgakilas et
al., 2015; Partha & Conyers, 2009), as well as catalysts (Haruta, 1997; Zhu et al., 2011). For the latter, theory predicts enhanced catalytic properties for clusters having specific number of atoms. For example, Al clusters containing 16-18 atoms can catalyze the splitting of H₂O molecules rather effectively, while Al clusters containing different numbers of atoms do not exhibit comparable catalytic properties (Roach et al., 2009). Another example of selective cluster-based catalysts are those that employ Au clusters for the oxidation of CO to CO₂ (Haruta, 1997; Kimble et al., 2004). Considering the non-scalability of the properties of the clusters with the number of atoms they contain, one can imagine a wide number of combinations for producing highly tunable materials for catalysis and other applications.

Despite the numerous potential applications, the use of atomic clusters in materials and devices is limited by their production in sufficient amounts (Jena, 2015; Khanna & Jena, 1995). The only exception to this limitation is the case of fullerenes, for which industrial-scale application is possible because they can be produced in bulk quantities from soot-producing flames (Krätschmer et al., 1990; Takehara et al., 2005).

Aside from fullerenes, atomic clusters are traditionally formed by cooling a hot plume of atoms through collisions with rare gas atoms in near-vacuum conditions. Some of the most commonly applied cluster beam generation methods include laser evaporation (Dietz et al., 1981; Duncan, 2012; Smalley, 1983), magnetron sputtering (Kelly & Arnell, 2000; Straňák et al., 2011; Window & Savvides, 1986), and Pulsed Arc Cluster Ionization Sources (PACIS) (Piazzoni & Milani, 2019; Siekmann et al., 1990). A great number of publications describing these different methods in great detail, including also book chapters and review articles, can be found in the literature. (Heiz & Landman, 2007; Piazzoni & Milani, 2019).

Earlier experimental studies have evaluated a number of size-specific properties of clusters including their electronic (Jackschath et al., 1992; Zhai et al., 2008) and thermodynamic properties (Aguado & Jarrold, 2011; Neal et al., 2007), their reactivity/stability (Kimble et al., 2004; Luo et al., 2016), as well as the cluster stoichiometry (Latif et al., 2019; Nakano et al., 2019; Remacle & Kryachko, 2005; Salisbury et al., 2000a). These experiments are typically carried out by combining the cluster beam generation with a mass selecting step, commonly using a quadrupole, where one specific cluster size can be separated from the distribution. For reactivity studies, mass-selected clusters are exposed to a controlled partial pressure of a certain reactant, such as O₂, H₂O, etc., in ion traps (Bernhardt, 2005; Bernhardt et al., 2005; Hagen et al., 2004), or flow tube reactors (Salisbury et al., 2000a; Wallace et al., 2003; Wallace & Whetten, 2000, 2002). Stoichiometry measurements are typically carried out in a tandem Ion Mobility Mass Spectrometry (IMMS) systems (Latif et al., 2019, 2019; Nakano et al., 2019;
Weis et al., 2002, 2004). While these experiments provided great insights into cluster size-dependent cluster behavior, most of the generation methods have a low production rate and consequently a low potential for upscaling.

Alternatively, atomic clusters can be synthesized by a range of atmospheric-pressure systems such as the hot wire generators (Domaschke et al., 2019; Peineke et al., 2009), spark discharge generators (Meuller et al., 2012; Tabrizi et al., 2009), and flame reactors (Wang et al., 2017). These techniques are more routinely used for the production of nanoparticles, but also showed potential for cluster production in the past. Producing atomic clusters at atmospheric pressure offers several advantages including simplicity and reduced cost of the required systems; two elements that are particularly attractive for industrial applications. In this respect, a very promising technique for producing atomic clusters, having sizes down to that of a single atom, is spark ablation (Maisser et al. 2015; Maisser, 2019; Schmidt-Ott, 2020). For many applications, spark ablation even has the potential of being scaled up to reach industrial particle quantities in a simple/economic and environmentally friendly way, as precursor chemicals are not necessary and no toxic wastes are produced (Feng et al., 2015a; Jena, 2015; Noh et al., 2017; Pfeiffer et al., 2014; Slotte & Zevenhoven, 2017). What is more, the ease of exchanging or mixing materials is inherent in this method (Feng et al., 2018; Tabrizi et al., 2010), providing a great means for tailoring nanomaterials for specific applications.

Mobility analysis of clusters produced by atmospheric-pressure spark ablation indicate that traces of oxidizing species further promote the formation of the more stable clusters at the cost of their less stable counterparts. This effect is known as oxidation etching and has been observed also in experiments using cluster beam generation methods (Bergeron & Castleman Jr, 2002; Leuchtner et al., 1991; Luo et al., 2012, 2012). In this paper we employ a Mass Spectrometer (MS) to determine the composition of clusters produced using spark ablation at atmospheric pressure in order to expand on our previous work (Maisser, 2019; Maisser et al., 2015) where we reported mobility spectra of clusters produced by the same technique. That MS measurements allow us to determine the selective combination of the produced atomic clusters with certain trace impurities, such as O₂ and H₂O, that are present in the carrier gas.

## 2. Materials and Methods

Spark ablation, also referred to as spark discharge generation (SDG), is a high-purity evaporation-condensation method introduced by Schwyn et al. in the late 1980s that is capable of producing nanoparticles (Feng et al., 2016; Meuller et al., 2012; Schwyn et al., 1988), and atomic clusters (Maisser et al., 2015) from conductive electrodes. In brief, material from two conductive electrode rods placed co-axially next to each other is evaporated by repeated spark discharges induced by a high potential
difference applied between the rods. The resulting vapour cloud is rapidly cooled down to room
temperature by a quenching gas flow passing through the gap between the two electrodes. Upon
cooling, the vapours form atomic clusters by nucleation, which grow to nanoparticles and
agglomerates by condensation and coagulation, respectively.

