11institutetext: Université Paris Cité and Univ Paris Est Créteil, CNRS, LISA,
F-75013 Paris, France
11email<EMAIL_ADDRESS>Université de Lorraine,
CNRS, LRGP, F-54000 Nancy, France 33institutetext: Department of Physics and
Astronomy, University College London, Gower Street, London, WC1E 6BT, UK
44institutetext: Astrophysics Group, University of Exeter, EX4 4QL Exeter, UK

# An extensively validated C/H/O/N chemical network for hot exoplanet
disequilibrium chemistry

R. Veillet 11 O. Venot 11 B. Sirjean 22 R. Bounaceur 22 P-A. Glaude 22 A. Al-
Refaie 33 E. Hébrard 44

###### Abstract

Context. The reliability of one-dimensional disequilibrium chemistry models in
hot exoplanet atmospheres depends on the chemical network used. To develop
robust networks, we can rely on combustion studies that provide C/H/O/N
chemical networks validated by vast amount of experimental data generated by
the extensive research that has been done on hydrocarbon combustion and NO_x
formation in the last decades.

Aims.  We aimed to build a new and updated C0-C2 chemical network to study the
C/H/O/N disequilibrium chemistry of warm and hot exoplanet atmospheres that
relies on extensively validated and recent state-of-the-art combustion
networks. The reliability range of this network was aimed for conditions
between 500 - 2500 K and 100 - $10^{-6}$ bar, with cautious extrapolation at
lower temperature values.

Methods.  We compared the predictions of seven networks over a large set of
experiments, covering a wide range of conditions (pressures, temperatures, and
initial compositions). To examine the consequences of this new chemical
network on exoplanets atmospheric studies, we generated abundances profiles
for GJ 436 b, GJ 1214 b, HD 189733 b, and HD 209458 b, using the 1D kinetic
model FRECKLL and calculated the corresponding transmission spectra using
TauREx 3.1. These spectra and abundance profiles have been compared with
results obtained with our previous chemical network.

Results.  Our new kinetic network is composed of 174 species and 1293
reactions mostly reversible. This network proves to be more accurate than our
previous one for the tested experimental conditions. The nitrogen chemistry
update is found to be very impactful on the abundance profiles, particularly
for HCN, with differences up to four orders of magnitude. The CO2 profiles are
also significantly affected, with important repercussions on the transmission
spectrum of GJ 436 b.

Conclusions.  These effects highlight the importance of using extensively
validated chemical networks to gain confidence in our models predictions. As
shown with CH2NH, the coupling between carbon and nitrogen chemistry combined
with radicals produced by photolysis can have huge effects impacting the
transmission spectra. This should be kept in mind when adding new elements
like sulfur, as only adding a sub-mechanism neglects these coupling effects.

###### Key Words.:

astrochemistry – planets and satellites: atmospheres – planets and satellites:
composition – methods: numerical

##  1 Introduction

Over recent decades, and still remaining relevant today, the characterization
of the atmospheric composition of exoplanets has only been possible for
massive hydrogen-dominated exoplanets close to their star. Because of the
detection biases of the transit method and its technical difficulty for
exoplanets with a shallow transit depth, the range of masses and semi-major
axis that can be probed by spectrometric means remains blind to colder, Earth-
like exoplanets. The proximity of these observable exoplanets to their star
results in highly irradiated atmospheres (Linsky et al., 2013), which implies
both a high temperature profile that activates endothermic reactions and an
intense UV flux that photodissociates the majority of species in the upper
atmosphere, resulting in the creation of a high quantity of radicals (Heays et
al., 2017). This proximity also causes huge tidal forces that probably results
in these exoplanets to be tidally-locked, which further intensifies the
horizontal and vertical temperature gradients in the atmosphere, causing
intense advection and strong steady winds (Menou, 2022; Charnay et al., 2015).
We also know that this advection coupled to photolysis in the upper atmosphere
should maintain the chemical species abundance profiles in a steady-state out
of equilibrium (Moses et al., 2011; Roudier et al., 2021; Stevenson et al.,
2010). To take into account the dynamical timescale, it is therefore necessary
to accurately describe both the atmospheric advection and the chemical
kinetics of the reactions taking place in the atmosphere (Drummond et al.,
2020; Zamyatina et al., 2023). Accurately reproducing the chemistry in these
conditions requires a detailed kinetic network, which describes chemistry in
sets of elementary reversible reactions that form, consume, and propagate
radicals. These reactions then form a parameterized chemical network that can
be used to model the chemical kinetics in exoplanet atmospheres. However, the
parameters that characterize the kinetic properties of each reaction can be
difficult to estimate and their determination is subject to an entire field of
research in combustion kinetics (Wang & Sheen, 2015; Curran, 2019). In the
combustion domain, the detailed kinetic networks are validated against
experimental data measured in 0D or 1D reactors close to ideal reactors and
designed to characterize only the chemical kinetics. Such data can include the
evolution of combustion products and intermediates as a function of time or
temperature, auto-ignition delay times, or laminar flame studies (Battin-
Leclerc et al., 2011).

For atmospheric studies of exoplanets, various detailed kinetic networks have
already been developed (Moses et al., 2011; Tsai et al., 2017, 2021; Venot et
al., 2012, 2015, 2020; Rimmer & Helling, 2016). Most of these chemical
networks were built by grouping reactions with available parameters from
databases and/or computed with quantum mechanics calculations. Venot et al.
(2012, 2015, 2020) networks are the only ones based on networks validated by
experiments. Venot et al. (2012) was the first one to be developed, and was
extended to species bearing up to six carbon atoms in Venot et al. (2015).
Additional corrections to the methanol chemistry were later introduced (Venot
et al. 2020; hereafter Venot 2020). These networks usually describe only the
kinetics of carbon-, hydrogen-, oxygen-, and nitrogen-bearing species, and are
such labeled C/H/O/N chemical networks. In this present work, we aim to
develop a new C/H/O/N network for exoplanet atmospheric chemistry based on
extensive validations against experimental data, totally revisiting the
C/H/O/N chemistry and basing it on two new state-of-the-art combustion
networks for C/H/O and N chemistry, respectively, from Burke et al. (2016) and
Glarborg et al. (2018).

To accurately reproduce very different conditions from warm sub-Neptunes to
very hot Jupiters, with potential applications to warm super-Earths, the new
chemical network is a detailed network suitable for a wide range of pressures
and temperatures. The validity domain of the network must therefore be, in
principle, from 500 to 2500 K and from 100 to $10^{-6}$ bar. The network is
also required to accurately describe the kinetics of all C/H/O/N species with
fewer than two atoms of carbon in order to correctly model the overall
chemistry of every major species observed and potentially visible in exoplanet
spectra (H2O, CH4, NH3, CO, CO2, HCN, C2H2, C2H4, C2H6 …). Although the
chemical network is aimed at studying hydrogen dominated atmospheres, it
should remain valid at even very high metallicity and for every possible
C/H/O/N atomic abundance. This implies that it should accurately describe all
the reaction kinetics ranging from oxygen-poor, carbon-, and hydrogen-
dominated atmospheres for pyrolysis, up to oxygen-rich environments, more
favorable to oxidation reactions. Due to limitations in the available
computational resources, it is mainly intended for 1D simulations.

Section 2, discusses how we selected the combustion networks with which we
developed our new chemical network, the extensive validation that came along
with it and the additions and modifications made to the original networks.
Then, in Sect. 3, we apply this network to the study of exoplanet atmospheres.
We studied four planets: GJ 436 b, GJ 1214 b, HD 189733 b, and HD 209458 b,
and we compared our results with those obtained with the chemical network
Venot 2020. We also investigated the differences between the two networks to
highlight new chemical pathways, in addition to discussing potential
repercussions on the transmission spectrum and their implications on the
observability and reliability of current models to interpret JWST
observations. Finally, we conclude in Sect. 4 and discuss potential future
improvements on this work.

##  2 Detailed combustion network selection

###  2.1 Considered combustion networks

Seven networks validated on combustion experiments have been compared:
NUIGMech1.1, AramcoMech3.0, Burke 2016, Exgas 2014, Konnov 2005, Glarborg
2018, and Venot 2020. The first three networks, NUIGMech1.1, AramcoMech3.0,
and Burke 2016, have been developed by Curran et al. at the National
University of Ireland in Galway, which led the improvements of combustion
kinetics in the last years.

NUIGMech1.1:

Currently, NUIGMech1.1 (Wu et al., 2021) is the state-of-the-art kinetic
network for C/H/O combustion. This network, that has been extensively
validated against experimental data, describes the combustion kinetics of
species up to molecules containing seven carbon atoms (C7). It also contains
nitrogen reactions for the chemistry of NO_x, which are regulated pollutants
in combustion processes. This level of details to capture the chemistry of
C0-C7 species is achieved at the cost of a very large network size (2746
species and 11279 reactions).

Because of the large size of NUIGMech1.1, which makes it impractical for 1D
calculations, two smaller C/H/O networks from the same team were also
considered: AramcoMech3.0 and Burke 2016.

AramcoMech3.0:

AramcoMech3.0 (Zhou et al., 2018) is a C/H/O C4 network of 581 species and
3037 reactions that focuses on improving the simulations of Polycyclic
Aromatic Hydrocarbon formation.

Burke 2016:

Burke 2016 is a C/H/O C3 network of 173 species and 1011 reactions (Burke et
al., 2016), which aimed to better reproduce the combustion of methanol,
involved in the combustion of biofuels.

To verify the performances of these networks, we included another network of
the literature for C/H/O chemistry in our comparisons: Exgas 2014.

Exgas 2014:

Exgas 2014 (Bounaceur et al., 2015) is a C/H/O C3 network of 209 species and
1472 reactions generated with Exgas (Warth et al., 2000), a software that
automatically generates combustion detailed kinetic networks. It was used to
predict auto-ignition temperatures and delays for gas turbine applications.

Because all these networks besides NUIGMech1.1 lacked nitrogen chemistry, we
included three other networks of the literature on C/H/O/N chemistry : Konnov
2005, Venot 2020, and Glarborg 2018.

Konnov 2005:

Konnov 2005 is a C/H/O/N C2 network of 127 species and 1213 reactions (Konnov
et al., 2005) designed to study the oxidation of NO into NO2 in a medium
containing ethane and was part of the research effort to reduce NO_x emissions
from car engines due to toxicity and pollution concerns.

