11institutetext: Space Research & Planetary Sciences, University of Bern,
Switzerland
11email<EMAIL_ADDRESS>22institutetext: Aix Marseille Univ, CNRS,
PIIM, Marseille, France
22email<EMAIL_ADDRESS>
# An overview of desorption parameters of
Volatile and Complex Organic Molecules

A systematic dig on experimental literature

N.F.W. Ligterink & M. Minissale 1122

(Received March 16, 2023; accepted June 15, 2023)

###### Abstract

Context. Many molecules observed in the interstellar medium are thought to
result from thermal desorption of ices. Parameters such as desorption energy
and pre-exponential frequency factor are essential to describe the desorption
of molecules. Experimental determinations of these parameters are missing for
many molecules, including those found in the interstellar medium.

Aims. The objective of this work is to expand the number of molecules for
which desorption parameters are available, by collecting and re-analysing
experimental temperature programmed desorption data that are present in the
literature.

Methods. Transition State Theory (TST) is used in combination with the Redhead
equation to determine desorption parameters. Experimental data and molecular
constants (e.g., mass, moment of inertia) are collected and given as input.

Results. Using the Redhead-TST method, the desorption parameters for 133
molecules have been determined. The Redhead-TST method is found to provide
reliable results that agree well with desorption parameters determined with
more rigorous experimental methods. The importance of using accurately
determined pre-exponential frequency factors to simulate desorption profiles
is emphasised. The large amount of data allows to look for trends, the most
important is the relationship log10($\nu$) = 2.65ln($m$) + 8.07, where $\nu$
is the pre-exponential frequency factor and $m$ the mass of the molecule.

Conclusions. The data collected in this work allow to model the thermal
desorption of molecules and help understand changes in chemical and elemental
composition of interstellar environments.

###### Key Words.:

Desorption - Complex Organic Molecules - Volatile Molecules - Interstellar
Medium - Transition State Theory - Redhead equation

##  1 Introduction

Desorption of molecules from and adsorption of gaseous species on a surface
play a pivotal role in regulating physical processes and setting the chemical
composition of environments in the interstellar medium (ISM), star- and
planet-forming regions, and solar system objects. For example, the chemical
composition of hot core and corinos, compact regions of warm and molecule-rich
gas surrounding protostars, are largely explained by the desorption of species
from ice-coated dust grains (e.g., Ligterink et al., 2018b, 2020, 2021, 2022;
Bøgelund et al., 2019; Gorai et al., 2020; Yang et al., 2021; Hsu et al.,
2022; Nazari et al., 2022; Bianchi et al., 2022; Zhang et al., 2023). The
temperature of interstellar grains and their ice mantles dictates which
molecules adsorb and consequently take part in chemical reactions (Jin &
Garrod, 2020; Garrod et al., 2022). The molecular composition of comets like
67P/Churyumov-Gerasimenko is largely set by which species have remained frozen
since its formation or have frozen-out on its surface since (e.g., Mumma &
Charnley, 2011; Goesmann et al., 2015; Altwegg et al., 2016; Rubin et al.,
2019). Frozen molecules are found on the surfaces of planets and moons in the
solar system, where seasonal changes alter sublimation rates and in turn
affect atmospheric processes and chemical composition (Fray & Schmitt, 2009),
for example on Triton (Bertrand et al., 2022) or Pluto (Johnson et al., 2021).
To interpret observational data and model physical and chemical processes,
empirical equations are employed to describe the desorption/adsorption
process. For these equations, molecule-specific parameters need to be known.

The theoretical framework and experimental methods for thermal desorption
studies are well described in astrophysical and astrochemical literature (see
reviews by Burke & Brown, 2010; Minissale et al., 2022). In short, to simulate
the desorption rate the Polanyi-Wigner equation is generally used:

$-\frac{dN}{dt}=\nu_{\rm n}\cdot N^{\rm n}\cdot{\rm exp}\left(\frac{E_{\rm
des}}{T}\right),$ (1)

where $\nu_{\rm n}$ the pre-exponential frequency factor with value
molecules1-n s-1 (also often denoted as $A_{\rm n}$ and where $n$ is the
desorption order with n = 0, 1, 2), $N$ the surface coverage in molecules cm-2
(also often denoted as $\theta$), n (= 0, 1, 2) the order of desorption,
$E_{\rm des}$ the desorption energy in K, and $T$ the temperature of the
surface. The order of desorption is given as zeroth, first, or second. Zeroth
order desorption is associated with multilayer desorption, while first order
desorption with (sub)monolayer desorption. The desorption energy can also be
given in Joule by changing the exponent to $E_{\rm des}$ $k_{\rm B}^{-1}$
($k_{\rm B}$ = Boltzmann constant) or in Joule mol-1 by changing the exponent
to $E_{\rm des}$ $R^{-1}$ ($R$ = ideal gas constant). Second order desorption
is possible, and is for example observed with processes such as recombinative
desorption, where two species react to form the desorbing product. However, as
this type of desorption is hardly encountered within the astrochemical
literature, second order desorption is ignored in the remainder of this
publication.

There are a variety of experimental methods to determine the desorption
parameters n, $\nu$, and $E_{\rm des}$, most of them based on the Temperature
Programmed Desorption (TPD) technique or a variation thereof. In a typical TPD
experiment, a surface held at low temperature under vacuum conditions is
exposed and covered with a given adsorbate. Next, the temperature of the
surface is linearly increased. At some point the adsorbate will start
desorbing and continues to do so until the adsorbate is fully removed from the
surface. The release of adsorbate to the gas-phase can be traced with a
variety of instruments, but usually a mass spectrometry technique is employed.
The measured desorption trace can be analysed to find the desorption
parameters, for example with leading edge, Redhead, heating variation,
inversion, or Arrhenius analysis (e.g., King, 1975; De Jong &
Niemantsverdriet, 1990a, b; Tait et al., 2005a).

There are a number of limitation to the experimental determination of
desorption parameters, specifically relating to experimental efforts and
safety. Currently, the number of molecules detected in the interstellar medium
is about 270111https://cdms.astro.uni-
koeln.de/classic/molecules,222http://astrochymist.org/astrochymist_mole.html
(McGuire, 2022) and more are detected every year. Because experiments are time
consuming, it is challenging to keep up with the number of detections and
provide desorption parameters for all species. Furthermore, some molecules are
difficult to work with, either because they are chemically unstable (e.g.,
PH2COOH, CH3OOH, c-C3H4O) or highly toxic (e.g., HCN, CH3NCO, H2P(O)OH). To
bridge the gap between the availability of experimentally determined
desorption parameters and the needs of the community, alternative approaches
are needed. Computational techniques such as Machine Learning (Villadsen et
al., 2022), Bayesian inference (Heyl et al., 2022), DFT calculations (Ferrero
et al., 2022; Piacentino & Öberg, 2022), or quantum mechanical methods
(Germain et al., 2022; Bovolenta et al., 2022; Tinacci et al., 2022) help fill
the gap. However, there also exists a rich experimental literature of TPD
experiments that have been used for the identification of molecules produced
in experiments that simulate chemical processes in extraterrestrial ice (for
brevity named “chemical TPDs”), but not to assess their desorption parameters.
Because this type of data is in essence the same as what is used for the
determination of desorption parameters, it raises the question if chemical TPD
traces can be used to determine pre-exponential factors and desorption
energies and in this way contribute more of these essential parameters to the
literature.

In this study, chemical TPD traces are collected from laboratory literature
and analysed with a combination of Transition State Theory and the Redhead
method (Redhead-TST) to determine the pre-factor and desorption energies of
133 molecules. The methods are presented in Sect. 2 and the resulting data in
Sect. 3. Implications for astrophysical and astrochemical studies are
discussed in Sect. 4.

##  2 Methods

This work makes use of an analysis method based on the Redhead equation and
Transition State Theory, and is indicated as the Redhead-TST method throughout
this manuscript. Furthermore, desorption energies and pre-exponential factors
are obtained from TPD data that are used for molecule identification. The
Redhead-TST method and the data set are introduced in the following sections
and a visual summary is presented in Fig. 1.

Figure 1: Visual summary of the Redhead-TST method. The pre-exponential
frequency factor ($\nu$) is determined with Transition State Theory (TST). The
main input are the translational ($q^{\ddagger}_{\rm tr,2D}$) and rotational
($q^{\ddagger}_{\rm rot,3D}$) partition functions, which increase as the
molecule transitions from a solid to gaseous state, and $T_{\rm peak}$ of the
molecule. The Redhead equation takes $T_{\rm peak}$, the heating rate $\beta$,
and $\nu_{\rm TST}$ as main inputs to calculate the desorption energy $E_{\rm
des}$.

###  2.1 Redhead-TST formalism

To determine the desorption energies of molecules from TPD data, the Redhead
equation is used (Redhead, 1962; King, 1975):

$E_{\rm Redhead}=T_{\rm peak}\cdot\left(\ln\left(\frac{\nu_{\rm TST}T_{\rm
peak}}{\beta}\right)-3.64\right)$ (2)

This equation takes the peak of the desorption trace ($T_{\rm peak}$, K) in
combination with the heating rate ($\beta$, K s-1) and a pre-exponential
factor ($\nu$, $s^{-1}$) to determine $E_{\rm des}$ in K energy units. This
equation only applies to first order desorption, which generally applies to
(sub-)monolayer coverage. It is not used for zeroth order desorption
processes, which is usually the case for multilayer desorption. Redhead (1962)
showed that for $\nu$/$\beta$ values of 108 – 1013 K-1, the relation between
$E_{\rm des}$ and $T_{\rm peak}$ is nearly linear, within a $\pm$1.5%
accuracy. For values of $\nu$/$\beta$ ¿ 1013 K-1, we verified that this
relationship holds by comparing literature results with those retrieved from
the Redhead equation when the same parameters are used (see Sect. 3.2). While
the Redhead equation is not the most accurate analysis method to determine
desorption energies (De Jong & Niemantsverdriet, 1990b, a), the simplicity of
this equation makes it very suitable for the analysis of TPD data that have
been recorded for other purposes, such as the molecule identification, rather
than their desorption parameters. Data of this type can be low in signal to
noise ratio or have a poorly defined desorption trace shape, which make other
methods, such as the leading edge analysis (De Jong & Niemantsverdriet, 1990b,
a) less suitable to analyse it with.

The value for $\nu$ used in the Redhead equation is usually assumed and taken
to be 1012 – 1013 s-1. While these values are suitable for small molecules and
atoms, they significantly underestimate the pre-exponential factor for larger
species with more degrees of freedom. In this work, the pre-exponential factor
is therefore calculated by Transition State Theory (TST), following the
equation:

$\nu_{\rm TST}=\frac{k_{\rm B}\cdot T_{\rm peak}}{h}\cdot q^{\ddagger}_{\rm
tr,2D}\cdot q^{\ddagger}_{\rm rot,3D},$ (3)

where $k_{\rm B}$ is the Boltzmann constant and $h$ the Planck constant. This
formalism is adopted from Minissale et al. (2022), which in turn is based on
work by Tait et al. (2005b). In short, TST takes the difference in rotational
and translational degrees of freedom between the adsorbed and transition state
into account. In equation 3, $q^{\ddagger}_{\rm tr,2D}$ and $q^{\ddagger}_{\rm
rot,3D}$ are the 2D translational partition function and the 3D rotational
partition function, respectively. The 2D translational partition function,
because the dimension orthogonal to the surface is assumed to be common to
both adsorbed and desorbed molecules. $q^{\ddagger}_{\rm tr,2D}$ is given by:

$q^{\ddagger}_{\rm tr,2D}=\frac{A}{\Lambda^{2}}.$ (4)

The parameter $A$ is the surface area of each adsorbed molecule, which is
fixed to 10-19 m2 and is the inverse of the generally assumed number of
binding sites (that is, 1$\times$1015 cm-2). For large molecules this value
could be different, but for simplicity we adopt a uniform value. $\Lambda$ is
the thermal wavelength of the molecule and calculated in the following way:

$\Lambda=\frac{h}{\sqrt{2\ \pi\ m_{\rm molecule}\ k_{\rm B}\ T_{\rm peak}}}$
(5)

In this equation, $m_{\rm molecule}$ is the mass of the particle in kg.
Finally, the rotational partition function, $q^{\ddagger}_{\rm rot,3D}$ is
given as:

$q^{\ddagger}_{\rm rot,3D}=\frac{\sqrt{\pi}}{\sigma h^{3}}\cdot(8\pi^{2}k_{\rm
B}T_{\rm peak})^{3/2}\cdot\sqrt{I_{\rm x}I_{\rm y}I_{\rm z}}$ (6)

Here, $\sigma$ is the symmetry factor of the molecule and indicates the number
of indistinguishable orientations of the particle. $I_{\rm x}$, $I_{\rm y}$,
and $I_{\rm z}$ are the principal moments of inertia for rotation of the
particle. The moments of inertia are determined using a rigid rotor
approximation and chemical structures from the
ChemSpider333http://www.chemspider.com database. These structure as calculated
with a Dreiding force fieled based geometry optimisation and are not a full
quantum mechanical treatment. For a handful of molecules their structures were
not available in this database and for these instances they have been
calculated with the Avogadro444Avogadro: an open-source molecular builder and
visualisation tool. Version 1.2.0 http://avogadro.cc/ software. These
equations are only applicable to molecules consisting of more than two atoms.
Tait et al. (2005b) note that this TST method gives a good approximation of
the pre-exponential factor, but can overestimate the value. Adsorbates are
assumed to be immobile on the surface and therefore have no rotational or
translational degrees of freedom when bound (in other words $q_{\rm ads}$ =
1). Since some molecules are found to migrate on the surface to sites with
higher binding energies, they have some degrees of freedom on the surface,
which results in $q_{\rm ads}$ ¿ 1. With a mobile adsorbate,
$q^{\ddagger}_{\rm tr,2D}\cdot q^{\ddagger}_{\rm rot,3D}$ / $q_{\rm ads}$ will
therefore be lower, thus lowering the pre-exponential factor.

We stress that TST suffers from some limitations. One of the main
approximations is that all molecules in the transition state reach the
Boltzmann distribution. This could not be the case for large molecules, and it
is the reasons why the TST could fail to treat large molecules. It is not easy
to quantitatively define the “large” word but based on the results of the
present work and on Minissale et al. (2022), we can tentatively claim that TST
starts to fail when the molecule presents more than 20 atoms. Moreover, TST
neglects quantum effects, which is important at low temperatures or when the
chemical reaction involves tunnelling. We point out that this a second order
limitation since, except for H2 or D2 or other peculiar cases, desorption
occurs at temperature where classical effects overcome of some orders of
magnitude quantum effects.

Several species considered in this work are salt complexes, which consist of
two molecules that have engaged in an acid-base reaction and are present as a
cation-anion pair. For these species, it is generally not possible to
determine the moments of inertia and subsequently its pre-exponential factor
with TST. To determine the desorption energies of these salts, we take their
pre-factor as the sum of the pre-factors of the individual acid and base. The
following values are used for individual components: 4.96$\times$1015 s-1 for
H2O, 1.63$\times$1017 s-1 for HCN, and 1.94$\times$1015 s-1 for NH3 (Minissale
et al., 2022) and 2.9$\times$1017 s-1 for HNCO, 3.9$\times$1017 s-1 for
CH3NH2, 6.9$\times$1019 s-1 for NH2COOH, 2.0$\times$1018 s-1 for HCOOH,
1.3$\times$1019 s-1 for CH3COOH, and 1.7$\times$1021 s-1 for the Acetaldehyde
Ammonia Trimer (AAT, this work).

###  2.2 Data set

The data used in this paper have been collected from a wide variety of
publications in the laboratory astrochemistry and surface science literature.
All these publications make use of TPD experiments in combination with a mass
spectrometry technique, such as quadrupole mass spectrometer (QMS) or time-of-
flight MS (TOF-MS), to detect and identify molecules.