Ag and Au electrodes (Goodfellow Inc., Pennsylvania, USA, purity ≥ 99.99%) were used to synthesize
the atomic clusters studied in this work. The electrodes were placed in a metallic holder and kept 1
mm apart from each other, while quenching with a 15- to 18-lpm flow of high purity (99.999%) Argon.
The circuit for the spark generator works similarly to a simple RLC circuit, consisting of a capacitor and
a high voltage power supply that determine the energy/spark and the spark repetition frequency (cf.
Fig. 1; and Tabrizi et al., 2009). Typical capacitance values used in our experiments were 6 nF for silver,
and 220-1000 nF for gold electrodes, whereas the current was 5 mA for all measurements. The
electrode diameter was 2 mm in all cases and the gap distance between both electrodes was kept at
approximately 0.5 mm, requiring a discharge voltage of about 600 V. The spark was operated with a
negative power supply for the production of positively-charged cluster ions, and with positive power
supply for negatively charged cluster ions. The cluster-containing gas stream was guided to an
atmospheric pressure interface time-of-flight mass spectrometer (API-ToF-MS; Tofwerk; Junninen et
al., 2010). The distance from the spark to the inlet of the MS was approximately 20 cm. The API-ToF-
MS samples directly with a flow rate of 0.8 lpm from atmospheric pressure, and guides the ions
through two quadrupoles and ion lenses to its ToF chamber. The excess flow was filtered and released
through the lab exhaust. The MS was operated either in negative or positive mode for measurements
of positive and negative cluster ions, respectively. As we did not employ any ionisation step upstream
the API-ToF-MS, neutral clusters were not measured in our study. With the operating conditions
employed in the experiments, the API-ToF-MS achieved ion transmission in the range of 0.1-1%, mass
resolution of around 4000 (m/Δm), and mass range up to ca. 1500 amu.

All measurements presented here were carried out without any gas purification of the quenching gas
flow passing through the spark discharge generator. This is because initial tests using gas purification
similar to the one employed in our earlier work (Maisser et al., 2015), showed a high variability of the
measured mass spectra, indicating that the performance of the purification unit has not been stable
over time. A possible explanation for that is the aging of the catalyst, which might have led to
incomplete regeneration. Additionally, quantification of the oxygen and hydrogen levels in the system
after purification was not possible in our experiments, and therefore using the gas stream directly
from the cylinder to quench the vapours produced by the spark ablation was considered to be of more
general value.
<figure><img src="image_4.png" /><figcaption>Fig. 1 Schematic illustration of the experimental setup consisting of the spark discharge generator electrical circuit, the spark chamber housing the electrodes, the Ar flow stream through the spark chamber and the API-ToF-MS for analysis of the generated clusters.</figcaption></figure>

### 3. Results and Discussion

Figs. 2 and 3 respectively show the mass spectra and the corresponding mass defect plots of negative and positive silver and gold clusters produced by atmospheric-pressure spark ablation. It should be noted here that a fraction of the generated clusters carries no charge. This fraction is relatively small for the smaller clusters due to the high charge density of the plasma of the spark, but increases when the clusters grow to become larger nanoparticles. According to other studies reported in literature where atomic clusters were produced by similar evaporation/condensation techniques, the expected fraction of charged clusters in the size range considered here should be above 80% (Maisser, 2019; Methling et al., 2001; Piazzoni & Milani, 2019; Wang et al., 2017). The discussion and conclusions that follow refer only to measurements of cluster ions. The neutral fraction of the clusters produced by the SDG has no influence on our results.

The peaks observed and identified in the mass spectra can be associated to pure and oxygen/hydrogen-containing metallic clusters, or to contaminants present in the system. The presence of oxygen/hydrogen-containing clusters and contaminants is expected in our measurements as we have trace impurities of oxygen and water in the carrier gas in addition to the metallic material evaporated from the electrodes. The contaminants present in the system are species outgassed from parts used in the experimental setup (e.g., O-rings, sealing material, etc.). The contaminants do not exhibit any reactivity towards the metallic clusters as evident from the measurements shown in Fig. 2. For most mass spectra shown in Fig. 2, the highest mass peak intensities observed correspond to atomic clusters and their reaction products with oxygen, hydrogen, OH or water (cf. Fig. 2a-c).
Exception to this generalisation is the mass spectrum of $Au_n^+$ clusters (cf. Figs. 2d, 3d), where the peaks of hydrocarbon contaminants are predominant, especially in the larger mass region, whereas the $Au_nO_kH_m^+$ clusters are of low abundance.

The mass defect plots (Fig. 3) show the difference of cluster measured mass from the sum of the mass of the protons and neutrons against the cluster mass. Negative mass defect indicates presence of heavier compounds than C, while positive mass defect typically indicates compounds with several H atoms. Metal cluster reaction products with H and O are observed as groups next to the pure cluster as marked in the plots. The hydrocarbon contaminants are easily spotted from these plots as the group with masses smaller than about 400 amu and positive mass defect (from relatively large amounts of hydrogen).