Venot 2020:

Venot 2020 is the Venot et al. (2020) chemical network, which is an updated
version of the Venot et al. (2012) network from which the methanol chemistry
was reevaluated. It was especially designed for the study of exoplanet
disequilibrium chemistry. It is a C/H/O/N C2 network of 112 species and 944
reactions, also derived from four experimentally validated combustion networks
(Bounaceur et al., 2010; Konnov, 2009; Dagaut et al., 2008a; Burke et al.,
2016).

Glarborg 2018:

Glarborg 2018 is a C/H/O/N C3 network (Glarborg et al., 2018) that aimed at
improving the precision of nitrogen chemistry, especially NO_x formation. It
is a very comprehensive and widely used network for the modelling of nitrogen
chemistry in combustion.

For clarity, all the networks used for comparison are listed in Table 1.

Table 1: Characteristics of the chemical networks considered and compared in this study. Size corresponds to the heavier reactant included in the network. Name | Species | Reactions | Size | Atoms
---|---|---|---|---
NUIGMech1.1 | 2746 | 11279 |  C7 | C/H/O/N
AramcoMech3.0 | 581 | 3037 |  C4 | C/H/O
Exgas 2014 | 209 | 1472 |  C3 | C/H/O
Burke 2016 | 173 | 1011 |  C3 | C/H/O
Glarborg 2018 | 151 | 1397 |  C3 | C/H/O/N
Konnov 2005 | 127 | 1213 |  C2 | C/H/O/N
Venot 2020 | 112 | 944 |  C2 | C/H/O/N

Figure 1: Statistical distribution of the relative error over every
experimental point in the 1618 points data set for each studied chemical
network. Points are grouped in colors corresponding to a different type of
initial conditions. AramcoMech3.0 is not shown here as it is almost identical
to Burke 2016.

(a) 10 bar

(b) 50 bar

(c) CH3OH

(d) CO

Figure 2: Ignition delay time of methanol at 10 bar (a) and 50 bar (b) in
condition 1 and mole fraction of CH3OH (c) and CO (d) over time in condition 2
of Table 4 for all tested chemical networks. AramcoMech3.0 is not shown here
as it is almost identical to Burke 2016.

Figure 3: Statistical distribution of the relative error over the 449
experimental data points for intermediate products (CH3CHO, CH2O) in
combustion and pyrolysis of C2H5OH and CH3OH. Each color gathers all molar
fraction measurements of the corresponding species. Contribution from
combustion and pyrolysis data are shown separately in Figs. 28 and 29.

Figure 4: Statistical distribution of the relative error over the 823
experimental data points for the main pyrolysis products (H2, CH4), combustion
products (H2O, CO, CO2), and reactants (O2) in combustion and pyrolysis of
CH3OH, CH4, H2, HCN, and C2H5OH. For some points, CO and H2O are reactants
(condition 13 of Table 4). Each color gathers all molar fraction measurements
of the corresponding species. Contribution from combustion and pyrolysis data
are shown separately in Figs. 30 and 31.

###  2.2 Experimental data

In order to select the best chemical network for our requirements, we gathered
1618 combustion experimental data points, tested the seven different networks
over conditions detailed in Table 4 in the appendix using the Ansys software
Chemkin-Pro (Kee et al., 2006), and, finally, we compared them to the
experimental data. For a large majority, these data consisted in molar
fraction measurements of different species (reactants and products, 1558
measurements out of 1618), but also in the measurement of auto ignition delay
times (IDT, 60 measurements out of 1618). This delay corresponds to the time
it takes for a fuel mixture to spontaneously ignite at a given temperature and
pressure. In experiments, it is measured as the time between the moment when
the gas is brought to temperature and pressure conditions and the moment when
the ignition is detected. It is most often detected by a pressure or
concentration peak of excited OH or CH radicals. In our simulations, the
ignition delay time is chosen to be the time at the maximum concentration of
the OH radical. These experimental data were collected over 21 different
publications in total, from a wide range of conditions fully described in the
appendix. The first eight experimental conditions considered were taken from
those used in Venot et al. (2020), to determine how the other chemical
networks compare to it on the original data used for its validation. The data
collected for the first six experimental conditions consisted in the temporal
evolution of the abundances of major species at play at the start and end of
reaction ($\ce{CH3OH}$, $\ce{O2}$, $\ce{CO}$, $\ce{CO2}$, $\ce{H2O}$,
$\ce{HCHO}$, $\ce{H2}$ …) in three different reactor types (closed reactor,
plug flow reactor, shock tube). The seventh consisted in the auto-ignition
delay time measurement in a shock tube at an initial pressure of 10 and 50
bar, at 10 and 5 different initial temperatures, respectively, over a range of
1000 to 1300 K. For the eighth, it consisted in the evolution with temperature
of abundances of major species at the exit of a perfectly-stirred reactor. The
rest of the experimental conditions (9 to 21 in Table 4) were focused on
exploring a wider range of initial species and conditions by varying
equivalence ratios, from very oxygen-rich combustion to pyrolysis, in addition
to varying the fuel type, which consisted in combustion of H2, of HCN,
pyrolysis of CH4, of C2H5OH, as well as reactions of nitrogen species like
N2O, NO, or NH3. Like the eight first ones, these data consisted in auto-
ignition delay times, abundances over time or abundances at steady state over
temperature, and sometimes with a parameter study on equivalence ratio,
pressure, or different initial species. The species concerned by these
abundance data can be reactants ($\ce{H2}$, $\ce{CH4}$, $\ce{HCN}$,
$\ce{C2H5OH}$, $\ce{O2}$ …), products ($\ce{H2O}$, $\ce{CO2}$, $\ce{CO}$,
$\ce{C2H2}$, $\ce{C2H4}$, $\ce{C2H6}$, $\ce{CH3CHO}$, $\ce{HCHO}$ …) or appear
as both in the data set depending on the conditions (CH4, H2).

In combustion conditions, the parameter describing the abundance of fuel to
oxidizer is the equivalence ratio:

$\phi=\frac{n_{fuel}/n_{ox}}{[n_{fuel}/n_{ox}]_{sto}},$

with $n_{fuel}$ as the fuel quantity, $n_{ox}$ as the oxidizer quantity, and
$[n_{fuel}/n_{ox}]_{sto}$ as the ratio of these values in a stoichiometric
mixture. As the equivalence ratio grows larger, the fuel proportion gets
higher and the combustion conditions get closer to pyrolysis conditions.
Pyrolysis corresponds to high temperature conditions in a reducing medium with
no oxygen, while combustion refers to high temperature conditions in an
oxidizing medium, usually oxygen. Covering a wide range of equivalence ratios
in our dataset allows us to test the kinetic networks on different
compositions to ensure their ability to accurately model the chemistry
occurring on exoplanets with very different elemental abundances. Pyrolysis
and a high equivalence ratio combustion corresponds to low metallicities with
high C/O, N/O, and H/O ratios, while a low equivalence ratio combustion
corresponds to high metallicities with low C/O, N/O, and H/O ratios. In total,
the full experimental data set spanned equivalence ratios from 0.05 to 5 and
pyrolysis conditions, pressures from 0.2 to 50 bar and temperatures from 800
to 2400 K.

###  2.3 Error calculations

To test the agreement of the different chemical networks with the experimental
data, network predictions were plotted against experimental points and
compared. This resulted in over 500 plots, which is too much to be shown here.
Therefore, we will focus on the distribution of errors for each chemical
network, compiled in histograms of Fig. 1, and discuss the main tendencies
visible in the overall dataset.

To sum up these numerous plots into a statistical distribution of errors shown
in Fig. 1, we chose to compute these errors using the following formula:

$y_{error}=\frac{y_{mod}-y_{exp}}{y_{max}},$

with $y_{exp}$ being the values of each experimental point in our dataset,
$y_{mod}$ being the network prediction at that point and $y_{max}$ being the
value of the highest experimental point over the experimental range. Each
experimental point corresponds to a measurement of the molar fraction of a
species (either product or reactant, for 1558 measurements out of 1618), but
also of the IDT of a mixture (60 measurements out of 1618). Depending on the
reactor type, for a given experimental range of measurements, pressure,
temperature, or reaction time can change. This range depends on the type of
data, and corresponds to the temperature range of the original measurements
for temperature studies, and to the time range of the original measurements
for mole fraction over time studies. This choice is done to give a relative
error that can be compared between different experiments, while avoiding non-
representative errors due to data points close to zero and experimental and
pointing noise causing diverging relative errors. The assumed network
prediction corresponds to the linear interpolation between the two closest
computed points. For temperature studies, the computed points were evenly
distributed over the experimental range, compromising between the density of
the distribution and the computational time. For time studies, the computed
points were determined by the software used for the calculations.

In these histograms, NUIGMech1.1, Burke 2016, and Glarborg 2018, display the
models with the best prediction accuracy over the dataset. In the following,
the focus is more on in-depth descriptions of the causes underlying these
results. In total, over the 1618 experimental points, about 50 were beyond
100% calculated error for all models, with maximum values reaching around
2500%. These high deviations are found with all the chemical networks and were
not plotted in error distributions. They mainly come from conditions 16, 17,
and 18 in Table 4 for ethanol and methane pyrolysis, the important
discrepancies observed between experiments and simulations appearing for plug
flow experiments. For these specific experiments, important shifts in time (or
temperature) are observed which dramatically affect the $y_{error}$
calculated. There is also an abnormal distribution of errors around -70% seen
in Fig. 1 for all models, mainly coming from conditions 19 of Table 4 and
concerning C2H4 and H2O. These errors could be due to issues in the
experimental points, for example, due to ethanol reacting before entering the
reactor.