To determine $E_{\rm Redhead}$ and $\nu_{TST}$, the peak desorption
temperature $T_{\rm peak}$ and heating rate $\beta$ are collected and
presented in Table LABEL:tab:redhead-tst. Table LABEL:tab:mol_const lists the
relevant molecular constants mass, $\sigma$, $I_{\rm x}$, $I_{\rm y}$, and
$I_{\rm z}$. The heating rate is usually indicated in the publication, but in
case $\beta$ is not provided it is set to 1 K min-1. This matches with most
heating rates applied in astrochemical laboratory work, but we note that
heating rates in this data set range from 0.1 K min-1 to over 1 K s-1. Peak
desorption temperatures can textually be indicated in the publication or be
determined by-eye from TPD traces presented in figures. The absolute
uncertainty on the recorded temperature is generally 0.5–2.0 K, depending on
the measurement technique used (e.g., thermocouple, diode). However, by-eye
analysis of TPD data is inherently more inaccurate and the $T_{\rm peak}$
uncertainty is therefore uniformly set to $\pm$5 K. Combined with the
uncertainties of the equations used, we apply a uniform uncertainty of
$\pm$10% on all determined desorption energies.

Table LABEL:tab:redhead-tst also list details about the substrate material,
precursor molecules (i.e., starting molecule or mixture of molecules), and the
ice processing source. We note that most data are collected for molecules that
are formed in-situ, instead of as pure or mixed deposited ices. This means
that while compositions of the ice at the beginning of the experiment are
given, by the time the TPD is started this composition has changed because new
molecules have been formed. In fact, some of the listed molecules may form in
reactions that are promoted by heating during the TPD. This makes
characterising the binding environment of a molecule challenging, as the
target molecule does not only interact with the substrate or surrounding
precursor species, but potentially with a host of molecules that are formed
during processing of the ice. Therefore, when the ice is processed and the
target species is formed in-situ, the binding environment is assumed to
consist of a combination of the substrate material (e.g., metal, carbon, etc)
and a residue that is a mix of different and undefined organic molecules. All
molecules are assumed to be present at (sub)monolayer coverage (1ML $\approx$
1$\times$1015 molecules cm-2), unless there is clear mention that multilayer
quantities ($\gg$1ML) of product are formed, for example determined from IR
spectroscopic measurements, in which case the molecule is excluded from the
dataset.

It is possible that the molecule under investigation co-desorbs with another
matrix species, either a precursor molecule or an new species that is
abundantly formed during the processing of the ice. Because co-desorbing
species have a peak desorption temperature that is governed by the matrix
molecule instead of the binding of the targeted species to the surface, these
molecules are excluded from the data set. For precursor molecules (e.g., H2O,
CH3OH) desorption temperatures are often known and therefore any target
molecule that has a $T_{\rm peak}$ close to this is considered to be co-
desorbing and excluded. However, during processing new molecules that act as
co-desorption matrix can be formed. Because the full molecular inventory is
not always described, it is difficult to identify co-desorption in these
cases. Only if there is mention or a strong suspicion that co-desorption is
occuring the entry is omitted.

A variety of ice processing techniques are used, ranging from UV and X-ray
processing to hydrogenation and electron/ion bombardment. We note that thermal
processing is also a possibility, but this is only listed when it is
explicitly mentioned as a step in the molecule formation process. The TPD
process itself is not considered thermal processing.

Finally, data of (sub)monolayer desorption studies presented in the literature
have also been collected and are presented in Table LABEL:tab:redhead-tst as
well. In some cases only $\nu_{\rm lit}$ and $E_{\rm lit}$ are presented,
while in other cases sufficient information is available (i.e., $T_{\rm peak}$
and $\beta$) that $\nu_{\rm TST}$ and $E_{\rm Redhead}$ can also be
determined.

Figure 2: Occurrences of CHNOPS atoms (top panel), functional groups (middle
panel), and binding surfaces (bottom panel) of the entries present in the data
set used in this paper. The category “other” in the bottom panel includes
surfaces like KBr and MgF2 windows.

##  3 Results and Discussion

In Table LABEL:tab:redhead-tst data of 133 molecules from 132 publications
have been collected, for a total of 328 entries. Information on the CHNOPS
elemental composition, functional groups, and binding surfaces in the data set
are presented in Fig. 2. The vast majority of molecules contain one or more
carbon and hydrogen atom, while a substantial fraction contains at least one
oxygen atom. About one third of the molecules included contains at least one
nitrogen atom, while phosphorus and sulfur are present in a minor percentage
of the data set. In terms of functional groups, we find that mostly alchols,
aldehydes or ketones, and amines are covered in the data set. Contrary to what
is found in the ISM (e.g., Cernicharo et al., 2020; McGuire et al., 2020;
Marcelino et al., 2021; Lee et al., 2021b, a; Loomis et al., 2021; Lee et al.,
2022; Cernicharo et al., 2022), cyanides only make up a small portion of this
data set and ethers and formates represent the smallest fraction of the
functional groups.

This figure highlights the large availability of data experimental data on
oxygen-bearing molecules, but relatively few molecules are covered that
contain nitrogen, phosphorus, or sulfur atoms. There are two primary
explanations for this. First, many precursor molecules with N, P, or S, such
as HCN, PH3, and H2S, are more difficult to work with in the laboratory or
toxic and therefore avoided by experimental researchers. In turn, fewer TPD
data on the formation of molecules containing these kind of atoms are
available. At the same time the limited amount of data available on N, P,
S-bearing species may also indicate that ice chemistry is less efficient at
forming such molecules or that molecules containing these atoms are refractory
and do not desorb at temperature $\leq$300 K.

Finally, the cold surfaces for these studies are dominated by metallic ones
(e.g., Au, Ag, Pt). Surfaces that are more relevant to the ISM, such as
crystalline or amorphous water, highly oriented pyrolytic graphite (HOPG),
graphene, silica (SiO2), and silicates (SiO${}_{4}^{4-}$) make up a smaller
percentage of the data set. Only few ice chemistry experiments make use of
surface that are not metallic (e.g., Potapov et al., 2022). Since many
molecules in the data set are obtained from such experiments, this explains
the dominance of metallic surfaces in the data set. However, as mentioned
earlier, these molecules are produced in-situ, likely together with a mixture
of other complex organic molecules that can remain on the surface at
temperatures $\geq$300 K. Consequently, the binding surface of the target
molecule will be a combination of the metallic surface and an organic
molecular residue. This environment be a relevant analogue to dust grains in
the ISM, which are presumably coated in a layer of organic molecules once
water-ice is removed from their surfaces.

In the following sections a deeper look will be taken at the Redhead-TST
method performance and collected desorption energy and pre-exponential factor
data.

###  3.1 Influence of TPD data on the Redhead-TST output

Figure 3: Values of $\nu_{\rm TST}$ and $E_{\rm redhead-TST}$ of all the
entries of H3CH2CHO (propionaldehyde), CH3COCOCH3 (2,3-Butanedione), NH2OH
(hydroxylamine), (CH2OH)2 (ethyleneglycol), HOCHCHOH (1,2-ethenediol),
HOCH2CH(OH)CHO (glyceraldehyde), and HOCH(CH2OH)2 (glycerol). Symbols indicate
a metallic surface (squares) or carbon surface (pentagon, NH2OH panel).

The Redhead-TST method relies on TPD data, which can show major differences in
$T_{\rm peak}$ between experiments. Consequently, this influences the derived
$\nu_{\rm TST}$ and $E_{\rm redhead-TST}$ values. Examples of this are shown
in Fig. 3, where $\nu_{\rm TST}$ and $E_{\rm redhead-TST}$ of all entries of
CH3CH2CHO (propionaldehyde), CH3COCOCH3 (2,3-Butanedione), NH2OH
(hydroxylamine), (CH2OH)2 (ethyleneglycol), HOCHCHOH (1,2-ethenediol),
HOCH2CH(OH)CHO (glyceraldehyde), and HOCH(CH2OH)2 (glycerol) are shown. In all
cases, marginal variations of at most one order of magnitude are seen in the
pre-exponential factors for each molecule. Variations on this level will have
a negligible effect on the simulated desorption profiles. However, in several
cases large shifts in the retrieved desorption energy are found, with the most
extreme scatter seen in CH3COCOCH3 values, which range from 6920 to 12690 K.
These shifts are directly correlated with the peak desorption temperature used
as input for the Redhead equation.

The scatter in peak desorption temperatures, and thus desorption energies, has
several explanations. First and foremost the coverage of a species of interest
is not known in the majority of cases. Therefore, molecules can span a wide
range of (sub)monolayer coverages. At lower coverage, molecules have a
tendency to settle in deeper binding sites, which have higher desorption
energies. Consequently, there is a correlation between coverage and desorption
temperature, where lower coverage results in a shift of desorption to higher
temperature (see e.g., Smith et al., 2016; He et al., 2016). This effect is
more pronounced on rough surfaces, which have a large range of binding sites,
like Amorphous Solid Water (ASW) or the organic residue that is presumably
formed in the experiments included in this work.

The surface on which the experiment is conducted can also play a role. Almost
all entries depicted in Fig. 3 are measured on a metallic surface, but this
classification groups many different materials, such as Au, Ag, and Cu, and
structure, such as rough, Pt(111), or Mo(110), together. Each metal and
surface structure will result in different desorption energies, which in turn
shift $T_{\rm peak}$. This shift is exacerbated when other categories of
surfaces are used, such as water ice or carbon. For the selected molecules, a
clear example is seen for NH2OH. One entry is measured on graphite (Ioppolo et
al., 2014) and its retrieved desorption energy is a clear outlier with respect
to the other entries of this molecule. Similarly, the type of organic residue
formed on a surface during an experiment can also influence the desorption
characteristics. This is highlighted by CH3COCOCH3, for which all entries are
measured on the same type of Ag surface, but produced with different
irradiated precursors, such as CH3CHO, CH3CHO:CH3COCOCH3, and H2O:CH3CHO.

Since the desorption parameters of molecules covered in this study have in
most cases not been reported, the values listed in this work can be used as an
approximation of the pre-factor and desorption energy, for example in chemical
models. However, it is important to be aware that the provided values are
limited by the quality of the TPD input data.

###  3.2 Redhead-TST method performance

Figure 4: Comparison between $\nu$ (top panel) and $E_{\rm des}$ (bottom
panel) values obtained from literature sources, but which have also been
analysed with the Redhead-TST method. Red squares indicate entries for which
the difference between $\nu_{\rm TST}$ and $\nu_{lit}$ is smaller than three
orders of magnitude, whereas the blue circles indicate those with a difference
larger than three orders of magnitude.

In Fig. 4 the Redhead-TST and literature $E_{\rm des}$ and $\nu$ values are
compared for a number of publications where both these values are available.
The top panel of this figure shows the pre-factors values, which are colored
based on whether the difference $\Delta$($\nu_{\rm TST}$ \- $\nu_{\rm Lit}$)
is smaller than $\pm$3 orders of magnitude (red squares) or larger than $\pm$3
orders of magnitude (blue circles). The literature $\nu$ values that are in
close agreement with their TST counterpart are found to derive from studies
that employ more rigorous methods to determine the pre-factor (e.g., Tait et
al., 2005b; Chaabouni et al., 2018; Behmard et al., 2019; Tylinski et al.,
2020), although a number of data points are also derived from studies that in
fact employ the same TST to calculate $\nu$ (Ulbricht et al., 2006). To some
extent, the large differences in pre-factors are not surprising, as in many
studies $\nu$ is assumed to be 1012 or 1013 s-1. This tendency is visible in
the top panel of Fig. 4. We conclude that the TST method gives accurate
approximations of the pre-exponential factor value.

The subsequent effect of the adopted $\nu$ on the desorption energy is visible
in the bottom panel of Fig. 4. The data points in the bottom panel are again
labeled according to the difference in pre-factor values. When the difference
in $\nu$ is $\leq$3 decades, the desorption energies are in good agreement and
fall on the parity line. Contrary, when there is a large difference between
$\nu_{\rm lit}$ and $\nu_{\rm TST}$ there is a prominent difference between
$E_{\rm Redhead}$ and $E_{\rm lit}$. Because in all these cases the literature
pre-factor is lower than $\nu_{\rm TST}$, the corresponding desorption energy
is also lower. This comparison shows that the Redhead-TST method gives
accurate desorption parameters that are in good agreement with those derived
in experiments, provided these experimental parameters are determined with
rigorous laboratory and analysis methods.

Figure 5: Desorption traces of CH3NCO, CH3C(O)NH2, and NH2C(O)NH2 with
desorption parameters obtained with the Redhead-TST method (red) and the
Redhead method, assuming a pre-exponential factor of 1013 s-1 (blue). Peak
desorption temperatures are indicated in the plot for each species. The
desorption profile is simulated with a first-order Polanyi-Wigner equation,
surface coverage of 1$\times$1015 molecules cm-2, and heating rate of 1 K
century-1 (solid lines), which is appropriate for the ISM, or the heating rate
of 5 K min-1 (dashed lines) used in these experiments.

One may argue that the large discrepancies between $E_{\rm Redhead-TST}$ and
$E_{\rm lit}$ when $\nu_{\rm lit}$ is assumed will actually not affect the
simulated desorption behaviour, as in either case the best fit parameters are
generated. An example of this is shown in Fig. 5, where data of the molecules
CH3NCO (methyl isocyanate), CH3C(O)NH2 (acetamide), and NH2C(O)NH2 (carbamide)
are analysed with the Redhead-TST method (red lines) and the Redhead method,
while assuming $\nu$ = 1013 s-1. The dashed lines show the simulated
desorption profiles at a heating rate of 5 K min-1, the same temperature ramp
as used in the studies where the TPD data of these molecules are taken from
(Ligterink et al., 2017, 2018a). The peak desorption temperatures determined
with a TST and assumed prefactor are found to be identical and match with the
$T_{\rm peak}$ value determined in the laboratory. However, if the heating
rate is changed to 1 K century-1, a value that is applicable to interstellar
environments, the peak desorption temperatures show a substantial deviation.
The simulated profiles with the assumed $\nu$ value, which are lower than the
value determined with TST, underestimate the peak desorption temperature by
about 10%. This example highlights the importance of determining desorption
parameters, including the pre-exponential factor, as accurately as possible.
However, it is important to be aware of the nuances of heating in interstellar
environments. Grains can be heated by photons and in the case of very small
particles and energetic photons, the grains can be flash heated at rates in
the order of K s-1. In these cases desorption energies with assumed pre-
exponential factors might be just as applicable, as Fig. 5 shows. In general,
it is recommended to use as accurate a value as possible.

###  3.3 Data trends

Due to the large amount of collected data, it is possible to investigate
trends in pre-factor values and desorption energies. In Fig. 6 $E_{\rm des}$
(top panels) and $\nu$ (bottom panels) are plotted against the molecule mass
in amu (left column) and number of atoms of the molecule (right column). Data
points obtained with the Redhead-TST method are indicated in red, while those
taken from literature are presented in blue. In addition, the recommended
desorption parameters of molecules from various surfaces listed in Minissale
et al. (2022) have been added in green points (data from their Table 2 and 3).
Each data point is labelled by its surface category, which includes metallic
(squares), carbon (pentagons), silicate (triangles), and water-ice (circles).

Figure 6: Desorption energy (top row) and pre-exponential factor (bottom row)
plotted against molecule mass (left column) and number of atoms (right
column). Values derived with the Redhead-TST method are coloured in red,
recommended desorption parameters adopted from Minissale et al. (2022) in
green, and other literature sources are presented in blue. Marker symbol
indicates whether a molecule is desorbing from a metallic (squares), water
(circles), silicate (triangle), or carbon (pentagon) surface.

Both Redhead-TST and literature desorption energies show a general tendency to
increase with increasing molecule mass or number of atoms. However, most
striking is the large spread in desorption energy values for a given molecular
mass or number of atoms, which often spans more than 5000 K (top panels Fig.
6. This spread makes it difficult to retrieve any empirical relationship
between the parameters. Even when only data of a specific surface or
containing certain functional groups are selected (not shown), no trend or
relationship is found.