In the measured Ag mass spectra all peaks corresponding to $Ag_nO_kH_m$ exhibit a number of isotopes as Ag has two stable isotopes at masses of 107, and 109 Da, respectively. Therefore, each $Ag_nO_kH_m$ cluster exhibits $n+1$ isotopes. Au has only one stable isotope at 197 Da and therefore only one peak of $Au_nO_kH_m$ exists for all $n$. The SDG always produces clusters and ions of both polarity, therefore, the resulting differences of the same metal of opposite polarity in the respective mass spectra can be linked to their gas phase ion kinetics. Gold has a higher gas phase electron affinity compared to silver, which means that it is more likely to acquire a negative, compared to a positive, charge (Johnson, 1975; Wu et al., 2010). This can be observed for the two mass spectra of Au clusters (cf. Fig. 2c, 2d, 3c and 3d).

Upon the introduction of chemical compounds into a mass spectrometer, it is likely that fragmentation or dissociation might occur. In the work presented in this manuscript we do not have any information on the rate or probability of fragmentation. However, it is possible that, e.g., dissociation of oxygen or hydrogen from an atomic cluster upon the introduction into the mass spectrometer might affect the measured mass distribution especially in the case that the hydrogenated or oxidised species is less stable than that of the respective pure cluster type. Experiments using a tandem setup of ion mobility measurement and mass measurement need to be carried out in order to further investigate this effect (Bernhardt, 2005; Maißer et al., 2015; Passananti et al., 2019).

The following sections provide a more detailed discussion on the measured contaminants, and the atomic clusters, respectively.
<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>
<figure><img src="image_8.png" /><figcaption>Fig. 2. Mass spectra of silver (a, and b) and gold (c and d) atomic clusters produced by atmospheric-pressure spark ablation. Subplots with blue bars correspond to negatively charged clusters, whereas those with red bars to their positively charged counterparts.</figcaption></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>
<figure><img src="image_11.png" /><figcaption>Fig. 3. Mass defect plots produced using the mass spectra shown in Fig. 2. The solid lines are to guide the eye where the pure Agₓ and Auᵧ cluster series lie. Example composition of the clusters is marked to the subplot a.</figcaption></figure>

### 3.1. Contaminants

A comparison of positive and negative mass spectra for ions having masses lower than 150 Da, and in the range from 150 to 400 Da are shown in Fig. 4 and Fig. S1a (supplemental material), respectively. A mass spectrum in the range from 350 to 760 Da gained from positive Au measurements is shown in Fig. S1b of the supplemental material. The mass spectra correspond to measurements using gold electrodes in the SDG, which explains the fact that in Fig. 4 only contaminant peaks, and no peaks of metallic atomic clusters, appear in the distribution (note that the mass of Au is 197 Da). As already mentioned above, the contaminant species are similar for both Ag and Au, therefore, we show only the comparison graph for Au in this manuscript. Additionally, Table S1 in the supplemental material shows all the identified contaminants throughout the entire mass range for Ag and Au spectra of both polarities.
<figure><img src="image_12.png" /><figcaption>Fig. 4 Mass spectra of positively (red bars) and negatively (blue bars) charged contaminant ions recorded during measurements using Au electrodes.</figcaption></figure>

The measurements shown in Fig. 4 indicate that the negatively charged fraction of the contaminants exhibits a higher variability (meaning different, non-related chemistry) and abundance compared to the positively charged fraction. The picture changes for masses higher than 150 Da (cf. Fig. S1a of the supplemental material) where a large number of positively charged contaminants can be found and identified, but most negatively charged mass peaks are related to atomic clusters from the spark. In the mass range above 350 Da only positive contaminants are observed (cf. Fig. S1b). This polarity difference is mostly related to the specific gas phase ionization energies of the clusters and the contaminants present in the system. We assume that the concentration of contaminants does not significantly vary among all our measurements which is a reasonable assumption considering that the contaminants are coming from parts of the experimental setup and are not changed among the different measurements.

### 3.1.1. Anionic Contaminants

In the spectrum of the negatively charged ionic clusters, the most abundant contaminants found in the range below 150 Da are mostly inorganic and have been identified as $Cl^-$, $O_3^-$, $I^-$, and $NO_3^-$ These species are common contaminants in mass spectrometry (Keller et al., 2008; Liu et al., 2020), however, the exact source in our experiments could not be identified.

Comparing the mass spectra recorded for the anionic species of Ag and Au (cf. Fig. 2a and 2c, respectively), it can be seen that similar species of contaminants are present. However, compared to the Ag and Au atomic clusters, their relative abundance varies in the two mass spectra. In the case of Ag the contaminant peaks, such as that of $Cl^-$, are of similar height to the cluster peaks, while for Au the cluster peaks are exceeding the contaminant peaks by up to a factor of 6. The absolute measured peak signal intensity of Au is much higher compare to that of the Ag clusters (cf. Fig. 2a and 2c). This is partially related to the number of stable isotopes of Ag which causes the total abundance of each
cluster species for each n to split among all existing isotopes. This means that the total abundance of
Ag$_n$ would be the sum of the signal intensity measured at all isotope masses. In contrast Au has only
one isotope and therefore the signal intensity of each peak represents the total abundance for each
cluster number n.

