A stochastic approach to grain surface chemical kinetics

Green, N. J. B.; Toniazzo, Thomas; Pilling, Michael J.; Ruffle, D. P.; Bell, N.; Hartquist, T. W.

doi:10.1051/0004-6361:20010961

Cited by 99 publications

(150 citation statements)

References 19 publications

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“…Caselli et al (1998a) took into account the discrete nature of the individual composition of grain mantles by modifiying the reaction rate coefficients, while Garrod et al (2008) went further by modifying the functional form of the reaction rates. Biham et al (2001) and Green et al (2001) introduced a stochastic method based on the resolution of the master equations (ME), in which the system solves the time derivatives of the probability of each discrete mantle composition. In spite of its good accuracy for grain surfaces, this method can only be used for small chemical networks since the number of equations grows exponentially with the number of species.…”

Section: The Computational Methodsmentioning

confidence: 99%

Multilayer modeling of porous grain surface chemistry

Taquet

Ceccarelli

Kahane

2012

A&A

145

157

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Context. Mantles of iced water mixed with carbon monoxyde, formaldehyde, and methanol are formed during the so-called prestellar core phase. In addition, radicals are also thought to be formed on the grain surfaces, and to react to form complex organic molecules later on, during the so-called warm-up phase of the protostellar evolution. Aims. We aim to study the formation of the grain mantles during the prestellar core phase and the abundance of formaldehyde, methanol, and radicals trapped in them. Methods. We have developed a macrosopic statistic multilayer model that follows the formation of grain mantles with time and that includes two effects that may increase the number of radicals trapped in the mantles: i) during the mantle formation, only the surface layer is chemically active and not the entire bulk; and ii) the porous structure of grains allows the trapping reactive particles. The model considers a network of H, O, and CO forming neutral species such as water, CO, formaldehyde, and methanol, plus several radicals. We ran a large grid of models to study the impact of the mantle multilayer nature and grain porous structure. In addition, we explored how the uncertainty of other key parameters influences the mantle composition. Results. Our model predicts relatively high abundances of radicals, especially of HCO and CH 3 O (10 −9 −10 −7 ). In addition, the multilayer approach enables us to follow the chemical differentiation within the grain mantle, showing that the mantles are far from being uniform. For example, methanol is mostly present in the outer layers of the mantles, whereas CO and other reactive species are trapped in the inner layers. The overall mantle composition depends on the density and age of the prestellar core as well as on some microscopic parameters, such as the diffusion energy and the hydrogenation reactions activation energy. Comparison with observations allows us to constrain the value of the last two parameters (0.5-0.65 and 1500 K, respectively) and provide some indications on the physical conditions during the formation of the ices.

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Section: The Computational Methodsmentioning

confidence: 99%

Multilayer modeling of porous grain surface chemistry

Taquet

Ceccarelli

Kahane

2012

A&A

145

157

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“…In this case, surface chemistry cannot be reliably described by the standard rate equation method, which tends to overestimate the rates. Other approaches like Monte Carlo techniques (e.g., Vasyunin et al 2009), master equations (e.g., Green et al 2001), and modified rate equations (e.g., Caselli et al 1998;Garrod et al 2009) should then be utilized. As shown by Vasyunin et al (2009), this only happens for rather dilute, warm gas and for very small grains and when quantum tunneling of hydrogen is allowed.…”

Section: Surface Reactionsmentioning

confidence: 99%

Chemistry in disks

Semenov

Hersant

Wakelam

et al. 2010

A&A

213

261

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Context. We describe and benchmark two sophisticated chemical models developed by the Heidelberg and Bordeaux astrochemistry groups. Aims. The main goal of this study is to elaborate on a few well-described tests for state-of-the-art astrochemical codes covering a range of physical conditions and chemical processes, in particular those aimed at constraining current and future interferometric observations of protoplanetary disks. Methods. We considered three physical models: a cold molecular cloud core, a hot core, and an outer region of a T Tauri disk. Our chemical network (for both models) is based on the original gas-phase osu_03_2008 ratefile and includes gas-grain interactions and a set of surface reactions for the H-, O-, C-, S-, and N-bearing molecules. The benchmarking was performed with the increasing complexity of the considered processes: (1) the pure gas-phase chemistry, (2) the gas-phase chemistry with accretion and desorption, and (3) the full gas-grain model with surface reactions. The chemical evolution is modeled within 10 9 years using atomic initial abundances with heavily depleted metals and hydrogen in its molecular form. Results. The time-dependent abundances calculated with the two chemical models are essentially the same for all considered physical cases and for all species, including the most complex polyatomic ions and organic molecules. This result, however, required a lot of effort to make all necessary details consistent through the model runs, e.g., definition of the gas particle density, density of grain surface sites, or the strength and shape of the UV radiation field. Conclusions. The reference models and the benchmark setup, along with the two chemical codes and resulting time-dependent abundances are made publicly available on the internet. This will facilitate and ease the development of other astrochemical models and provide nonspecialists with a detailed description of the model ingredients and requirements to analyze the cosmic chemistry as studied, e.g., by (sub-) millimeter observations of molecular lines.

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“…Similarly, the survivability of microhaloes in the tidal field of the parent galaxy is still poorly understood. On the one hand, fly-by encounters with individual stars (Green & Goodwin 2007) and the tidal field of the Milky Way disc (D'Onghia et al 2010;Errani et al 2017) may wipe out a large fraction of the subhalo population with small orbital pericentres. On the other hand, the steep density profile of microhaloes (Anderhalden & Diemand 2013;Angulo et al 2016) greatly increases their resilience to tidal mass stripping (Goerdt et al 2007;Peñarrubia et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Fluctuations of the gravitational field generated by a random population of extended substructures

Peñarrubia

2017

Monthly Notices of the Royal Astronomical Society

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A large population of extended substructures generates a stochastic gravitational field that is fully specified by the function p(F), which defines the probability that a tracer particle experiences a force F within the interval F, F + dF. This paper presents a statistical technique for deriving the spectrum of random fluctuations directly from the number density of substructures with known mass and size functions. Application to the subhalo population found in cold dark matter simulations of Milky Way-sized haloes shows that, while the combined force distribution is governed by the most massive satellites, the fluctuations of the tidal field are completely dominated by the smallest and most abundant subhaloes. In light of this result we discuss observational experiments that may be sufficiently sensitive to Galactic tidal fluctuations to probe the "dark" low-end of the subhalo mass function and constrain the particle mass of warm and ultra-light axion dark matter models.

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A stochastic approach to grain surface chemical kinetics

Cited by 99 publications

References 19 publications

Multilayer modeling of porous grain surface chemistry

Multilayer modeling of porous grain surface chemistry

Chemistry in disks

Fluctuations of the gravitational field generated by a random population of extended substructures

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