The situation is different for the pre-exponential factors. While the
laboratory literature data is affected by the tendency to assume $\nu$ = 1013
s-1, there is a clear trend in the TST data showing a relationship between
$\nu_{\rm TST}$ and the molecule mass. These data are fitted with an equation
of the form log10($\nu$) = a$\cdot$ln($m$) + b, where $m$ is the mass of the
molecule in amu (atomic mass units, and the best-fit values are found to be a
= 2.65 and of b = 8.07. Because both studied make use of Transition State
Theory to determine the pre-factor, the data points of both this work (red)
and those of Minissale et al. (2022) (green) are used for the fit. The TST
data points show a scatter of about one decade around the best-fit line, an
uncertainty that has a marginal effect on any simulated desorption profile.
The recommended values presented by Minissale et al. (2022) are in good
agreement with the found equation and show that the equation can also be used
for lower mass molecules. This empirical relationship can be used to estimate
the pre-exponential factor of a molecule solely based on its mass. This may
find use when analysing laboratory data to determine $E_{\rm des}$ values or
as an easy way to determine pre-factors for molecules in astrochemical models
(see also Sect. 4.1).

Table 1: Mean desorption parameters.

Molecule $\nu$ $E_{\rm des}$ $T_{\rm peak,ISM}^{a}$ Molecule $\nu$ $E_{\rm
des}$ $T_{\rm peak,ISM}$ s-1 K K s-1 K K C2H4 9.1e+15 2602 45 CH3CHCHOH
1.6e+19 8330 125 C2H6 4.8e+16 2773 46 CH3OCH2OH 1.9e+19 8471 127 C3H8 6.5e+17
3721 59 (CH3)2NCHO 6.7e+19 8785 129 CH2CHCH3 3.9e+17 3709 60
[NH${}_{4}^{+}$][OCN-] 2.9e+17 8117 129 CH3PH2 4.4e+17 4662 74 CH3OCH2CH3
2.1e+19 8633 129 CH3I 9.3e+17 4758 75 C6H5CCH 1.1e+20 8915 130 CH3CCH 1.9e+17
4624 75 CH3CH2CH2OH 3.8e+19 8909 132 C4H10 5.7e+18 4946 76 c-C3H2O 5.8e+18
8766 133 CH3CHO 9.7e+17 4882 77 C6H5CHO 3.0e+20 9502 136 H2SO2 2.6e+18 5035 78
CH2CH(OH)CH3 3.3e+19 9415 139 H2CCO 1.7e+17 4895 79 CH3COCOCH3 1.4e+20 9621
139 N2H2 5.9e+16 4891 81 NH2OH 1.0e+18 8954 140 c-C3H4O 2.9e+18 5319 82
[NH${}_{4}^{+}$][CH3COO-] 1.3e+19 9408 141 HNCO 2.9e+17 5154 83 NH2CH2CN
2.4e+19 9529 142 CH2NH 5.3e+16 5106 84 CH3CH2C6H5 5.3e+20 10126 144 CH3OOCH3
4.2e+18 5560 86 H2PCOOH 6.4e+19 9852 144 CH3CHCH2O 4.4e+18 5715 88 c-H2C3O
1.2e+19 9605 144 CH3CH2OCHO 1.6e+19 5833 88 H2POH 3.0e+18 9446 145 CH3OCHO
4.7e+18 5715 88 [NH${}_{4}^{+}$][HCOO-] 2.0e+18 9426 145 CH3NC 2.9e+17 5657 91
NH2CH(CH3)OH 4.2e+19 9910 146 NO2 3.7e+17 5676 91 C10H22 2.7e+21 10517 146
HCOPH2 3.7e+18 6006 92 CH3CH2CH2SH 9.6e+19 10070 147 CH3CH2NH2 2.3e+18 6033 94
c-NCHCH2 4.3e+18 9733 148 P2H4 2.2e+18 6183 96 CH3OCOOH 6.6e+19 10151 149
CH3NH2 4.9e+17 6025 96 HOCH2OH 1.1e+19 9911 149 CH3CH2CHO 8.8e+18 6359 97
CH2CHCH2OH 3.3e+19 10139 150 HCOSH 3.8e+18 6301 97 (CH2OH)2 4.2e+19 10214 151
CH3COCH3 1.0e+19 6459 98 [CH3NH${}_{3}^{+}$][NCO-] 6.8e+17 9688 152 CH3CHNH
1.9e+18 6327 98 HOCH2NH2 1.2e+19 10138 152 CH2CHNH2 1.9e+18 6326 98
CH2C(OH)COOH 1.5e+20 10578 153 H3SiC(H)CO 7.9e+18 6439 98 C10H8 5.7e+20 10831
154 C6H14 8.5e+19 6874 101 HOCH2CN 3.0e+19 10375 154 CH3P2H3 1.4e+19 6675 101
HCOCOOH 7.8e+19 10838 158 CH3SH 1.8e+18 6522 101 CH3NHCHO 3.1e+19 10708 158
CH3OCH3 2.0e+18 6503 101 CH3CONH2 4.0e+19 10763 159 H2S2 2.8e+18 6560 101
CH3COCOOH 1.9e+20 11169 161 CH3NCO 6.8e+18 6842 104 H2CC(OH)2 3.4e+19 10985
162 c-C6H12 6.3e+19 7187 106 (CH2-O)x 4.5e+17 10267 162 HC2CHO 4.3e+18 6957
107 HOCHCHOH 2.7e+19 10918 162 CH2CHOH 2.1e+18 6957 108 c-C6H5CN 4.1e+20 11628
166 N2H4 4.4e+17 6779 108 C6H5CHCH2 1.1e+21 12136 170 CH2CHCHO 8.1e+18 7162
109 HOCH2CH(OH)CHO 4.4e+20 12034 171 (CHO)2 6.2e+18 7163 109 C6H15N3 1.7e+21
12279 171 (CH3)2C6H4 1.4e+20 7457 109 H2CO3 5.3e+19 12160 178 C4H4S 5.0e+19
7492 111 [NH${}_{4}^{+}$][NH2COO-] 6.9e+19 12166 178 HCOOH 2.0e+18 7135 111
C14H10 3.2e+21 13019 180 [H3O+][OCN-] 3.0e+17 7087 113 HOCH2COOH 1.6e+20 12730
184 CH3CH2OH 5.2e+18 7400 113 CH3P(O)(OH)2 2.5e+20 13241 190 CH3C6H5 4.2e+19
7698 114 HOCH(CH2OH)2 5.7e+20 13545 192 C6H6 1.2e+19 7710 116 NH2COOH 6.9e+19
13276 194 CH3COOH 1.3e+19 7680 116 NH2CONH2 3.4e+19 13284 196 CH3C(O)OCH3
4.1e+19 7807 116 [C6H15N3H+][HCOO-] 1.7e+21 14186 198 HOCH2CHO 1.6e+19 7797
117 NH2CH2COOH 3.2e+20 14390 205 HCCNH2 1.6e+18 7587 118 S3 2.8e+20 14846 212
[NH${}_{4}^{+}$][CN-] 1.6e+17 7417 119 S4 8.3e+20 15497 218 CHOCH2CN 2.8e+19
8099 120 H2P(O)OH 8.2e+19 15207 221 CH2OCHCN 2.8e+19 8099 120 CH3SCH3 1.1e+20
15350 222 CH3OOH 4.2e+18 7845 120 C5H6N2O2 6.4e+21 20232 276 (CH2NH2)2 1.9e+19
8097 121 C24H12 4.9e+22 21013 279 CH3COCN 3.3e+19 8125 121 C4H5N3O 1.7e+22
23675 318 P3H5 4.4e+19 8227 122 C5H5N5 1.5e+22 23812 320 C8H18 6.0e+20 8728
124 C5H5N5O 3.2e+22 26209 348 PH2CH2PH2 2.8e+19 8307 124 C32H14 1.0e+24 28012
356 HCCOH 1.2e+18 8029 125 C60 4.1e+24 33634 399 555Molecules are sorted by
increasing $T_{\rm peak}$ value. aThe $T_{\rm peak}$ is determined from a
desorption profile simulated with a first-order Polanyi-Wigner equation,
surface coverage of 1$\times$1015 molecules cm-2, and heating rate of 1 K
century-1. Note that these mean values in many cases consists of a
superposition of binding environments, many of which are not relevant to
interstellar environments (i.e. metallic surfaces). These values should be
taken as best available.

###  3.4 Desorption parameters and caveats

For several molecules a large number of entries of the same species have been
collected in Table LABEL:tab:redhead-tst. This amount of data can be useful in
several cases, such as to analyse the influence of the desorption surface.
However, often a single $\nu$ and $E_{\rm des}$ value is desired, for example
to be used chemical models. The mean values for $\nu$ and $E_{\rm des}$ for
molecules analysed in this work with the Redhead-TST method are presented
Table 1.

It is important to note several caveats of the data provided in Table 1.
First, these desorption parameters are the mean of the available data.
Monolayer desorption is a more nuanced process and desorption energies will
differ depending on coverage and available binding sites. The mean values
presented in this table should only be used when no data is available from
more rigorous studies and only for specific applications, such as input for
astrochemical models.

Second, binding environments are grouped together, but one has to be aware
that the desorption energies of these molecules will be different when, for
example, amorphous water ice or graphite are considered. The most prominent
source of desorption parameter data comes from experiments that make use of
metallic surfaces (e.g., Au, Ag, Pt substrates). Often the molecule is formed
in situ together with other species, and there the binding surface can be seen
as a combination of metallic and a refractory organic residue, which makes
these environments more realistic.

In connection to the above points, it is important to note that for some
molecules extensive studies have been performed to determine the pure ice
(sub)monolayer desorption parameters, including on different surfaces. In
particular molecules that are often and abundantly observed in the ISM are on
this list, such as CH3NH2, CH3CHO, HCOOH, CH3CH2OH, CH3OCHO, CH3COOH, and
HOCH2CHO (e.g. Lattelais et al., 2011; Bertin et al., 2011; Burke et al.,
2015a, b, c; Chaabouni et al., 2018; Corazzi et al., 2021; Ferrero et al.,
2022). While these studies are invaluable to assess the influence of different
binding environments, in many cases the pre-exponential frequency factor is
assumed (e.g., to be 1012 or 1013 s-1), which will affect the determined
desorption energy. In Table LABEL:tab:redhead-tst the Redhead-TST analysis of
these data are presented when possible. If desorption parameters on surfaces
like amorphous or crystalline water, HOPG, or silicates are required, it is
recommended to adopt these values.

While some molecules are well studied, it is also noteworthy that some
prominent interstellar molecules have not rigorously been studied in the
laboratory, such as dimethyl ether (CH3OCH3), ethylene glycol ((CH2OH)2), and
acetamide (CH3C(O)NH2). For some molecules like ethylcyanide (CH3CH2CN) and
vinylcyanide (CH2CHCN) only limited data can be found in the literature (Toumi
et al., 2016; Kimber et al., 2018; Couturier-Tamburelli et al., 2018) and only
for the case of multilayer desorption. Because these species are routinely
observed in the ISM (e.g., Nazari et al., 2022), dedicated laboratory studies
of these molecules are desired. The parameters of these molecules presented in
this study rely solely on chemical TPD data and should be considered the best
currently available.

Figure 7: Difference between $\nu_{\rm TST}$ and $\nu_{Hasagawa}$ for
molecules presented in this study. Frequencies normalised to one are
presented. Figure 8: Comparison of desorption profiles generated with the TST
(red) or the Hasegawa (blue) prefactor. Left panel: desorption profiles of
CH3NCO, CH3C(O)NH2, and NH2C(O)NH2. The desorption energies are determined
with the Redhead-TST method using data from Ligterink et al. (2017) and
Ligterink et al. (2018a) (see Table LABEL:tab:redhead-tst and top left
corners). Desorption profiles are plotted for $\nu_{\rm TST}$ (red) and with
$\nu_{\rm Hasegawa}$ (blue). The desorption profile is simulated with a first-
order Polanyi-Wigner equation, surface coverage of 1$\times$1015 molecules
cm-2, and heating rate of 1 K century-1. Peak desorption temperatures decrease
by $\sim$30% when realistic pre-factors are used. Right panel: ice (solid) and
gas (dashed) abundances plotted against radius (au) of a protoplanetary disk
for $\nu_{\rm TST}$ (red) and with $\nu_{\rm Hasegawa}$ (blue). An average
disk temperature profile of $T(r)=200\times(r/1AU)^{0.62}$ K is used (Andrews
& Williams, 2007). For each 1 K temperature step, the number of desorbed
molecules is determined, which summed give the gas-phase column density and
subtracted from a starting value of 1$\times$1015 cm-2 give the ice column
density. The TST abundances are offset by a factor of 10 for easier viewing.
The peak radii of the ice-gas inversion are indicated and are shown to shift
outward by $\sim$50% when realistic pre-factors are used.

##  4 Astrophysical implications

###  4.1 Impact of the pre-factor on astrochemical models

Astrochemical models make use of the Polanyi-Wigner equation or a variation
thereof to calculate the desorption rates of molecules on dust grains (e.g.,
Kulterer et al., 2020). While for these models desorption energies are
generally taken from experimental and theoretical literature, the $\nu$ value
is calculated with an empirical formula, often the harmonic oscillator
relation presented in Hasegawa et al. (1992):

$\nu=\sqrt{2k_{\rm B}n_{\rm ss}E_{\rm des}/\pi^{2}m},$ (7)

which uses the molecule mass $m$, the molecule binding energy $E_{\rm des}$,
and the number of binding sites per grain surface area $n_{\rm ss}$ as input
to calculate the pre-factor. Except for small molecules and atoms, this
equation underestimates the value of $\nu$, often by multiple decades
(Minissale et al., 2022). This is also found for molecules considered in this
study. Figure 7 shows the difference between $\nu_{\rm TST}$ and $\nu_{\rm
Hasegawa}$ and visualises that for the majority of cases $\nu_{\rm TST}$ is at
least seven decades larger than the pre-factor calculated with the Hasegawa
equation.

The difference in adopted pre-factor can have a strong impact on astrochemical
models. Combining a desorption energy that is determined with Redhead-TST or
any other accurate method with a pre-factor determined by the Hasagawa
equation can severely misrepresent the desorption behaviour of the molecule
(see Fig. 2 in Ceccarelli et al., 2022). This is exemplified in Fig. 8 for the
molecules CH3NCO, CH3C(O)NH2, and NH2C(O)NH2. For these species $E_{\rm
Redhead-TST}$ is determined with data from Ligterink et al. (2017) and
Ligterink et al. (2018a) and subsequently plotted with $\nu_{\rm TST}$ (red)
and $\nu_{\rm Hasegawa}$ (blue). The desorption profiles (left panel) plotted
with $\nu_{Hasegawa}$ are all shifted to higher temperatures and misrepresent
the actual desorption profile. For these examples the peak desorption
temperature is about 30% higher than what it should be. In the right panel the
gas and ice column densities are plotted against the radius of a
protoplanetary disk, which has the average temperature profile
$T(r)=200\times(r/1AU)^{0.62}$ K, as found by Andrews & Williams (2007). For
each 1 K temperature step, the number of desorbed molecules is determined,
which summed give the gas-phase column density and subtracted from a starting
value of 1$\times$1015 cm-2 give the ice column density. As expected from
their peak desorption temperatures, the ice-gas inversion point lies at a
larger radius when the accurate TST pre-factor is used. Using $\nu_{\rm
Hasegawa}$ results in the inversion point shifting $\sim$50% inwards.

Due to its impact on astrochemical models, it is recommended to use more
accurate pre-exponential factor values. One way is to implement the empirical
equation presented in Sect. 3.3, with the caveat that this equation is ill
suited to determine pre-factors for atoms and diatomic molecules. Better yet
is to use the $\nu$ and $E_{\rm des}$ that belong together as presented in the
literature source as direct input for the chemical model, to prevent any
misrepresentation.

