Is There Any Linkage between Interstellar Aldehyde and Alcohol?

More than 200 molecular species have been identified in the interstellar medium (ISM) and circumstellar shells (McGuire 2018, https://cdms.astro.uni-koeln.de/classic/molecules), which resolved the unexplained mystery of the molecular universe. Interstellar grains accelerate the formation of complex organic molecules (COMs; molecules with >six atoms) in space. During the warm-up stage of a star-forming core, radicals or simple molecules on the grain surface become mobile and could produce various COMs (Chakrabarti et al. 2006a, 2006b; Garrod & Herbst 2006; Das et al. 2008a, 2010, 2016; Garrod et al. 2008; Das & Chakrabarti 2011; Sil et al. 2018). These ice-phase species may transfer to the gas phase by various desorption mechanisms.

Alcohols and aldehydes were identified in various parts of the ISM. Methanal (formaldehyde, H₂CO) is the simplest form of aldehyde, which was first observed in space by Snyder et al. (1969). It is an intermediary of the large COMs, which can help constrain physical conditions in the star-forming regions (Persson et al. 2018). Methanol (methyl alcohol, CH₃OH) is the simplest alcohol and one of the most abundant interstellar molecules. It was widely observed toward various sources (Allamandola et al. 1992; Pontoppidan et al. 2003) and is used as a reliable tracer of high-density environments (Menten et al. 1988). A large number of studies reported the chemical origin of methanol starting from formaldehyde (Watanabe & Kouchi 2002; Fuchs et al. 2009; Goumans 2011; Song & Kästner 2017). Methanol is ubiquitous in star-forming regions (Weaver et al. 2018) and mainly formed on the interstellar dust via successive hydrogen addition reactions with carbon monoxide (Fedoseev et al. 2015; Chuang et al. 2016; Butscher et al. 2017).

Woon (2002a) carried out a theoretical study of the formation of H₂CO and CH₃OH. Quantum chemical calculations were carried out to determine the activation barrier for the hydrogen addition reactions (H + CO and H + H₂CO) in gas, with water clusters ${({{\rm{H}}}_{2}{\rm{O}})}_{n}$ (n ≤ 3) and in ice phases. He included explicit water molecules to show the catalytic effect on the activation barrier by surrounding water molecules. Das et al. (2008b) and Rimola et al. (2014) studied methanol formation by successive hydrogen addition with CO in water ice. They compared their simulated abundance with observations. Recently, Song & Kästner (2017) derived the tunneling rate constant of the H₂CO + H reaction on amorphous solid water (ASW) for various product channels: CH₂OH, CH₃O, and H₂ + HCO. They also provided the activation barrier of these three product channels and rate coefficients.

Identification of ethanal (acetaldehyde, CH₃CHO) was first reported by Gottlieb & Snyder (1973) toward Sgr B2 and Sgr A. They reported an emission at 1065 MHz, which was assigned to the 1₁₀ → 1₁₁ transition of CH₃CHO. This assignment was based on the agreement in radial velocity with other molecules, which were observed in the galactic center region. Then Fourikis et al. (1974) observed it toward Sgr B2 with the Parkes 64 m telescope. Its successor, ethanol (ethyl alcohol, C₂H₅OH), is among the earliest complex molecules and was identified toward Sgr B2 (Zuckerman et al. 1975).

Propanal (propionaldehyde, CH₃CH₂CHO) is identified in Sgr B2(N) (Hollis et al. 2004), where it coexists with propynal (HC₂CHO) and propenal (CH₂CHCHO). The detection of these three aldehydes toward the same source suggests that successive hydrogen additions might be an efficient process to establish the chemical linkage between propynal, propenal, and propanal (Hollis et al. 2004). Propenal (also known as acrolein) is the simplest unsaturated aldehyde. It is the second most stable isomer of the C₃H₄O isomeric group (Etim et al. 2018). Allyl alcohol (CH₂CHCH₂OH) is an isomer of propanal and the corresponding alcohol of propenal (Zaverkin et al. 2018). Acetaldehyde is supposed to be a potential ancestor of carbohydrates and propenal (also known as acrolein; Pizzarello & Weber 2004; Córdova et al. 2005).

The corresponding alcohol of propanal is 1-propanol (CH₃CH₂CH₂OH), which is yet to be observed in space. However, trans-ethyl methyl ether (CH₃CH₂OCH₃), which is one of the isomers of the same isomeric group, was tentatively detected toward Orion KL (Tercero et al. 2015). Zaverkin et al. (2018) reported that the propanal to 1-propanol production by the hydrogenation reaction might be inefficient due to the efficient hydrogen abstraction reaction.

The simplest form of sugar, glycolaldehyde (HOCH₂CHO), plays an essential role in forming amino acids and complex sugars (Collins et al. 1995; Weber 1998). Glycolaldehyde was also detected outside of the Galactic center toward the hot core G31.41+0.31 (hereafter G31; Beltrán et al. 2009). Ethylene glycol (HOCH₂CH₂OH) is the corresponding alcohol of glycolaldehyde. It was detected in a wide variety of sources, such as the Galactic center (Hollis et al. 2002; Belloche et al. 2013), NGC 1333–IRAS 2A (Maury et al. 2014), and the Orion bar (Brouillet et al. 2015), as well as comets C/1995 O1 (Hale–Bopp) (Crovisier et al. 2004), C/2012 F6 (Lemmon), and C/2013 R1 (Lovejoy) (Biver et al. 2014). It was also identified in the Murchinson and Murray meteorites (Cooper et al. 2001). The abundance ratio of glycolaldehyde and ethylene glycol in star-forming regions and comets can help in understanding the possible preservation of COMs (Coutens et al. 2018). Methyl formate (CH₃OCHO) and acetic acid (CH₃COOH) are two isomers of glycolaldehyde. The linkage between methyl formate, acetic acid, and glycolaldehyde can constrain the understanding of the chemical and physical conditions of the star-forming core (Woods et al. 2013). Acetone (CH₃COCH₃) is one of the most important molecules in organic chemistry, and it was the first species containing 10 atoms identified in the ISM (Combes et al. 1987). Acetone was also found to be present in comet 67P/Churyumov–Gerasimenko (Altwegg et al. 2017). Ethenone (ketene, CH₂CO) was identified for the first time toward Sgr B2(OH) by Turner (1977) through microwave measurements. Recently, Turner et al. (2020) performed an experiment combined with high-level quantum chemical calculations and provided compelling evidence of ketene formation in the processed mixture of water and carbon monoxide ices explaining the observed ketene detection in deep space. The simplest enol compound, vinyl alcohol (ethenol, CH₂CHOH), was identified in an interstellar cloud of dust and gas near the center of the Milky Way toward Sgr B2(N) utilizing its millimeter-wave rotational transitions (Turner & Apponi 2001). In the dark cloud, the nonenergetic process plays an active role in processing the ice constituents. In contrast, in the latter stages of star and planet formation, various energetic procedures play a crucial role (Fedoseev et al. 2017). Laboratory experiments of methanol dissociation studied through various energetic processes, such as photons, electrons, vacuum ultraviolet photons, X-rays, etc., LM could lead to the synthesis of various COMs. Öberg et al. (2009) found that the ratio between ethylene glycol to ethanol and methyl formate to ethanol varies with the ice temperature and irradiation.

In this work, Atacama Large Millimeter/submillimeter Array (ALMA) archival data of a hot molecular core, G10.47+0.03 (hereafter G10), which is located at a distance of 8.6 kpc, is analyzed (Sanna et al. 2014). This is a young cluster (Cesaroni et al. 1998; Rolffs et al. 2011) whose luminosity is ∼5 × 10⁵ L_⊙ (Cesaroni et al. 2010). Many simple species, complex organics, and molecules with a higher upper energy state (such as HC₃N and HCN) were observed in this source, suggesting an active site for chemical enrichment (Wyrowski et al. 1999; Ikeda et al. 2001; Gibb et al. 2004; Rolffs et al. 2011; Gorai et al. 2020).

Recently, Qasim et al. (2019) showed that the aldehyde and primary alcohol may be linked via successive hydrogen addition reactions. A similar connection is also observed between ketones and secondary alcohols; i.e., isopropanol (CH₃CHOHCH₃) can be synthesized via two successive hydrogen additions with acetone (Hiraoka et al. 1998). Here several aldehydes and alcohols are identified. Obtained column densities, rotation temperatures, and spatial distributions of these molecules are discussed to understand the morphological correlation between these aldehydes and alcohols. The astrochemical model is implemented to compare with the observational finding. This work demonstrates that consecutive hydrogen addition could contribute to forming some complex interstellar molecules. Here the kinetics of the consecutive hydrogen addition reactions of alcohol production from aldehyde, ketone, and ketene are examined. Binding energies (BEs) of a species with a grain substrate are essential for constructing a chemical model. Most COMs are primarily produced on the dust surface and further desorbed back to the gas phase (Requena-Torres et al. 2006). Most of the time, the BE of these species is estimated, which may induce uncertainties in the results. Our estimated BE values with the water-ice surface are included in our chemical model to see their impact. The proton affinity (PA) is not directly included in our model. But it is computed to check the reactivity of these species with H⁺. The role of the PA of various species and their intermediate steps is also discussed to synthesize new COMs. Some of our obtained kinetic parameters are directly included in our chemical model to compare between the observation and modeling yields.

The remainder of this paper is organized as follows. In Section 2, the observational results are presented. The computational details adopted to explain the chemical linkage between aldehyde and alcohol are given in Section 3. Astrochemical modeling and its implication are discussed in Section 4, and finally, in Section 5, we conclude.

2.1. Observations and Data Analysis

2.2. Synthesized Images

2.3. Line Analysis

The observed spectra obtained within the circular region having a diameter of 20 (∼17,200 au) centered at R.A. (J2000) = (18^h08^m38232), decl. (J2000) = (−19°51'504) are continuum-subtracted for the analysis. The line width (ΔV), local standard of rest velocity (V_LSR), and integrated intensity (∫T_mb dV) of each transition that is obtained after Gaussian fitting to the observed transition is noted. All of the line parameters corresponding to an observed transition, such as associated quantum numbers ${J}_{{K}_{a}^{{\prime} }{K}_{c}^{{\prime} }}^{{\prime} }$–${J}_{{K}_{a}^{{\prime\prime} }{K}_{c}^{{\prime\prime} }}^{{\prime\prime} }$, rest frequency (ν₀), ΔV, ∫T_mb dV, upper-state energy (E_u ), and V_LSR, are presented in Table 1. Multiple transitions of methanol (CH₃OH), acetaldehyde (CH₃CHO), ethylene glycol [(CH₂OH)₂], glycolaldehyde (HOCH₂CHO), propanal (CH₃CH₂CHO), acetone (CH₃COCH₃), methyl formate (CH₃COH), and dimethyl ether (CH₃OCH₃) are identified. This is the first time ethylene glycol and propanal are identified in G10. Only two lines of glycolaldehyde are identified. Therefore, it is considered a tentative detection in this source. The observed spectra of these species and the fitted spectra are shown in Appendix B (see Figure B1). The integrated intensity for the methanol, ethylene glycol, and glycolaldehyde is obtained by simple Gaussian fitting to each transition. In the case of acetaldehyde, propanal, acetone, methyl formate, and dimethyl ether, torsional substates due to the internal rotation of the methyl group are noticed. For acetone and dimethyl ether, these substates are AA, EE, EA, and AE. In the case of acetaldehyde, propanal, and methyl formate, A and E substates are noticed. But these transitions are found to be blended with different substates. These transitions were not resolved due to the low spectral resolution of the present spectra. The integrated intensity is obtained with the Gaussian fitting and then divided according to the S μ² values (obtained from https://www.cv.nrao.edu/php/splat). The transition with maximum intensity between the torsional substates was considered in the rotation diagram analysis to calculate the rotation temperature and column density.