The differences in peak heights cannot be explained only by the number of isotopes as they are also
related to the differences in gas phase electron affinity between Au and Ag. Silver has a significantly
lower gas phase electron affinity compared to that of the typically found contaminants such as Cl, O<sub>3</sub>,
or NO<sub>3</sub> (Arnold et al., 1994; Bowen et al., 1983; Davidson et al., 1977). During spark discharge a plasma
is generated between the two electrodes which leads to the formation of a large number of charges.
High electron affinity leads to a high probability of acquiring a negative charge. Despite that in the
setup we have only traces of contaminants outgassed from its different components, we have a high
concentration silver vapour cloud. Therefore, a large number of silver clusters are competing with a
significantly smaller amount of contaminants for the negative charge carriers. This results into similar
peak heights of clusters and contaminants in the Ag<sup>-</sup> mass spectrum. In contrast Au has a high gas
phase electron affinity in the range or only slightly below that of contaminants, and, therefore,
acquires a negative charge more easily compared to Ag, yielding a much higher absolute measured
peak signal intensity, both in absolute numbers and relatively to the contaminant peaks (cf. Fig.2c).

3.1.2. Cationic Contaminants

Positively charged contaminants have been identified as PDMS (protonated polydimethylsiloxane, (C₂HOSi)ₙH⁺, with n=3-10), which has a mass step of 74 Da (Asbach et al., 2016; Keller et al., 2008; Timko et al., 2009; Yu et al., 2009), as well as a phthalate (Bis(2-ethylhexyl) phthalate, C₂₄H₃₈O₄H⁺) and its fragmentation masses (i.e., protonated 1,2-benzyldicarboxylic acid, C₁₆H₂₂O₄H⁺, and phthalate esters with saturated alkyl side chains (CH₂)ₙH⁺) (Jecklin et al., 2009), (cf. Fig. S1, and Table S1 of the supplemental material). Tubing materials and O-rings used in our experimental setup contain silicon rubber, which consist mainly of PDMS, and are therefore the most likely source of these contaminants observed. Phthalates are plasticizers used in polymeric materials. The presence of these contaminants can be explained by commonly used tubing material, as well as plastic ferules in connection pieces (e.g. Swagelok). The phthalate fragments show reactivity towards NO₂, leading to high abundance of contaminants in the mass range below 400 Da, for positively charged ions, with mass steps of 14 Da. The positively charged contaminants observed in our measurements have been identified to be very similar to those reported in other studies, where ions generated by commonly employed, commercial aerosol neutralizers using e.g. Po²¹⁰ radioactive (Maißer et al., 2015), or X-ray (Liu et al., 2020) radiations sources where analysed by mass spectrometry. As mentioned above, the plasma formed during spark ablation between the electrodes yields a high abundance of charge carriers, which can
charge any volatile material present in the system. Similarities of non-metallic ions produced by Po²¹⁰ or X-ray sources, with those produced by spark ablation, can be explained by the use of similar connection pieces (e.g. Swagelok plastic ferrules, tubing materials, electrically insulating materials, etc.) in the setups.

It should be noted here that it is unclear which of the contaminants are present and at which stage they are introduced in our experimental setup. Due to the fact that the sources of the contaminants can be linked to certain parts used in the experimental setup, it seems likely that some of the observed contaminants are not present during the cluster generation, but are added in the tube lines after the cluster production section and before the API-ToF-MS. This suggests that some of these sources could be avoided by the use of higher purity connection parts and sealing materials throughout the entire experimental setup. Future experiments using a tandem system combining mobility separation and mass analysis are planned in order to investigate if a simple mobility separation of atomic clusters from the contaminants is feasible.

## 3.2. Atomic Cluster Ions

In all the experiments carried out in this work we find pure clusters as well as clusters with adsorbed oxygen, hydrogen and/or water (cf. Fig. 2). The measured mass spectra for negative clusters (Figs. 2a and 2c) show that the peaks of the pure atomic clusters are clearly separated from peaks corresponding to metallic clusters with adsorbed trace impurities. Furthermore, the negative clusters are only impure due to adsorption of constituents originating from the trace impurities in the carrier gas, which results into a predictable pattern signature for all cluster sizes.

The mass spectra of the positively charged clusters produced by ablating silver electrodes (cf. Fig. 2b) also exhibit a predictable pattern throughout the entire mass range, whereas those of the gold cations (cf. Fig. 2d) exhibit a large variety of contaminant peaks throughout the mass range. This abundance of contaminants disrupts the pattern of metallic cluster peaks, making it difficult to identify them, and consequently to draw any conclusions about the cluster reactivity and stability. The following sections will focus mainly on the analysis of the silver clusters, both positively and negatively charged, and negatively charged gold.

### 3.2.1. Silver Clusters

The full mass spectra of negatively and positively charged silver clusters are shown in Fig. 2a and b, respectively. Silver has two stable isotopes with masses of 106.9 and 108.9 Da. As mentioned above, this leads to an increasing number of stable cluster combinations with increasing numbers of Ag atoms, which can be observed for both polarities, as shown in Fig. 5.
<figure><img src="image_13.png" /></figure>

Fig. 5 Mass spectra of positively and negatively charged clusters in the range of 100 to 600 Da including inserts of the Ag<sub>3</sub> and Ag<sub>4</sub>. Red solid bars correspond to positively charged clusters, blue dotted lines to negatively charged clusters. The height of the bars reflects the relative abundance of each cluster species.