###  4.2 Peak desorption temperatures

For simplicity and consistency, desorption profiles and peak desorption
temperatures are calculated using Eq. 1 and a heating rate of 1 K century-1 in
this work. However, this heating rate is not appropriate for all interstellar
environments and peak desorption temperatures are often determined with other
equations, such as the adsorption-thermal-desorption balance presented by
Hasegawa et al. (1992) (see also Nazari et al., 2021):

$\frac{n_{\rm ice}}{n_{\rm gas}}=\frac{\pi a^{2}_{\rm grain}n_{\rm
grain}\sqrt{3k_{\rm B}Tm^{-1}}}{e^{-E_{\rm des}/T}\sqrt{2k_{\rm B}n_{\rm
ss}E_{\rm des}\pi^{-2}m^{-1}}}.$ (8)

As input, this equation takes the grain size $a_{\rm grain}$ (set to 0.1
$\mu$m), the grain number density $n_{\rm grain}$ (set to 1.0$\times$10-12
$n_{\rm H}$, with $n_{\rm H}$ the gas density), the sticking efficiency $S$
(set to 1), the gas and grain temperature $T$, the molecule mass $m$, the
molecule binding energy $E_{\rm des}$, and the number of binding sites per
grain surface area $n_{\rm ss}$ to calculate the ice-to-gas ratio. The peak
desorption temperature is taken as the point where ice and gas molecular
abundances are equal, that is $n_{\rm ice}$ / $n_{\rm gas}$ = 1. Instead of
the peak desorption temperature depending on the heating rate values, in this
equation it primarily depends on the gas density of the environment.
Furthermore, we note that this equation in its original form makes use of the
harmonic oscillator relation to calculate the pre-exponential frequency in the
denominator. As discussed Sect. 4.1, this equation gives inaccurate values for
larger molecules and therefore the equation should rather take accurate pre-
factor into account:

$\frac{n_{\rm ice}}{n_{\rm gas}}=\frac{\pi a^{2}_{\rm grain}n_{\rm
grain}\sqrt{3k_{\rm B}Tm^{-1}}}{\nu_{\rm TST}e^{-E_{\rm des}/T}}.$ (9)

Using the latter equation, the peak desorption temperatures where calculated
for $n_{\rm H}$ = 1$\times$107 cm-3 (appropriate for molecular clouds) and
$n_{\rm H}$ = 1$\times$1012 cm-3 (appropriate for protoplanetary disk
environments) and compared with the $T_{\rm peak}$ determined from the peak of
the Polanyi-Wigner desorption profile using a heating rate of $\beta$ = 1 K
century-1. Desorption parameters listed in Table 1 were used. In Table 2 the
results for a selection of twenty molecules are presented.

Table 2: Comparison peak desorption temperatures.

Molecule | $T_{\rm p,PW}$ | $T_{\rm p,ATD}$ | Diff. | $T_{\rm p,ATD}$ | Diff.
---|---|---|---|---|---
|  | $n_{\rm H,7}$ |  | $n_{\rm H,12}$ |
| K | K | % | K | %
C2H4 | 45 | 43 | 96 | 54 | 119
CH3CHO | 77 | 75 | 98 | 92 | 119
H2CCO | 79 | 78 | 98 | 95 | 120
CH2NH | 84 | 83 | 99 | 102 | 121
CH3SH | 101 | 100 | 99 | 121 | 120
CH3OCH3 | 101 | 99 | 98 | 121 | 120
CH3NCO | 104 | 102 | 98 | 124 | 119
HC2CHO | 107 | 105 | 98 | 127 | 119
CH2CHOH | 108 | 106 | 98 | 129 | 120
(CHO)2 | 109 | 107 | 99 | 130 | 119
HCOOH | 111 | 109 | 98 | 133 | 119
CH3CH2OH | 113 | 111 | 99 | 135 | 120
CH3COOH | 116 | 114 | 98 | 138 | 119
NH2OH | 140 | 139 | 99 | 169 | 121
H2PCOOH | 144 | 143 | 99 | 172 | 119
HOCH2CN | 154 | 152 | 99 | 184 | 119
HCOCOOH | 158 | 157 | 99 | 188 | 119
NH2CONH2 | 196 | 195 | 99 | 235 | 120
S4 | 218 | 216 | 99 | 258 | 118
C60 | 399 | 417 | 104 | 487 | 122
666Peak desorption temperatures for various molecules, using the Polanyi-
Wigner equation ($T_{\rm p,PW}$, with $\beta$ = 1 K century-1), and the
adsorption-thermal-desorption equation ($T_{\rm p,ATD}$), for $n_{\rm H}$ =
1$\times$107 cm-3 ($n_{\rm H,7}$) and $n_{\rm H}$ = 1$\times$1012 cm-3
($n_{\rm H,12}$). The differences between the adsorption-thermal-desorption
and Polanyi-Wigner peak desorption temperatures are indicated in percent of
($T_{\rm p,ATD}$ / $T_{\rm p,PW}$). Desorption parameters from Table 1 are
used.

The adsorption-thermal-desorption equation a low density ($n_{\rm H}$ =
1$\times$107 cm-3) shows a small difference with the Polanyi-Wigner results.
One aspect that affects this comparison is the definition of the peak
desorption temperature of the adsorption-thermal-desorption balance equation,
which is located at the point where $n_{\rm ice}$ / $n_{\rm gas}$ = 1.
However, for the $T_{\rm peak}$ from the Polanyi-Wigner equation this is
usually $n_{\rm ice}$ / $n_{\rm gas}\leq$ 0.1. Correcting for this discrepancy
raises the adsorption-thermal-desorption balance peak desorption temperatures
by several K. In turn, this make $T_{\rm peak,PW}$ and $T_{\rm peak,ATD}$ for
the low-density scenario virtually identical. Quite different is the situation
for the high-density ($n_{\rm H}$ = 1$\times$1012 cm-3) results of the
adsorption-thermal-desorption balance equation, which show peak desorption
temperatures that are approximately 20% higher than those obtained with the
Polanyi-Wigner equation. The peak desorption temperatures listed in this study
might therefore not be representative for all interstellar environments and
source-specific modelling is required to accurately determine desorption
fronts.

###  4.3 Salt desorption

Figure 9: Desorption profiles of acids (red), bases (blue), and the resulting
salt (black). Desorption energies and pre-factors for H2O, NH3, and HCN are
obtained from Minissale et al. (2022), while the parameters for the remaining
molecules are taken from Table 1. Peak desorption temperatures are indicated
in the plot for each species. The desorption profile is simulated with a
first-order Polanyi-Wigner equation, surface coverage of 1015 molecules cm-2,
and heating rate of 1 K century-1.

Several molecules detected in the interstellar medium can be classified as
acids ([AH], e.g., HCOOH, HNCO, CH3COOH) or bases ([B], e.g., NH3, CH3NH2). In
ice mantles, these species can engage in acid-base reactions and form organic
salts via the reaction [AH] + [B] $\rightarrow$ [A-][BH+]. Several of such
salts are included in this study and their desorption energies are determined.
The pre-factors of these salts are determined by adding the pre-exponential
factor values of its individual components ([AH] and [B]), see Sect. 2.1.
While there is a chemical diversity in the salts, they often include ammonia
as the base. In the interstellar medium, ammonia is also expected to be a
prominent base, due to its high ice abundance (up to 10% w.r.t. H2O, Boogert
et al., 2015). In all cases, $E_{\rm des}$ of the salt is larger than the
value of its individual components and therefore the salts will reside on dust
grains at higher temperatures (see also e.g., Kruczkiewicz et al., 2021). This
is exemplified in Fig. 9 where the desorption profiles of various salts and
their corresponding acid and base are plotted. A first-order Polanyi-Wigner
equation is used, with a monolayer coverage of 1015 molecules cm-2 and a
heating rate of 1 K century-1. Differences in peak desorption temperatures
range from just over 10 K for the H2O–HNCO system, to more than 60 K for the
CH3NH2–HNCO system.

The ability to lock up molecules in the form of salts can have number of
implications. Large quantities of organic molecules with amino (-NH2) or
carboxylic acid (-COOH) groups can be trapped in organic salt complexes and
remain on dust grains and larger bodies at elevated temperatures. Recent
analysis of samples collected and return from the astroid Ryugu show that it
contains high concentrations of amines (e.g., CH3NH2) and acids (HCOOH and
CH3COOH, Naraoka et al., 2023). Because these molecules are found to not be
trapped in minerals or other organic matter, the authors suggest that these
species reside in the material as salts in order to explain why these
volatiles are still present. Our results underline this conclusion, although
questions remains if the salts included in this study, which are still
relatively volatile, could have survived the heating ($\geq$ 300 K) and
hydrothermal stages during the formation of Ryugu (Nakamura et al., 2022).
Further desorption studies of salts complexes could help unravel in which salt
form amines and acids are locked up in asteroid Ryugu.

Organic salt complexes also provide a molecular reservoir that remains
available for grain surface chemistry for a longer time (or rather to higher
temperatures) but not for gas-phase reactions. Ammonia is the dominant
nitrogen carrier in observed interstellar ice, but it is also relatively
volatile. Locking ammonia up in ammonium salts (NH${}_{4}^{+}$) could mean
that a substantial atomic nitrogen reservoir is available on interstellar
grains long after NH3 or H2O have desorbed, see Fig. 9. Laboratory
investigations of the processing of salts with energetic radiation at elevated
temperatures ($\geq$100 K) and in water-free environments, are relevant
avenues to investigate the formation of prebiotic molecules (see for example
Bossa et al., 2009b).

As salts desorb at higher temperatures, they can give misleading indications
of sublimation fronts as determined with observations of interstellar
environments. For example, Lee et al. (2022) measure the spatial distribution
of various molecules toward the HH212 protoplanetary disk and find similar
radial distributions of HNCO and NH2CHO. If the presence of these molecules in
the gas can entirely be explained by ice desorption, this co-spatial
distribution is peculiar as these molecules have significantly different
desorption parameters of 2.9$\times$1017 s-1 and 5154 K (this work) and
3.69$\times$1018 s-1 and 9561 K (Minissale et al., 2022) for HNCO and NH2CHO,
respectively. One would expect that HNCO desorbs at a much lower temperature
or larger radius than NH2CHO. However, if instead most of the HNCO available
in the ice has reacted with NH3, it would be locked up in the
[NH${}_{4}^{+}$][OCN-] salt. This salt has the desorption parameters
2.9$\times$1017 s-1 and 8117 K, which are close to those of formamide. The
salt can therefore explain the co-spatial distribution of HNCO and NH2CHO due
to thermal desorption of the [NH${}_{4}^{+}$][OCN-] salts, which upon
desorption dissociates into its individual components, HNCO and NH3 (see also
Lee et al., 2022).

Thus far two molecules have been detected in the interstellar medium that are
presumed to be part of salt complexes. These are the cyanate anion (OCN-) and
the ammonium cation NH${}_{4}^{+}$ (Lacy et al., 1984; Keane et al., 2001;
Pontoppidan et al., 2003; Van Broekhuizen et al., 2005). Recently, these
species have also been detected in observations with the new James Webb Space
Telescope (JWST, McClure et al., 2023). The unprecedented sensitivity of JWST
opens up two avenues of research. First, it is possible to look for other salt
components at lower abundance or with weaker spectroscopic features, for
example the cyanide anion (CN-) and carboxylate anions (R-COO-). Second,
searches for salts in water poor or free interstellar environments, for
example molecular clouds that interact with the warm gas of a protostellar
outflow or the edges of photo dominated regions (PDRs), can be conducted.
Because the bulk ice species have been removed from the grains in these
regions, the less abundant salt and organic species are easier to detect.
Furthermore, the presence and shape of salt spectroscopic signatures will
provide information about the chemical and physical history of the
environment.

###  4.4 Chemical and elemental composition above the water-snow line

Figure 10: Ice elemental composition as [X]/[Carbon] for Hydrogen, Nitrogen,
Oxygen, and Sulphur as a function of temperature. Peak desorption temperatures
for selected species are indicated in the plot (vertical dashed lines).
Elemental compositions of Soluble Organic Matter (SOM) obtained from the
Murchison meteorite (Schmitt-Kopplin et al., 2010) and averaged Insoluble
Organic Matter (IOM, Alexander et al., 2017) are indicated (horizontal dashed
lines). The desorption profiles are simulated with a first-order Polanyi-
Wigner equation, variable surface coverages, and a heating rate of 1 K
century-1. Low and high OM represent low and high fraction of organic
molecules, respectively.  Figure 11: Functional chemical group composition
[X]/[-NH2] for oxygen-bearing groups -OH, -COOH, -C(O)-, -C(O)O-, and -O-
(solid red lines) and nitrogen-bearing groups -NC(O)- and -CN (dashed blue
lines) as a function of temperature. The desorption profiles are simulated
with a first-order Polanyi-Wigner equation, variable surface coverages, and a
heating rate of 1 K century-1.

Within star- and planet-forming environments temperature gradients and the
associated desorption fronts of molecules, also called snow lines, play an
important role in forming planetary objects and setting their chemical and
elemental composition. Examples of such lines are the nitrogen and water
snowline or the soot line, which is driven by the sublimation of large carbon-
dominated molecules such as polycyclic aromatic hydrocarbons (PAHs). These
lines are invoked to explain the high atomic nitrogen abundance in Jupiter or
the comparatively low carbon content of Earth (Bosman et al., 2019; Öberg &
Bergin, 2021; Li et al., 2021). Because of their high abundances,
observational, modelling, and experimental efforts have focused on the main
ice species to investigate desorption fronts. Since most organic molecules
except for CH3OH and CH4 are found at low abundances, these species will not
affect the elemental composition of a protoplanetary disk or create snow lines
that are observable. However, the combined inventory of organic molecules may
affect the elemental composition. ISO and Spitzer IR observation of various
interstellar sources have found significant features in the 5–8 $\mu$m region
(Gibb & Whittet, 2002; Boogert et al., 2008). These features have at least in
part been assigned to an organic residue consisting of a variety of molecules,
including HCOOH and HCOO-. Therefore, the data presented in this paper are
used in this section to investigate how a large reservoir of diverse organic
molecules affects elemental ratios and the chemical composition, for example
in a protoplanetary disk.

Elemental ratios of hydrogen, nitrogen, oxygen, phosphorus, and sulphur over
carbon are determined for a molecular inventory that consists of the species
listed in this work and desorption parameters presented in Table 1,
supplemented by bulk ice species with parameters taken from Table 3 of
Minissale et al. (2022). The ice abundances are set with respect to water. The
following fractions are used: CO, N2, and CO2 at 0.25, CH3OH at 0.1, O2, CH4,
NH3, and H2CO at 0.05, and H2S, CS, and HCN at 0.01. Combined, the
aforementioned species are indicated as the bulk ice species. All remaining
organic molecules listed in this work, as well as C2H2, CH3CN, NH2CHO taken
from Minissale et al. (2022) are included in a low and high fraction scenario
where each organic molecule contributes at 10-4 and 10-3 level, respectively.
The desorption profile, including ice and gas abundances are simulated with a
first-order Polanyi-Wigner equation, variable abundances according to
fractions with respect to water, and a heating rate of 1 K century-1. The
elemental composition is obtained by multiplying the ice or gas abundance of
each molecule with its elemental composition (e.g., for CH3OH carbon =
abundance $\times$ 1, hydrogen = abundance $\times$ 4, and oxygen = abundance
$\times$ 1) and subsequently summing all contributions to an element.

The resulting ice elemental compositions for [Hydrogen]/[Carbon],
[Nitrogen]/[Carbon], [Oxygen]/[Carbon], and [Sulphur]/[Carbon] are shown in
Fig. 10. Figure 12 in the Appendix shows the same plot, but with elemental
ratios plotted versus protoplanetary disk radius by using an average
protoplanetary disk temperature profile $T(r)=200\times(r/1AU)^{0.62}$ K
(Andrews & Williams, 2007). The elemental ratio of [Phosphorus]/[Carbon] is
shown in Fig. 13 in the Appendix. The bulk ice elemental ratios are plotted in
blue, while the addition of a low (10${}^{-4}\times$[H2O]) and high
(10${}^{-3}\times$[H2O]) fraction of organic molecules are plotted in red and
green, respectively. The peak desorption temperatures of several bulk ice
species are indicated, as well as the elemental composition of the
extraterrestrial substances Soluble Organic Matter (SOM, Sephton, 2002) and
Insoluble Organic Matter (IOM, Alexander et al., 2017). Both SOM and IOM are
regularly extracted from meteorites and SOM shows similarities with the
organic material measured on several solar system objects, such as comet 67P/
Churyumov-Gerasimenko (Hänni et al., 2022). In this work, we use the average
elemental composition of C100H155N3O20S3 for SOM obtained from the Murchison
meteorite (Schmitt-Kopplin et al., 2010) and the average IOM composition of
C100H77O14N3S2 presented in Alexander et al. (2017).