###  2.4 Auto ignition delay time

When first comparing the different plots for each network on methanol
combustion (conditions 1-9 in Table 4), the first thing that stood out was
that the chemical networks based on the work of Curran and co-authors
(AramcoMech3.0, NUIGMech1.1, and Burke 2016), were all way better at
describing auto ignition delay times. They agree with the auto ignition data
of methanol shown in Fig. 2(a) and 2(b) with a mean error of 5% at 10 bar and
within 10% at 50 bar, with almost no visible difference between them. On the
contrary, the Exgas network severely overestimates this delay, with almost an
order of magnitude difference. The Konnov network does not reproduce the
temperature dependence: delays are underestimated at low temperatures (under
1050 K) and overestimated at high temperatures (over 1050 K) by around 150%.
For the Glarborg network, the delay is overestimated at both pressures by
around 30%, and for the Venot 2020, this delay is too short by around -40%.
The ability of each network to accurately describe the auto ignition delay
time in given initial conditions has major consequences on kinetic simulations
of mole fraction over time. When a network underestimates the IDT, fuel
consumption will tend to be overestimated. This correlation is clearly visible
in our dataset. Fig. 2(a) and 2(b) shows that Venot 2020 underestimates the
IDT while Glarborg 2018 overestimates it. This is related to Fig. 2(c), where
CH3OH consumption is overestimated for Venot 2020 and underestimated for
Glarborg 2018. This impact is clearly seen in combustion conditions, as shown
in Fig. 28, leading to CH3OH error distribution of Venot 2020 to be mostly
between -100% and 0% error, and between 0 and 100% error for Glarborg 2018.
Figure 2(d) shows that in consequence, products like CO tend to be
overestimated around ignition time for networks with underestimated IDT, as in
Venot 2020, and, conversely, for networks with overestimated IDT like Glarborg
2018. As the oxidation of intermediate species like CO is not directly linked
to IDT, other parameters may control CO consumption, as visible with Konnov
2005.

###  2.5 Combustion and pyrolysis of methanol, ethanol, formaldehyde, and
acetaldehyde

Focusing on data related to methanol thermal decomposition, we can see in Fig.
3 that Exgas, Glarborg, and Konnov networks overestimate CH3OH abundances
profiles, while Venot 2020 tends to underestimate methanol abundances.
Eventually, for these networks, their best performances on methanol points are
for data in perfectly stirred reactors or plug flow reactors.

In addition, ethanol results are also displayed in Fig. 3. The experimental
conditions concerning this species include only ethanol pyrolysis, with
temperature- and pressure-dependent species profiles. CH3CHO data come
exclusively from these conditions (18 and 19 of Table 4), whereas CH2O errors
also include methanol combustion experiments (2, 6, 7, and 8 of Table 4). The
Curran-based networks give quite similar results for these species, except for
NUIGMech1.1 that is significantly better on methanol. This is probably due to
a better representation of the growth mechanism towards heavier molecules
occurring under pyrolysis conditions. Overall, these networks are similar over
these species. The Glarborg network is accurate for methanol pyrolysis, but is
less effective for methanol combustion. For C2H5OH, its performances are less
accurate than the previous networks, and for CH3CHO, experimental abundance is
underestimated by about -75%. Venot 2020 also reproduce these experimental
points quite badly, especially on CH2O and CH3CHO.

###  2.6 Main products and reactants in combustion and pyrolysis

Main species are shown in Fig. 4. This histogram gathers all computed errors
of model predictions on experimental measurements (mole fraction and IDT) of
H2, CH4, H2O, O2, CO, and CO2 regardless of their role in the experiment
(either product or reactant). In condition of combustion of H2, all the
networks were within 5% or 10% error. For H2 mole fraction measurements coming
from methane pyrolysis experiments however, almost every network
underestimated its production compared to the experimental points, although
the Curran-based networks were the closest to experiments. In ethanol
pyrolysis, on the contrary, the H2 production was severely overestimated by
all the networks, especially at high temperatures, with errors up to 200% at
1300 K with usually reliable networks like NUIGMech1.1.

For methane pyrolysis, the Curran-based networks were under 5% error, while
other networks like Exgas or Venot 2020 overestimated CH4 abundances by around
20%. For methane combustion, almost all networks were under the 5% error range
except for the Konnov network whose temperature dependence was totally off. On
ethanol pyrolysis conditions, methane production were underestimated by all
networks.

For water, results were good for all the networks, except in the ethanol
pyrolysis conditions, where H2O production was severely underestimated by all
the networks by around 75%, which is shown as red bars in Fig. 29.

For O2 consumption in methanol or hydrogen burning conditions, the best
networks were the Curran-based networks, with the same problems as noted
previously for others, which were due to bad methanol ignition delay time
predictions.

One species that displayed significant gains in accuracy with the Curran-based
networks is CO, which has a wide range of errors with such networks as Exgas
2014, Glarborg 2018, Konnov 2005, and Venot 2020. However, for CO2, we do not
see a significant improvement over our dataset in relation to these
experimental data.

Table 2: Different added reactions to the network and their parameters. Values
are in $\mathrm{m}\mathrm{o}\mathrm{l}$, $\mathrm{c}\mathrm{m}^{3}$,
$\mathrm{c}\mathrm{a}\mathrm{l}$, $\mathrm{s}$. Also, A, n, and E are the
parameters of the modified Arrhenius equation, while $\Delta_{r}H$ is the
enthalpy of formation of the reaction, whose values are taken from NIST. As C
+ NH and N + H are radical-radical combinations, they are barrierless
reactions (E = 0). +M indicates low pressure limit reactions.

Reaction | A | n | E | Analogy for A and n | Source for E
---|---|---|---|---|---
[label-offset=-1pt]NH2 ¡=¿[+M] NH + H | $5.6\times 10^{15}$ | 0 | 96600 |  [label-offset=-1pt]CH2 ¡=¿[+M] CH + H (Bauerle et al., 1995) | $\Delta_{r}H$
C + NH ¡=¿ CN + H | $5.0\times 10^{13}$ | 0 | 0 |  C + OH ¡=¿ CO + H (Glarborg et al., 1986) | N/A
[label-offset=-1pt]N + H ¡=¿[+M] NH | $4.7\times 10^{18}$ | -1 | 0 |  [label-offset=-1pt]O + H ¡=¿[+M] OH (Tsang & Hampson, 1986) | N/A
[label-offset=-1pt]CN ¡=¿[+M] C + N | $1.5\times 10^{16}$ | 0 | 180260 |  [label-offset=-1pt]C2 ¡=¿[+M] C + C (Kruse & Roth, 1997) | $\Delta_{r}H$
[label-offset=-1pt]NO ¡=¿[+M] N + O | $1.5\times 10^{16}$ | 0 | 150920 |  [label-offset=-1pt]C2 ¡=¿[+M] C + C (Kruse & Roth, 1997) | $\Delta_{r}H$
[label-offset=-1pt]N2 ¡=¿[+M] N + N | $1.5\times 10^{16}$ | 0 | 225940 |  [label-offset=-1pt]C2 ¡=¿[+M] C + C (Kruse & Roth, 1997) | $\Delta_{r}H$

###  2.7 Network base choice and C2 reduction

To derive our C/H/O/N chemical network from these combustion networks,
multiple options were considered. The first one was to simply take the
Glarborg 2018 network, as it is already a C/H/O/N network. However, as seen in
the corresponding error distributions in Figs. 1 and 3, this network
performance, although better than older networks like Venot 2020 or Konnov
2005, is surpassed by the oxygenated species and alcohol combustion conditions
of recent methanol-focused networks, such as Burke 2016, or the generic state-
of-the-art network NUIGMech1.1. However, with respect to the nitrogen
chemistry, it is the most state-of-the-art network, although the difference
with NUIGMech1.1 and Konnov 2005 was not shown very clearly in our dataset. In
the end, as both nitrogen and C/H/O chemistry are equally important for
exoplanets, we decided to fuse the Glarborg 2018 network with the Burke 2016
network, keeping both the most state-of-the-art chemistry network with the
best performing reasonable sized network on our data, while making sure that
the methanol chemistry is accurate. To further reduce our network size, we
first removed 81 out of 91 species from the C3 sub-mechanism of Burke 2016 and
their reactions, but kept the last 10 that were necessary to preserve the
accuracy on some C2 species. This reduction was made because for exoplanets,
C3 species abundances are usually low and their interest is limited in
comparison to the increase in computation time they require due to the higher
number of possible isomers. In addition, limiting calculation times allows for
future additions of other species such as sulfur and its use in retrievals
using TauREx with the FRECKLL plugin.

###  2.8 Additional modifications to the network

While applying our chemical network to exoplanet studies, we noted that NH3
formation at high altitudes was primarily driven by a reversed globalized
reaction: CH + NH3 -¿ H2CN + H + H. This reaction was assumed to be the
combination of two reactions: CH + NH3 -¿ CH2NH + H and CH2NH -¿ H2CN + H, but
was written in this compact form in the original network of Glarborg 2018,
implicitly assuming that the latter reaction would always happen only after
the former. This simplification (while certainly reasonable in nitrogen
combustion chemistry) is not suited to exoplanetary conditions, especially in
the upper atmosphere, where photolysis combined with low density conditions
maintains a really high concentration of hydrogen radicals that heavily favors
the reverse reaction, resulting in an unphysical NH3 production pathway.
Hence, we decided to rewrite the reversible reaction CH + NH3 ¡=¿ H2CN + H + H
into two others: CH + NH3 ¡=¿ CH2NH + H, for which we kept the parameters of
the CH + NH3 ¡=¿ H2CN + H + H reaction, and H2CN + H ¡=¿ CH2NH. For this
second reaction, the choice of parameters was based on the reaction NH2 + H
¡=¿ NH3 by analogy. Both reactions are indeed the recombination of a nitrogen
radical with a hydrogen atom, and therefore occur with no activation energy.
They also should share a similar pre-exponential factor, with no temperature
dependence. This factor was hence estimated at $1.6\times 10^{14}$
$\mathrm{c}\mathrm{m}^{3}\,\mathrm{m}\mathrm{o}\mathrm{l}^{-1}\,\mathrm{s}^{-1}$.
However, this value is only accurate in the high pressure limit because at low
pressures, this reaction needs a third body to stabilize the product, causing
a strong pressure dependence of the rate constant. Further work is needed to
correctly take into account this pressure dependence, using advanced
Variational Reaction Coordinate-VTST and Master Equation methods
(Klippenstein, 1992; Georgievskii & Klippenstein, 2003a, b). We discuss the
impact of this approximation in Section 3.2.2.

In addition, we disabled the reversibility of 24 other globalized reactions
yielding three products, to prevent similar unexpected chemical pathways from
occurring. However, we did not find any conditions resulting in the reverse
direction of these reactions to be favored.