The insets of Fig. 5 show the details of the mass spectra around the masses of the pure of Ag<sub>3</sub><sup>+/-</sup> and Ag<sub>4</sub><sup>+/-</sup>atomic clusters. Evidently, only peaks of the exact masses of the four stable isotopes corresponding to Ag<sub>3</sub><sup>+</sup> are observed while for Ag<sub>4</sub><sup>+</sup>, also peaks in between those of the pure isotopes are observed having a mass difference of 1 Da. This suggests that successive peaks correspond to clusters having a difference of one adsorbed H atom. The measurements show that clusters having *n* = 1 and 3 do not show Ag<sub>n</sub>H<sup>+</sup> peaks, while clusters of *n* = 2 and 4 do show hydrogen adsorption. For *n* ≥ 5, all the clusters exhibit Ag<sub>n</sub>H<sup>+</sup> peaks, in addition to those of pure Ag<sub>n</sub><sup>+</sup>. For negatively charged clusters, no clear trend of the Ag<sub>n</sub>H<sup>-</sup> peaks can be observed (cf. Fig. 5), however, this may be due to the generally low concentrations of the cluster species with no oxygen adsorbed to them. The pure silver anions throughout the entire cluster mass spectrum exhibit lower abundance compared to the respective clusters containing oxygen atoms (cf. Fig. 2a). The species of the highest abundance in the distributions is Ag<sub>1</sub>O<sub>2</sub>H<sub>m</sub><sup>-</sup>. In general, the most stable clusters for *n* = 1 and 3 are the ones containing O<sub>2</sub>H<sub>m</sub>, while for *n* = 2 the cluster containing OH<sub>m</sub> are the most stable species. For higher cluster numbers, a relative increase of the clusters containing higher oxygen numbers is observed.
<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

Fig. 6 Total abundance of all isotopes of Ag<sub>n</sub>O<sub>k</sub>H<sub>m</sub><sup>+</sup> (a) and Ag<sub>n</sub>O<sub>k</sub>H<sub>m</sub><sup>-</sup> (b), having n that ranges from 1 to 10, k from 0 to 5, and m from 0 to 10. Each bar corresponds to the sum of the measured signal intensities of all isotopes.

Fig. 6 shows the total abundance of Ag<sub>n</sub>O<sub>k</sub>H<sub>m</sub> clusters for cations and anions, respectively, where n = 1-10 for cations, and 1-7 for anions and, k = 1-5, and m = 0-10. This means that for each specific n and k, the sum of the measured abundance of all isotopes results in the total abundance depicted in the graphs. For example, in the case of Ag<sub>1</sub> the total abundance is the combined measured peak intensity of Ag<sup>106.9Da</sup> and Ag<sup>108.9Da</sup>, and the abundance of Ag<sub>1</sub>O<sub>1</sub>H<sub>m</sub> is the combined measured peak intensity of Ag<sup>106.9Da</sup>O<sub>1</sub>H<sub>m</sub> and Ag<sup>108.9Da</sup>O<sub>1</sub>H<sub>m</sub>. The number of H molecules in the cluster are not specified because it is not possible to distinguish the corresponding peaks, for example a Ag<sup>108.9Da</sup>O and a Ag<sup>106.9Da</sup>H<sub>2</sub>O cluster overlap in the mass spectrum as the masses of these two species are equal.
Fig. 6a shows that the smaller Ag<sub>n</sub><sup>+</sup> clusters having n ≤ 4, show enhanced stability for cluster species with oxygen and hydrogen combinations adsorbed to them as can be seen by the high measured signal intensity of e.g. Ag<sub>1</sub>O<sub>1</sub>H<sub>m</sub><sup>+</sup>, and Ag<sub>2</sub>O<sub>1</sub>H<sub>m</sub><sup>+</sup>. For clusters with n = 5-12, the pattern of mass peaks exhibits the highest relative abundance of the pure species. The total measured peak abundance decreases with increasing n, but the pattern of the peaks and those corresponding to their reaction products with oxide and hydroxide remains constant.

Fig. 6b shows that for negative Ag clusters the abundance of the pure cluster is relatively low compared to its oxidized form. The highest abundance peak is the Ag<sub>1</sub>O<sub>2</sub>H<sub>m</sub><sup>-</sup> cluster peak. It can be seen that for clusters with n ≤ 3 a stabilization trend can be observed with an increasing number of oxygen atoms (k) adsorbed to the clusters.

Our measured mass graphs were also compared to results from previous experiments. There exist two studies where atomic silver clusters where generated using atmospheric pressure gas phase production technique similar to the one described here. The first one used a glowing wire generator and the other one used a spark ablation method identical to the one employed in this study (Maisser et al., 2015; Peineke et al., 2009). Both studies employed differential mobility analysis (DMA) which is an ion mobility classifier that operates under atmospheric pressure. The mobility distributions exhibit an odd-even alternation in the measured peak heights, which can be well explained by the stability of specific clusters. In these previous studies it was assumed that the mobility peaks were caused by pure clusters (Maisser et al., 2015) or clusters with a potassium cation (Peineke et al., 2009) adsorbed to them. Here we compare the previous results by Maisser et al. 2015, with the mass measurements of the current manuscript. This is reasonable since the experimental setup for the spark ablation has been identical with our study. The mass results suggest that the peaks in the mobility distribution correspond to Ag<sub>n</sub>O<sub>k</sub>H<sub>m</sub> (where, n=1-12, k=0-6, and m=0-10). Simple considerations show that their mobilities were affected only to a minor degree by the adsorbed species of O<sub>k</sub> and H<sub>m</sub> due to the much larger cross section of the Ag atom compared to O and H.