First, we note that the addition of organic material affects the elemental
ratios at low temperatures, when bulk ice dominates. The combined contribution
of organic elemental carbon lowers all elemental ratios. The effect is most
pronounced for the high fraction organic matter model where it can lower
ratios by up to 100%. In this scenario we only take the contribution of the
133 molecules studied in this work into account. In a natural environment the
diversity of the organic components may be many times larger (e.g., Hänni et
al., 2022) and therefore have an even larger impact on elemental ratios.

Second, we note the importance of the organic material to set the ice
elemental composition when the bulk ice species are removed from the solid
state. In our model, all the bulk ice species are lost before $\sim$115 K,
while the species that contribute to the nitrogen and sulphur budget are gone
at lower temperatures of $\sim$100 K and $\sim$75 K, respectively. These
differences in bulk ice desorption explain the dips seen in [N]/[C] and
[S]/[C] between 75 and 100 K. At these points the nitrogen (NH3) and sulphur
(CS) reservoirs have desorbed, but the main carrier of carbon (CH3OH) is still
present. It is likely that these dips will be less pronounced in reality due
to the trapping of the volatile nitrogen and sulphur carriers in the
dominating water-phase, which desorbs together methanol. After desorption of
the bulk ice reservoir the organic material starts to dominate the overall
elemental composition on the dust grains.

The elemental ratios are biased towards the molecules included in this work
and the assumption that molecules contribute equally to the total molecular
budget is unlikely to be realistic. Therefore, care needs to be taken with any
interpretation of Fig. 10. However, some general observations can be made.
First, at low temperature($\leq$115 K) or larger radii ($\geq$3 au), the bulk
ices dominate the elemental budget and ratios are seen to vary in a stepwise
manner. Objects formed in this regime will have predictable elemental
compositions, depending on the “step” they form at. However, in the warmer
disk regions closer to the (proto)star and where water has desorbed, the
elemental budget is set by the large mixture of organic molecules, which
desorb at different temperatures. Consequently, the elemental composition is
seen to be much more varied, especially in [Hydrogen]/[Carbon] and
[Oxygen]/[Carbon]. Objects that form in this regime could thus be expected to
have a much more varied or less predictable elemental signature.

It is interesting to point out that the organic molecules that remain on the
grains at $T\geq$100 K show similarities with, and in some cases are identical
to, the molecules used by Kudo et al. (2002) to determine the sticking
velocity of organics-coated grains. These authors found that organics-coated
grains rapidly coagulate in the 2.3 – 3.0 au region of a disk. This region is
in good agreement with the location where we find most organic molecules still
coating the grains after water has desorbed.

Finally, the evolution of various functional chemical groups are plotted in
Fig. 11. The abundances of the functional groups have been determined in the
same way that the elemental abundances have. Ratios of amides ([-NC(O)-]),
cyanides ([-CN]), alcohols ([-OH]), carboxylic acids ([-COOH]), aldehydes +
ketones ([-C(O)-]), esters ([-C(O)O-]), and ethers ([-O-]) with respect to
amines ([-NH2]) are plotted. This plot reflects several observations already
made in Sect. 3 about the occurrence in of certain functional groups in the
data set, for example that the alcohol (-OH) group is most abundant. It also
shows that some of these functional groups can be quickly depleted as the
environment warms up. In particular esters and ethers will be removed quickly.
On the other hand, amides and amines seem to remain present to higher
temperatures. In this way we see how chemical make-up and average properties
of an organics-coated grain may change with temperature and could potentially
be used as a tracers of thermal history of a particle in the solar system.
However, it is important to acknowledge that this view is biased towards
relatively small and volatile organic molecules. For example, IOM is rich in
ether (-O-) cross links (Remusat et al., 2005) and because this substance is
very refractory it will ensure that the ether functional group will remain
prominent component of the organic coating of grains at elevated temperatures.

##  5 Conclusion

This study presents a large number of desorption parameters, that is
desorption energies ($E_{\rm des}$) and pre-exponential frequency factors
($\nu$). These parameters will find use in astrochemical models and help to
understand the evolution of the chemical and elemental composition in star and
planet-forming regions. Because the list of molecules is dominated by organic
molecules and salts of medium-volatility these data are of particular
importance to assess the surface chemistry in regions were water ice has been
removed from dust grains.

To expand the number of molecules for which desorption parameters are
available, experimental temperature programmed desorption data have been
collected from the literature and analysed with the Redhead Transition State
Theory (Redhead-TST) method to determine the pre-exponential frequency factor
($\nu$) and the desorption energy ($E$). A comparison with literature $\nu$
and $E_{\rm des}$ values shows that the Redhead-TST method provides reliable
results that are on par with results of rigorous experimental methods. We
emphasise that the usage of accurately determined pre-factor values, instead
of assumed values or the often used Hasagawa equation, is essential to
properly simulate the desorption profiles of molecules. Due to the large
amount of data collected in this study, trends can be searched for. No
relationship between molecule mass or number of atoms is found, but a
relationship between the pre-factor and molecule mass in the form of
log10($\nu$) = 2.65ln($m$) + 8.07 is, which can be used to determine this
parameter in future studies. Mean desorption parameters are provided and used
to highlight how the desorption of these species can affect chemical and
elemental compositions.

###### Acknowledgements.

The authors thank E.F. van Dishoeck, E.G. Bøgelund, and C. Ceccarelli for
helpful discussions and feedback. Thanks go out to K.-J. Chuang for making
unpublished TPD plots of CH2CHNH2 and CH3CHNH available for analysis. The
authors thank the many astrochemists and surface scientists who have
contributed data to the literature on which this study relies. N.F.W.L.
acknowledges support from the Swiss National Science Foundation (SNSF)
Ambizione grant 193453 and NCCR PlanetS. M.M. acknowledges the French national
programme “Physique et Chimie du Milieu Interstellaire” (PCMI) of CNRS/INSU
with INC/INP cofunded by CEA and CNES.

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##  Appendix A Desorption parameters and molecular constants

The analysis techniques used in this publication make use of a number of
molecular constants, such as the moments of inertia, molecule mass, and
symmetry factor, which are listed in Table LABEL:tab:mol_const. All the
literature studies used in this work and the pre-exponential frequency factors
and desorption energies determined with the Redhead-TST method are presented
in Table LABEL:tab:redhead-tst. This table provides an overview of all
relevant data to determine the desorption constants (that is, $T_{peak}$,
$\beta$), but also information on the desorption surface and how the molecule
was produced.

Table 3: Molecular constants Molecule | Name | Mass | $I_{\rm x}$ | $I_{\rm y}$ | $I_{\rm z}$ | $\sigma$
---|---|---|---|---|---|---
|  | amu | amu $A^{2}$ |
C2H4 | Ethylene | 28 | 19.08 | 16.17 | 2.92 | 2
CH2NH | Methanimine | 29 | 16.47 | 14.43 | 2.04 | 2
(CH2-O)x | POM | 30 | 14.33 | 12.86 | 1.47 | 2
C2H6 | Ethane | 30 | 25.17 | 25.17 | 6.31 | 1
N2H2 | Diazine | 30 | 14.05 | 13.00 | 1.05 | 1
CH3NH2 | Methylamine | 31 | 22.75 | 21.84 | 4.96 | 1
N2H4 | Hydrazine | 32 | 20.55 | 18.71 | 3.55 | 1
NH2OH | Hydroxylamine | 33 | 18.70 | 17.25 | 2.59 | 1
CH3CCH | Propyne | 40 | 53.74 | 53.74 | 3.17 | 3
CH3NC | methylisocyanide | 41 | 45.53 | 45.53 | 3.17 | 3
HCCNH2 | Ethynamine | 41 | 59.06 | 57.52 | 1.54 | 1
c-NCHCH2 | 2H-azirine | 41 | 30.27 | 19.54 | 14.32 | 1
CH2CHCH3 | Propene | 42 | 58.92 | 51.88 | 10.18 | 1
H2CCO | Ketene | 42 | 48.01 | 46.54 | 1.47 | 2
HCCOH | Ethynol | 42 | 49.29 | 48.46 | 0.83 | 1
CH2CHNH2 | Vinylamine | 43 | 52.82 | 44.71 | 8.15 | 1
CH3CHNH | Ethanimine | 43 | 52.95 | 44.79 | 8.18 | 1
HNCO | Isocyanic acid | 43 | 45.07 | 44.48 | 0.60 | 1
C3H8 | Propane | 44 | 68.51 | 60.81 | 17.11 | 1
CH2CHOH | Vinyl alcohol | 44 | 50.29 | 43.50 | 6.78 | 1
CH3CH2NH2 | Ethylamine | 44 | 64.69 | 56.89 | 15.38 | 1
CH3CHO | Acetaldehyde | 44 | 52.38 | 46.79 | 8.74 | 1
N2O | Nitrous oxide | 44 | 39.93 | 39.93 | 0.00 | 1
[NH${}_{4}^{+}$][CN-] | Ammonium cyanide | 44 | 146.76 | 146.73 | 2.83 | 1
CH3CH2OH | Ethanol | 46 | 63.28 | 55.54 | 14.04 | 1
CH3OCH3 | Dimethylether | 46 | 52.46 | 46.86 | 8.78 | 1
HCOOH | Formic acid | 46 | 44.30 | 38.99 | 5.32 | 1
NO2 | Nitrogen dioxide | 46 | 35.88 | 32.57 | 3.30 | 2
HOCH2NH2 | Aminomethanol | 47 | 60.30 | 52.63 | 12.12 | 1
CH3OOH | Methylhydroperoxide | 48 | 51.92 | 45.92 | 10.77 | 1
CH3PH2 | methylphosphine | 48 | 34.89 | 34.54 | 5.94 | 1
CH3SH | Methanethiol | 48 | 40.03 | 38.25 | 4.95 | 1
HOCH2OH | Methanediol | 48 | 59.02 | 51.41 | 10.77 | 1
H2POH | Hydroxyphospine | 50 | 29.81 | 28.86 | 3.63 | 1
HC2CHO | Propynal | 54 | 96.61 | 88.73 | 7.88 | 1
c-H2C3O | Cyclopropenone | 54 | 73.92 | 58.67 | 15.25 | 1
CH2CHCHO | Propenal | 56 | 111.51 | 102.48 | 9.03 | 1
NH2CH2CN | Amino acetonitrile | 56 | 108.85 | 97.55 | 15.76 | 1
c-C3H4O | cyclopropanone | 56 | 82.11 | 64.18 | 25.05 | 1
CH3NCO | Methyl isocyanate | 57 | 107.16 | 100.76 | 9.58 | 1
HOCH2CN | Glycolonitrile | 57 | 108.34 | 97.44 | 14.06 | 1
C4H10 | n-Butane | 58 | 150.85 | 141.82 | 21.56 | 1
CH2CHCH2OH | Allyl alcohol | 58 | 126.85 | 118.55 | 16.30 | 1
CH3CH2CHO | Propanal | 58 | 115.73 | 112.38 | 19.84 | 1
CH3CHCH2O | Propylene oxide | 58 | 86.19 | 78.08 | 27.92 | 1
CH3CHCHOH | 1-Propenol | 58 | 108.21 | 87.30 | 24.05 | 1
CH3COCH3 | Acetone | 58 | 97.26 | 57.70 | 45.87 | 1
CH2CH(OH)CH3 | 2-propenol | 59 | 94.16 | 52.54 | 44.76 | 1
CH3CONH2 | Acetamide | 59 | 90.84 | 52.18 | 41.81 | 1
CH3NHCHO | N-Methylformamide | 59 | 114.82 | 107.36 | 10.60 | 1
CH3OCH2CH3 | Ethyl methyl ether | 59 | 136.12 | 128.10 | 17.51 | 1
(CH2NH2)2 | Ethylenediamine | 60 | 143.37 | 134.37 | 18.46 | 1
(CHO)2 | Glyoxal | 60 | 104.37 | 96.66 | 7.71 | 1
CH3CH2CH2OH | 1-Propanol | 60 | 145.92 | 136.47 | 18.86 | 1
CH3COOH | Acetic acid | 60 | 87.60 | 49.39 | 41.37 | 1
CH3OCHO | Methyl formate | 60 | 107.99 | 102.32 | 8.84 | 1
H2CC(OH)2 | 1,1-Ethenediol | 60 | 84.34 | 45.49 | 38.85 | 1
HOCH2CHO | Glycolaldehyde | 60 | 111.18 | 108.06 | 16.40 | 1
HOCHCHOH | 1,2-Ethenediol | 60 | 115.96 | 108.00 | 7.96 | 1
NH2CONH2 | Carbamide | 60 | 84.30 | 44.12 | 40.18 | 2
OCS | Carbonyl sulfide | 60 | 87.51 | 87.51 | 0.00 | 1
[NH${}_{4}^{+}$][OCN-] | Ammonium cyanate | 60 | – | – | – | –
c-C3H2O | Cyclopropenone | 60 | 73.92 | 58.67 | 15.25 | 2
NH2CH(CH3)OH | alpha-aminoethanol | 61 | 104.80 | 60.80 | 56.14 | 1
NH2COOH | Carbamic acid | 61 | 81.24 | 41.21 | 40.04 | 1
[H3O+][OCN-] | Hydronium cyanate | 61 | – | – | – | –
(CH2OH)2 | Ethylene glycole | 62 | 107.48 | 89.15 | 33.76 | 1
CH3OCH2OH | Methoxymethanol | 62 | 132.67 | 124.49 | 14.55 | 1
CH3OOCH3 | Dimethylperoxide | 62 | 118.60 | 111.24 | 13.74 | 1
CH3SCH3 | Dimethyl sulphide | 62 | 88.29 | 66.38 | 28.27 | 1
H2CO3 | Carbonic acid | 62 | 84.91 | 42.46 | 42.45 | 1
HCOPH2 | Formylphosphine | 62 | 95.53 | 86.63 | 8.90 | 1
HCOSH | Thioformic acid | 62 | 84.10 | 75.66 | 8.45 | 1
[NH${}_{4}^{+}$][HCOO-] | Ammonium formate | 63 | – | – | – | –
SO2 | Sulfurdioxide | 64 | 61.48 | 42.00 | 0.00 | 1
H2P(O)OH | Phosphinic acid | 66 | 70.60 | 54.68 | 21.39 | 1
H2S2 | Hydrogen disulfide | 66 | 73.02 | 72.66 | 3.58 | 1
H2SO2 | Sulfoxylic acid | 66 | 73.97 | 51.93 | 25.33 | 1
P2H4 | Diphosphine | 66 | 55.84 | 55.26 | 5.62 | 1
CH2OCHCN | Cyanoethylene oxide | 69 | 141.53 | 135.73 | 27.90 | 1
CH3COCN | Acetyl cyanide | 69 | 150.27 | 107.53 | 45.89 | 1
CHOCH2CN | Cyanoacetaldehyde | 69 | 141.53 | 135.73 | 27.90 | 1
H3SiC(H)CO | Silylketene | 72 | 147.38 | 138.24 | 9.14 | 1
(CH3)2NCHO | N-N-Dimethylformamide | 73 | 163.06 | 115.34 | 55.81 | 1
CH3C(O)OCH3 | Methyl acetate | 74 | 159.72 | 120.32 | 46.17 | 1
CH3CH2OCHO | Ethyl formate | 74 | 213.27 | 199.56 | 20.02 | 1
HCOCOOH | Glyoxylic acid | 74 | 144.61 | 104.85 | 39.76 | 1
[CH3NH${}_{3}^{+}$][NCO-] | Methylammonium cyanate | 74 | – | – | – | –
HC5N | Cyanobutadiyne | 75 | 328.74 | 328.74 | 0.00 | 1
NH2CH2COOH | Glycine | 75 | 152.20 | 122.27 | 50.91 | 1
CH3CH2CH2SH | n-Propanethiol | 76 | 227.55 | 216.26 | 20.70 | 1
CH3OCOOH | Carbonic acid monomethyl ester | 76 | 150.24 | 113.70 | 39.89 | 1
HOCH2COOH | Glycolic acid | 76 | 153.30 | 122.07 | 49.05 | 1
NCCCCN | Dicyanoacetylene | 76 | 327.44 | 327.44 | 0.00 | 1
[NH${}_{4}^{+}$][CH3COO-] | Ammonium acetate | 77 | – | – | – | –
C6H6 | Benzene | 78 | 178.19 | 89.10 | 89.09 | 6
H2PCOOH | Phosphinecarboxylic acid | 78 | 145.75 | 104.19 | 44.42 | 1
[NH${}_{4}^{+}$][NH2COO-] | Ammoniumcarbamate | 78 | 287.19 | 248.62 | 49.82 | 1
CH3P2H3 | methyldiphosphine | 80 | 144.88 | 134.69 | 18.63 | 1
PH2CH2PH2 | methylenediphosphine | 80 | 144.84 | 134.66 | 18.56 | 1
C4H4S | Thiophene | 84 | 157.93 | 98.37 | 59.56 | 1
c-C6H12 | Cyclohexane | 84 | 201.70 | 116.67 | 116.65 | 1
C6H14 | Hexane | 86 | 473.94 | 458.27 | 34.41 | 1
CH3COCOCH3 | 2,3-Butanedione / diacetyl | 86 | 219.84 | 148.14 | 87.40 | 1
CH2C(OH)COOH | 2-Hydroxyacrylic acid | 88 | 209.94 | 133.12 | 76.82 | 1
CH3COCOOH | Pyruvic acid | 88 | 213.52 | 136.19 | 80.48 | 1
HOCH2CH(OH)CHO | Glyceraldehyde | 90 | 285.64 | 243.94 | 57.82 | 1
CH3C6H5 | Toluene | 92 | 288.03 | 199.12 | 92.08 | 3
HOCH(CH2OH)2 | Glycerol | 92 | 311.26 | 269.19 | 62.35 | 1
CH3P(O)(OH)2 | Methylphosphonic acid | 96 | 110.04 | 108.06 | 94.31 | 1
S3 | Trisulfur | 96 | 187.14 | 143.31 | 43.83 | 2
P3H5 | Triphosphane | 98 | 154.78 | 128.94 | 34.16 | 1
C6H5CCH | Phenylacetylene | 102 | 401.67 | 312.32 | 89.35 | 2
c-C6H5CN | Benzonitrile | 103 | 401.22 | 311.94 | 89.28 | 2
C6H5CHCH2 | Styrene | 104 | 412.59 | 319.77 | 98.38 | 1
(CH3)2C6H4 | p-Xylene | 106 | 437.34 | 348.78 | 94.87 | 2
C6H5CHO | Benzaldehyde | 106 | 412.51 | 316.64 | 95.87 | 1
CH3CH2C6H5 | Ethylbenzene | 106 | 411.55 | 340.25 | 112.86 | 1
C4H5N3O | Cytosine | 111 | 715.27 | 471.06 | 244.23 | 1
C8H18 | Octane | 114 | 1069.05 | 1046.62 | 47.45 | 1
C5H6N2O2 | Thymine | 126 | 476.79 | 329.82 | 150.13 | 1
C10H8 | Naphthalene | 128 | 571.60 | 411.14 | 160.46 | 2
S4 | Tetrasulfur | 128 | 371.39 | 246.23 | 125.16 | 2
C6H15N3 | AAT | 129 | 543.23 | 302.24 | 299.79 | 1
C5H5N5 | Adenine | 135 | 530.47 | 319.98 | 210.54 | 1
C10H22 | Decane | 142 | 2046.04 | 2017.46 | 59.88 | 1
CH3I | Methyl iodide | 142 | 66.39 | 66.39 | 3.17 | 3
C5H5N5O | Guanine | 151 | 715.27 | 471.06 | 244.23 | 1
[C6H15N3H+][HCOO-] | AATF salt | 175 | – | – | – | –
C14H10 | Phenanthrene | 178 | 1231.44 | 921.82 | 309.62 | 2
C24H12 | Coronene | 300 | 3009.29 | 1504.65 | 1504.64 | 6
C32H14 | Ovalene | 398 | 5511.77 | 3406.56 | 2105.21 | 2
C60 | Fullerene | 720 | 5214.32 | 5214.30 | 5214.24 | 3
Table 3: continued. 777POM = Polyoxymethylene, AAT = Acetaldehyde Ammonia
Trimer, AATF salt = Acetaldehyde Ammonia Trimer Formate salt. For the salts no
moments of inertia or symmetry factors have been listed.