Table 1. Summary of the Line Parameters of the Observed Molecules toward G10

Species	${J}_{{K}_{a}^{{\prime} }{K}_{c}^{{\prime} }}^{{\prime} }$–${J}_{{K}_{a}^{{\prime\prime} }{K}_{c}^{{\prime\prime} }}^{{\prime\prime} }$	Frequency	Eu	ΔV	Imax	VLSR	S μ2	∫Tmb dV
		(GHz)	(K)	(km s−1)	(K)	(km s−1)	(D2)	(K km s−1)
Methanol	21−0,21 − 211,21E, vt = 0	129.720384	546.2	9.6 ± 0.2	30.1 ± 0.6	66.9 ± 0.1	19.8	308.7 ± 14.5
(CH3OH)	17−4,13 − 18−3,16E, vt = 0	131.134094	450.9	11.1 ± 0.2	45.7 ± 0.5	66.6 ± 0.5	21.8	540.0 ± 17.7
	7−1,7 − 60,6E, vt = 1	147.943673	356.3	11.1 ± 0.8	32.1 ± 1.9	66.2 ± 0.4	14.4	378.5 ± 52.8
	15−0,15 − 151,15E, vt = 0	148.111993	290.7	10.5 ± 0.6	44.7 ± 1.5	66.5 ± 0.2	29.02	500.0 ± 47.2
	8−7,1 − 7−6,1E, vt = 1	153.128697	664.5	10.4 ± 0.2	9.15 ± 0.1	66.8 ± 0.1	15.9	101.1 ± 3.1
	12−0,12 − 151,12E, vt = 0	153.281282*	193.8	11.37 ± 0.2	40.28 ± 0.6	66.3 ± 0.1	30.7	487.4 ± 14.5
	11−0,11 − 111,11 E, vt = 0	154.425832*	166.1	10.6 ± 0.3	36.02 ± 0.7	66.64 ± 0.1	30.5	408.0 ± 18.7
Ethylene glycol	3310,23 − 3211,22, 1-1	130.115657	323	7.7 ± 0.07	2.55 ± 0.01	67.8 ± 0.03	53	20.9 ± 0.3
(CH2OH)2	139,4 − 129,3, 0-1	130.998583	83.7	7.9 ± 0.5	5.6 ± 0.24	68.2 ± 0.178	98.4	48.1 ± 5.02
	287,21 − 286,23, 1-0	131.229156	223.4	7.8 ± 0.1	4.8 ± 0.02	68.8 ± 0.04	158.3	39.8 ± 0.7
	273,25 − 272,26, 0-0	148.082465	185.5	7.2 ± 0.13	4.8 ± 0.15	69.3 ± 0.12	87.3	36.89 ± 1.8
	398,32 − 397,33, 1-1	153.325448	414	10.5 ± 0.5	3.9 ± 0.1	69.4 ± 0.15	385.1	44.14 ± 3.13
	84,5 − 73,5, 1-0	153.567383	25.4	8.9 ± 0.27	13.8 ± 0.33	66.9 ± 0.11	60.4	131.45 ± 7.08
	133,11 − 122,10, 0-0	159.768239	48.8	8.73 ± 0.25	9.32 ± 0.15	68.4 ± 0.08	62.8	86.65 ± 3.87
Acetaldehyde	85,4 − 75,3,A	154.161467	89.8	7.3 ± 0.7	5.0 ± 0.4	67.6 ± 0.3	61.7	38.8 ± 0.10
(CH3CHO)	83,5 − 73,4,A	154.173895	114.4	6.6 ± 0.2	1.3 ± 0.1	67.8 ± 0.1	44.3	9.2 ± 0.53
	84,4 − 74,3,A	154.201471	69.5	8.8 ± 0.5	5.7 ± 0.2	67.0 ± 0.2	75.9	52.9 ± 0.08
	83,6 − 73,5,A	154.274686	53.7	8.4 ± 0.2	5.3 ± 0.1	67.1 ± 0.7	87.0	47.5 ± 0.01
	83,5 − 73,4,E	154.296489	53.7	8.3 ± 0.2	4.5 ± 0.1	66.8 ± 0.1	87.0	39.6 ± 0.03
Propanal	107,4 − 106,5, A	147.867697	54.6	12.1 ± 1.2	12.3 ± 0.8	68.9 ± 0.4	12.0	158.4 ± 6.5
(CH3CH2CHO)	97,3 − 96,4, A	148.005214	49.6	10.1 ± 0.3	11.5 ± 0.3	67.6 ± 0.1	9.3	123.8 ± 1.5
	253,22 − 252,23, A	153.554511	172.7	9.0 ± 0.3	13.2 ± 0.3	68.9 ± 0.1	31.2	126.2 ± 3.5
	275,23 − 274,24, A	154.067527	206.2	8.5 ± 0.3	13.5 ± 0.3	69.8 ± 0.1	41.6	123.4 ± 3.3
	162,15 − 151,14,A	159.932413	68.6	9.9 ± 0.2	18.7 ± 0.2	68.3 ± 0.1	38.1	198.2 ± 3.2
Glycolaldehyde	150,15 − 14114	153.597996	60.5	8.1 ± 0.3	3.9 ± 0.1	66.9 ± 0.1	72.9	11.7 ± 2.3
(HOCH2CHO)	207,13 − 206,14	153.614231	147.0	7.1 ± 0.6	2.1 ± 0.2	66.4 ± 0.2	60.4	11.5 ± 2.5
Acetone	2510,15 − 259,16, AE	130.708968	241	8.2 ± 0.3	4.43 ± 0.1	65.8 ± 0.1	536.9	38.8 ± 2.1
(CH3COCH3)	122,11 − 111,10, AA	130.924799	44.1	10.6 ± 0.2	7.7 ± 0.1	66.3 ± 0.1	876.4	86.6 ± 2.9
	115,7 − 104,6, EE	147.684364	48.4	9.6 ± 0.2	6.13 ± 0.1	67.5 ± 0.1	783.8	62.7 ± 2.5
	2610,17 − 269,18, EE	149.190766	251	10.9 ± 0.7	7.4 ± 0.3	65.5 ± 0.2	1309.1	86.1 ± 8.2
	132,11 − 123,10, AE	149.395864	55.8	10.8 ± 1.1	8.4 ± 0.5	68.3 ± 0.4	517.6	96.7 ± 15.7
	142,12 − 133,11, EE	159.247634	63	8.7 ± 0.2	14.8 ± 0.2	67.5 ± 0.1	1516.9	137.3 ± 5.5
	246,18 − 245,19, EE	159.415955	198	8.8 ± 0.2	9.6 ± 0.1	68.4 ± 0.1	902.1	89.16 ± 2.6
	224,18 − 223,19, EE	160.118153	157.1	9.3 ± 0.2	4.3 ± 0.1	65.5 ± 0.1	607.6	42.4 ± 2.0
Methyl formate	112,10 − 102,9, A	130.016790	40.7	9.5 ± 0.20	17.55 ± 0.3	66.1 ± 0.07	28.04	178.3 ± 6.44
(CH3OCHO)	112,10 − 102,9, E	130.010105	40.7	9.4 ± 0.17	17.14 ± 0.12	66.4 ± 0.04	28.04	171.7 ± 4.46
	121,12 − 111,11, A	131.377495	230	9.38 ± 0.2	6.28 ± 0.05	66.7 ± 0.05	31.39	62.7 ± 1.83
	124,8 − 114,7, E	148.575217	244.1	8.65 ± 0.9	8.13 ± 0.73	66.9 ± 0.4	28.3	74.87 ± 14.48
	124,9 − 114,8, A	148.805941	56.8	8.52 ± 0.44	18.13 ± 0.7	66.3 ± 0.2	28.4	164.50 ± 14.98
	133,11 − 123,10, A	158.704392	59.6	9.4 ± 0.2	24.29 ± 0.4	66.2 ± 0.1	32.57	243.05 ± 9.17
	1310,4 − 1210,3, A	159.662793	120.04	8.71 ± 0.3	18.78 ± 0.3	66.64 ± 0.07	14.1	174.12 ± 4.38
	137,6 − 127,5, A	160.178942	86.2	9.03 ± 0.25	16.05 ± 0.22	66.4 ± 0.08	24.5	154.27 ± 6.30
	137,6 − 127,5, A	160.193496	86.2	9.5 ± 0.15	35.15 ± 0.32	66.48 ± 0.04	24.5	358.00 ± 3.00
Dimethyl ether	61,6 − 50,5, EE	131.405796	19.9	11.8 ± 0.5	28.7 ± 0.5	66.1 ± 0.2	82.4	359.9 ± 20.6
(CH3OCH3)	90,9 − 81,8, EE	153.054818	40.4	9.4 ± 0.2	26.5 ± 0.3	66.8 ± 0.1	126.4	264.02 ± 6.5
	244,20 − 243,21, EE	153.385902	297.5	12.4 ± 0.4	11.5 ± 0.1	67.2 ± 0.1	379.5	151.6 ± 3.6
	111,10 − 102,9, EE	154.455083	62.9	10.6 ± 0.2	11.6 ± 0.1	66.1 ± 0.1	68.8	130.3 ± 3.7

Note. Optically thick.

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2.4. Spatial Distribution of Aldehydes and Alcohols

2.5. Rotation Diagram Analysis

Here a rotation diagram analysis is performed by assuming the observed transitions are optically thin and in LTE. For optically thin lines, the column density can be expressed as (Goldsmith & Langer 1999)

$$\begin{eqnarray}&&\displaystyle \frac{{N}_{u}^{\mathrm{thin}}}{{g}_{u}}=\displaystyle \frac{3{k}_{{\rm{B}}}\int {T}_{\mathrm{mb}}{dV}}{8{\pi }^{3}\nu S{\mu }^{2}},\end{eqnarray} \tag{ 1 }$$

where g_u is the degeneracy of the upper state, k_B is the Boltzmann constant, ∫ T_mb dV is the integrated intensity, ν is the rest frequency, μ is the electric dipole moment, and S is the line strength. Under LTE conditions, the total column density would be written as

$$\begin{eqnarray}&&\displaystyle \frac{{N}_{u}^{\mathrm{thin}}}{{g}_{u}}=\displaystyle \frac{{N}_{\mathrm{total}}}{Q({T}_{\mathrm{rot}})}{\exp }^{-{E}_{u}/{k}_{{\rm{B}}}{T}_{\mathrm{rot}}},\end{eqnarray} \tag{ 2 }$$

where T_rot is the rotational temperature, E_u is the upper-state energy, and Q(T_rot) is the partition function at the rotational temperature. The above two equations can be rearranged as

$$\begin{eqnarray}&&\mathrm{ln}\left(\displaystyle \frac{{N}_{u}^{\mathrm{thin}}}{{g}_{u}}\right)=-\left(\displaystyle \frac{1}{{T}_{\mathrm{rot}}}\right)\left(\displaystyle \frac{{E}_{u}}{{k}_{{\rm{B}}}}\right)+\mathrm{ln}\left(\displaystyle \frac{{N}_{\mathrm{total}}}{Q({T}_{\mathrm{rot}})}\right).\end{eqnarray} \tag{ 3 }$$