In order to compare the mobility and the mass measurements, the peak area in the mobility distributions reported in previous publications must correspond to the total abundance of Ag<sub>n</sub>O<sub>k</sub>H<sub>m</sub>. The results of this comparison for positively charged silver clusters is shown in Fig. 7. To create this graph, we employed the mobility distributions reported by Maisser et al. 2015, which is also provided in the supplements to this manuscript (cf. Fig. S2). As shown in Fig. S2, the first 6 measured peaks of the mobility graph were fitted using Gaussian curves. In order to evaluate the peak area of each curve the integral of the resulting best fit Gaussian equation was determined (cf. Fig. S2, in the supplement). The result of the calculated area under the curve for each respective peak reflects the abundance of
each corresponding cluster species. All peaks were normalized to the peak of the species with the highest abundance (i.e., the peak of the distribution with the largest area). As shown in Fig.7 a good agreement between the relative abundance of the mass graph and the mobility graphs is achieved when assuming that each mobility peak includes all Ag<sub>n</sub>O<sub>k</sub>H<sub>m</sub> (for a specific *n*, but all *k* and *m*) and not of just the pure clusters. An exception is the first peak of the mobility graph which exhibits a significantly lower abundance in the mobility distribution compared to the mass spectrum. It can be seen that when summarizing all pure clusters with each oxidized cluster species for one *n*, the odd-even alteration observed previously in the mobility measurement is maintained. However, the most abundant peak is not that of Ag<sub>3</sub><sup>+</sup> but of the Ag<sub>2</sub>O<sub>k</sub>H<sub>m</sub><sup>+</sup> cluster.

The comparison of the mass and mobility measurements for silver cations allows to draw some conclusions on the differences of the cluster generation to the generation of larger nanoparticles by spark ablation. It has been shown that once the clusters are formed, growth by condensation and coagulation is assisted by coalescence to yield spherical particles having sizes in the sub-10-nm range.

The geometric mean diameter of these particles can be approximated by $\overline{D_p} \approx (\frac{3\dot{m}\beta V}{\pi\rho Q^2})^{\frac{1}{3}}$ where $\rho$ is the density of the particle material ($\rho = 10.49 \text{ g/cm}^3$ for Ag), $V$ is the volume occupied by the flow between the spark and the position in the setup where the particle reaches a diameter of $\overline{D_p}$, $\dot{m}$ is the mass production rate which is the mass ablated/spark multiplied by the spark frequency (mainly determined by the energy input), $Q$ is the volume flow rate, and $\beta$ is the coagulation coefficient (Feng et al., 2015b). The distribution follows a lognormal function with a geometric standard deviation of 1.35.

It should be noted here that $\beta$ is weakly size dependent, and the assumption of $\beta$ corresponding to the final particle size is a reasonable approximation for particles larger than 2 nm, whereas below that size it correctly describes the trend when spark operating parameters are changed. This means that a decrease of the factor $\dot{m}V/(Q^2\beta)$ further reduces the size, until the size domain of atomic clusters (below ca. 1 nm) is reached. While this qualitative statement remains correct, the assumption the equation is based on is that every collision leads to irreversible sticking, which is evidently not fulfilled in the atomic cluster size range as can be seen in the measured mass spectra. The selective, non-scalable growth (corresponding to the size range where the sticking probability cannot be assumed to be 1) in the atomic cluster range is the reason for deviations from the lognormal distribution, which still characterizes the overall shape. However, stable (less reactive) clusters species exhibit an increased abundance compared to the best lognormal fit, and non-stable ones are more weakly represented. A sticking probability that is much smaller than unity leads to a cluster coagulation coefficient $\beta_c \ll \beta$. The observed high abundance of Ag<sub>2</sub>O<sub>k</sub>H<sub>m</sub><sup>+</sup> instead of Ag<sub>3</sub><sup>+</sup> can therefore be
explained by differences in the sticking probability caused by the cluster reactivity or stability, which causes individual cluster sizes deviate from the lognormal curve. However, an overall log-normal-like behaviour is still maintained, as can be seen in Fig.6. The operating conditions mainly define the amount of material condensing and the coagulation time. If they slightly shift the peak to the right (higher initial concentration and/or longer coagulation time), this has a large influence on the cluster sizes left of the peak (n = 2) and a much smaller influence on the concentrations right of the peak. This dependence of the resulting overall mass or mobility distribution on the spark operating conditions might explain the large difference for n = 1 while the rest is about the same when the distributions are matched at n = 2 (cf. Fig.7). Another reason for differences observed in the comparison might be related to differences in the transmission efficiencies between the mass and the mobility measurements. The transmission in the mobility measurement rapidly decreases at very small cluster sizes due to increasing diffusivity. In the mass measurements the transmission efficiency has been found to be stable in the range of 100 to several hundreds of Da (Heinritzi et al., 2016). This can also partially explain the low relative abundance of Ag⁺ in the mobility measurement compared to the mass measurement.

A similar comparison has been attempted for the negative clusters, however, the best fit curves were very broad leading to a relatively large peak area. This suggests that the mobility peaks in the negatively charged cluster of different numbers of *n* might be overlapping, which makes a direct comparison of mass and mobility abundance difficult. The overlapping could be related to the enhanced stability of highly oxidized clusters containing 4 or more oxygen molecules adsorbed to the cluster resulting into mixed mobility peaks for two neighbouring cluster containing *n* and *n*+1 Ag atoms. This needs to be further analysed in a combined mass and mobility measurement.

<figure><img src="image_16.png" /></figure>
Fig. 7 Relative abundance of Ag$_{n}$O$_{k}$H$_{m}$⁻ clusters determined by the mass measurement and the mobility measurements (cf. Fig. S2).