Table 4: Literature and Redhead-TST desorption energies Moleculea |  Surfaceb |  Precursor(s)c |  Processingd | $T_{\rm peak}$ | $\beta$ | $\Lambda$ | $q_{rot,3D}$ | $q_{tr,3D}$ | $\nu_{lit}$ | $E_{\rm des,lit}$ | $\nu_{TST}$ | $E_{\rm des,Red}$ | Reference
---|---|---|---|---|---|---|---|---|---|---|---|---|---
|  |  |  | K |  K s-1 | pm |  |  |  s-1 | K |  s-1 | K |
C2H4 | (P) ASW |  C2H4 | No | 68 | 0.033 | 40 | 125 | 62 | 4.0E+15 | 2800 | 1.1E+16 | 2780 | Behmard et al. (2019)
C2H4 | Compact ASW |  C2H4 | No | 60 | 0.033 | 43 | 103 | 55 | 4.0E+15 | 2400 | 7.1E+15 | 2420 | Behmard et al. (2019)
CH2NH | Ag |  CH3NH2 |  5 keV e- | 115 | 0.008 | 30 | 201 | 109 | – | – | 5.3E+16 | 5110 | Zhu et al. (2019b)
(CH2-O)x | Au |  H2CO:H2CO | UV | 230 | 0.067 | 21 | 426 | 226 | – | – | 4.6E+17 | 10390 | Butscher et al. (2016)
(CH2-O)x | Au |  H2O:NH3:H2CO | Thermal | 225 | 0.067 | 21 | 412 | 221 | – | – | 4.3E+17 | 10140 | Duvernay et al. (2014)
C2H6 |  MgF2 |  CH4 | UV | 66 | 0.017 | 39 | 503 | 65 | – | 2060 | 4.5E+16 | 2840 | Carrascosa et al. (2020)
C2H6 | (P) ASW |  C2H6 | No | 67 | 0.033 | 39 | 514 | 66 | 6.0E+16 | 3000 | 4.7E+16 | 2840 | Behmard et al. (2019)
C2H6 | Compact ASW |  C2H6 | No | 58 | 0.033 | 42 | 414 | 57 | 6.0E+16 | 2500 | 2.9E+16 | 2420 | Behmard et al. (2019)
C2H6 | ASW |  C2H6 | No | – | 1.000 | 0 | 0 | – | 9.5E+15 | 2490 | – | – | Smith et al. (2016)
C2H6 | Graphene |  C2H6 | No | – | 1.000 | 0 | 0 | – | 5.1E+28 | 2980 | – | – | Smith et al. (2016)
C2H6 | MgO(100) |  C2H6 | No | 75 | 0.600 | 37 | 609 | 74 | 7.9E+14 | 2670 | 7.0E+16 | 3000 | Tait et al. (2005a)
N2H2 | Ag |  CH4:NH3 |  5 keV e- | 110 | 0.008 | 30 | 237 | 108 | – | – | 5.9E+16 | 4890 | Förstel et al. (2017)
CH3NH2 | Ag |  CH4:NH3 |  5 keV e- | 108 | 0.008 | 30 | 827 | 110 | – | – | 2.0E+17 | 4930 | Förstel et al. (2017)
CH3NH2 | Cu | HCN | H | 148 | 0.083 | 26 | 1326 | 151 | – | – | 6.2E+17 | 6630 | Theulé et al. (2011a)
CH3NH2 | Cu |  CH2NH | H | 125 | 0.083 | 28 | 1029 | 127 | – | – | 3.4E+17 | 5510 | Theulé et al. (2011a)
CH3NH2 | HOPG |  CH3NH2 | No | 160 | 0.200 | 25 | 1490 | 163 | – | 7000 | 8.1E+17 | 7080 | Chaabouni et al. (2018)
CH3NH2 | np-ASW |  CH3NH2 | No | 137 | 0.200 | 27 | 1181 | 139 | – | 4200 | 4.7E+17 | 5970 | Chaabouni et al. (2018)
N2H4 | Ag |  CH4:NH3 |  5 keV e- | 145 | 0.008 | 26 | 957 | 152 | – | – | 4.4E+17 | 6780 | Förstel et al. (2017)
NH2OH | (A) silicate | NO | H | 188 | 0.017 | 22 | 1107 | 204 | 1.0E+13 | 6520 | 8.8E+17 | 8840 | Congiu et al. (2012)
NH2OH |  (C) H2O | NO | H | 188 | 0.017 | 22 | 1107 | 204 | – | – | 8.8E+17 | 8840 | Congiu et al. (2012)
NH2OH | Au | NO | H | 170 | 0.083 | 23 | 952 | 184 | – | – | 6.2E+17 | 7640 | Fedoseev et al. (2016)
NH2OH | Au | NO:CO | H | 200 | 0.083 | 21 | 1214 | 217 | – | – | 1.1E+18 | 9140 | Fedoseev et al. (2016)
NH2OH | (A) Silicate |  NH3:O | O | 180 | 1.000 | 23 | 1037 | 195 | – | – | 7.6E+17 | 7690 | He et al. (2015)
NH2OH | Rh |  NH3:O2 |  5 keV e- | 191 | 0.008 | 22 | 1133 | 207 | – | – | 9.3E+17 | 9130 | Tsegaw et al. (2017)
NH2OH | Ag |  NH3:H2O |  5 keV e- | 180 | 0.008 | 23 | 1037 | 195 | – | – | 7.6E+17 | 8550 | Zheng & Kaiser (2010)
NH2OH | Graphite |  NO2 | H | 245 | 0.017 | 19 | 1646 | 265 | – | – | 2.2E+18 | 11810 | Ioppolo et al. (2014)
CH3CCH | Pt(111) |  CH3CCH | No | 138 | 4.000 | 23 | 767 | 181 | – | – | 4.0E+17 | 5580 | Peck et al. (1998)
CH3CCH | Ag |  C2H2:NH3 |  5 keV e- | 105 | 0.017 | 27 | 509 | 138 | – | – | 1.5E+17 | 4690 | Turner et al. (2021)
CH3CCH | (P) ASW |  C3H4 | No | 97 | 0.033 | 28 | 452 | 127 | 1.0E+19 | 4700 | 1.2E+17 | 4230 | Behmard et al. (2019)
CH3CCH | Compact ASW |  C3H4 | No | 92 | 0.033 | 29 | 418 | 121 | 1.0E+19 | 4400 | 9.7E+16 | 3990 | Behmard et al. (2019)
CH3NC |  $\alpha$-quartz(0001) |  CH3NC | No | 120 | 0.200 | 25 | 527 | 161 | 3.0E+17 | 4990 | 2.1E+17 | 5120 | Bertin et al. (2017)
CH3NC |  $\alpha$-quartz(0001) |  CH3NC | No | 120 | 0.200 | 25 | 527 | 161 | 3.0E+17 | 5340 | 2.1E+17 | 5120 | Bertin et al. (2017)
CH3NC | ASW |  CH3NC | No | 135 | 0.167 | 23 | 629 | 182 | 1.0E+18 | 6270 | 3.2E+17 | 5850 | Bertin et al. (2017)
CH3NC | ASW |  CH3NC | No | 135 | 0.167 | 23 | 629 | 182 | 1.0E+18 | 6380 | 3.2E+17 | 5850 | Bertin et al. (2017)
CH3NC |  (C) H2O |  CH3NC | No | 138 | 0.167 | 23 | 650 | 186 | 5.0E+16 | 5690 | 3.5E+17 | 6000 | Bertin et al. (2017)
CH3NC |  (C) H2O |  CH3NC | No | 138 | 0.167 | 23 | 650 | 186 | 5.0E+16 | 5690 | 3.5E+17 | 6000 | Bertin et al. (2017)
HCCNH2 | Ag |  C2H2:NH3 |  5 keV e- | 160 | 0.017 | 22 | 2175 | 215 | – | – | 1.6E+18 | 7590 | Turner et al. (2021)
c-NCHCH2 | Ag |  C2H2:NH3 |  5 keV e- | 200 | 0.017 | 19 | 3862 | 269 | – | – | 4.3E+18 | 9730 | Turner et al. (2021)
CH2CHCH3 | (P) ASW |  C3H6 | No | 86 | 0.033 | 29 | 2087 | 119 | 6.0E+18 | 4100 | 4.4E+17 | 3860 | Behmard et al. (2019)
CH2CHCH3 | Compact ASW |  C3H6 | No | 80 | 0.033 | 30 | 1873 | 110 | 6.0E+18 | 3800 | 3.4E+17 | 3560 | Behmard et al. (2019)
H2CCO | Ag |  CO:CH4 |  5 keV e- | 93 | 0.008 | 28 | 381 | 128 | – | – | 9.5E+16 | 4160 | Maity et al. (2014a)
H2CCO | Ag |  CH3OH |  5 keV e- | 121 | 0.008 | 24 | 566 | 167 | – | – | 2.4E+17 | 5560 | Maity et al. (2015)
H2CCO | Ag |  CH3OH:CO |  5 keV e- | 123 | 0.008 | 24 | 580 | 169 | – | – | 2.5E+17 | 5660 | Maity et al. (2015)
H2CCO | Ag |  H2O:CO |  5 keV e- | 95 | 0.017 | 28 | 394 | 131 | – | – | 1.0E+17 | 4200 | Turner et al. (2020)
HCCOH | Ag |  H2O:CO |  5 keV e- | 170 | 0.017 | 21 | 1460 | 234 | – | – | 1.2E+18 | 8030 | Turner et al. (2020)
CH2CHNH2 | Au |  C2H2:NH3 | UV | 138 | 0.083 | 23 | 3336 | 195 | – | – | 1.9E+18 | 6330 | Chuang in prep.
CH3CHNH | Au |  C2H2:NH3 | UV | 138 | 0.083 | 23 | 3351 | 195 | – | – | 1.9E+18 | 6330 | Chuang in prep.
HNCO | HOPG | HNCO | H | 120 | 0.200 | 24 | 676 | 169 | 1.0E+28 | 3960 | 2.9E+17 | 5150 | Noble et al. (2015)
HNCO | HOPG | HNCO | H | 120 | 0.200 | 24 | 676 | 169 | 1.0E+28 | 3730 | 2.9E+17 | 5150 | Noble et al. (2015)
C3H8 |  MgF2 |  CH4 | UV | 84 | 0.017 | 29 | 3050 | 121 | – | 2580 | 6.5E+17 | 3860 | Carrascosa et al. (2020)
C3H8 | (P) ASW |  C3H8 | No | 73 | 0.033 | 31 | 2471 | 105 | 4.0E+18 | 3500 | 4.0E+17 | 3250 | Behmard et al. (2019)
C3H8 | Compact ASW |  C3H8 | No | 84 | 0.033 | 29 | 3050 | 121 | 4.0E+18 | 4000 | 6.5E+17 | 3800 | Behmard et al. (2019)
C3H8 | ASW |  C3H8 | No | – | 1.000 | 0 | 0 | – | 1.1E+16 | 2840 | – | – | Smith et al. (2016)
C3H8 | Graphene |  C3H8 | No | – | 1.000 | 0 | 0 | – | 4.2E+28 | 3750 | – | – | Smith et al. (2016)
C3H8 | MgO(100) |  C3H8 | No | 93 | 0.600 | 27 | 3553 | 134 | 4.0E+15 | 3490 | 9.2E+17 | 3980 | Tait et al. (2005a)
CH2CHOH | Au |  O2:C2H2 | H | 145 | 0.083 | 22 | 3157 | 209 | – | – | 2.0E+18 | 6660 | Chuang et al. (2020)
CH2CHOH | Ag |  CO:CH4 |  5 keV e- | 147 | 0.008 | 22 | 3222 | 212 | – | – | 2.1E+18 | 7100 | Abplanalp et al. (2016)
CH2CHOH | Ag |  CO:CH4 |  5 keV e- | 147 | 0.008 | 22 | 3222 | 212 | – | – | 2.1E+18 | 7100 | Zhu et al. (2022b)
CH3CH2NH2 | Ag |  CH4:NH3 |  5 keV e- | 125 | 0.008 | 24 | 4933 | 180 | – | – | 2.3E+18 | 6030 | Förstel et al. (2017)
CH3CHO | Au |  O2:C2H2 | H | 130 | 0.083 | 23 | 3218 | 188 | – | – | 1.6E+18 | 5930 | Chuang et al. (2020)
CH3CHO | Ni |  CH3CHO | No | 105 | 1.210 | 26 | 2333 | 151 | 1.0E+12 | 2850 | 7.7E+17 | 4410 | Corazzi et al. (2021)
CH3CHO | Olivine grains |  CH3CHO | No | 103 | 1.210 | 26 | 2270 | 149 | – | – | 7.2E+17 | 4320 | Corazzi et al. (2021)
CH3CHO | Ni |  CH3CHO:H2O | No | 108 | 1.210 | 25 | 2420 | 155 | 1.0E+12 | 3080 | 8.4E+17 | 4530 | Corazzi et al. (2021)
CH3CHO | Olivine grains |  CH3CHO:H2O | No | 108 | 1.210 | 25 | 2444 | 156 | – | – | 8.6E+17 | 4560 | Corazzi et al. (2021)
CH3CHO | ASW |  CH3CHO | No | 100 | 0.017 | 26 | 2171 | 144 | – | 4330 | 6.5E+17 | 4610 | Lasne et al. (2012)
CH3CHO | Ag |  CO:CH4 |  5 keV e- | 122 | 0.008 | 24 | 2926 | 176 | – | – | 1.3E+18 | 5820 | Zhu et al. (2022b)
N2O | (A) silicate | NO | H | 85 | 0.017 | 29 | 0 | 123 | 1.0E+13 | 2770 | – | – | Congiu et al. (2012)
[NH${}_{4}^{+}$][CN-] | Au |  HCN:NH3 | Thermal | 160 | 0.083 | 21 | 7415 | 231 | – | – | 1.6E+17 | 6970 | Danger et al. (2011a)
[NH${}_{4}^{+}$][CN-] | Au |  HCN:NH3:CH2NH | Thermal | 180 | 0.083 | 20 | 8848 | 260 | – | – | 1.6E+17 | 7860 | Danger et al. (2011a)
CH3CH2OH | Ag |  CH3OH:CH4 |  5 keV e- | 147 | 0.008 | 21 | 5875 | 222 | – | – | 4.0E+18 | 7200 | Bergantini et al. (2018d)