The rotational temperature and total column density of a species are simultaneously determined from this expression. All of the spectroscopic parameters for this analysis are taken from either the CDMS (https://cdms.astro.uni-koeln.de; Müller et al. 2001, 2005; Endres et al. 2016) or the JPL (http://spec.jpl.nasa.gov; Pickett et al. 1998) database. Rotation diagrams of methanol (CH₃OH), acetaldehyde (CH₃CHO), ethylene glycol [(CH₂OH)₂], glycolaldehyde (HOCH₂CHO), propanal (CH₃CH₂CHO), acetone (CH₃COCH₃), methyl formate (CH₃OCHO), and dimethyl ether (CH₃OCH₃) are shown in Figure 1. The derived rotational temperatures and column densities are summarized in Table 2. In Table 2, we have noted the errors. The relative percentage errors of the rotational temperature and column density are noted in the footnote of the table. The rotation diagram of glycolaldehyde is constructed with only two data points. Therefore, the rotational temperature has been assumed to some constant value. Methanol transitions with a wide range (166–664 K) of upper-state energies are observed. Some of these transitions are optically thick (i.e., 153.281 and 154.425 GHz). Therefore, it is challenging to construct the rotation diagram with all seven unblended methanol lines (noted in Table 1). Since two transitions are optically thick, these are excluded from the rotation diagram analysis. Furthermore, by considering the rest of the five transitions, considerable uncertainty in the estimated column density ((3.7 ± 3) × 10¹⁸ cm⁻²) and rotational temperature (345 ± 198 K) is obtained. Comparatively, a better linear fit is possible if we exclude the 148.111 GHz transitions from the rotation diagram (by choosing the transitions with upper-state energy >300 K). It yields a column density and rotational temperature of methanol of (4.3 ± 3.4) × 10¹⁸ cm⁻² and 206 ± 66 K, respectively.

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Table 2. Estimated Rotation Temperature and Column Density of the Observed Molecules in G10

Molecule	Trot (K)	Ntot (cm−2) a	Literature Ntot (cm−2)
CH3OH	206 ± 66	(4.3 ± 3.4) × 1018	9.0 × 1018 b
(CH2OH)2	185 ± 61	(5.0 ± 2.1) × 1017
CH3CHO	72 ± 11	(6.8 ± 1.1) × 1015	(2.1 ± 0.7) × 1014 c
CH3CH2CHO	234 ± 42	(2.4 ± 0.23) × 1017
HOCH2CHO	155	1.3 × 1016
CH3COCH3	224 ± 41	(5.4 ± 0.7) × 1017	5.0 × 1017 b
CH3OCHO	191 ± 24	(9.1 ± 0.7) × 1017	7.0 × 1017 b
CH3OCH3	148 ± 42	(1.5 ± 0.4) × 1017	1.5 × 1018 b

Notes. In the last column, the obtained column densities are compared with other observations of G10. Estimated values have 12%−33% and 8%−79% uncertainty for temperature and column density, respectively.

^a This work. ^b Rolffs et al. (2011); θ_b = 028–427, θ_s = 15, T_ex = 200 K. ^c Ikeda et al. (2001); θ_b = 16''–26'', θ_s = 20'', T_rot = 30 ± 4 K.

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For the sample of considered molecules, our estimated rotation temperature varies from 72 to 234 K. A low rotation temperature of ∼72 ± 11 K for acetaldehyde is obtained, which is in agreement with the rotation temperature of acetaldehyde obtained by Ikeda et al. (2001). Olmi et al. (1996) derived a temperature of 160 K for CH₃CN. Rolffs et al. (2011) considered a temperature of 200 K to give an optimum fit to the data for line identification purposes. The average rotational temperature obtained from the species observed here is 177 K, which is in close agreement with Olmi et al. (1996) and Rolffs et al. (2011). There could be a possibility of beam dilution on the derived column densities and excitation temperatures. If the beam dilution effect is included in the rotation diagram, it is noticed that, on average, our column densities and temperatures increase by a factor of 3.2 and 1.4, respectively. Derived column density and rotational temperature are further used in the LTE module of CASSIS to produce synthetic LTE spectra and compare with our observations (see Figure B1). The average FWHM of each species from Table 1 is used for this computation. A source size of 2'' with the ALMA telescope is considered. Figure B1 shows a good agreement with the observed feature, except for the three transitions of methanol and all of the transitions of dimethyl ether. The intensity of the three methanol transitions is overproduced. Among them, the 153.281 and 154.425 GHz transitions are optically thick. All of the transitions of dimethyl ether are underproduced. During the line analysis, it is noticed that these transitions consist of multiple substates. The strongest transition is only considered based on their S μ² value. Thus, the obtained FWHMs are possibly overestimated during the Gaussian fitting, which can underproduce the intensity of these transitions in Figure B1.

Here an extensive theoretical investigation is carried out to understand the formation mechanism of some aldehydes, alcohols, ketene, and ketone, which are observed in G10. All the quantum chemical results presented in this paper are carried out using the Gaussian 09 suite of programs (Frisch et al. 2013). The density functional theory (DFT) method with a B3LYP functional (Becke 1993) and 6-31+G(d,p) basis set is used in computing the activation barrier and enthalpy of the reactions. Both the activation and reaction energies are calculated including the harmonic zero-point vibrational energy (ZPVE) and considering a temperature of ∼298.15 K and ∼1 atmosphere pressure. A fully optimized ground-state structure is verified to be a stationary point (having a nonimaginary frequency) by harmonic vibrational frequency analysis. For the transition state (TS) calculation, the QST2 method of the Gaussian 09 program is used. All of the TSs are verified using the internal reaction coordinate analysis, which connects reactants and products through the minimum energy path of the potential energy surface (PES). Further, the higher-order method is employed to examine the effect on the activation barrier calculation. The single-point energy calculation of the TS structure and the optimized structure of the reactants at the CCSD(T)/aug-cc-pVTZ level is carried out. The ice mixture is considered embedded within a continuum solvation field to include the passive impact of bulk ice on the activation barrier and enthalpy of reaction. To mimic the ice-phase feature, water is used as the solvent. A self-consistent reaction field (SCRF) is routinely applied in the electronic structure calculations to study the spectroscopy and thermochemistry parameters of any species in the ice phase. Explicit water molecules can also generate a more realistic (inhomogeneous) field. Since the typical size of the interstellar water cluster (number of water molecules required) is not known with certainty, it is preferred to use the SCRF method (Woon 2002b). The polarizable continuum model (PCM) with different integral equation formalism (IEFPCM) variants as a default SCRF method is used for this purpose (Cancès et al. 1997; Tomasi et al. 2005). A similar level of theory and basis set is used for the gas-phase calculations. This method creates a solute cavity by overlapping spheres (Pascual-Ahuir et al. 1987; Tomasi & Persico 1994). The implicit solvation model places the molecule of interest inside a cavity in a continuous homogeneous dielectric medium (in this case, water) that represents the solvent. This method is proven to produce convincing results for ices. The PCM is used to consider the reaction field of bulk ice. Here the cluster is treated explicitly to avoid spurious boundary effects. In this framework, the reaction fields issuing from a dielectric constant of 78 for water or about 100 for ice (Aragones et al. 2011) are essentially identical. Hence, the solid-phase calculations and the thermochemistry parameters reported here are close to those in ice. The BE and PA of the aldehydes, ketone, ketene, and corresponding alcohols are also calculated quantum chemically discussed in Section 3.2.

3.1. Rate Constants

A crossover temperature, T_c , is defined as the maximum temperature below which a tunneling reaction dominates and above which a thermal activation reaction takes over. The T_c is calculated by the following relation:

$$\begin{eqnarray}&&{T}_{c}=\displaystyle \frac{h\omega }{4{\pi }^{2}{k}_{{\rm{B}}}},\end{eqnarray} \tag{ 4 }$$

where h is Planck's constant, k_B is the Boltzmann constant, and ω is the absolute value of the imaginary frequency at the TS. For all gas-phase reactions, the TS theory (TST) is applied. The gas-phase rate constant can be calculated by the following expression:

$$\begin{eqnarray}&&K=\left(\displaystyle \frac{{k}_{{\rm{B}}}T}{{{hc}}^{0}}\right)\exp (-{\rm{\Delta }}{G}^{\unicode{x02021}}/{RT}),\end{eqnarray} \tag{ 5 }$$

where ΔG^‡ is the Gibbs free energy of activation, c⁰ is the concentration (∼1), R is the ideal gas constant, and T is the temperature.

High-level quantum chemical calculations of six aldehyde–alcohol pairs with one ketone–alcohol and one ketene–alcohol pair are presented and discussed in detail. Aldehyde-to-alcohol, ketone-to-alcohol, and ketene-to-alcohol formation may occur through successive hydrogen addition reactions in two steps. The first step is aldehyde/ketone/ketene + H → radical-1, and the second step is radical-1 + H → alcohol. The first step of all these reactions is a neutral–radical reaction, whereas the second step is a radical–radical reaction. The first step of all these reactions thus has an activation barrier, whereas the second step is barrierless, as both reactants have free electrons, which makes them very reactive. Our calculations show that six aldehydes (methanal, ethanal, propanal, propenal, propynal, and glycolaldehyde), one ketone (acetone), and one ketene (ethenone) are forming alcohols (methanol, ethanol, 1-propanol, allyl alcohol, propargyl alcohol, ethylene glycol, isopropanol, and vinyl alcohol, respectively) via two successive hydrogen addition reactions. In terms of stability, the observed alcohols are thermodynamically favorable compared to their isomers. The formation of complex molecules on the grain surface through successive hydrogen additions is found to be dominant. The formation pathways of these molecules are discussed below, and a graphical representation of the PESs of their formation mechanism considering the IEFPCM and DFT-B3LYP/6-31G+(d,p) level of theory is shown in Figure 2.

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3.1.1. Methanal and Methanol

Here the activation barrier of the reaction H+H₂CO is computed for the production of CH₂OH or CH₃O (reactions 1a and 2a of Table 3). It is noticed that the hydrogen addition to the carbon atom of H₂CO (i.e., the formation of CH₃O, methoxy radical) is more favorable than that of its addition to the O atom of H₂CO (i.e., the formation of CH₂OH) in both the gas and ice phases considering both the DFT-B3LYP/6-31+G(d,p) and CCSD(T)/aug-cc-pVTZ levels of theory. This is consistent with earlier experimental and theoretical results (Chuang et al. 2016; Song & Kästner 2017). Song & Kästner (2017) studied the TS of reactions 1a and 2a both in the gas phase and on five different binding sites of the ASW surface and calculated the corresponding activation energies. We compare our activation energy values with their gas and ice phases for binding site ASW 5 in Table 3, which shows a good agreement when we consider a CCSD(T)/aug-cc-pVTZ level of theory. Woon (2002a) predicted the barrier height of reaction 2a as 5.04 kcal mol⁻¹ for the gas phase, and it decreases to 4.81 kcal mol⁻¹ when the reaction is embedded with the isodensity surface polarized continuum model (IPCM) field. In our case, we also found a similar decreasing trend from the gas phase to the ice phase considering both levels of theory. Song & Kästner (2017) also computed the BE of H₂CO for different sites on the ASW surface and found that the BE values vary between 1000 and 9370 K. This uncertainty in the BE value may lead to a very different abundance of CH₃OH under different astrophysical environments. The hydrogen abstraction reaction of methanol also plays an essential role in controlling the abundance of COMs. The methanol abstraction reaction barrier and the rate constant are taken from Song & Kästner (2017).