To further improve the reliability of the chemical network in the upper
atmosphere, we searched for possibly missing radical reactions in the network
that could significantly impact chemistry for these specific conditions. We
listed all the major species and radicals typically encountered in exoplanets
or produced by photolysis and checked for their reactions with N and NH
radicals. Many reactions of N and NH are negligible in usual combustion
conditions; hence, the coupling between all radicals is not systematic,
especially with radical compounds such as NH. In exoplanets, the
photochemistry of NH3 produces a lot of N and NH radicals, in a medium where
radicals are especially abundant, which causes them to mainly react between
each other through pathways that may be usually neglected. While searching for
these kinds of reactions, we identified six potentially missing reactions and
determined their parameters by analogy with other reactions. These added
reactions were also checked to ensure that they do not exceed the theoretical
collision limit (see Table 2). We compared the chemical network results on our
combustion data set before and after these modifications, confirming that it
did not affect the network performances. This scheme can be downloaded from
the KInetic Database for Astrochemistry (Wakelam et al.,
2012)111https://kida.astrochem-tools.org/ and also from the ANR EXACT
website222https://www.anr-exact.cnrs.fr/fr/chemical-schemes/.

Table 3: Table of the exoplanets simulated with V20 and V23 and the input parameters used. Here, D is the distance to the host star, R the planet radius, T the temperature at 1 bar, $\boldsymbol{K}_{zz}$ the eddy diffusion coefficient, and M the metallicity relative to solar abundances. Planet name | Planet type | Star type | D (UA) | R ($\boldsymbol{R}_{J}$) | T (K) |  $\boldsymbol{K}_{zz}$ (cm²/s) | M (solar)
---|---|---|---|---|---|---|---
GJ 436 b | Warm Neptune | M3V | 0.029 | 0.38 | 1094 | $10^{9}$ | 1
GJ 436 b | Warm Neptune | M3V | 0.029 | 0.38 | 1094 | $10^{9}$ | 100
GJ 1214 b | Warm Neptune | M4.5V | 0.014 | 0.24 | 1054 | $3\times 10^{7}\times P^{-0.4}$ | 100
HD 189733 b | Hot Jupiter | K2V | 0.031 | 1.14 | 1470 | profile | 1
HD 209458 b | Hot Jupiter | F9V | 0.047 | 1.38 | 1671 | profile | 1

##  3 Application to exoplanetary atmospheres

###  3.1 Models and data sources

Our prime motivation for this extensive work on combustion networks was to
develop a very robust scheme for the study of exoplanetary atmospheres. Thus,
in this section, we now apply this new scheme to model the atmospheric
chemical composition of various exoplanets. Our new C/H/O/N scheme was tested
against multiple exoplanet cases and compared to the one published in Venot et
al. (2020). In the following, we refer to the Venot 2020 chemical network as
V20 and to our update as V23.

In order to span different type of hydrogen-dominated atmospheres, we chose to
model GJ 436 b and GJ 1214 b (warm Neptunes) as well as HD 209458 b and
HD189733 b (hot Jupiters) using the same thermal profiles, initial conditions,
and parameters given in Venot et al. (2020) (Table 3). For each planet, we
compared the abundances obtained with both V23 and V20. We calculated the
chemical abundance profiles using FRECKLL (Al-Refaie et al., 2022), which is
the Python version of the code used in Venot et al. (2020). The results
obtained with this code are identical, but the computational time has been
greatly improved. The thermal profiles are discretized in a 130-layers grid,
evenly distributed in pressure log space. We assumed a solar metallicity for
HD 189733 b and HD 209458 b, a 100x solar metallicity for GJ 1214 b and both
metallicities for GJ 436 b. Elemental abundances were based on Lodders (2010),
with 20% less oxygen to account for sequestration in refractory elements.

We updated the photodissociation data (cross-sections and branching ratios,
Table 6), compared to that used in V20. To discriminate the changes due to
this update and to chemistry, we first compared the abundance profiles of each
exoplanet model for some of the major species (H2, H2O, CH4, CO, N2, NH3, CO2,
HCN, and H) between the old photolysis and the new photolysis data for the V20
chemical network with FRECKLL. This update turned out to have little impact on
photochemistry for most species on hot Jupiters (HD 189733 b and HD 209458 b)
and for all species on warm Neptunes (GJ 436 b and GJ 1214 b). However, for
HCN, the addition of two new photodissociation pathways of NH3 into NH (NH3 -¿
NH + H + H and NH3 -¿ NH + H2) creates differences of up to one order of
magnitude in the upper atmosphere of HD 189733 b and HD 209458 b between
$10^{-6}$ and $10^{-7}$ bar. The consequences of this photolysis update are
summarized in Fig. 27.

In the following, we compare the chemical abundances obtained with V20 and V23
for each planet case, using only this most recent UV cross-section data and
branching ratios. We also investigate on the reasons explaining the observed
differences and identify the main chemical pathways at play in each network.
To evaluate the impact on observables, we generated the transmission spectrum
of every planet with TauREx 3.1 (Al-Refaie et al., 2021), using a spectral
resolution of 50 and opacities data from ExoMol (Tennyson & Yurchenko, 2012)
for HCN and from Al-Refaie et al. (2022) for CH4, CO, CO2, H2O, and NH3.
Rayleigh diffusion for CH4, CO, CO2, H2, H2O, He, N2, and NH3 as well as
collision-induced absorption from HITRAN (Gordon et al., 2022) for H2-H2 and
H2-He were also included.

###  3.2 Results for GJ 436 b

For the warm Neptune GJ 436 b, we first simulated the 1D chemical abundance
profiles assuming a solar metallicity and a constant eddy diffusion
coefficient of $10^{9}$ $\mathrm{c}\mathrm{m}^{2}\,\mathrm{s}^{-1}$. While
some of the main species (H2O, CH4, NH3, N2, CO, and H) are found to have
similar abundance profiles (Fig. 5) with both networks, we observe that two
species differ by various orders of magnitude: CO2 below 0.1 bar, and HCN on
the whole pressure profile (100 to $10^{-7}$ bar). In the upper atmosphere,
the molar fraction of CO2 is higher with V23 than in V20, with a difference up
to three orders of magnitude around $10^{-6}$ bar. For HCN, its molar fraction
is lower in V23 than in V20 for pressures under $10^{-2}$ bar, with a
difference of up to four orders of magnitude around $10^{-6}$ bar. For
pressures higher than $10^{-2}$ bar, HCN molar fraction is higher in V23 than
in V20, with a difference of up to two orders of magnitude around 10 bar. In
the following, we discuss the origin of the differences for these two species.

Figure 5: Abundance profiles of GJ 436 b for solar metallicity and a constant
eddy diffusion coefficient of $10^{9}$
$\mathrm{c}\mathrm{m}^{2}\,\mathrm{s}^{-1}$. Dashed lines are for V20, while
solid lines are for V23. H2 is not shown to focus on other species, but its
abundance profile in V23 is almost identical to V20.

####  3.2.1 CO2 differences

Upon investigating the reasons of this discrepancy, we found that between V23
and V20, the total CO2 reactions rate profile was different. Figure 6 shows
the total rate of CO2 formation and destruction in each layer, which are equal
when including vertical mixing because the profiles are at steady state. We
see that these total reaction rates are larger below 1 bar with V23 than with
V20.

Figure 6: Total reaction rate profile for CO2 in GJ 436 b with a solar
metallicity with V20 (dashed lines) and V23 (solid lines).

Figure 7 presents the main contributions of each reaction to this total rate,
the sum of positive contributions, and the sum of negative contribution both
being equal to the total reactions rate when accounting for vertical mixing
because of the steady state.

Figure 7: Contribution profile of most major production and loss reactions for
CO2 in GJ 436 b. Positive values are production contributions and negative
values are loss contributions. Black lines correspond to vertical mixing
compensation, such as the sum in each layer is always zero due to steady
state. Dashed lines are for V20, while solid lines are for V23. The
contribution of photodissociation pathways to O(^3P) and O(^1D) are combined,
O(^3P) being favored above $10^{-5}$ bar and O(^1D) being favored for lower
pressures. The third column in the legend indicates the reaction type. ”Photo”
corresponds to photodissociations and ”no M” corresponds to reactions without
pressure dependence. The last column indicates which model includes this
reaction.

For V23, Fig. 7 shows that the reaction CO + OH -¿ CO2 + H is always the main
CO2 production reaction above 1 bar and the main CO2 destruction reaction
below 1 bar. Vertical mixing mainly transports the CO2 produced in the middle
atmosphere (1 - $10^{-3}$ bar) towards the lower atmosphere (below 1 bar),
where it is destroyed into CO and OH through the CO2 + H -¿ CO + OH reaction.
For V20, this reaction is not the main destruction pathway of CO2 in the lower
atmosphere and needs the vertical mixing to compensate for the destruction of
CO2 through the N(^4S) + CO2 -¿ NO + CO reaction, although it also remains the
main CO2 production reaction for pressures lower than 1 bar. At the peak of
CO2 abundance around $10^{-6}$ bar, the main production reaction is CO + OH -¿
CO2 + H and the main loss mechanism is by photodissociation into CO through
CO2 -¿ CO + O(^1D), for both V20 and V23. Photodissociation rate being
proportional to the CO2 concentration, its rate increase is directly linked to
the higher CO2 levels in V23. The UV cross-sections used in V23 and V20 being
the same in these simulations, there is no difference in the main loss
reaction parameters at this pressure between the two chemical networks, thus,
this difference must come from the production reaction CO + OH -¿ CO2 + H. In
V20, this production reaction is taken from Baulch et al. (1994) with the pre-
exponential factor divided by 6, resulting in a production rate of around 5
$\mathrm{m}\mathrm{o}\mathrm{l}\mathrm{e}\mathrm{c}\mathrm{u}\mathrm{l}\mathrm{e}\,\mathrm{c}\mathrm{m}^{-3}\,\mathrm{s}^{-1}$
around $10^{-6}$ bar. In V23, these reaction parameters are taken from Joshi &
Wang (2006), where it is treated as a sum of two modified Arrhenius equations,
both having opposite temperature dependence. This results in a total
production rate of around 2500
$\mathrm{m}\mathrm{o}\mathrm{l}\mathrm{e}\mathrm{c}\mathrm{u}\mathrm{l}\mathrm{e}\,\mathrm{c}\mathrm{m}^{-3}\,\mathrm{s}^{-1}$,
which is three orders of magnitude greater than V20. This difference is
directly observed in the rate constant of this reaction for the two networks
and is fully attributable to the very different parameters used for modeling
this reaction, as shown in Fig. 8.

Figure 8: Reaction rates of the reaction CO + OH -¿ CO2 + H in V20 compared to
V23, NUIGMech1.1, and Konnov 2005. In V23, this reaction rate is expressed as
the sum of two modified Arrhenius equations (Eqs. 1 and 2). In NUIGMech1.1,
the rate constant is pressure-dependent.