CH3CH2OH | Au |  CH3CHO:H | H | 160 | 0.033 | 20 | 6671 | 241 | – | – | 5.4E+18 | 7670 | Bisschop et al. (2007)
CH3CH2OH | HOPG |  CH3CH2OH | No | 151 | 0.500 | 21 | 6116 | 228 | – | – | 4.4E+18 | 6790 | Burke et al. (2008)
CH3CH2OH | Au |  O2:C2H2 | H | 162 | 0.083 | 20 | 6797 | 244 | – | – | 5.6E+18 | 7630 | Chuang et al. (2020)
CH3CH2OH |  (C) H2O |  CH3CH2OH | No | 169 | 0.167 | 20 | 7242 | 255 | – | – | 6.5E+18 | 7880 | Lattelais et al. (2011)
CH3CH2OH | HOPG |  CH3CH2OH | No | 162 | 1.000 | 20 | 6797 | 244 | 2.0E+15 | 6010 | 5.6E+18 | 7230 | Ulbricht et al. (2006)
CH3OCH3 | Ag |  CH3OH:CH4 |  5 keV e- | 135 | 0.008 | 22 | 3419 | 204 | – | – | 2.0E+18 | 6500 | Bergantini et al. (2018d)
CH3OCH3 |  (C) H2O |  CH3OCH3 | No | – | 0.167 | 0 | 0 | – | 1.0E+14 | 4080 | – | – | Lattelais et al. (2011)
HCOOH | Au | HCOOH | No | 145 | 0.200 | 21 | 2482 | 219 | – | – | 1.6E+18 | 6510 | Chaabouni et al. (2020)
HCOOH | Au |  CO:O2 | H | 160 | 0.008 | 20 | 2877 | 241 | – | – | 2.3E+18 | 7760 | Ioppolo et al. (2011)
NO2 |  (SiO)2 | NO + O | O | 129 | 0.083 | 23 | 676 | 195 | – | – | 3.5E+17 | 5690 | Minissale & Dulieu (2014)
NO2 |  (SiO)2 |  NO + O2 | O | 122 | 0.083 | 23 | 621 | 184 | – | – | 2.9E+17 | 5350 | Minissale & Dulieu (2014)
NO2 |  (SiO)2 |  NO + O3 | O | 125 | 0.083 | 23 | 644 | 189 | – | – | 3.2E+17 | 5500 | Minissale & Dulieu (2014)
NO2 | HOPG |  NO2 | No | 144 | 0.500 | 21 | 797 | 217 | 6.0E+16 | 4450 | 5.2E+17 | 6170 | Ulbricht et al. (2006)
HOCH2NH2 | Au |  H2O:NH3:H2CO | Thermal | 205 | 0.017 | 18 | 8543 | 316 | – | 6980 | 1.2E+19 | 10180 | Bossa et al. (2009a)
HOCH2NH2 | Au |  H2CO:NH3:HCN | Thermal | 214 | 0.083 | 17 | 9112 | 330 | 3.0E+12 | 6980 | 1.3E+19 | 10330 | Danger et al. (2012)
HOCH2NH2 | Au |  HOCH2NH2 | Thermal | 200 | 0.083 | 18 | 8233 | 308 | – | – | 1.1E+19 | 9590 | Danger et al. (2012)
HOCH2NH2 | Ag |  CH3NH2:O2 |  5 keV e- | 210 | 0.017 | 18 | 8858 | 324 | – | – | 1.3E+19 | 10450 | Singh et al. (2022)
CH3OOH | Ag |  CH3OH:O2 |  5 keV e- | 162 | 0.017 | 20 | 4903 | 255 | – | – | 4.2E+18 | 7850 | Zhu et al. (2022a)
CH3PH2 | Ag |  CH4:PH3 |  5 keV e- | 102 | 0.017 | 25 | 1294 | 161 | – | – | 4.4E+17 | 4660 | Turner et al. (2016)
CH3SH | Au(111) |  CH3SH | No | 139 | 3.000 | 21 | 2116 | 219 | 1.0E+13 | 4030 | 1.3E+18 | 5830 | Liu et al. (2002)
CH3SH | Au(111) |  CH3SH | No | 160 | 0.240 | 20 | 2614 | 252 | – | – | 2.2E+18 | 7220 | Rzeźnicka et al. (2005)
HOCH2OH | Au |  H2O:NH3:H2CO | Thermal | 225 | 0.067 | 17 | 9052 | 354 | – | – | 1.5E+19 | 10940 | Duvernay et al. (2014)
HOCH2OH | Ag |  CH3OH:O2 |  5 keV e- | 181 | 0.017 | 19 | 6531 | 285 | – | – | 7.0E+18 | 8880 | Zhu et al. (2022a)
H2POH | Ag |  CO2:PH3 |  5 keV e- | 205 | 0.017 | 17 | 2433 | 336 | – | – | 3.5E+18 | 9940 | Turner et al. (2018)
H2POH | Ag |  H2O:PH3 |  5 keV e- | 205 | 0.017 | 17 | 2433 | 336 | – | – | 3.5E+18 | 9940 | Turner et al. (2018)
H2POH | Ag |  H2O:PH3 |  5 keV e- | 177 | 0.017 | 19 | 1952 | 290 | – | – | 2.1E+18 | 8460 | Turner et al. (2018)
HC2CHO | Ag |  CO:C2H2 |  5 keV e- | 140 | 0.002 | 20 | 6387 | 248 | – | – | 4.6E+18 | 7090 | Abplanalp & Kaiser (2019)
HC2CHO | Ag |  C2H2:CO |  5 keV e- | 135 | 0.002 | 20 | 6048 | 239 | – | – | 4.1E+18 | 6820 | Kleimeier et al. (2021a)
c-H2C3O | Ag |  CO:C2H2 |  5 keV e- | 190 | 0.002 | 17 | 9994 | 337 | – | – | 1.3E+19 | 9890 | Abplanalp & Kaiser (2019)
c-H2C3O | Ag |  C2H2:CO |  5 keV e- | 180 | 0.002 | 18 | 9215 | 319 | – | – | 1.1E+19 | 9320 | Kleimeier et al. (2021a)
CH2CHCHO | Ag |  CO:C2H4 |  5 keV e- | 140 | 0.008 | 20 | 7896 | 257 | – | – | 5.9E+18 | 6900 | Abplanalp et al. (2015)
CH2CHCHO | Ag(111) |  CH2CHCHO | No | 164 | 1.000 | 18 | 10011 | 301 | – | – | 1.0E+19 | 7420 | Muir et al. (2020)
NH2CH2CN | Au |  HCN:NH3:NHCH2 | Thermal | 185 | 0.083 | 17 | 15275 | 340 | – | – | 2.0E+19 | 8970 | Danger et al. (2011a)
NH2CH2CN | Au |  CH3CN:NH3 | UV | 205 | 0.067 | 16 | 17818 | 377 | – | – | 2.9E+19 | 10080 | Danger et al. (2011b)
c-C3H4O | Ag |  CO:C2H4 |  5 keV e- | 110 | 0.008 | 22 | 6220 | 202 | – | – | 2.9E+18 | 5320 | Abplanalp et al. (2015)
CH3NCO | Au |  CH4:HNCO | UV | 145 | 0.083 | 19 | 8334 | 271 | – | – | 6.8E+18 | 6840 | Ligterink et al. (2017)
HOCH2CN | Au |  H2CO:NH3:HCN | Thermal | 214 | 0.083 | 16 | 17898 | 400 | 3.0E+12 | 6980 | 3.2E+19 | 10510 | Danger et al. (2012)
HOCH2CN | Au |  HCN:H2CO:H2O | Thermal | 208 | 0.067 | 16 | 17151 | 389 | – | – | 2.9E+19 | 10240 | Danger et al. (2014)
C4H10 |  Al2O3(0001) |  C4H10 | No | – | – | 0 | 0 | – | 4.0E+10 | 4230 | – | – | Slayton et al. (1995)
C4H10 | MgO(100) |  C4H10 | No | 110 | 0.600 | 22 | 11628 | 209 | 5.0E+15 | 4200 | 5.6E+18 | 4920 | Tait et al. (2005b)
C4H10 | MgO(100) |  C4H10 | No | 111 | 0.600 | 22 | 11787 | 211 | 5.0E+15 | 4200 | 5.8E+18 | 4970 | Tait et al. (2005a)
CH2CHCH2OH | Pd(111) |  CH3CH2CHO | No | 200 | 0.017 | 16 | 20780 | 381 | – | 6010 | 3.3E+19 | 10140 | Dostert et al. (2016)
CH3CH2CHO | Ag(111) |  CH3CH2CHO | No | 162 | 1.000 | 18 | 15542 | 308 | – | – | 1.6E+19 | 7400 | Muir et al. (2020)
CH3CH2CHO | Au |  C2H2:CO | H | 125 | 0.083 | 21 | 10534 | 238 | – | – | 6.5E+18 | 5870 | Qasim et al. (2019)
CH3CH2CHO | Ag |  CO:CH3CH3 |  5 keV e- | 127 | 0.008 | 20 | 10788 | 242 | – | – | 6.9E+18 | 6270 | Abplanalp et al. (2016)
CH3CH2CHO | Ag | P:C:E |  5 keV e- | 120 | 0.008 | 21 | 9908 | 228 | – | – | 5.7E+18 | 5890 | Abplanalp et al. (2016)
CH3CHCH2O | Ag |  CO2:C3H6 |  5 keV e- | 117 | 0.008 | 21 | 8139 | 223 | – | – | 4.4E+18 | 5710 | Bergantini et al. (2018b)
CH3CHCHOH | Ag |  CO:CH3CH3 |  5 keV e- | 165 | 0.008 | 18 | 14991 | 314 | – | – | 1.6E+19 | 8330 | Abplanalp et al. (2016)
CH3COCH3 | ASW |  CH3C(O)CH3 | No | 130 | 0.017 | 20 | 11159 | 247 | – | 4690 | 7.5E+18 | 6340 | Lasne et al. (2012)
CH3COCH3 |  (C) H2O |  CH3C(O)CH3 | No | 140 | 5.000 | 19 | 12471 | 266 | 1.0E+13 | 4210 | 9.7E+18 | 6080 | Schaff & Roberts (1994)
CH3COCH3 | ASW |  CH3C(O)CH3 | No | 157 | 5.000 | 18 | 14811 | 299 | 1.0E+13 | 4810 | 1.4E+19 | 6900 | Schaff & Roberts (1994)
CH3COCH3 | ASW |  CH3C(O)CH3 | No | 157 | 4.500 | 18 | 14811 | 299 | 1.0E+13 | 4690 | 1.4E+19 | 6910 | Schaff & Roberts (1998)
CH3COCH3 | (P) ASW |  CH3C(O)CH3 | No | 145 | 0.083 | 19 | 13145 | 276 | – | – | 1.1E+19 | 6910 | Souda (2011)
CH3COCH3 | non-(P) ASW |  CH3C(O)CH3 | No | 120 | 0.083 | 21 | 9897 | 228 | – | – | 5.7E+18 | 5620 | Souda (2011)
CH2CH(OH)CH3 | Ag(111) |  CH2CH(OH)CH3 | No | 202 | 1.000 | 16 | 20048 | 391 | – | – | 3.3E+19 | 9420 | Muir et al. (2020)
CH3CONH2 | Au |  NH2CH(CH3)OH | UV | 220 | 0.067 | 15 | 21555 | 426 | – | – | 4.2E+19 | 10920 | Duvernay et al. (2010)
CH3CONH2 | Au |  HNCO:CH4 | UV | 215 | 0.083 | 16 | 20825 | 416 | – | – | 3.9E+19 | 10600 | Ligterink et al. (2018a)
CH3NHCHO | Au |  CH3NH2:CO | UV | 200 | 0.067 | 16 | 15173 | 387 | – | – | 2.4E+19 | 9800 | Bossa et al. (2012)
CH3NHCHO | Ag |  CH3NH2:CO |  5 keV e- | 225 | 0.008 | 15 | 18105 | 436 | – | – | 3.7E+19 | 11610 | Frigge et al. (2018b)
CH3OCH2CH3 | Ag |  H2O:CH4 |  5 keV e- | 170 | 0.008 | 17 | 18173 | 329 | – | – | 2.1E+19 | 8630 | Bergantini et al. (2018c)
(CH2NH2)2 | Ag |  CH3NH2 |  5 keV e- | 160 | 0.008 | 18 | 17907 | 315 | – | – | 1.9E+19 | 8100 | Zhu et al. (2019b)
(CHO)2 | Ag |  CH3OH:CO |  5 keV e- | 145 | 0.008 | 19 | 7227 | 285 | – | – | 6.2E+18 | 7160 | Maity et al. (2015)
CH3CH2CH2OH | Ag(111) |  CH3CH2CH2OH | No | 219 | 1.000 | 15 | 29470 | 431 | – | – | 5.8E+19 | 10350 | Muir et al. (2020)
CH3CH2CH2OH | Au |  CH3CH2CHO | H | 155 | 0.083 | 18 | 17548 | 305 | – | – | 1.7E+19 | 7470 | Qasim et al. (2019)
CH3COOH | Ag |  CH4:CO2 |  5 keV e- | 165 | 0.017 | 18 | 13305 | 325 | – | – | 1.5E+19 | 8200 | Bergantini et al. (2018a)
CH3COOH | ASW |  CH3COOH | No | 157 | 0.017 | 18 | 12349 | 309 | 1.0E+00 | – | 1.2E+19 | 7770 | Bertin et al. (2011)
CH3COOH | HOPG |  CH3COOH | No | 155 | 0.500 | 18 | 12114 | 305 | – | – | 1.2E+19 | 7130 | Burke et al. (2015b)
CH3COOH |  (C) H2O |  CH3COOH | No | 160 | 0.500 | 18 | 12705 | 315 | – | – | 1.3E+19 | 7390 | Burke et al. (2015c)
CH3COOH |  (C) H2O |  CH3COOH | No | 160 | 0.017 | 18 | 12705 | 315 | – | – | 1.3E+19 | 7930 | Lattelais et al. (2011)
CH3COOH | Ag |  CH4:CO2 |  5 keV e- | 155 | 0.017 | 18 | 12114 | 305 | – | – | 1.2E+19 | 7660 | Zhu et al. (2018b)
CH3OCHO | ASW |  CH3OCHO | No | 128 | 0.017 | 20 | 6717 | 252 | 1.0E+14 | 4450 | 4.5E+18 | 6180 | Bertin et al. (2011)