Table 3. Calculated Gibbs Free Energy of the Activation, Activation Barrier, and Reaction Enthalpy of Alcohol Formation via Successive Hydrogen Addition with Aldehydes, Ketone, and Ketene by Using Lower [B3LYP/6-31+G(d,p)] and Higher [CCSD(T)/aug-cc-pVTZ] Levels of Theory Considering a 298.15 K Temperature and 1 Atmospheric Pressure

Serial No.	Reaction Type	Reactions	Gibbs Free Energy of Activation		Activation Barrier		Enthalpy of Reaction
			(kcal mol−1)		(kcal mol−1)		(kcal mol−1)
			Gas Phase	Ice Phase	Gas Phase	Ice Phase	Gas Phase	Ice Phase
Methanal–Methanol
1a	NR	H2CO + H → CH2OH	10.57	11.32	5.59 (9.35) a [10.35] (i)	6.49 (10.02) a [11.16] (i)	−32.31	−32.39
1b	RR	CH2OH + H → CH3OH	⋯	⋯	0.00	0.00	−94.75	−94.93
2a	NR	H2CO + H → CH3O	7.10	6.97	1.89 (3.35) a [4.29] (i)	1.76 (3.10) a [3.26] (i)	−27.61	−27.07
2b	RR	CH3O+H → CH3OH	⋯	⋯	0.00	0.00	−99.44	−100.24
Ethanal–Ethanol
3a	NR	CH3CHO + H → CH3CHOH	11.41	12.44	5.91 (9.38) a	6.91 (10.24) a	−27.96	−26.80
3b	RR	CH3CHOH + H → CH3CH2OH	⋯	⋯	0.00	0.00	−92.61	−93.21
4a	NR	CH3CHO + H → CH3CH2O	10.10	10.24	4.11 (5.36) a	4.25 (5.38) a	−21.50	−20.27
4b	RR	CH3CH2O+H → CH3CH2OH	⋯	⋯	0.00	0.00	−99.07	−99.74
Propanal-1–Propanol
5a	NR	CH3CH2CHO + H → CH3CH2CHOH	11.12	12.08	5.47 (9.27) a [9.82] (ii)	6.41 (9.87) a	−28.10 [−26.31] (ii)	−18.42
5b	RR	CH3CH2CHOH + H → CH3CH2CH2OH	⋯	⋯	0.00	0.00	−92.82	−101.80
6a	NR	CH3CH2CHO + H → CH3CH2CH2O	10.08	10.27	3.99 (4.97) a [5.71] (ii)	4.19 (4.96) a	−22.00 [−19.74] (ii)	−20.76
6b	RR	CH3CH2CH2O+H → CH3CH2CH2OH	⋯	⋯	0.00	0.00	−98.91	−99.46
Propenal–Allyl Alcohol
7a	NR	CH2CHCHO + H → CH2CHCHOH	9.56	10.48	4.00(6.30) a [7.89] (ii)	2.36 (10.47) a	−40.29 [−38.93] (ii)	−39.59
7b	RR	CH2CHCHOH + H → CH2CHCH2OH	⋯	⋯	0.00	0.00	−76.72	−77.07
8a	NR	CH2CHCHO + H → CH2CHCH2O	10.32	10.24	4.43 (5.79) a [6.43] (ii)	4.38 (6.60) a	−18.00 [−16.16] (ii)	−16.72
8b	RR	CH2CHCH2O+H → CH2CHCH2OH	⋯	⋯	0.00	0.00	−99.00	−99.95
Propynal–Propargyl Alcohol
9a	NR	HC2CHO + H → HC2CHOH	8.86	9.65	3.41 [7.41] (ii)	4.20 (8.28) a	−43.92 [−42.42] (ii)	−43.22
9b	RR	HC2CHOH + H → HC2CH2OH	⋯	⋯	0.00	0.00	−79.53	−80.44
10a	NR	HC2CHO + H → HC2CH2O	9.63	9.47	3.74 [5.78] (ii)	3.59 (5.68) a	−22.42 [−21.00] (ii)	−22.20
10b	RR	HC2CH2O+H → HC2CH2OH	⋯	⋯	0.00	0.00	−101.02	−101.46
Glycolaldehyde–Ethylene Glycol
11a	NR	HOCH2CHO + H → HOCH2CHOH	11.42	12.31	5.50 (8.75) a [9.51] (iii)	6.32 (9.29) a	−29.43	−30.43
11b	RR	HOCH2CHOH + H → HOCH2CH2OH	⋯	⋯	0.00	0.00	−92.18	−92.44
12a	NR	HOCH2CHO + H → HOCH2CH2O	9.43	9.67	3.34 (4.47)[4.97] (iii)	3.38 (4.17) a	−22.38	−22.61
12b	RR	HOCH2CH2O+H → HOCH2CH2OH	⋯	⋯	0.00	0.00	−99.22	−100.25
Acetone–Isopropanol
13a	NR	CH3COCH3 + H → CH3COHCH3	11.27	12.44	5.56 (9.15) a	6.66 (9.85) a	−25.76	−24.35
13b	RR	CH3COHCH3 + H → CH3CHOHCH3	⋯	⋯	0.00	0.00	−90.73	−91.08
14a	NR	CH3COCH3 + H → CH3CHOCH3	12.97	12.97	6.29	6.55 (—) b	−16.40	−24.35
14b	RR	CH3CHOCH3 + H → CH3CHOHCH3	⋯	⋯	0.00	0.00	−100.10	−91.08
Ethenone–Vinyl Alcohol
15a	NR	CH2CO + H → CH2COH	17.52	18.22	11.80 (5.39)	12.49 (17.59) a	−15.60	−16.96
15b	RR	CH2COH + H → CH2CHOH	⋯	⋯	0.00	0.00	−45.67	−45.30
16a	NR	CH2CO + H → CH2CHO	10.04	9.92	4.36 (3.97)	4.26 (5.43) a	−41.76	−43.87
16b	RR	CH2CHO + H → CH2CHOH	⋯	⋯	0.00	0.00	−19.51	−18.40

Notes. Here NR refers to neutral–radical reactions and RR to radical–radical reactions.

^a Bracketed values are calculated using the CCSD(T)/aug-cc-pVTZ level. ^b We did not find true TSs. (i) Song & Kästner (2017), (ii) Zaverkin et al. (2018), (iii) Álvarez-Barcia et al. (2018).

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3.1.2. Ethanal and Ethanol

Here the formation of C₂H₅OH via two successive hydrogen addition reactions with CH₃CHO is considered. The kinetic information (i.e., activation barrier and reaction enthalpy) of these two reactions is noted in Table 3. Looking at the activation barriers reported in Table 3, it is clear that, similar to methanol formation, here the hydrogen addition at the carbon atom position of CH₃CHO (i.e., the formation of CH₃CH₂O; reaction 4a) is more efficient compared to the hydrogen addition at the O atom position (i.e., the formation of CH₃CHOH; reaction 3a). The relative abundances between the acetaldehyde and ethyl alcohol seem to depend upon the evolution time, availability of the hydrogen atoms, ice thickness, and ice morphology (Bisschop et al. 2007, 2008). Only the hydrogenation reactions are not enough in explaining the observed abundances of ethanol. It is necessary to consider the ice-phase reaction between CH₃ and CH₂OH for the formation of ethanol in the ice phase.

3.1.3. Propanal and 1-propanol

The coexistence of propanal, propynal, and propenal in Sgr B2(N) (Hollis et al. 2004) could be explained by the following successive hydrogenation sequence: HC₂CHO (propynal) → CH₂CHCHO (propenal) → CH₃CH₂CHO (propanal). Recently, Qasim et al. (2019) studied propanal formation by the radical–radical reaction between HCO and H₃CCH₂. They used the sequential hydrogenation of propanal to form 1-propanol. The first step of the reaction has a high activation barrier. But this reaction can change the saturated bond to the unsaturated radicals, which may again undergo some radical–radical recombination reactions. Qasim et al. (2019) obtained a significant production of 1-propanol with this pathway. Our TST calculation for the successive hydrogenation pathways is shown in Table 3. It is noticed that the hydrogen addition at the carbon atom position of propanal (CH₃CH₂CH₂O formation; reaction 6a of Table 3) is favorable compared to the oxygen atom position of propanal (CH₃CH₂CHOH formation; reaction 5a). The gas-phase activation and reaction energies (including ZPVE) of Zaverkin et al. (2018) are compared with ours in Table 3, which is in good agreement with our values calculated using a higher level of theory. In addition to the successive hydrogenation reactions, following ice-phase reactions, are also considered for the formation of propynal, propenal, and propanal:

$$\begin{eqnarray*}&&{{\rm{C}}}_{2}{\rm{H}}+{{\rm{H}}}_{2}\mathrm{CO}\to \mathrm{propynal}+{\rm{H}},\end{eqnarray*}$$

$$\begin{eqnarray*}&&{\rm{O}}+{{\rm{C}}}_{3}{{\rm{H}}}_{3}\to \mathrm{propynal}+{\rm{H}},\end{eqnarray*}$$

$$\begin{eqnarray*}&&\mathrm{HCO}+{{\rm{C}}}_{3}{{\rm{H}}}_{3}\to \mathrm{propenal}+{\rm{H}},\end{eqnarray*}$$

$$\begin{eqnarray*}&&\mathrm{HCO}+{{\rm{H}}}_{3}{\mathrm{CCH}}_{2}\to \mathrm{propanal}+{\rm{H}}.\end{eqnarray*}$$

3.1.4. Propenal and Allyl Alcohol

Propenal is also a product of the two successive hydrogenation reactions with propynal:

$$\begin{eqnarray*}&&\mathrm{propynal}+2{\rm{H}}\to \mathrm{propenal}\ ({\mathrm{CH}}_{2}\mathrm{CHCHO}).\end{eqnarray*}$$

Here the other product channel of this reaction is studied. First, the hydrogenation reaction of propynal would form propenal. In the second step, propenal could undergo another hydrogenation reaction to form allyl alcohol. The TS calculation of the first step found an activation barrier of 2.60 kcal mol⁻¹ in the gas phase and 2.57 kcal mol⁻¹ in the ice phase, considering a lower level of theory (see Figure 2). Table 3 shows the activation barriers for the hydrogen addition at the propenal's carbon and oxygen atom position. It is noted that, unlike the other cases, here the hydrogen addition at the oxygen atom of propenal (CH₂CHCHOH formation; reaction 7a) seems to be more favorable than it is with the carbon atom position (CH₂CHCH₂O formation; reaction 8a) considering a lower level of theory. But when considering a higher level of theory, the opposite trend is found, which agrees well with the values calculated by Zaverkin et al. (2018), shown in Table 3 for comparison.

3.1.5. Propynal and Propargyl Alcohol

Transformation from propynal (HC₂CHO) to propargyl alcohol was reported by Gorai et al. (2017b). This happened via two successive hydrogenations with propynal:

$$\begin{eqnarray*}&&\mathrm{propynal}+2{\rm{H}}\to \mathrm{propynol}\ (\mathrm{propargyl}\ \mathrm{alcohol},{\mathrm{HC}}_{2}{\mathrm{CH}}_{2}\mathrm{OH}).\end{eqnarray*}$$

Table 3 shows a different trend for the hydrogen addition for reactions 9a and 10a in the gas phase. The hydrogen addition to the oxygen atom position (reaction 9a) is more favorable than that of the carbon atom position (reaction 10a) using a lower level of theory, whereas Zaverkin et al. (2018) found the opposite trend. In contrast, the opposite trend is noticed for the ice phase using both lower and higher levels of theory.