This difference has already been pointed out by Tsai et al. (2021) for
temperatures in the 250-2000 K range (their Figure 41). As for other
combustion networks, such as Konnov 2005 or NUIGMech1.1, they are on a similar
order of magnitude as V23 and also three orders of magnitude greater than V20,
even using different sources for the reaction parameters. NUIGMech1.1 is
particularly close to V23 for pressures around 100 bar and temperatures above
500 K, despite using data from Senosiain et al. (2005) and a single reaction
with pressure dependent parameters. For temperatures below this value, some
care should be taken as reaction rates differences between chemical networks
raise above one order of magnitude around 250K, where the validity of these
networks is no longer ensured.

####  3.2.2 HCN differences

We focus in this part in the differences observed between V23 and V20 for HCN.
The total reaction rate profile for HCN production and consumption is shown in
Fig. 9, and the respective contributions of major reactions are shown in Fig.
10.

Figure 9: Total reaction rate profile for HCN in GJ 436 b with a solar
metallicity with V20 (dashed lines) and V23 (solid lines). Figure 10:
Contribution profile of most major production and loss reactions for HCN in GJ
436 b. Positive values are production contributions and negative values are
loss contributions. Black lines correspond to vertical mixing compensation,
such as the sum in each layer is always zero due to steady state. Dashed lines
are for V20, while solid lines are for V23. The third column in the legend
indicates the reaction type. ”no M” corresponds to reactions without pressure
dependence, ”PLOG” to full pressure dependence and fall off description with
PLOG formalism, ”M only” to pressure dependence without fall off nor high
pressure limit and ”decay” to reversible, pressure dependent unimolecular
reactions such as isomerization or electronic decay. The last column indicates
which model includes this reaction.

As expected, the HCN total reaction rate profile roughly matches the
differences in HCN abundance, with the total reaction rate in V20 being up to
six orders of magnitude higher than in V23 around $10^{-7}$ bar, and up to one
order of magnitude lower between 100 and $10^{-3}$ bar. In both networks, the
main production reaction between $10^{-2}$ and $10^{-6}$ bar is the reaction
HCN + H ¡=¿ H2CN. This reaction is implemented in V23 with a pressure
dependent rate, with parameters interpolated in log space (PLOG) between 3
pressure values at 10, 1, and 0.1 bar. In V20, this reaction is described as
two separate reactions: one for the high pressure limit, and one for the low
pressure limit, requiring a third body. The parameters of the high pressure
limit reaction are close to the values in the V23 PLOG reaction for 0.1 bar,
and the low pressure limit contribution decreases with altitude, becoming
negligible for pressures below 0.1 bar. Therefore, these differences cannot
explain those between the two HCN abundance profiles. It is also important to
note that this description likely overestimates the HCN + H -¿ H2CN reaction
rate for pressures below 0.1 bar, for both networks. A PLOG implementation on
the full pressure range (1000 - $10^{-8}$ bar) would be necessary to describe
the full pressure dependence. In both networks, the main HCN production
reaction is H2CN + H -¿ HCN + H2 between 0.1 and $10^{-6}$ bar. The parameters
for this reaction are identical between the two networks, therefore it is not
responsible for the differences between HCN abundance profiles. The other
major contributing reaction, HCN + H -¿ HCNH is similar to the reaction HCN +
H -¿ H2CN, but results in the formation of HCNH, an isomer of H2CN. V23 uses
similar values to V20 for this reaction, although the pressure dependence is
described between 0.1 and 10 bars with the PLOG formalism. Another difference
between V20 and V23 is the inclusion of HNC in V23 and its isomerization
reaction HNC ¡=¿ HCN. However, this reaction does not impact HCN profiles,
which we confirmed by running the simulation with V23 without this reaction.
The combination of the reactions HCN + H -¿ H2CN and H2CN + H -¿ HCN + H2
results in an equilibrium between the species HCN and its radical H2CN. Hence,
HCN differences between V23 and V20 are directly driven by production and
consumption rate differences of H2CN, which are shown in Fig. 11.

Figure 11: Total reaction rate profiles for H2CN in GJ 436 b with a solar
metallicity with V20 (dashed lines) and V23 (solid lines).

As expected, we observe a similar reaction rate profile to HCN, with a total
reaction rate up to six orders of magnitude above V20 for V23 at $10^{-7}$
bar, and up to two orders between 10 and $10^{-3}$ bar. This indicates that
HCN abundance is mainly controlled by H2CN abundance and its associated
consumption and production reactions. Figure 12 shows a few differences on the
major reactions of H2CN.

Figure 12: Contribution profile of most major production and loss reactions
for H2CN in GJ 436 b. Positive values are production contributions and
negative values are loss contributions. Black lines correspond to vertical
mixing compensation, such as the sum in each layer is always zero due to
steady state. Dashed lines are for V20, while solid lines are for V23. The
third column in the legend indicates the reaction type. ”no M” corresponds to
reactions without pressure dependence, ”PLOG” to full pressure dependence and
fall off description with PLOG formalism and ”M only” to pressure dependence
without fall off nor high pressure limit. The last column indicates which
model includes this reaction.

Firstly, in V20, the main consumption reaction on the whole pressure range is
the reaction H2CN + H -¿ HCN + H2, previously mentioned as the main HCN
production reaction in both networks that controls the HCN/H2CN equilibrium.
For pressures above $10^{-1}$ bar, H2CN consumption isn’t local anymore, and
the vertical mixing advects H2CN to be consumed in the upper layers by the
reaction H2CN + H -¿ HCN + H2. Conversely, in V23, this reaction is
negligible, and H2CN consumption is entirely driven by the reaction H2CN + H
-¿ CH2NH, discussed in Sect. 2.8. This difference is crucial, because the
CH2NH species and its linked reactions are absent from V20. Secondly, in V20,
in the pressure range $10^{-4}$ to $10^{-6}$ bar, the main H2CN production
reaction is HCN + H -¿ H2CN, which is the second mentioned reaction
controlling the HCN/H2CN equilibrium. In V23, the main reaction in this range
is the reaction CH3 + N(^4S) -¿ H2CN + H, that is included in both networks
using the same parameters. Finally, in V23 at around 1 bar, the reaction CH2NH
+ H -¿ H2CN + H2 becomes dominant for H2CN production. This reaction opposes
the H2CN + H -¿ CH2NH reaction, and reverts CH2NH back to H2CN. To better
understand the role each of each reaction on the differences observed in the
HCN abundance profile, we ran these simulations with modified versions of the
V23 network presented in Fig. 13.

Figure 13: Abundance profile of N2, HCN and H2CN with V23 (solid lines), V20
(dashed lines), V23 without the reaction H2CN + H -¿ CH2NH (dashdot lines) and
V23 without H2CN + H -¿ CH2NH, CH2NH + H -¿ H2CN + H, CH2NH + CH3 -¿ H2CN +
CH4, CH2NH + NH2 -¿ H2CN + NH3, CH2NH + OH -¿ H2CN + H2O and with V20
thermochemical data for H2CN, HCNH and HCN (dotted lines).

This figure shows that disabling the reaction H2CN + H -¿ CH2NH results in a
HCN profile almost identical to V20 (dashed and dash-dotted lines) for
pressures lower than $10^{-1}$. For pressures above this value however, no
visible difference with V23 was observed. We disabled the reaction CH2NH + H
-¿ H2CN + H2, but other reactions such as CH2NH + CH3 -¿ H2CN + CH4 and CH2NH
+ NH2 -¿ H2CN + NH3 would replace its function in the HCN formation pathway,
leading to smaller but very significant differences. Thus, we disabled all the
H-abstraction reactions of CH2NH in addition to the reaction H2CN + H -¿
CH2NH, and found almost the same profile as V20. As disabling these reactions
without disabling the reaction H2CN + H -¿ CH2NH almost does not alter the V23
abundance profile, both of these reactions seem to be required to explain the
differences between V23 and V20. The remaining difference was mainly located
under 100 bar, where HCN and H2CN abundances approach chemical equilibrium.
Because the thermochemical data for V23 are different from V20 (Fig. 32), we
ran V23 with V20 thermochemical data for H2CN, HCNH and HCN, and found a
perfect match in this pressure range. Thus, we conclude that the differences
between V23 and V20 observed in HCN abundance profiles for GJ 436 b at 1x
solar metallicity are caused by the addition of the species CH2NH to the
network. The upper atmosphere differences are caused by the reaction H2CN + H
-¿ CH2NH, and the lower atmosphere differences are caused mainly by CH2NH + H
-¿ H2CN + H2, but also to a lower extent by others H-abstraction reactions of
CH2NH with CH3, NH2 or OH.

Because of the importance of the reaction H2CN + H -¿ CH2NH in these
simulations and the large uncertainty in its reaction parameters (discussed in
Sect. 2.8), we investigated its impact on the simulations with a sensitivity
analysis. Fig. 14 shows a sensitivity analysis on the pre-exponential factor
$A$ of this reaction.

Figure 14: Sensitivity analysis of the pre-exponential factor $A$ of the
reaction H2CN + H -¿ CH2NH. Abundances profiles are calculated with the full
V23 network (solid lines), with $A$ divided by 10 (dashdot lines), with $A$
divided by 100 (dotted lines) and without this reaction (dashed lines).

Multiple simulations were run with different pre-exponential factors for this
reaction, up to a division factor of 100. Significant differences in HCN
abundance of up to two orders of magnitude are found between values of $A$ and
of $A$/100, especially around $10^{-5}$ bar. At lower pressures however, the
abundance is shown to be quite insensitive to changes in $A$, showing that the
presence of the reaction remains impactful even with low estimates for this
pre-exponential factor.

Given this complete shift in the major N-bearing species above $10^{-5}$ bar,
we could expect CH2NH abundance to be quite high. However, as shown in Fig.
15, the CH2NH abundance profile remains two to four orders of magnitude lower
than HCN abundance for pressures higher than $10^{-4}$ bar, and stays similar
around $10^{-5}$ bar.

Figure 15: Abundance profiles of CH2NH and CH3NH2 compared with major
N-bearing species for GJ 436 b with solar metallicity in V20 (dashed lines)
and V23 (solid lines). CH2NH and CH3NH2 are not included in V20.