Our experimental mass distributions can also be related to cluster reactivity and stability measurements carried out by other groups (Bernhardt et al., 2005, 2009; Bréchignac et al., 1999; Sharpe & Cassady, 1992). The dominant peak of Ag$_{2}$OH⁺ shown in the mass spectra has also been observed by Sharpe & Cassady (1992), who produced pure silver and silver oxides by fast atom bombardment using an 8-10kV xenon atom beam and a 2×10⁻⁵mbar argon pulse for thermalization. Collision-induced dissociation experiments were carried out in a pressure range of 1-10×10⁻⁷mbar of xenon or argon. In addition to silver oxides, the resulting mass distributions show also varying amounts of adsorbed hydrogen. The hydrogen in those experiments is assumed to originate from water residues in the silver oxide lattices, making it comparable to our results, where also water and oxygen traces were in the carrier gas flow.

In another study reporting mass spectra of Ag cationic clusters in low pressure He and Ar gas mixture when water and oxygen is present in the carrier gas,(Bréchignac et al., 1999) bare silver clusters were created in a gas-aggregation type source. The vapour was effused into a mixture of He and Ar gas of 20 mbar and 3 mbar pressure, respectively. Less than 1% of oxygen and water was added to the carrier gas. The neutral clusters were ionized with a pulsed laser and subsequently measured in a time-of-flight mass spectrometer. The measurements are mostly on clusters with $n = 4-12$. Despite the differences in the conditions of those measurements, the results carry some similarities to those of our study, such as the stabilization of even-number clusters in the presence of oxygen and water. However, the relative oxygen concentration in those measurements was higher compared to that in our measurements, which can explain the differences in the mass spectra of the cluster species reported by the two studies.

Although no study investigating the reactions of Ag⁻ with water was found in the literature, some reports on the reactivity of Ag⁻ with oxygen have been published (Bernhardt et al., 2009; Hagen et al., 2004). These studies employed guided beam technology. Clusters are generated by sputtering of metal targets and are mass-selected via quadrupole techniques. The reaction studies were carried out using an ion trap filled with helium buffer gas (approximately 1 Pa) and small partial pressures of the reactant gas. The consensus of these reports is that in the presence of oxygen the silver oxide peaks clearly show higher abundance while the respective pure silver clusters almost vanish. Specifically, experiments with ion traps show that Ag$_{2}$⁻ clusters react with atomic oxygen, while Ag$_{1}$⁻ and Ag$_{3}$⁻ have a tendency to react with molecular oxygen O$_{2}$. This can be explained by the open shell electronic structure of the dimer anions, which stabilizes with the adsorbed oxygen (Bernhardt et al., 2005). The same trend can also be observed in our experimental results. Additionally, it has been shown that
increasing *n* leads to increasing number of *m* in the Ag<sub>n</sub>O<sub>m</sub> clusters (Hagen et al., 2004), which is also
in line with our findings (cf. Fig. 6b).

It should be mentioned that according to the Jellium model magic numbers would be expected in the
case of pure silver clusters with n = 3 and 9 for cations, as well as with n = 7 for anions (Castleman Jr
& Khanna, 2009; de Heer et al., 1987; Knight et al., 1987). However, no enhanced stability of magic
number peaks has been observed for silver of both polarities in our measurements. This can be linked
to the presence of trace impurities of oxygen and water. A similar disruption of the odd-even pattern
and disappearing magic number peaks was reported in the literature for silver cations when O<sub>3</sub> was
added to the clusters in the gas phase (Wleklinski et al., 2015).

3.2.2. Gold clusters

As mentioned above, the high abundance peaks in the cationic mass spectrum measured using Au
electrodes (cf. Fig. 2d) are mostly identified to be associated to contaminants such as fragmentation
products of a phthalate and PDMS (cf. Table S1 and Fig. S1 in the supplement). This is related to the
high gas phase electron affinity of gold which leads to a very low probability for gold clusters to acquire
a positive charge. The SDG method generates both positive and negative charge carriers, therefore
the gas phase ion kinetics of the evaporated electrode material has a strong influence on the final
results. The results suggest that due to this specific gas phase behaviour the SDG method is not
applicable for the generation of cationic gold clusters.

In the distribution of gold anions (Fig. 2c) the pure cluster peaks for n = 1-3 are the most abundant
species, and the highest peak intensity was observed for Au<sub>2</sub>-. Additionally, the peaks for AuO<sub>2</sub>− and
Au<sub>2</sub>O<sub>2</sub>− are sticking out in the distribution with high signal intensity. Fig. 8 shows the compiled
abundance of all measured Au<sub>n</sub>O<sub>k</sub>H<sub>m</sub>− clusters with n = 1-7, k = 0-5, and m= 0-10. The first two bars
show the cluster abundance of Au<sub>n</sub>− and Au<sub>n</sub>H<sub>m</sub>−, respectively. It can be seen that for low numbers of n
(n = 1-3) most of non-oxidized peaks are pure, meaning that they are also not hydrated. For n ≥ 4, it
can be seen that the clusters containing even number of Au atoms are more likely to be hydrated
compared to the odd-number peaks. The abundance of the pure gold anions also shows a trend of
odd-even alternation for cluster sizes with n ≥ 4, with relatively higher abundance of the odd number
clusters compared to the even number clusters. For n ≥ 4, the peak intensity decreases rapidly, but
the even numbers show a tendency to stabilize with O<sub>2</sub>, while the odd numbers remain pure. The
general decrease of abundance of clusters with higher masses can be observed in all mass spectra,
and is likely to be related to higher losses in the mass spectrometer.
There is a number of studies that have investigated the reactivity with and binding energies of oxygen to gold atomic clusters (Bernhardt et al., 2005; Kimble et al., 2004, 2006). Kimble et al. (2004) investigated the reactivity of gold anions towards oxygen and the oxidation of CO. In their experiments, charged clusters are formed and equilibrated by laser vaporization of a rotating and translating gold rod in a helium/O₂ flow stream at an overall pressure of 100 Torr (Kimble et al., 2004). This has the effect that O₂ has been present during the initial formation of the clusters. This difference in the experimental setup leads to different observations compared to studies where O₂ has been introduced only after the cluster generation process. If O₂ is present already in the laser ablation section of the setup also odd-number atomic clusters show reactivity towards O₂. In contrast to exposure after the laser ablation region. This is in line with our findings, where we also observed reactivity of odd number clusters towards O₂, leading for instance to the formation of Au₃O₂Hm⁻ (cf. Fig. 8). They concluded from their work that pre-oxidized Au⁻ clusters can serve as an effective catalyst for the oxidation of CO.

