Astrochemical Model Digs Into the Universe's Missing Sulfur

Sulfur is one of the most abundant elements in the universe. If you peer into a diffuse interstellar cloud, you find loads of it - about the amount expected based on fusion patterns of the stars it was born in. The science-journalism coverage adds useful context, while the strongest evidential footing still comes from the underlying data, papers or institutional documentation.

That matters because chemistry gains force when a claimed structure or process can be described with enough precision to be reproduced by others. Synthetic routes, spectroscopic signatures, yield under defined conditions and stability under realistic operating parameters are the currency of credibility in chemistry, and a result that lacks these details cannot be evaluated independently. The distance between a discovery on a laboratory bench and a process that works reliably at scale is measured in years of optimization, and each step reveals constraints that were invisible at smaller scale. However, if you look at a dense, cold, molecular cloud - the kind where those stars actually form - it seems like 99% of the sulfur that is expected to be there is missing. A new paper published in Astronomy & Astrophysics from the Max Planck Institute for Extraterrestrial Physics and the Centro de Astrobiologia describes a new computer simulation.

The paper marks the first successful model of the chemistry of a multicomponent interstellar ice analog with a rate-equation simulation. The authors focused on simulating the results of one particular lab experiment focusing on sulfur that was performed in 2024.

During this experiment, a mixture of carbon dioxide (CO2) and carbon disulfide (CS2) was cooled down to 10K and then blasted with vacuum-ultraviolet (VUV) photons. During the physical experiment, this processing broke the molecules apart and created a mix-mash of new sulfur-bearing chemicals such as sulfur dioxide, carbonyl sulfide, and even.

Mimicking this experiment in simulation was the goal of the current paper, and it held some interesting new breakthroughs. First was how the molecules actually move.

The broader interest lies in whether the claimed property or reaction pathway can be characterized with enough precision to support replication by other groups. Chemistry has a replication problem that is less discussed than the one in psychology or medicine, but it is real: synthetic procedures that work reliably in one laboratory sometimes fail to transfer, for reasons ranging from impure starting materials to undocumented temperature sensitivities. A result that comes with full experimental detail and a clear characterization of the product is far more valuable than one that reports a discovery without the procedural backbone.

Enabling “non-diffusive chemistry” - where atoms can interact with their neighbors immediately upon breaking off from their host molecule - was the key to getting the reaction to. Turns out the answer is about 100 “monolayers” - or single sheets of ice molecules.

Because this item comes through Universe Today as science journalism, it should be treated as contextual reporting rather than primary evidence. Good science reporting can identify why a result matters, connect it to the wider literature and make technical work readable, but the decisive evidence remains in the original paper, dataset, mission release or technical record. That distinction is especially important when a story is later repeated by aggregators, because repetition increases visibility, not evidential strength.

The next step is to see whether independent groups working with orthogonal techniques reach compatible conclusions, and whether the result scales beyond the conditions used in the original study. Chemical discoveries that matter tend to be ones whose key properties can be measured by multiple spectroscopic, crystallographic or computational methods that are unlikely to share the same blind spots. Scalability, cost and long-term stability under realistic operating conditions are additional filters that come into play before any practical application becomes viable.

Source