CH3OCHO |  (C) H2O |  CH3OCHO | No | 129 | 0.017 | 20 | 6795 | 254 | 1.0E+14 | 4570 | 4.6E+18 | 6230 | Bertin et al. (2011)
CH3OCHO | HOPG |  CH3OCHO | No | 104 | 0.500 | 22 | 4919 | 205 | – | 2160 | 2.2E+18 | 4570 | Burke et al. (2015b)
CH3OCHO | HOPG |  CH3OCHO | No | 104 | 0.500 | 22 | 4919 | 205 | – | 3320 | 2.2E+18 | 4570 | Burke et al. (2015b)
CH3OCHO | HOPG |  CH3OCHO | No | 104 | 0.500 | 22 | 4919 | 205 | – | 3910 | 2.2E+18 | 4570 | Burke et al. (2015b)
CH3OCHO | HOPG |  CH3OCHO | No | 104 | 0.500 | 22 | 4919 | 205 | – | 4140 | 2.2E+18 | 4570 | Burke et al. (2015b)
CH3OCHO | ASW |  CH3OCHO | No | 127 | 0.500 | 20 | 6638 | 250 | – | 4410 | 4.4E+18 | 5690 | Burke et al. (2015c)
CH3OCHO |  (C) H2O |  CH3OCHO | No | 127 | 0.500 | 20 | 6638 | 250 | – | 4620 | 4.4E+18 | 5690 | Burke et al. (2015c)
CH3OCHO |  (C) H2O |  CH3OCHO | No | 127 | 0.017 | 20 | 6638 | 250 | 1.0E+14 | 4480 | 4.4E+18 | 6120 | Lattelais et al. (2011)
CH3OCHO | Ag(111) |  CH3OCHO | No | 144 | 1.500 | 19 | 8015 | 283 | – | 4450 | 6.8E+18 | 6380 | Schwaner et al. (1997)
CH3OCHO | Ag(111) |  CH3OCHO | No | 160 | 1.500 | 18 | 9387 | 315 | – | – | 9.9E+18 | 7160 | Schwaner et al. (1997)
CH3OCHO | Cu(111) |  CH3OCHO | No | 155 | 2.000 | 18 | 8950 | 305 | – | – | 8.8E+18 | 6870 | Sexton et al. (1985)
CH3OCHO | Ni(111) |  CH3OCHO | No | 142 | 0.500 | 19 | 7848 | 280 | – | – | 6.5E+18 | 6440 | Zahidi et al. (1994)
CH3OCHO | Mo(110) |  CH3OH |  20 / 1k eV e- | 105 | 0.017 | 22 | 4990 | 207 | – | – | 2.3E+18 | 4970 | Sullivan et al. (2016)
H2CC(OH)2 | Ag |  CO2:CH4 |  5 keV e- | 215 | 0.017 | 15 | 18061 | 423 | – | – | 3.4E+19 | 10920 | Kleimeier & Kaiser (2021)
H2CC(OH)2 | Ag |  CH3OH |  5 keV e- | 210 | 0.008 | 16 | 17435 | 413 | – | – | 3.2E+19 | 10790 | Maity et al. (2015)
H2CC(OH)2 | Ag |  CH3OH:CO |  5 keV e- | 218 | 0.008 | 15 | 18440 | 429 | – | – | 3.6E+19 | 11240 | Maity et al. (2015)
HOCH2CHO | HOPG |  HOCH2CHO | No | 145 | 0.500 | 19 | 11502 | 285 | – | – | 9.9E+18 | 6640 | Burke et al. (2015b)
HOCH2CHO | ASW |  HOCH2CHO | No | 160 | 0.500 | 18 | 13332 | 315 | – | – | 1.4E+19 | 7390 | Burke et al. (2015c)
HOCH2CHO |  (C) H2O |  HOCH2CHO | No | 160 | 0.500 | 18 | 13332 | 315 | – | – | 1.4E+19 | 7390 | Burke et al. (2015c)
HOCH2CHO | Au |  H2CO:H2CO | UV | 200 | 0.067 | 16 | 18632 | 394 | – | – | 3.1E+19 | 9850 | Butscher et al. (2016)
HOCH2CHO | Ag |  CH3OH |  5 keV e- | 166 | 0.008 | 17 | 14089 | 327 | – | – | 1.6E+19 | 8380 | Maity et al. (2014b)
HOCH2CHO | Mo(110) |  CH3OH |  1k eV e- | 145 | 0.017 | 19 | 11502 | 285 | – | – | 9.9E+18 | 7130 | Sullivan et al. (2016)
HOCHCHOH | Ag |  CH3OH |  5 keV e- | 210 | 0.008 | 16 | 14261 | 413 | – | – | 2.6E+19 | 10750 | Kleimeier et al. (2021b)
HOCHCHOH | Ag |  CH3OH |  5 keV e- | 225 | 0.008 | 15 | 15816 | 443 | – | – | 3.3E+19 | 11590 | Kleimeier et al. (2021b)
HOCHCHOH | Ag |  CH3OH:CO |  5 keV e- | 198 | 0.008 | 16 | 13056 | 390 | – | – | 2.1E+19 | 10080 | Kleimeier et al. (2021b)
HOCHCHOH | Ag |  CH3OH:CO |  5 keV e- | 217 | 0.008 | 15 | 14980 | 427 | – | – | 2.9E+19 | 11140 | Kleimeier et al. (2021b)
HOCHCHOH | Ag |  CH3OH |  5 keV e- | 210 | 0.008 | 16 | 14261 | 413 | 1.0E+00 | – | 2.6E+19 | 10750 | Maity et al. (2014b)
HOCHCHOH | Ag |  CH3OH:CO |  5 keV e- | 218 | 0.008 | 15 | 15083 | 429 | 2.0E+00 | – | 2.9E+19 | 11200 | Maity et al. (2014b)
NH2CONH2 | Ag |  CO:NH3 |  5 keV e- | 250 | 0.008 | 14 | 11337 | 492 | – | – | 2.9E+19 | 12870 | Förstel et al. (2016a)
NH2CONH2 | Ag |  CO:NH3 |  5 keV e- | 260 | 0.008 | 14 | 12024 | 512 | – | – | 3.3E+19 | 13430 | Förstel et al. (2016b)
NH2CONH2 | Au |  HNCO:CH4 | UV | 267 | 0.083 | 14 | 12512 | 526 | – | – | 3.7E+19 | 13210 | Ligterink et al. (2018a)
NH2CONH2 | KBr |  H2O:NH3:CO2 | UV | 270 | 0.033 | 14 | 12724 | 532 | – | – | 3.8E+19 | 13620 | Potapov et al. (2022)
OCS | HOPG |  CS2:O | O | 78 | 0.017 | 26 | 0 | 154 | 5.3E+14 | 2430 | – | – | Ward et al. (2012)
[NH${}_{4}^{+}$][OCN-] | Au |  CO:N2 | H / N | 185 | 0.083 | 17 | 0 | 364 | – | – | 2.9E+17 | 8190 | Fedoseev et al. (2015b)
[NH${}_{4}^{+}$][OCN-] | Au | NO:CO | UV / H | 160 | 0.083 | 18 | 0 | 315 | – | – | 2.9E+17 | 7060 | Fedoseev et al. (2016)
[NH${}_{4}^{+}$][OCN-] | Au |  HNCO:CH4 | UV | 205 | 0.083 | 16 | 0 | 404 | – | – | 2.9E+17 | 9100 | Ligterink et al. (2018a)
c-C3H2O | Ag |  CO:C2H2 | UV | 177 | 0.008 | 17 | 4493 | 348 | – | – | 5.8E+18 | 8770 | Wang et al. (2022a)
NH2CH(CH3)OH | Au |  NH3:CH3CHO | Thermal | 200 | 0.067 | 16 | 25103 | 400 | – | – | 4.2E+19 | 9910 | Duvernay et al. (2010)
NH2COOH | Au |  CO2:NH3 | Thermal | 265 | 0.083 | 14 | 23435 | 530 | 1.0E+13 | 9150 | 6.9E+19 | 13280 | Noble et al. (2014)
[H3O+][OCN-] | Cu |  HNCO:H2O | No | 155 | 0.017 | 18 | 0 | 310 | 2.9E+12 | 4810 | 3.0E+17 | 7090 | Theulé et al. (2011b)
(CH2OH)2 | Ag |  CH3OH:CH4 |  5 keV e- | 195 | 0.008 | 16 | 22980 | 397 | – | – | 3.7E+19 | 10040 | Bergantini et al. (2018d)
(CH2OH)2 | Au |  H2CO:H2CO | UV | 212 | 0.067 | 15 | 26050 | 431 | – | – | 5.0E+19 | 10550 | Butscher et al. (2016)
(CH2OH)2 | Au | CO+H | H | 200 | 0.083 | 16 | 23870 | 407 | – | – | 4.0E+19 | 9860 | Fedoseev et al. (2015a)
(CH2OH)2 | Au |  HOCH2CHO:CO | H | 200 | 0.333 | 16 | 23870 | 407 | – | – | 4.0E+19 | 9580 | Fedoseev et al. (2017)
(CH2OH)2 | Rh |  HOCH2CHO | H | 214 | 0.017 | 15 | 26420 | 435 | – | – | 5.1E+19 | 10960 | Leroux et al. (2021)
(CH2OH)2 | Ag |  CH3OH |  5 keV e- | 200 | 0.008 | 16 | 23870 | 407 | – | – | 4.0E+19 | 10320 | Maity et al. (2014b)
(CH2OH)2 | Ag |  CH3OH:CO |  5 keV e- | 198 | 0.008 | 16 | 23513 | 403 | – | – | 3.9E+19 | 10210 | Maity et al. (2014b)
(CH2OH)2 | Ag |  CH3OH |  5 keV e- | 198 | 0.008 | 16 | 23513 | 403 | – | – | 3.9E+19 | 10210 | Maity et al. (2015)
(CH2OH)2 | Ag |  CH3OH:CO |  5 keV e- | 198 | 0.008 | 16 | 23513 | 403 | – | – | 3.9E+19 | 10210 | Maity et al. (2015)
CH3OCH2OH | Ag |  CH3OH:CH4 |  5 keV e- | 162 | 0.008 | 17 | 15000 | 330 | – | – | 1.7E+19 | 8180 | Bergantini et al. (2018d)
CH3OCH2OH | Mo(110) |  CH3OH |  20 eV e- | 165 | 0.017 | 17 | 15419 | 336 | – | – | 1.8E+19 | 8230 | Boamah et al. (2014)
CH3OCH2OH | Ag |  CH3OH |  5 keV e- | 170 | 0.008 | 17 | 16125 | 346 | – | – | 2.0E+19 | 8620 | Maity et al. (2015)
CH3OCH2OH | Ag |  CH3OH:CO |  5 keV e- | 184 | 0.008 | 16 | 18157 | 374 | – | – | 2.6E+19 | 9400 | Maity et al. (2015)
CH3OCH2OH | Ta(110) |  CH3OH | UV | 155 | 0.017 | 18 | 14038 | 315 | – | – | 1.4E+19 | 7690 | Schneider et al. (2019)
CH3OCH2OH | Ag |  CH3OH |  5 keV e- | 161 | 0.008 | 17 | 14861 | 328 | – | – | 1.6E+19 | 8130 | Zhu et al. (2019a)
CH3OCH2OH | Mo(110) |  CH3OH |  1k eV e- | 180 | 0.017 | 17 | 17568 | 366 | – | – | 2.4E+19 | 9050 | Sullivan et al. (2016)
CH3OOCH3 | Ag |  CH3OH:CH4 |  5 keV e- | 113 | 0.008 | 21 | 7590 | 230 | – | – | 4.1E+18 | 5510 | Bergantini et al. (2018d)
CH3OOCH3 | Ag |  CH3OH |  5 keV e- | 115 | 0.008 | 21 | 7792 | 234 | – | – | 4.4E+18 | 5610 | Zhu et al. (2019a)
CH3SCH3 | ? |  H2S:CH3OH:NH3:H2O |  10 keV e- | 290 | 0.008 | 13 | 29827 | 590 | – | – | 1.1E+20 | 15350 | Mahjoub et al. (2017)
H2CO3 | Ag |  H2O:CO2 |  5 keV e- | 245 | 0.017 | 14 | 22261 | 498 | – | – | 5.7E+19 | 12600 | Jones et al. (2014)
H2CO3 | Al | OH:H:O:CO | OH | 235 | 0.067 | 14 | 20912 | 478 | – | – | 4.9E+19 | 11720 | Oba et al. (2010)
HCOPH2 | Ag |  PH3:CO |  5 keV e- | 125 | 0.017 | 20 | 5629 | 254 | – | – | 3.7E+18 | 6010 | Frigge et al. (2018a)
HCOSH | Rh |  CO:H2S |  5 keV e- | 131 | 0.017 | 19 | 5157 | 266 | – | – | 3.8E+18 | 6300 | Wang et al. (2022b)
[NH${}_{4}^{+}$][HCOO-] | CsI |  NH3:HCOOH | No | 203 | 0.083 | 15 | 0 | 420 | – | – | 2.0E+18 | 9400 | Bergner et al. (2016)
[NH${}_{4}^{+}$][HCOO-] | Au |  HCOOH:NH3 | No | 208 | 0.200 | 15 | 0 | 430 | 7.7E+15 | 8290 | 2.0E+18 | 9450 | Kruczkiewicz et al. (2021)
SO2 | Au(111) |  SO2 | No | 142 | 3.000 | 18 | 0 | 298 | – | 3920 | – | – | Liu et al. (2002)
H2P(O)OH | Ag |  CO2:PH3 |  5 keV e- | 290 | 0.017 | 13 | 21057 | 628 | – | – | 8.0E+19 | 15070 | Turner et al. (2018)
H2P(O)OH | Ag |  H2O:PH3 |  5 keV e- | 295 | 0.017 | 13 | 21604 | 639 | – | – | 8.5E+19 | 15350 | Turner et al. (2018)
H2S2 | ? |  H2S | UV | 138 | 0.023 | 18 | 3315 | 299 | – | – | 2.8E+18 | 6560 | Jimenez-Escobar & Muñoz Caro (2011)
H2SO2 | ? |  H2S:H2O | UV | 106 | 0.017 | 21 | 5052 | 230 | – | – | 2.6E+18 | 5040 | Jimenez-Escobar & Muñoz Caro (2011)