Another study investigated the adsorption of oxygen on hydrated gold cluster anions by using a helium/H₂O flow stream (Wallace et al., 2003). The pressure in the vicinity of the evaporation zone was close to 1 atm. Downstream the cluster formation region, the clusters are exposed to O₂ using a pulsed extraction valve (Salisbury et al., 2000b). The results of their study show the different mass graphs of hydrated gold cluster ions before and after exposure to O₂. They found that in the presence of OH, also the odd-number clusters would react with O₂. In addition, they conclude that co-adsorption of H₂O, O₂, and CO increases the catalytic properties of Auₙ⁻ clusters for CO oxidation. This is highly relevant for our work as it suggests that some cluster species with highly interesting catalytic properties might benefit from the fact that co-adsorption of various trace reactions is occurring in our system.

While these studies are qualitatively comparable to our experiments as the trace reactants present during the cluster formation procedure are alike the relative abundance of the specific cluster and reactants vary in some cases significantly. The high signal intensity of the pure Au anionic clusters is found in all studies including ours (cf. Figs. 2c and 8). However, the highest peak intensity of Au₂O₂ cannot be fully understood from the literature review and is probably related to the sticking probability in analogy to the high peak intensity of Ag₂OkHm⁺. Another possible explanation enhancing this effect of relatively high abundance of Au₂O₂⁻, could be related to dissociation of oxygen or water from the clusters. It has been found that the dissociation energies for the loss of molecular oxygen are lower for the even number clusters, which would mean a low probability for reactions of Au₂O₂⁻ into Au₂⁻ + O₂. For the fragmentation of atomic oxygen, the even number clusters displayed lower
dissociation energies (Kimble et al., 2004). This could additionally explain the relatively high
abundance of Au$_{2}^{-}$.

Enhanced stability is expected for Au<sup>7-</sup> as this corresponds to a magic number of shell closing according
to the Jellium model. In the measurements, it can be seen that Au<sup>7-</sup> is sticking out from the mass
spectrum, however, with a low relative signal intensity. The low overall signal is most likely related to
poor transmission for that mass range in the MS.

<figure><img src="image_17.png" /><figcaption>Fig. 8 Total abundance of all isotopes of Au$_{n}$O$_{k}$H$_{m}^{+}$ having *n* that ranges from 1 to 7, *k* from 0 to 5, and *m* from 0 to 10</figcaption></figure>

**4. Conclusion**

This study shows that generation of atomic clusters in the gas phase using spark discharge generation
at atmospheric pressure is feasible. Trace impurities of water and oxygen that are present in the
carrier gas exhibit a size- and polarity-dependent reactivity towards the metallic clusters. This is in line
with previous experimental results for e.g. hot wire generated TiO$_{n}$ clusters (Domaschke et al., 2019),
or also with reactivity studies of size selected clusters of Ag and Au clusters in low pressure systems
such as flow reactors (Salisbury et al., 2000b; Wallace et al., 2003), or ion traps (Bernhardt, 2005).
The highest abundances of pure clusters (i.e., clusters without any adsorbed contaminant) are observed for silver cations and gold anions, while the highest overall cluster peak intensity was observed for pure gold anions. Silver anionic clusters show low stability in the measured spectra, but stabilize with the adsorption of multiple oxygens. The number of oxygen atoms necessary for stabilisation increases with increasing cluster size. Gold cations showed no detectable stability throughout the entire mass range for the pure species, and for species with adsorbed oxygen and/or hydrogen.

In summary our measurements show that the charge and size-dependent reactivity for silver and gold clusters are in general similar to those reported in previous studies. Differences in the cluster reactivities reported previously in some studies are related to variabilities in the concentration of the trace reactants such as oxygen and hydrogen. As mentioned above, one major difference in our study is that the observed reactivity happens at atmospheric pressure where the collision rate between clusters and contaminants is much higher compared to vacuum systems. Despite that, certain species exhibit high stability under these experimental conditions, which is very promising for potential applications.

Apart from the reactivity of clusters with trace impurities, a major concern in cluster synthesis at atmospheric pressure has been their interference with contaminants of the experimental system. While contaminants cannot be easily avoided at atmospheric pressure cluster synthesis, most of them seem to either be of significantly lower mass or of low abundance compared to the metallic clusters. Therefore, the interference with the cluster masses in the spectra was found to be negligible in many cases.

A future goal will be to determine which cluster species with interesting catalytic properties can be generated with high production rates and high purity with the SDG to bring this technology one step closer towards applications.

## Acknowledgments

This work was supported by UHEL Faculty of Science support grant (75284140) and 3-year grant (75284132) and Finnish Academy of Science project 1325656

The project INFRASTRUCTURES/1216/0070 (NANO²LAB) is co-funded by the European Regional Development Fund and the Republic of Cyprus through the Research Innovation Foundation (Strategic Infrastructure).

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 856612 and the Cyprus Government.
## Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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