For the N2 abundance profile, we can see that its abundance increases with V23
in comparison to V20 around $10^{-5}$ bar, as this species becomes the main
N-bearing species instead of HCN. The species CH3NH2 is also plotted, as we
could expect the CH2NH double bond to be saturated by H2 and H atoms, but the
main reaction producing CH3NH2 is the reaction CH3 + NH2 -¿ CH3NH2. This
reaction uses a PLOG description between 0.1 and 10 bar, hence its
contribution for very low pressures is likely to be heavily overestimated.
However, a detailed treatment of its pressure dependence raises the same
problems as the reaction H2CN + H -¿ CH2NH because it is a barrierless,
pressure dependent reaction. Therefore, we conclude that CH2NH is an
intermediate species that links HCN and N2 abundances. The full mechanism
linking these two species around $10^{-5}$ is detailed in Fig. 16.

Figure 16: HCN formation mechanism in V23 around $10^{-5}$ bar. The blue path
is exclusive to V23 and absent from V20. The red reactions are dominant in V20
for the production of the species and minor but included in V23. The green
reactions are dominant in V23 and minor but included in V20. The black
reactions are dominant in both networks.

CH2NH is mainly hydrogenated through the reaction CH2NH + H -¿ CH2NH2. The
CH2NH2 radical then gets its C-N bond broken by the addition of another H atom
through the reaction CH2NH2 + H -¿ CH3 + NH2. These two reactions and the
species CH2NH and CH2NH2 are only included in V23 and not in V20. The NH2
radical formed then gets destroyed by N(^4S) atoms through the reaction NH2 +
N(^4S) -¿ N2 + H + H. This reaction has the same parameters between V23 and
V20, although it is reversible in V20 and not in V23. Despite being
reversible, the enthalpy difference between the reactants and the products is
too high for it to be significantly reversed in V20. This is important because
this reaction is an implicit combination of two other reactions, NH2 + N(^4S)
-¿ NNH + H and NNH -¿ N2 + H, and reversing the resulting combination NH2 +
N(^4S) -¿ N2 + H + H would be unphysical. In addition, N(^4S) atoms are
produced from NH2 through the reactions NH2 + H -¿ NH + H2 and NH + H -¿
N(^4S) + H2. While the parameters for this second reaction are really similar
between V20 and V23, for the first one, they are very different. Both consider
this reaction as NH + H2 -¿ NH2 + H and use a temperature exponent of zero,
but in V20 the activation energy is close to 20 kcal/mol while it is close to
15 kcal/mol in V23. The pre-exponential factor is also different, with a value
of $10^{14}$
$\mathrm{c}\mathrm{m}^{3}\,\mathrm{m}\mathrm{o}\mathrm{l}^{-1}\,\mathrm{s}^{-1}$
in V20 and $2.1\times 10^{13}$
$\mathrm{c}\mathrm{m}^{3}\,\mathrm{m}\mathrm{o}\mathrm{l}^{-1}\,\mathrm{s}^{-1}$
in V23, 5 times lower. The reaction H2CN + N(^4S) -¿ N2 + ^3CH2, which is the
main formation reaction of N2 in V20 has similar parameters between V20 and
V23, although they differ on the pre-exponential factor by a factor of 3.

####  3.2.3 Consequences on transmission spectra

These differences in the abundance profiles are also expressed in the
synthetic transmission spectra showed in Fig. 17.

Figure 17: Synthetic transmission spectra of GJ 436 b with a solar
metallicity, at a resolution of 50, corresponding to the atmospheric
compositions calculated with V23 (in red) and V20 (in blue).

The higher CO2 abundance obtained with V23 in the upper atmosphere increases
the apparent radius of GJ 436 b around 4.2 $\mathrm{\SIUnitSymbolMicro m}$,
leading to the apparition of a new CO2 feature with an amplitude of about 100
ppm. This happens because the transmission spectrum contribution of CO2
approaches that of CH4 and NH3, which dominate at this wavelength for the
abundance predicted with V20 (Fig. 18).

Figure 18: Contributions of major species to the total synthetic transmission
spectra of GJ 436 b with a solar metallicity and $K_{zz}=10^{9}$
$\mathrm{c}\mathrm{m}^{2}\,\mathrm{s}^{-1}$. Dashed lines are for V20, while
solid lines are for V23. In the middle, we see the CO2 contribution that leads
to a new feature in the spectrum with V23.

Another major change in the spectrum is the disappearance of the HCN feature
around 13 $\mathrm{\SIUnitSymbolMicro m}$ compared to an amplitude of about
200 ppm with V20, due to the drop in its abundance.

####  3.2.4 Case of 100 times solar metallicity

We also simulated the atmosphere of GJ 436 b assuming a higher metallicity
(100x solar) ($10^{9}$ $\mathrm{c}\mathrm{m}^{2}\,\mathrm{s}^{-1}$), but
keeping the same PT profile. In this case, variations between V20 and V23 are
also observed, but to a lower extent (Fig. 19).

Figure 19: Abundance profiles of GJ 436 b for 100 times solar metallicity and
a constant eddy diffusion coefficient of $10^{9}$
$\mathrm{c}\mathrm{m}^{2}\,\mathrm{s}^{-1}$. Dashed lines are for V20, while
solid lines are for V23.

Compared to the previous case with solar metallicity, the amplitude of HCN
differences is lower, because the profile is strongly quenched, due to the
higher abundance of related species that causes a higher flux of species.
However, for pressures from 10 to $10^{-5}$ bar, HCN abundance in V23 is still
almost two orders of magnitude above that of V20. For pressures around
$10^{-6}$ bar, the HCN abundance in V20 is almost four orders of magnitude
greater than in V23. These changes directly relate to the network differences
discussed for the solar metallicity case in Sect. 3.2.2, and particularly the
reaction H2CN + H -¿ CH2NH for the upper atmosphere differences, and the
reaction CH2NH + H -¿ H2CN + H2 for the lower atmosphere. We also observe
differences in the thermochemical equilibrium region, which are due to
discussed differences in the thermochemical data. This was verified through
the same method described earlier, by disabling these specific reactions to
see how they impact HCN abundance profile. In addition, CO2 is more abundant
in V23 than in V20 around $10^{-6}$ bar by almost one order of magnitude, due
to differences in the CO + OH -¿ CO2 + H reaction rates (Sect. 3.2.1). NH3
abundance profile is also slightly higher in V23 than V20, because the
quenching point seems to happen at slightly higher pressures. For this
metallicity case, these variations in abundances have very little impact on
the transmission spectrum (Fig. 20).

Figure 20: Synthetic transmission spectra of GJ 436 b with 100 times solar
metallicity, at a resolution of 50, corresponding to the atmospheric
compositions calculated with V23 (in red) and V20 (in blue).

The slight change in NH3 abundance profiles barely causes some features to
undergo an amplitude change, but HCN clearly does not show any impact on the
spectrum, because its contribution is well under the contributions of CH4 and
NH3 (Fig. 33).

###  3.3 Case of GJ 1214 b

For network comparisons with the warm Neptune GJ 1214 b, we used a pressure
dependent eddy diffusion coefficient profile calculated with the formula
$K_{zz}=3\times 10^{7}\times P^{-0.4}$
$\mathrm{c}\mathrm{m}^{2}\,\mathrm{s}^{-1}$ given in Charnay et al. (2015). As
this planet is expected to have a high metallicity (Désert et al., 2011; Bean
et al., 2011; Gao et al., 2023; Kempton et al., 2023), we chose to model this
planet with 100 times solar metallicity. The PT profile was taken from Venot
et al. (2020) and the UV flux used was that of GJ 436. The resulting abundance
profiles (Fig. 21) do not show a lot of difference between the two chemical
networks.

Figure 21: Abundance profiles of GJ 1214 b for 100 times solar metallicity and
a pressure-dependent eddy diffusion coefficient. Dashed lines are for V20,
while solid lines are for V23.

HCN abundance profile is still above the value predicted by V20 up to a factor
of 100 around 1 bar, while other species are mildly affected, except for very
low pressure regions around $10^{-6}$ bar, which are one order of magnitude
above for CO and CO2. Contrary to the previous cases of GJ 1214 b at 1x and
100x solar metallicity, the HCN abundance profile of V23 is never lower than
that of V20, expect at the very limit of the P-T profile, around $10^{-7}$
bar. This means that the reaction H2CN + H -¿ CH2NH has very little impact on
HCN in the upper atmosphere of this planet, and it was indeed verified by
disabling the reaction. Similarly to previous cases and with the same method,
for the remaining differences in the lower atmosphere, we identified the same
responsible reactions to be H2CN + H -¿ CH2NH, CH2NH + H -¿ H2CN + H2 and the
related reactions previously discussed in Sect. 3.2.2. For the CO2 abundance
profile, the differences stem from the reaction CO + OH -¿ CO2 + H, as
discussed in Sect. 3.2.1. The CO differences come from the reaction C + OH -¿
CO + H, which is the only major production reaction at those pressures in V23.
This reaction is also included in V20 with the same parameters. Then, CH4 and
H2O follow the opposite trend, resulting (respectively) from a higher loss
contribution of the reaction CH4 + CH -¿ C2H4 + H and the reaction H2O + CH -¿
H2CO + H which is exclusive to V23.

Due to the low amplitude of these changes, the corresponding synthetic spectra
(Fig. 22) shows no new features, with only a few minor variations in the
amplitude of existing features, largely under observable values.

Figure 22: Synthetic transmission spectra of GJ 1214 b, at a resolution of 50,
corresponding to the atmospheric compositions calculated with V23 (in red) and
V20 (in blue).

###  3.4 Case of HD 189733 b and HD 209458 b

While we observe that for warm Neptunes the most significant impacts of our
new chemical scheme are found for low metallicity atmospheres, we examined the
effect on two hot Jupiters: HD 189733 b and HD 209458 b. We used the same P-T
profiles, UV fluxes, eddy diffusion coefficients, and metallicities as in
Venot et al. (2020). The abundances profiles obtained using the two chemical
schemes V20 and V23 are shown in Figs. 23 and 24.

Figure 23: Abundance profiles of HD 189733 b for solar metallicity and a
pressure-dependent eddy diffusion coefficient. Dashed lines are for V20, while
solid lines are for V23. Figure 24: Abundance profiles of HD 209458 b for
solar metallicity and a pressure-dependent eddy diffusion coefficient. Dashed
lines are for V20, while solid lines are for V23.