3.1.6. Glycolaldehyde and Ethylene Glycol

Glycolaldehyde is the simplest form of the aldose family. Sugars like glucose, ribose, and erythrose belong to this family. Therefore, the presence of glycolaldehyde in space is an indication of the precursor of biologically relevant molecules. Identification of the numerous COMs in the Galactic center makes it an active area of research. Specifically, the study of the chemical evolution of the grain mantle, where various energetic processes (like UV radiation, X-rays, cosmic rays, etc.) may reprocess the mantle composition, is fascinating. The C and O addition to the formyl radical and then three successive hydrogen additions yield glycolaldehyde. The two subsequent hydrogen additions to the glycolaldehyde can produce ethylene glycol (Charnley 2001). Fedoseev et al. (2015) proposed that two HCO radicals can recombine to produce glyoxal (HC(O)CHO), which can also subsequently convert to glycolaldehyde. Coutens et al. (2018) investigated the formation mechanism of these two species (glycolaldehyde and ethylene glycol) using a gas-grain chemical model. They studied the EG/GA ratio for various luminosities. Álvarez-Barcia et al. (2018) studied two successive hydrogen addition reactions with acetaldehyde that can produce ethylene glycol. They also studied the hydrogen abstraction reactions of GA and EG. Table 3 shows the kinetics involved in converting ethylene glycol from glycolaldehyde. Like the methanal–methanol, ethanal–ethanol, propanal-1–propanol, and ice-phase pathways of propynal–propargyl alcohol pairs, the favorable position of the hydrogen addition is noted to be the carbon atom (HOCH₂CH₂O formation; reaction 12a) instead of the oxygen atom (HOCH₂CHOH formation; reaction 11a) of glycolaldehyde.

3.1.7. Acetone and Isopropanol

Acetone (propanone, CH₃COCH₃) and isopropanol (isopropyl alcohol, 2-propanol, CH₃CHOHCH₃) might be chemically connected. Table 3 shows the two successive hydrogenations of acetone (reactions 13 and 14). We check the TST calculations for the hydrogen addition either with the oxygen atom (activation energy 5.56 kcal mol⁻¹) or with the carbon atom (activation energy 6.29 kcal mol⁻¹) position of acetone and find that the former one is more favorable than the latter in the gas phase considering a lower level of theory. In contrast, the opposite trend was noted in the ice phase. We could not find a proper TS using a higher level of theory for reaction 14a in the ice phase.

3.1.8. Ethenone and Vinyl Alcohol

Vinyl alcohol is a planar molecule that has two conformations. The "syn" conformer is found to be more stable than the "anti" conformer (Turner & Apponi 2001). Here also, the "syn" conformer of vinyl alcohol is considered for the quantum chemical calculations. Here the TS of the two successive hydrogen addition reactions to ketene is examined for the formation of vinyl alcohol. The first step has an activation barrier, whereas the second step is a radical–radical reaction and assumed to be barrierless. Vinyl alcohol can also form from acetaldehyde via the isomerization process (Käser et al. 2020). Table 3 indicates that the hydrogen addition at the carbon atom position is more favorable than the oxygen atom position of ethenone. Table 3 shows that the H addition to the O atom position of ketene (reaction 15a) requires a significantly higher activation barrier than the H addition to the O atom position of the other species (reactions 1a, 3a, 5a, 7a, 9a, 11a, and 13a) reported here. It could be due to the change of the hybridization of the C atom of ketene from sp (linear) to sp2 (bent) during the H addition reaction. On the other hand, there is no change of hybridization upon H addition to the other aldehyde, which requires only a small structural change; thus, the activation energy could be small. Carr et al. (1968) studied the reaction of ketene with atomic H at room temperature, where the reaction H + H₂CCO →CH₃CO^* → CH₃ + CO occurs rather slowly (rate constant 1.3 × 10⁻¹³ cm³ mol⁻¹ s⁻¹), while neither H + H₂CCO →CH₂CHO nor H abstraction occur very significantly.

3.2. BE and PA

A sizable portion (∼60%–70% of the surface coverage) of the interstellar icy layers may contain water molecules. That is why the BE of the interstellar species is usually explored with the H₂O surface. However, the rest (∼30%–40%) of the grain mantle would comprise other impurities. Here CO₂, CO, and CH₃OH can occupy a sizable portion of the grain mantle in different lines of sight. Keane et al. (2001) summarized that the relative abundance of CO, CO₂, and CH₃OH relative to water ice could vary in the range of 0.4–15, 0.17–21, and 1.5–30, respectively, in different lines of sight. So, the BE with these surface species is also important in the various evolutionary stages. It is needed to construct a model that can consider the BE depending on the surface composition of the ice. Furuya & Persson (2018) reported a model in which the BE depends on the surface composition of ice mantles. However, showing the results of such a chemical model is outside the scope of this work.

A realistic estimation of the BEs with various substrates is not available. In the absence of any experimental values, the quantum chemical method provides an educated estimate (Wakelam et al. 2017; Das et al. 2018). Here CO and CH₃OH molecules are considered a substrate for this purpose.

The Gaussian 09 suite of programs employed for the quantum chemical calculations is utilized to evaluate the BE values. To calculate the BE, the optimized energy for the complex system (where a species is placed at a suitable distance from the grain surface) is subtracted from the tomaal optimized energies of the grain surface and species. To find the optimized energy of all structures, a second-order Møller–Plesset (MP2) method with an aug-cc-pVDZ basis set (Dunning 1989) is used following Das et al. (2018). The ZPVE and basis set superposition error correction are not considered for BE calculations.

Obtained BE values are shown in Table 4. It is interesting to note that a higher BE for the alcohol is obtained in most cases than for the corresponding aldehyde, ketone, or ketene. Obtained values with CO and CH₃OH differ significantly from that of water. In terms of average values, the BE value with a water monomer is ∼2.85 times higher than the CO monomer and 1.16 times lower in comparison to the CH₃OH monomer. Here, in our chemical model, the obtained BE values with CO and CH₃OH are not included because they are not available for the other species. However, BE values obtained with the c-tetramer water configuration are included in our model with the appropriate scaling factor suggested in Das et al. (2018). In Table 4, the ground-state spin multiplicity of the species used to calculate the BE is also noted. Separate calculations (job type "opt+freq" in the Gaussian 09 suite) with different spin multiplicities are used. The lowest-energy electronic state in between these is noted as the ground state.

Table 4. Calculated BE Values of the Aldehydes, Alcohols, Ketone, and Ketene Considered in This Work

Species	Ground State	BE (K)
	Used	CO Monomer	CH3OH Monomer	H2O Monomer	H2O Tetramer (Scaled by 1.188)	Available
Methanal	Singlet	641	2942	2896	3242 a (3851)	2050, b 4500 ± 1350 c
Methanol	Singlet	1247	3227	3124	4368 a (5189)	5534, b 5000 ± 1500 c
Ethanal	Singlet	1015	1964	3189	3849 a (4573)	2450, b 5400 ± 1620 c
Ethanol	Singlet	873	3408	2824	5045 (5993)	6584, b 5400 ± 1620 c
Propanal	Singlet	680	3679	3522	3396 (4034)	4500 ± 1350 c
1-Propanol	Singlet	1298	3932	2609	4791 (5692)	⋯
Propenal	Singlet	1043	3236	3170	3495 (4152)	5400 ± 1620 c
Allyl alcohol	Singlet	1381	3625	2958	5964 (7085)	⋯
Propynal	Singlet	1174	3072	2318	3379 (4014)	⋯
Propargyl alcohol	Singlet	1031	4119	2870	4284 (5089)	⋯
Glycolaldehyde	Singlet	826	3254	2871	5131 (6096)	⋯
Ethylene glycol	Singlet	933	4666	3144	4602 (5467)	⋯
Acetone	Singlet	728	3769	3623	4420 (5251)	3500 b
Isopropanol	Singlet	1369	3640	2879	5230 (6213)	⋯
Ethenone	Singlet	732	1450	1317	2847 (3382)	2200, b 2800 ± 840 c
Vinyl alcohol	Singlet	1236	3407	2836	3786 (4497)	⋯

Notes.

^a Das et al. (2018). ^b KIDA old BE. ^c KIDA new BE (Wakelam et al. 2017).

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The protonation reactions (i.e., X + H⁺ → XH⁺) and proton transfer reactions (i.e., X + YH⁺ → XH⁺ + Y) have a significant impact on the interstellar chemistry (Herbst & Leung 1986; Wakelam et al. 2010). The ab initio quantum chemical approaches provide a reliable value of the PA for small molecules, where the experimental determination of these parameters is a difficult task. A systematic study to calculate the adiabatic PA of aldehydes, ketone, ketene, and the corresponding alcohols is considered in this work. The PA is defined as the energy released when a proton is added to a system. Thus, the energy difference between a species and a species with an additional proton (H⁺) is estimated. It is calculated as the difference in energy (electronic + zero-point energy) between a neutral species and its protonated analog, i.e.,

$$\begin{eqnarray}&&\mathrm{PA}={E}_{{\rm{X}}}-{E}_{{\mathrm{XH}}^{+}},\end{eqnarray} \tag{ 6 }$$

where E_X is the optimized energy of the species, X, and ${E}_{{\mathrm{XH}}^{+}}$ is the optimized energy of the protonated species, XH+. To find the optimized energy of all structures, an MP2 method with an aug-cc-pVDZ basis set is again used. This is then verified via harmonic frequency calculations with the equilibrium geometries having only real frequencies. Zero-point correction to energies is not considered to evaluate the PA of the species reported here. The protonation of a neutral species can occur in more than one position, which yields different protonated species with other PAs. To avoid any ambiguity, only the highest PA is noted in Table 5. A protonated form with a higher PA of a neutral species is found to be more stable and abundant (in some cases) and thus may be detectable. In contrast, another protonated form with a lower PA forming from the same neutral species is less stable. This species is very reactive, and the high reactivity of this protonated species may reduce its interstellar abundance. We notice that ethenone has the highest and methanal has the lowest PA among all of the species considered for this study.

Table 5. Calculated PA of These Species

Species	Protonation Reactions	PA (kJ mol−1)
		Our Calculation	Available
Methanal	HCHO + H+ → CH2OH+	692	712.9 ± 1.1 a
Methanol	CH3OH + H+ → CH3OH2 +	740	754.3 a
Ethanal	CH3CHO + H+ → CH3CHOH+	749	768.5 ± 1.6 a
Ethanol	C2H5OH + H+ → C2H5OH2 +	764
Propanal	CH3CH2CHO + H+ → HCH3CH2CHO+	769
1-Propanol	CH3CH2CH2OH + H+ → HCH3CH2CH2OH+	770
Propenal	CH2CHCHO + H+ → CH2CHCHOH+	764
Allyl alcohol	CH2CHCH2OH + H+ → CH2CHCH2OH2 +	773
Propynal	HC2CHO + H+ → HC2CHOH+	733
Propargyl alcohol	${\mathrm{HC}}_{2}{\mathrm{CH}}_{2}\mathrm{OH}\ +\ {{\rm{H}}}^{+}\ \to \ {\mathrm{HC}}_{2}{\mathrm{CH}}_{2}{\mathrm{OH}}_{2}^{+}$	742
Glycolaldehyde	HOCH2CHO + H+ → H2OCH2CHO+	759
Ethylene glycol	HOCH2CH2OH + H+ → HOCH2CH2OH2 +	797
Acetone	CH3COCH3 + H+ → CH3COHCH3 +	793
Isopropanol	CH3CHOHCH3 + H+ → CH3CHOH2CH3 +	782
Ethenone	CH2CO + H+ → CH3CO+	822	825.3 ± 3 a
Vinyl alcohol	CH2CHOH + H+ → CH3CHOH+	800	882.8 b

Notes.

^a Hunter & Lias (1998). ^b Turner & Apponi (2001).

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The chemical model for molecular clouds (CMMC; Das et al. 2015a, 2015b, 2019, 2021; Gorai et al. 2017a, 2017b, 2020; Sil et al. 2018, 2021; Bhat et al. 2021) is implemented for studying the formation of aldehydes, ketone, ketene, and their corresponding alcohols. The low metallic elemental abundances are used as the initial abundances (Wakelam & Herbst 2008; EA1 set). The gas-phase pathways of the CMMC are mainly adopted from the UMIST database (McElroy et al. 2013). Additionally, a complete network for the formation of alcohols and aldehydes is considered. For the construction of these reaction sets, the ice-phase reactions noted in Table 3 are considered. The activation energy barriers calculated with the CCSD(T)/aug-cc-pVTZ level are adopted. Since, for reaction 13a of Table 3, a true TS was not obtained with the CCSD(T)/aug-cc-pVTZ level of theory, the activation barrier obtained with the B3LYP/6-31+G(d,p) level of theory is used. For the gas-phase destruction of these species, interstellar photoreactions, cosmic ray–induced reactions, and ion–molecular reactions with major ions (He⁺, ${{\rm{H}}}_{3}^{+}$, HCO⁺, H₃O⁺, C⁺, etc.) are considered. The destruction of some of these reactions was already available in McElroy et al. (2013). If it is not available, a very similar reaction with the same rate constants is adopted for these reactions. A cosmic-ray rate of 1.3 × 10⁻¹⁷ s⁻¹ is considered in all of our models. Cosmic ray–induced and chemical desorption with an efficiency of 1% is considered. For the all-grain surface species, a photodesorption rate of 3 × 10⁻³ per incident UV photon (Öberg et al. 2007) is adopted. A sticking coefficient of 1.0 is considered for the neutral species. Sticking coefficients of H and H₂ are taken from Chaabouni et al. (2012). On the grain surface, the diffusive surface reaction can lead to chemical complexity. However, to successfully continue the diffusive reaction mechanism, the reactants should be in close proximity until they react. There is a possibility that two reactants can move apart or be desorbed back to the gas phase. So it is essential to consider the competition between diffusion, desorption, and reaction. Following Garrod & Pauly (2011), this possibility is included here. At a high density (∼10⁷ cm⁻³) and around 10 K, a notable portion of the grain surface would be covered by molecular hydrogen. At this stage, an encounter desorption of H₂ (Hincelin et al. 2015; Chang et al. 2021; Das et al. 2021) needs to be considered to avoid an unnecessary surge in the grain molecular hydrogen. The grain mantle chemical composition at this phase is crucial to delivering the surface species into the gas phase during the warm-up stage. Here the encounter desorption mechanism of H₂ is considered in our model.

The BE (E_d ) of the surface species plays a decisive role in controlling the chemical complexity of the interstellar ice. A straightforward relation between the diffusion energy (E_b ) of a species with the BE is considered by following ${E}_{b}={{RE}}_{d}$, where R may vary between 0.35 and 0.8 (Garrod et al. 2007). Here R = 0.35 and 0.50 are used to show their effects on the chemical complexity. These BEs are taken from either the KIDA database ¹³ or Das et al. (2018). Das et al. (2018) provided BE values for some relevant interstellar species with the water surface. They computed the interaction energies, varying the size of the substrate. They showed that their method will yield a minimum deviation with the experimentally obtained BE values when a higher-order cluster is used. Table 4 shows the computed BEs of these aldehydes, alcohols, ketone, and ketene with various substrates. The ground state used to calculate the BE of these molecules is also pointed out along with the BE values in Table 4. Das et al. (2018) suggested a scaling factor of ∼1.188 while using the tetramer water configuration for the computation of the BE. The scaled BE values are also noted in Table 4. Except for the glycolaldehyde and ethylene glycol pair, for all cases, obtained BEs with the tetramer configuration are noted to be comparatively higher than their related aldehyde, ketone, or ketene. These new sets of BEs with the tetramer configuration of water molecules are included in our chemical model with appropriate scaling.

Depending on the BEs and R values, four sets of BEs are constructed (see Table 6). For sets 1 and 2, BEs are used from the KIDA database (Wakelam et al. 2017). The only difference between sets 1 and 2 is that for set 1, R = 0.5 is used, whereas for set 2, R = 0.35 is used. Recently, Das et al. (2018) provided a set of BEs for 100 relevant interstellar species with the water surface. They used the tetramer configuration of water molecules for this calculation. They suggested using a scaling factor of 1.188 with this to estimate the BEs of these species. Set 3 is constructed with the BE values provided by Das et al. (2018). Sil et al. (2017) reported the BEs of H and H₂ for a comprehensive collection of surfaces (benzene, silica, and water). They showed that the BEs of H and H₂ are very sensitive to the choice of substrates. These variations should be expected for all species if various substrates are considered. But showing all of the changes here would be outside the scope of this work. Here one additional BE set is considered, where the BE of H and H₂ are kept the same as in KIDA, but for the others, it is the same as in set 3. Thus, in set 3, BEs of H and H₂ of 148 and 627 K, respectively, are used from Das et al. (2018) after scaling by 1.188, whereas in set 4, 650 and 440 K, respectively, are used for H and H₂ (Wakelam et al. 2017). Here R = 0.35 is considered for both set 3 and set 4.

4.1. Physical Conditions

Here a physical condition is used that is suitable to explain the properties of a hot core. Three models (A, B, and C) are implemented to describe the chemical complexity around this region. The physical condition of our model A is divided into three distinct stages. In the first stage (10⁵–10⁶ yr), an isothermal (gas and grain temperature are kept the same) collapse of the cloud is considered from a minimum hydrogen number density (${{n}_{{\rm{H}}}}_{\min }$) 3 × 10³ cm⁻³ to a maximum hydrogen number density (${{n}_{{\rm{H}}}}_{\max }$) 10⁷ cm⁻³. During this stage, the visual extinction parameter (A_V ) can gradually increase due to increased density. The following relation between A_V and n_H is used to consider this situation (Lee et al. 1996; Das & Chakrabarti 2011; Gorai et al. 2020):

$$\begin{eqnarray}\begin{array}{rcl}{A}_{V} & = & \max \left({{A}_{V}}_{\min },(\sqrt{{n}_{{\rm{H}}}/{{n}_{{\rm{H}}}}_{\min }}-1)/(\sqrt{{{n}_{{\rm{H}}}}_{\max }/{{n}_{{\rm{H}}}}_{\min }}-1)\right.\\ & & \left.\times {{A}_{V}}_{\max }\right).\end{array}\end{eqnarray} \tag{ 7 }$$

It is considered that A_V can vary from a minimum value (${{A}_{V}}_{\min }=2$) to a maximum value (${{A}_{V}}_{\max }=200$). In the second stage (5 × 10⁴ yr), the cloud remains constant at ${{n}_{{\rm{H}}}}_{\max }$, ${{A}_{V}}_{\max }$. At the end of this stage, the gas and dust temperatures can reach up to 200 K. The third or last stage is a post–warm-up stage (period of 1.0 × 10⁵ yr), where n_H, A_V , and temperature are kept fixed at their respective highest values.

In the case of model B, the prestellar collapsing stage is considered instead of the initial isothermal collapsing stage. In this stage, the dust temperature is allowed to decrease as the collapse proceeds. The gas temperature is kept constant at 10 K during this collapse. The other two stages are considered the same as model A. The dust temperature of the prestellar collapsing stage is calculated by using the following empirical relation proposed by Hocuk et al. (2017) and used in Shimonishi et al. (2020):

$$\begin{eqnarray}&&{{T}_{d}}^{{\rm{H}}{\rm{o}}{\rm{c}}}=[11+5.7\tanh (0.61-{{\rm{l}}{\rm{o}}{\rm{g}}}_{10}({A}_{V}))]{\chi }_{{\rm{U}}{\rm{V}}}^{1/5.9},\end{eqnarray} \tag{ 8 }$$

where χ_UV is the Draine UV field strength (Draine 1978) corresponding to 1.7G₀ using the Habing field (Habing 1968). Here G₀ is the far-ultraviolet radiation field in the Habing unit (∼1.6 × 10⁻³ erg cm⁻² s⁻¹) integrated over the energy range 6–13.6 eV. Here χ_UV = 1 is used.

Model C is the same as model A. The only difference between model A and model C is the isothermal collapsing timescale of the first stage. In model C, a shorter timescale (10⁵ yr) for the isothermal collapse is considered. It is more relevant for high-mass star-forming regions. Thus, in the case of model A, we have a density slope of ∼10 cm⁻³ yr^–1, whereas it is ∼100 cm⁻³ yr^–1 in the collapsing stage. The warm-up and post–warm-up timescales are kept the same as in model A.

The physical conditions described here are summarized in Figure 3. The time evolution of density, temperature, and visual extinction in the three distinct stages is shown for models A, B, and C. The left axis of Figure 3 represents the variation of the temperature and visual extinction parameters in the linear scale, whereas the right axis represents the density curve in the logarithmic scale. For better visibility, in the top panel, temperature is plotted by scaling by a factor of 2. Models A and B start with an isothermal collapsing stage. In this case, the dust and gas temperatures are kept the same. Here a region having an initial temperature between 8 and 20 K is explored to check the sensitivity of our model A to the initial choice of temperature. The temperature range for the initial stage of models A and C is highlighted with the red shaded region. The visual extinction for the initial stage (isothermal collapsing stage) of models A and C is kept constant at 2. In the case of model B, the gas temperature of the first stage is kept fixed at 10 K, and the dust temperature is estimated based on Equation (8). The initial visual extinction of model B is varied between 0.1 and 10 (corresponds to an initial dust temperature of 16–5 K from Equation (8)) to check its effect on chemical evolution. The initial choice of visual extinction parameter and the estimated dust temperature of model B are highlighted in green and yellow, respectively, in Figure 3. In the warm-up stage of models A, B, and C, A_V and n_H are kept constant at their highest value, and the temperature is allowed to increase up to 200 K. It is the typical temperature of a hot core (Garrod et al. 2008). In the post–warm-up stage, both of the models have identical conditions.

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4.2. Modeling Results

4.2.1. Abundances

In model A, various cases starting with the initial temperature between 8 and 20 K are considered. Figure 4 shows the variation of peak abundances with respect to H₂ obtained during the simulation timescale. Six pairs of aldehyde–alcohol, one pair of ketone–alcohol, and one pair of ketene–alcohol shown. Observed abundances in G10, G31, Sgr B2(N), and Orion KL are shown in solid blue, red, cyan, and orange curves, respectively. In Figure 4, the solid violet line represents the case with set 1 BEs reported in Table 6. The solid black line represents the case with set 2. A lower value of R provides more mobility to the surface species keeping the same resident time on grains. It yields a dramatic change in the production of surface species. The changes in the chemical composition of interstellar ice would reflect a significant difference between the gas-phase abundances of the species shown in Figure 4. The case with set 3 is represented with a solid green line in Figure 4. The set 4 case is represented with a solid brown line in Figure 4. It is evident from Figure 4 that the resulting peak abundance is highly sensitive to the choice of initial temperature—more specifically, the dust temperature. Some cases of model A are tested with a different gas temperature than dust and do not find significant differences in the peak abundance.