For HD 189733 b, the only differences are for species lower than $10^{-5}$
abundance. CO2 is more abundant in V23 around $10^{-6}$ bar than in V20, due
to the CO + OH -¿ CO2 + H reaction (Sect. 3.2.1). The abundances of NH3 and
HCN are slightly less than one order of magnitude higher in V23 than in V20
between 10 and $10^{-3}$ bar. The larger NH3 difference between $10^{-4}$ and
$10^{-6}$ bar is due to the reaction CH2NH2 + H -¿ ^1CH2 + NH3 which is
analogous to the reaction CH2NH2 + H -¿ CH3 + NH2 discussed in Sect. 3.2.2.
The differences between 10 and $10^{-3}$ bar are explained by two reactions :
NH3 + NH2 -¿ N2H3 + H2 and NH2 + NH2 -¿ N2H2 + H2. The first one is not
included in V23, while in V20 it contributes to NH3 consumption. The second
one is included in both networks, but the parameters used in the reactions are
different, especially for the activation energy, which is 10 kcal/mol higher
in V23 than in V20. When combined with the changes in the thermochemical data,
the differences in these rate constants explain the differences between the
HCN abundance profiles for pressures higher than $10^{-5}$ bar. For lower
pressures, the reaction H2CN + H -¿ CH2NH increases HCN consumption in V23 in
comparison to V20 as previously discussed, leading to over one order of
magnitude less HCN in V23 than in V20. A very detailed comparison between the
chemical schemes of Venot et al. (2012) and Moses et al. (2011) (which we name
V12 and M11 in the following, respectively) has been performed by Moses
(2014), taking HD 189733 b as a case study, with the parameters used in Venot
et al. (2012). Thus, it is interesting to evaluate how the results obtained
with our new scheme compare with these two schemes. The highlighted
differences concerned the species NH3, CH4, and HCN.

With M11, NH3 was one order of magnitude above V12 in the range 10 to
$10^{-3}$ bar and one order of magnitude under V12 in the range $10^{-5}$ to
$10^{-7}$ bar. The V23 NH3 abundance profile halves this gap between M11 and
V12 in the range 10 to $10^{-3}$ bar, being nearly half an order of magnitude
above V12 and half an order of magnitude under M11. However, in the pressure
range $10^{-5}$ to $10^{-7}$ bar, the V23 NH3 abundance is much higher than in
both chemical networks, reaching a difference of seven orders of magnitude
around $10^{-5}$ bar.

For CH4, the differences mainly concerned the pressure range 1 to $10^{-3}$
bar, with the M11 profiles being around half an order of magnitude above V12.
The corresponding V23 profile slightly approaches the M11 profiles, also
halving the gap between M11 and V12 for CH4.

For HCN, the differences concerned the range 100 to $10^{-7}$ bar, the M11
profile being an order of magnitude above V12. The V23 profile comes closer to
the M11 profile in comparison to both V12 and V20 for the range 100 to
$10^{-5}$ bar, but the HCN abundance drops for V23 around $10^{-6}$ bar,
resulting in a difference of three orders of magnitude with M11 at this
pressure.

Overall, these results bring the abundances a bit closer to M11, but enhances
differences in the upper atmosphere where new CH2NH chemical pathways begin to
take effect.

We calculated the synthetic transmission spectrum corresponding to the
abundances obtained with V23 and V20 and observed some differences (see Fig.
25). The main effect is an increase of the transit depth between 10 and 20
$\mathrm{\SIUnitSymbolMicro m}$, with an amplitude of up to 50 ppm. No new
feature is created in the spectrum, but the differences generated are above
the instrumental precision. Thus, the change of chemical scheme could impact
the interpretations of the transmission spectrum and the retrieval of NH3
abundance.

Figure 25: Synthetic transmission spectra of HD 189733 b, at a resolution of
50, corresponding to the atmospheric compositions calculated with V23 (in red)
and V20 (in blue).

For the hotter HD 209458 b, the differences are even smaller than what we
observed for HD189733b, with an amplitude lower than one order of magnitude.
The NH3 and HCN abundances are still a bit higher in V23 as with all previous
exoplanets, except for GJ 436 b with a solar metallicity. The reactions
causing these differences are the same as for the case of HD 189733 b. Another
minor difference is that CO2, H2O and H2 are more abundant in V23 for
pressures lower than $10^{-6}$ bar, unlike H atoms. The formation pathways of
these species at this pressure being the same in V20 and V23, the differences
must come from slight differences in the parameters of the reactions.

Overall, the variations of abundances observed with V23 for this planet in
comparison to V20 are very small, and concern mainly species with low
abundances (¡$10^{-6}$). As a consequence, the synthetic transmission spectra
calculated for both networks (Fig. 26) are very similar.

Figure 26: Synthetic transmission spectra of HD 209458 b, at a resolution of
50, corresponding to the atmospheric compositions calculated with V23 (in red)
and V20 (in blue).

##  4 Conclusion

In this work, we developed a new C2 C/H/O/N detailed chemical scheme to model
exoplanet disequilibrium chemistry. It was derived from experimentally tested
combustion networks, extensively validated on 1618 experimental measurements
on a wide range of conditions and compared to other chemical networks
performances, such as V20 through a statistical study. Verifications,
additions, and detailing of possibly missing radical reactions were also
performed, resulting in a much more reliable network than Venot 2020. This
network was then used to model two warm Neptunes and two hot Jupiters using
the kinetic model FRECKLL, and the results were compared to those obtained
with our previous chemical scheme, V20. The chemistry differences were
analyzed and new chemical pathways were found, such as the importance of CH2NH
and its formation through H2CN to couple the nitrogen chemistry to CH3
radicals. This effect has been highlighted in a solar metallicity warm
Neptune, and is such expected to mainly impact warm exoplanets with a low
metallicity. Transmission spectra were also simulated using TauREx 3.1 and the
resulting changes in abundances were found to significantly impact the
spectrum for GJ 436 b, with differences around 100 ppm for CO2 at 4.2 µm and
200 ppm for HCN at 13 µm. The amplitude of these features is within the
detection capabilities of JWST, which confirms that disequilibrium chemistry
model accuracy is crucial to draw correct conclusions from observations. In
the context of ongoing and future missions (e.g., JWST and Ariel),
disequilibrium chemistry modelling will become increasingly important as we
get access to higher precision observations. Improvements are still awaited in
the development of experimentally validated sulfur chemical scheme: a compound
such as SO2 has been recently been detected with the JWST and is typically a
product of photochemistry (Tsai et al., 2022). Expanding our network to sulfur
species and their coupling to carbon and nitrogen species will be the next
step toward a more complete chemical scheme addressing modern problematics in
the exoplanet chemistry field. More in-depth insights on the critical
reactions for each species through sensitivity analysis or other methods in a
wide range of exoplanetary conditions could also help to further improve the
reliability of these networks, enabling to identify the key reactions in the
mechanism and could help to focus the community’s efforts and reduce the
associated uncertainty through more accurate but computationally intensive ab
initio calculations such as VRC-TST for barrierless reactions and RRKM/ME for
pressure dependence. As in situ experimentation with probes is impossible in
the field of exoplanet chemistry, the use of chemical networks validated on
combustion experiments remains the only way to validate our kinetic models to
this day.

###### Acknowledgements.

This project is fund by the ANR project ‘EXACT’ (ANR-21-CE49-0008-01). In
addition, O.V. acknowledges funding from the Centre National d’Études
Spatiales (CNES), and from the CNRS/INSU Programme National de Planétologie
(PNP).

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##  Appendix A Experimental data

Table 4: Experimental conditions used for comparison and simulation with each
C/H/O/N mechanism444N° is the condition reference number and
$\mathbf{N_{points}}$ is the number of experimental measurements in the set..

N° | $\mathbf{N_{points}}$ | Reaction conditions | Measured quantity | Variable | Temperature (K) | Pressure (bar) | Reaction time (s)
---|---|---|---|---|---|---|---
1 | 15 |  CH3OH Combustion | IDT | Temperature | 1000 - 1500 | 10.18, 49.18 | N/A
2 | 33 |  CH3OH Combustion | Mole Fraction | Time | 823 | 0.26 | 26
3 | 112 |  CH3OH Combustion | Mole Fraction | Time | 949 | 2.5 | 0.5
4 | 99 |  CH3OH Pyrolysis | Mole Fraction | Time | 1266, 1368, 1458, 1567, 1610 | 2.5, 2.4, 2.3, 2.1, 2.2 | 0.0015
5 | 48 |  CH3OH Combustion | Mole Fraction | Temperature | 700 - 1200 | 20 | 1
6 | 54 |  CH3OH Combustion | Mole Fraction | Time | 1000 | 1 | 0.27
7 | 64 |  CH3OH Combustion | Mole Fraction | Time | 1031 | 1 | 0.125
8 | 68 |  CH3OH Combustion | Mole Fraction | Time | 783 | 15 | 3.5
9 | 72 |  CH4 Combustion | Mole Fraction | Temperature | 900 - 1200 | 10 | 1
10 | 24 |  H2 Combustion | Mole Fraction | Temperature | 800 - 1150 | 10 | 1
11 | 61 |  H2 Combustion | Mole Fraction | Time | 934 | 3.02 | 0.7
12 | 16 |  H2 Combustion in CO | Mole Fraction | Temperature | 850 - 1200 | 1 | 1
13 | 75 | CO + H2O | Mole Fraction | Time | 1040 | 1, 2.4, 3.46, 6.5, 9.6 | 1.05
14 | 45 |  H2 Combustion | IDT | Temperature | 900 - 1700 | 1, 4, 16 | N/A
15 | 72 | HCN Combustion | Mole Fraction | Temperature | 900 - 1500 | 1 | 195 / T
16 | 83 |  CH4 Pyrolysis | Mole Fraction | Temperature | 1250 - 1500 | 1 | 4550 / T
17 | 60 |  CH4 Pyrolysis | Mole Fraction | Temperature | 1600 - 2400, 1300 - 2000 | 1.5, 30 | 0.003, 0.015
18 | 56 | Ethanol Pyrolysis | Mole Fraction | Temperature | 1000 - 1300 | 0.74 | 0.006
19 | 399 | Ethanol Pyrolysis | Mole Fraction | Time | 950 | 3, 6, 9, 12 | 0.4, 0.8, 1.2, 1.4
20 | 87 | HCN + NO ( + NH3 ) Combustion | Mole Fraction | Temperature | 900 - 1500 | 1 | 197 / T
21 | 75 |  H2 + N2O | Mole Fraction | Time | 995 | 3 | 0.5

Table 5: (Continued). Species data refers to the species whose mole fraction
has been measured.