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It is noticed that the set 1 and 2 BEs are helpful to explain the abundances of these aldehydes and alcohols, except glycolaldehyde and ethylene glycol. The set 3 BEs deviate very much from the observed value. This is mainly because of the adopted BE values of H (148 K) and H₂ (627 K). However, with the set 4 BEs, the observed abundance of ethylene glycol could be explained. This is because set 4 considered the BE of H and H₂ to be the same as that in sets 1 and 2 (650 and 440 K, respectively, for H and H₂). The ice-phase origin of these species is evident from the results obtained from model A. The results obtained from model A seem to be highly sensitive to the initial dust temperature, which suggests that either these species were formed during the initial cold phase or the seeds of these species were produced during the initial stage. The final gas-phase abundance of these species is highly affected by the surface species present at the beginning of the warm-up stage. The surface species at the warm-up stage are reprocessed with the elevated temperature, where radicals take an active part in building the chemical complexity. Once the warm-up stage starts, the desorption of the surface species enhances and might reach their respective sublimation temperatures and populate the gas phase. With the set 4 BE and model A, it is possible to explain the observed abundances, except for glycolaldehyde.

The results obtained with model B are also shown in Figure 4. All four cases mentioned in the context of model A are also considered with model B. To visualize the differences between model A and model B, the model B cases are shown with dashed curves in the same colors. It is interesting to note that for model B, significant changes of abundance for the adopted BE parameters are noticed. Like model A, the results of model B are not very sensitive to the initial choice of dust temperature. The reason behind this deviation is the adopted dust temperature at the first stage of models A and B (see Figure 3). In model A, the dust remains at a constant temperature for ∼10⁶ yr, whereas in model B, in between a few times 10³ yr, it drops down to a temperature of <10 K. This affects the mobility of the surface species. Since it stays at <10 K during its first stage of evolution, model B does not show dramatic changes between the variation of the initial dust temperature.

The orange curve of Figure 4 represents the variation of the peak abundances of these species with set 4 and model C. It is interesting to note that with the shorter collapsing timescale (relevant for the hot core) of model C, the peak abundances of the COMs are significantly enhanced. Most of the observed abundances could be explained with set 4 of model C. The increase in the abundances of the COMs with the shorter collapsing time is not so straightforward. Many parameters are involved in it. But the shorter collapsing time leads to a much steeper slope for the density, which means that the depletion will be much faster, and the complex molecules, once produced, will have a comparatively shorter time to be destroyed further. In Figure 5, the time evolution of the gas- and ice-phase abundances of aldehydes, alcohols, ketone, and ketene with respect to H₂ are shown. Only the results obtained with model C by considering an initial gas and ice temperature of 10 K are shown. There is some uncertainty in the observations. Additionally, the chemical model includes lots of estimated parameters (BEs, reaction pathways, reaction rates, interaction with dust, etc.) that can induce severe skepticism. For these reasons, it is not easy to match all of the observed abundances simultaneously. In Figure 4, the peak abundance is taken beyond the isothermal collapsing stage. Figure 5 shows that beyond the isothermal stage, these peak gas-phase abundances appeared during the warm-up stage, when the temperature varied between ∼100 and 120 K. So our time uncertainty is relatively small (∼10⁴ yr).

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A comparison between our obtained gas-phase peak abundance and the observational results is shown in Table 7. Only the results obtained with set 4 of model C are used to show this comparison. Minimum and maximum peak abundances obtained within the initial temperature range considered for model C and set 4 are noted in Table 7. The errors in the obtained abundances are only shown for those species that are identified in G10. The errors are calculated by taking the uncertainty in estimating the H₂ column density and the column density obtained from rotation diagram analysis. Gorai et al. (2020) obtained an average column density of ∼1.35 × 10²⁵ cm⁻². The error in estimating the column density was ±1.0 × 10²² cm⁻², which mainly arose from the uncertainty of the measured flux. The error in estimating the column density of the species is taken from Table 2. For estimating the abundance errors for glycolaldehyde, only the uncertainty in predicting the H₂ column density is considered.

Table 6. Different Sets of BE Used Based on the Ratio of the Diffusion Energy to BE (R)

	BEs Used	R
Set 1	Wakelam et al. (2017)	0.5
Set 2	Wakelam et al. (2017)	0.35
Set 3	1.188 × BE of Das et al. (2018)	0.35
Set 4	1.188 × BE of Das et al. (2018), BE of	0.35
	H and H2 from Wakelam et al. (2017)

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Table 7. Comparison between the Observed Abundance and Our CMMC Results with Model C (Set 4)

Species	Obtained Abundances	Obtained Abundance	Observed Abundances
	from Model	Ratio from Model	G31	Sgr B2 (N)	Orion KL	G10
Methanal	3.3(−9) − 5.8(−7)		[1.9(−7)]e	[3.2(−08)]m		[6.1(−7)]b
Methanol	3.5(−8) − 1.4(−5)	1.0(1) − 5.3(1)	[1.9(−6)]e	[7.7(−7)]m [7.1(−6)]x	[2.2(−7)]l	[3.2 ± 2.5(−7)] a , [1.8(−6)]b
Ethanal	1.8(−13) − 5.0(−9)		[1.0(−9)]n	[1.4(−08)]m [9.5(−8)]x		[5.0 ± 0.8(−10)] a
Ethanol	7.4(−10) − 9.7(−8)	8.0(−2) − 2.6(4)	[2.0(−8)]n [1.3(−9)]f	[1.1(−7)]m [3.6(−7)]x	[1.3(−8)]l	[1.2(−7)]b
Propanal	7.3(−10) − 4.8(−8)					[1.8 ± 0.2(−8)] a
1-Propanol	2.4(−12) − 1.1(−7)	2.6(−4) − 1.3(2)
Propenal	2.0(−13) − 1.9(−9)					[1.2(−10)]*d
Allyl alcohol	8.7(−15) − 1.7(−9)	3.6(−2) − 2.9(0)
Propynal	3.1(−11) − 2.2(−09)
Propargyl alcohol	6.4(−15) − 1.3(−11)	1.4(−4) − 1.0(−2)
Glycolaldehyde	2.8(−15) − 1.3(−06)		[8.3(−11)]f	[3.2(−10)]m [1.9(−8)]x	[1.5(−10)]l	[9.6 ± 0.007(−10)] a
Ethylene glycol	5.5(−17) − 1.6(−5)	1.9(−2) − 4.45(2)	[1.7(−10)]f	4.1(−10)]m	[2.3(−9)]l	[3.7 ± 1.5(−8)] a
Acetone	6.6(−14) − 1.7(−8)			[2.6(−8)]m		[4.0 ± 0.5(−8)]a , [1.0(−7)]b
Isopropanol	1.2(−13) − 4.4(−9)	9.7(−2) − 7.4(3)
Ethenone	4.6(−13) − 5.5(−9)					[8.0(−8)]b
Vinyl alcohol	3.5(−11) − 1.4(−8)	1.9(0) − 7.7(1)				[ ≤2.3(−9)]*d
Methyl formate	2.3(−11) − 1.3(−6)		[2.6(−9)]f	[7.7(−8)]m [9.5(−8)]x	[1.2(−7)]k	[6.7 ± 0.5(−8)]a , [1.4(−7)]b
Dimethyl ether	3.5(−9) − 1.5(−7)	1.2(−1) − 1.5(2)	[4.2(−9)]f	[3.5(−7)]m	[3.0(−7)]k	[1.1 ± 0.3(−8)]a , [3.0(−7)]b

Note.

G31 observation:^e Gorai et al. (2021; used ALMA with θ_b = 098–119 and ${N}_{{{\rm{H}}}_{2}}=1.53\times {10}^{25}$ cm⁻²), ^f Rivilla et al. (2017; used SMA with θ_b = 075–35 and ${N}_{{{\rm{H}}}_{2}}=1.2\times {10}^{26}$ cm⁻²), ⁿ Nummelin et al. (1998; used SEST and IRAM with θ_b = 10'' and ${N}_{{{\rm{H}}}_{2}}=1.6\times {10}^{23}$ cm⁻²). Sgr B2 observation: ^x Belloche et al. (2014; used ALMA with θ_b = 12–19 and ${N}_{{{\rm{H}}}_{2}}=5.6\times {10}^{24}$ cm⁻²), ^m Belloche et al. (2013; used IRAM with θ_b = 10''–25 and ${N}_{{{\rm{H}}}_{2}}=5.6\times {10}^{24}$ cm⁻²; Belloche et al. 2014). Orion KL observation: ^l Brouillet et al. (2015; used ALMA with 14–19 and ${{\rm{N}}}_{{H}_{2}}=2.0\times {10}^{24}$ cm⁻²; Favre et al. 2011), ^k Tercero et al. (2015; used ALMA with 129–2'' and ${N}_{{{\rm{H}}}_{2}}=2.0\times {10}^{24}$ cm⁻²; Favre et al. 2011). G10 observation: ^a This work (used ALMA with θ_b = 139–244 and ${N}_{{{\rm{H}}}_{2}}=1.35\times {10}^{25}$ cm⁻²),^b Rolffs et al. (2011; used SMA with θ_b = 028–427 and ${N}_{{{\rm{H}}}_{2}}=5\times {10}^{24}$ cm⁻²), ^*d Dickens et al. (2001; used SEST and NRAO for possible detection of propenal and upper limit for vinyl alcohol with θ_b = 45'' and ${N}_{{{\rm{H}}}_{2}}={10}^{23}$ cm⁻²).

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It is noticed that our results can successfully explain the observed abundances.

4.2.2. Molecular Ratios

In Figure 6, the temperature variation of the ratio obtained between the peak abundance of six pairs of alcohol–aldehyde, one pair of alcohol–ketone, and one pair of alcohol–ketene for model A (solid curve), model B (dashed curve), and model C (dotted–dashed curves) is shown. The observed ratios for G31, G10, Sgr B2(N), and Orion KL are shown with red, blue, cyan, and orange solid lines, respectively. The simulated ratio between the peak abundance of alcohol and aldehyde/ketone/ketene obtained with set 4 and model C, along with the observed abundances of these species, is noted in Table 7. Table 7 depicts the observed abundances in G31, G10, Sgr B2, and Orion KL, along with our modeled abundance. Mainly, the interferometric observations (wherever available) are noted to facilitate a comparative study of the chemical complexity of these hot cores. However, it is worth pointing out that all of these observations are carried out with different facilities with very diverse angular and spatial resolutions. The facility used for each observation, along with the H₂ column density used to derive the abundances, is noted in the footnotes of Table 7. Here only the band 4 archival data of G10 are analyzed and report only one alcohol–aldehyde pair (ethylene glycol–glycolaldehyde pair). These two species are recognized for the first time in this source. Earlier, Rolffs et al. (2011) obtained the methanol–methanal pair. Any transitions of methanal are not identified within our spectral bands. So, a direct comparison between our obtained ratios and others could not be performed. For example, Table 7 denotes a higher methanol abundance than ours, obtained by Rolffs et al. (2011). This is because the beam size of our band 4 observation of ALMA varies between 139 and 244 (∼12,000–21,000 au, considering 8.6 kpc distance). In contrast, the beam size of the Submillimeter Array observation at 345 GHz of Rolffs et al. (2011) is 03 (∼2600 au, considering 8.6 kpc distance). The integrated emission map of the methanol transitions shown in Figure C3 suggests that the methanol is more compact. So, as expected, with the higher spatial resolution, Rolffs et al. (2011) could trace the more inner region of the G10; thus, they obtained a higher abundance than us. Furthermore, for deriving the abundances from Rolffs et al. (2011), a hydrogen column density of 5 × 10²⁴ cm⁻² is used. In contrast, our abundance is derived by considering 1.35 × 10²⁵ cm⁻² (Gorai et al. 2020), which might also create significant variation. Since our chemical model does not consider the spatial variation of the abundances, explaining the observed ratio with the suitable beam size is also outside the scope of this work.

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The observed ratio is calculated from the observed pair from the same source (if available). It shows that the methanol-to-methanal abundance ratio in these sources can vary between 2.95 and 23.8, whereas our calculated methanol-to-methanal ratio varies between 10 and 53. The observed ratio of ethanol to ethanal varies between 5 and 261, whereas our model results show it ranges between 0.08 and 25,600. The observed ethylene glycol–to–glycolaldehyde ratio varies between 1.3 and 37, whereas our model derives 0.019–445. The observed ratio of vinyl alcohol to ethenone is 0.021, whereas our chemical model estimates 1.78–77. In brief, the observed ratio of three alcohol–aldehyde pairs (methanal–methanol, ethanal–ethanol, and glycolaldehyde–ethylene glycol) is >1 in G10, G31, Sgr B2(N), and Orion KL. Thus, in the high-mass star-forming regions, the abundances of these alcohols are generally higher than their respective aldehydes. Our estimated molecular ratios for these alcohol–aldehyde pairs are similar to the observed values in the temperature range 12−18 K (see Figure 6). However, in the case of vinyl alcohol to ethenone, the observed abundance ratio could not be explained. It could be because of the consideration of the upper limit of the vinyl alcohol observation from the single-dish observation of Dickens et al. (2001) for deriving the molecular ratio. A little ambiguity between our obtained ratio of dimethyl ether (DE) and methyl formate (MF) is obtained. Here DE/MF < 1 is received from our observational analysis, whereas Rolffs et al. (2011) noted it as >1 for the same source. Also, in other high-mass sources, the ratio is >1 (4.5 for Sgr B2, 2 for G31, and 2.5 for Orion KL). Our modeling results find that the ratio of DE to MF can vary between 0.12 and 152.

Here our CMMC model, coupled with the observational study, is used to explain the abundance of some interstellar aldehydes, ketone, and ketene, along with their corresponding alcohols. It is noticed that there is both observational and theoretical evidence for the production of alcohols via hydrogenation of precursor aldehydes, ketones, and ketenes. The following are the main highlights of this paper.

• 1.  
  Various aldehydes, alcohols, and a ketone are identified in G10. Among them, ethylene glycol and propanal are detected for the first time in G10, and glycolaldehyde is tentatively detected.
• 2.  
  The column densities and fractional abundances of the observed molecules are estimated assuming LTE conditions. The kinetic temperatures of the gas are also evaluated, which vary from 72 to 234 K.
• 3.  
  The spatial distributions of the observed species are investigated, and their zeroth-order moment maps and emitting regions are provided. However, molecular emissions are not well spatially resolved or, at best, marginally resolved to an extent. Thus, we need high angular resolution data to understand their detailed distributions in G10.
• 4.  
  Successive hydrogenation reactions can lead to the formation of alcohol starting from the aldehyde/ketone/ketene. Extensive quantum chemical calculations are carried out to yield the TS of some of these reactions. The first step of the hydrogen addition reactions has an activation barrier, whereas the second step could be considered barrierless radical–radical reactions. Our higher-level (CCSD(T)/aug-cc-pVTZ) ice-phase calculations depict the first step of the hydrogenation reaction at the carbon atom position for all pairs (except acetone–isopropanol) and are comparatively more favorable than the oxygen atom position. In the acetone–isopropanol pair, an actual TS could not be obtained for the hydrogenation to the carbon atom position. However, the lower-level calculation (DFT-B3LYP/6-31+G(d,p)) in the ice phase shows the same trend.
• 5.  
  The BEs of these species are computed with various substrates, such as H₂O, CH₃OH, and CO. For most cases, the BE of alcohols is found to be greater than that of the corresponding aldehyde/ketone/ketene. On average, the computed BE with the CH₃OH monomer is found to be ∼1.16 times higher, and with the CO monomer, it is ∼2.85 times lower than that of the water monomer. These new sets of BEs will help in understanding the chemical complexity, where CO and CH₃OH will also efficiently cover the icy mantle. The BEs of the species reported in this study for the alcohol, aldehyde, ketone, and ketene is a little bit on the higher side (∼3000 K) with the H₂O c-tetramer configuration. Comparatively, a higher surface temperature is needed to transfer these surface species in the gas phase. Such a high temperature is usually achieved deep inside the cloud in the hot-core stage. If the grain surface pathways are the only deriving route, then species with lower BE are expected to have more extended emission than species with relatively higher BE. Since our observed beam size is large, it is not possible to spatially resolve the inner region and comment on the dependency between the BE and size of the emitting region.
• 6.  
  Our CMMC model with the shorter collapsing timescale (∼10⁵ yr) was able to reproduce the observed abundance and ratio between various species.

This paper makes use of the following ALMA data: ADS/JAO.ALMA #2016.1.00929.S. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), MOST and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO and NAOJ. S.K.M. acknowledges a CSIR fellowship (file No. 09/904(0014)/2018-EMR-I). P.G. acknowledges support from a Chalmers Cosmic Origins postdoctoral fellowship. M.S. gratefully acknowledges DST, the Government of India, for providing financial assistance through the DST-INSPIRE Fellowship [IF160109] scheme. A.D. acknowledges the ISRO respond project (grant No. ISRO/RES/2/402/16-17) for partial financial support. J.C.T. acknowledges support from the Chalmers Foundation and a VR grant. T.S. and A.D. acknowledge the Indo-Japan Collaborative Science Programme. This research was possible in part due to a Grant-in-Aid from the Higher Education Department of the Government of West Bengal.

Software: Gaussian 09 (Frisch et al. 2013), CASA 4.7.2 (McMullin et al. 2007), CASSIS (Vastel et al. 2015).

The emitting diameter of each transition of the observed molecules is shown in Table A1. Here the average emitting diameter refers to the average source size. Two-dimensional Gaussian fittings of a region (the area enclosing a 50% line peak) of the moment map image are performed. After averaging the semiminor and semimajor axes, the emission region's diameters are obtained.

Table A1. Emitting Diameter of Each Transition of Observed Species toward G10

Species	${J}_{{K}_{a}^{{\prime} }{K}_{c}^{{\prime} }}^{{\prime} }$-${J}_{{K}_{a}^{{\prime\prime} }{K}_{c}^{{\prime\prime} }}^{{\prime\prime} }$	Frequency	Eu	Emitting Diameter (Average)
		(GHz)	(K)	${\theta }_{s}^{{\prime\prime} }$
Methanol	110,11 − 111,11 E, vt = 0	154.425832*	166.1	1.81
(CH3OH)	120,12 − 151,12E, vt = 0	153.281282*	193.8	1.79
	150,15 − 151,15 E, vt = 0	148.111993	290.7	1.78
	7−1,7 − 60,6 E, vt = 1	147.943673	356.3	1.56
	17−4,13 − 18−3,16 E, vt = 0	131.134094	450.9	1.45
	210,21 − 211,21 E, vt = 0	129.720384	546.2	1.33
	8 8−7,1 − 7−6,1 E, vt = 1	153.128697	664.5	1.23
Ethylene glycol	84,5 − 73,5, 1-0	153.567383	25.4	1.83
(g-HOCH2CHO)	133,11 − 122,10, 0-0	159.768239	48.8	1.85
	139,4 − 129,3,0-1	130.998583	83.8	1.04
	273,25 − 272,26, 0-0	148.082465	185.5	1.04
	287,21 − 286,23, 1-0	131.229156	223.4	1.21
	3310,23 − 3211,22, 1-1	130.115657	323.0	1.60
	398,32 − 397,33, 1-1	153.325448	414	1.27
Acetaldehyde	83,6 − 73,5,A	154.2746864	53.7	1.49
(CH3CHO)	83,5 − 73,4,E	154.296489	53.7	1.53
	84,4 − 74,3, A	154.201471	69.5	1.38
	85,4 − 75,3, A	154.161467	89.8	1.33
	83,5 − 73,4, A	154.173895	114.4	1.0
Propanal	97,2 − 96,3, A	148.005042	49.6	1.18
(CH3CH2CHO)	107,3 − 106,4, E	147.867178	54.6	1.27
	162,15 − 151,14, E	159.932346	68.6	0.83
	253,22 − 252,23, A	153.554511	172.7	1.87
	275,23 − 274,24, E	154.066288	206.2	1.08
Glycolaldehyde	150,15 − 14114	153.597995	60.5	1.26
(HOCH2CHO)	207,13 − 206,14	153.614231	147.0	0.89
Acetone	122,11 − 111,10, AA	130.924799	44.1	1.28
(CH3COCH3)	115,7 − 104,6, EE	147.684364	48.4	1.83
	132,11 − 123,10, AE	149.395864	55.8	1.56
	143,12 − 132,11, EE	159.2476343	63.4	1.29
	224,18 − 223,19, EE	160.118153	157.1	1.38
	246,18 − 245,19, EE	159.415955	198.9	1.50
	2510,15 − 259,16, AE	130.708968	241.0	1.0
	2610,17 − 269,18, EE	149.190766	251.0	1.35
Methyl formate	112,10 − 102,9, A	130.016790	40.7	1.87
(CH3OCHO)	112,10 − 102,9, E	130.010105	40.7	1.80
	124,9 − 114,8, A	148.805941	56.8	1.95
	133,11 − 123,10, A	158.704392	59.6	1.85
	137,6 − 127,5, A	160.178942	86.2	1.82
	137,6 − 127,5, A	160.193496	86.2	1.66
	1310,4 − 1210,3, A	159.662793	120.0	1.67
	121,12 − 111,11, A	131.377495	230.0	1.0
	124,8 − 114,7, E	148.575217	244.1	1.69
Dimethyl ether	61,6 − 50,5, AA	131.406552	19.9	1.91
(CH3OCH3)	90,3 − 81,8, AE	153.055196	40.4	1.83
	111,10 − 102,9, AE	154.456506	62.9	1.95
	244,20 − 243,21, EE	153.385902	297.5	1.44

Note. Optically thick.

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The observed, fitted, and LTE spectra of the observed transitions are shown in Figure B1.

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The integrated intensity distribution of all observed molecules is shown in Figures C1–C4. The integrated intensity is obtained in the velocity range where the emission line is seen. It is typically obtained between 58 and 78 km s⁻¹. This range slightly varies for some species. Figures C1 and C2 show the intensity distribution of the observed molecules with an upper energy state of <100 K, and Figures C3 and C4 show the intensity distribution for the molecules with an upper energy state of >100 K.

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