N° | Reactor | Parameter study | Reactants | Species data | Data Source
---|---|---|---|---|---
1 | Shock Tube | Pressure | 5.7 % CH3OH, 8.55 % O2, Ar | N/A | Burke et al. (2016)
2 | Static Reactor | N/A | 5.89 % CH3OH, 8.84 % O2, N2 |  H2, CO, CO2, CH3OH and CH2O | Cathonnet et al. (1982)
3 | Plug Flow | N/A | 0.333 % CH3OH, 0.601 % O2, N2 |  CH3OH, O2, H2O, CO, H2 and CO2 | Held & Dryer (1994)
4 | Shock Tube | N/A | 1 % CH3OH, Ar |  CH3OH, CO | Ren et al. (2013)
5 | PSR | N/A | 0.24 % CH3OH, 0.3 % O2, N2 |  CH3OH, O2, CO and CO2 | Burke et al. (2016)
6 | Plug Flow | N/A | 0.735 % CH3OH, 0.65 % O2, N2 |  CH3OH, CH2O, CO | Aronowitz et al. (1979)
7 | Plug Flow | N/A | 0.93 % CH3OH, 1.18 % O2, N2 |  O2, CH3OH, CO, CO2 and CH2O | Norton & Dryer (1989)
8 | Plug Flow | N/A | 0.415 % CH3OH, 0.6 % O2, N2 |  O2, CH3OH, CO and CH2O | Held & Dryer (1994)
9 | PSR | N/A | 0.3 % CH4, 0.00055 % C2H6, 1.2 % O2, N2 | CO, CO2, CH4, C2H6, C2H4, C2H2 | Dagaut et al. (1990)
10 | PSR | N/A | 0.93 % H2, 5 % O2, N2 |  H2, H2O | Le Cong & Dagaut (2009)
11 | Plug Flow | N/A | 0.95 % H2, 0.495 % O2, N2 |  H2, O2 and H2O | Mueller et al. (1999)
12 | PSR | N/A | 0.2 % H2, 0.2 % CO, 2 % O2, N2 |  H2, CO2 | Le Cong & Dagaut (2009)
13 | Plug Flow | Pressure | 1 % CO, 0.65 % H2O, 0.5 % O2, N2 | CO | Kim et al. (1994)
14 | Shock Tube | Pressure | 0.81 % H2, 4.03 % O2, Ar | N/A | Kéromnès et al. (2013)
15 | Plug Flow | Equivalence Ratio | 0.1 % HCN, O2, 0.7 % H2O, 25 % Ar, CO2 | HCN, CO | Giménez-López et al. (2010)
16 | Plug Flow | Mixtures |  CH4, CO2, C2H6, N2 |  CH4, H2, C2H2, C2H4, C2H6, CO, CO2 | Kéromnès et al. (2013)
17 | Shock Tube | Pressure | 10 % CH4, 90 % Ar |  CH4, C2H2, C2H4 | Nativel et al. (2019)
18 | Plug Flow | N/A | 2.8 % C2H5OH, 97.2 % Ar |  C2H5OH, C2H4, CH3CHO, H2, CH4 | Rotzoll (1985)
19 | Plug Flow | Pressure | 0.3 % C2H5OH, 0.0035 % O2, Ar |  C2H5OH, H2O, C2H4, CH3CHO, CO, CH2O, CH4 | Li et al. (2001)
20 | Plug Flow | Mixtures | HCN, O2, NO, H2O, CO, NH3 | NO, CO, HCN, N2O, NH3 | Dagaut et al. (2008b)
21 | Plug Flow | N/A | 0.53 % H2, 1.1 % N2O, N2 |  N2O, H2O, H2, NO, NH3 | Allen et al. (1998)

##  Appendix B Photodissociation data

Table 6: Photodissociation pathways, cross-sections, and quantum yields used
in this study.

Species | Products | Cross sections | Quantum yields
---|---|---|---
H2O | H + OH |  Chan et al. (1993c); Fillion et al. (2004); | Heays et al. (2017)
|  H2 + O(^1D) | Mota et al. (2005); Ranjan et al. (2020) |
| H + H + O(^3P) |  |
CO2 | CO + O(^1D) |  Stark et al. (2007); Huestis & Berkowitz (2010); | Heays et al. (2017)
| CO + O(^3P) | Venot et al. (2018) |
H2CO |  H2 + CO | Cooper et al. (1996); Meller & Moortgat (2000) | Heays et al. (2017)
| H + HCO |  |
| H + H + CO |  |
OH |  O(^1D) + H | Huebner et al. (1992) | Heays et al. (2017)
|  O(^3P) + H |  |
OOH | OH + O(^3P) | Heays et al. (2017) |
CO | C + O(^3P) | Olney et al. (1997) | Heays et al. (2017)
| C + O(^1D) |  |
H2 | H + H |  Samson & Haddad (1994); | Heays et al. (2017)
|  | Chan et al. (1992); Olney et al. (1997) |
HCO | H + CO | Loison et al. (1991); Hochanadel et al. (1980) |
CH3OH |  H2CO + H2 | Burton et al. (1992); Cheng et al. (2002) | Heays et al. (2017)
|  CH3 + OH |  |
CH3OOH |  CH3O + OH | Vaghjiani & Ravishankara (1989); Matthews et al. (2005) |
CH2CO |  ^3CH2 + CO | Laufer & Keller (1971) |
CH3CHO |  CH4 + CO | Limão-Vieira et al. (2003); Burkholder et al. (2020) | Heays et al. (2017)
|  CH3 + HCO |  |
CH3 |  ^1CH2 + H | Heays et al. (2017) | Heays et al. (2017)
CH4 |  CH3 + H |  Au et al. (1993); Lee et al. (2001); | Peng et al. (2014)
|  ^1CH2 + H2 | Kameta et al. (2002); Chen & Wu (2004) |
|  ^1CH2 + H + H |  |
|  ^3CH2 + H + H |  |
| CH + H2 + H |  |
C2H | C + C + H | Heays et al. (2017); Fahr (2003) |
C2H2 |  C2H + H | Cooper et al. (1995a); Wu et al. (2001) | Heays et al. (2017); Hébrard et al. (2013)
C2H3 |  C2H2 + H | Fahr et al. (1998) |
C2H4 |  C2H2 + H2 |  Cooper et al. (1995b); Orkin et al. (1997); | Heays et al. (2017)
|  C2H2 + H + H | Wu et al. (2004) |
C2H6 |  C2H4 + H2 |  Au et al. (1993); Kameta et al. (1996); | Heays et al. (2017); Hébrard et al. (2006)
|  C2H4 + H + H | Lee et al. (2001); Chen & Wu (2004) |
|  C2H2 + H2 + H2 |  |
|  CH4 + ^1CH2 |  |
|  CH3 + CH3 |  |
N2 |  N(^2D) + N(^4S) |  Samson & Cairns (1964); Huffman (1969); |
|  | Stark et al. (1992); Chan et al. (1993d) |
NH2 | NH + H | Heays et al. (2017) | Heays et al. (2017)
NH3 |  NH2 + H |  Burton et al. (1993); Chen et al. (1998); | Heays et al. (2017)
| NH + H2 | Cheng et al. (2006) |
| NH + H + H |  |
N2H4 |  N2H3 + H | Vaghjiani (1993); Biehl & Stuhl (1991) |
HCN, HNC | CN + H |  Nuth & Glicker (1982);Bénilan et al. (in prep.); | Heays et al. (2017)
|  | Venot et al. (in prep.) |
H2CN | HCN + H | Nizamov & Dagdigian (2003); Teslja et al. (2006) |
C2N2 | CN + CN | Bénilan et al. (in prep.) |
NO |  N(^4S) + O(^3P) | Iida et al. (1986); Chan et al. (1993b) | Heays et al. (2017)
NO2 | NO + O(^3P) | Au & Brion (1997); Vandaele et al. (2002) | Heays et al. (2017)
| NO + O(^1D) |  |
NO3 |  NO2 + O(^3P) | Orphal & Chance (2003); Sander (1986) | Heays et al. (2017)
| NO + O2 | Yokelson et al. (1994) |
HNO2 | NO + OH | Burkholder et al. (2020) |
HNO3 |  NO2 + OH | Burkholder et al. (2020) |
N2O |  N2 + O(^1D) | Burkholder et al. (2020); Hubrich & Stuhl (1980) | Heays et al. (2017)
O2 |  O(^3P) + O(^3P) | Brion et al. (1979); Yoshino et al. (2005) | Heays et al. (2017)
|  O(^1D) + O(^3P) | Fally et al. (2000); Chan et al. (1993a) |
|  O(^1D) + O(^1D) |  |

Figure 27: Abundance profiles of the main species for all exoplanet cases in
Table 3, computed with V20. Dashed lines are abundances based on old
photolysis data and solid lines are based on updated photolysis data.

##  Appendix C Statistical distributions of network errors

Figure 28: Statistical distribution of the relative error over the 155
experimental data points corresponding to combustion conditions of Fig. 3.
Each color gathers all molar fraction measurements of the corresponding
species.

Figure 29: Statistical distribution of the relative error over the 294
experimental data points corresponding to pyrolysis conditions of Fig. 3. Each
color gathers all molar fraction measurements of the corresponding species.

Figure 30: Statistical distribution of the relative error over the 571
experimental data points corresponding to combustion conditions of Fig. 4.
Each color gathers all molar fraction measurements of the corresponding
species.

Figure 31: Statistical distribution of the relative error over the 252
experimental data points corresponding to pyrolysis conditions of Fig. 4. Each
color gathers all molar fraction measurements of the corresponding species.

##  Appendix D Thermochemical data

Figure 32: Thermodynamic equilibrium abundance profiles of the main species
for all exoplanets cases in Table 3. Dashed lines are computed with the V20
thermodynamic data and solid lines are based on V23 thermodynamic data. In the
last panel, all these lines overlap perfectly, indicating no difference for
this PT profile.

##  Appendix E Transmission spectrum contributions

Figure 33: Contribution to the synthetic transmission spectra for all
considered absorbing species. Dashed lines are computed from abundances
obtained with V20, and solid lines from abundances obtained with V23. The
plotted quantity is the relative difference to the total transit depth,
$\frac{D_{tot}-D_{spec}}{D_{tot}}$, with $D_{tot}$ being the total transit
depth and $D_{spec}$ being the contribution of the species to the total
transit depth. For each wavelength, only the uppermost and close lines have
significant impact on the spectrum.