A Thermodynamic Resolution of Molecular Clustering: Eliminating Sticking Efficiency

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The paper examines how molecular clustering models enforce thermodynamic equilibrium when forward and reverse reaction rates should be related by Gibbs free energy, noting that many models neglect reverse rates for low or negative ΔG associations and instead use an empirical sticking efficiency η. Using a parameter-free framework based on microscopic reversibility and statistical mechanics, the author applies it to argon dimer formation and reports that the model reproduces transient and equilibrium behavior without approximation; a stated limitation is that it is demonstrated on a chemically inert argon dimer system. When η is computed after the fact within this framework, it is found to become negative and collapse to zero at equilibrium, indicating a structural inconsistency in the sticking-efficiency parameterization when reversibility is restored. The paper relates to endometriosis and/or adenomyosis only indirectly—its discussion of molecular clustering is relevant to none of those conditions specifically, though it is included because the upstream search indexed it under endometriosis-adjacent keywords. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract In gas-phase chemistry, forward and reverse rates are governed by Gibbs free energy to ensure equilibrium. Yet in molecular clustering models, particularly for low or negative $\Delta G$ associations, reverse rates are often neglected and replaced with an empirical “sticking efficiency” $\eta$. This substitution violates thermodynamic consistency. We present a parameter-free framework in which equilibrium and dynamics emerge from microscopic reversibility and statistical mechanics. Applied to argon dimer formation—a chemically inert system with no free parameters, the model reproduces both transient and equilibrium behavior without approximation. Post hoc computation of $\eta$ reveals that it becomes negative and collapses to zero at equilibrium, exposing a structural failure in the parameter when reversibility is restored. This result is not merely theoretical. Climate models rely on molecular clustering to simulate black carbon and secondary organic aerosol formation, key drivers of radiative forcing. Models using $\eta$ inherit its inconsistencies. This framework offers a thermodynamically grounded and general replacement.
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Yet in molecular clustering models, particularly for low or negative $\Delta G$ associations, reverse rates are often neglected and replaced with an empirical “sticking efficiency” $\eta$. This substitution violates thermodynamic consistency. We present a parameter-free framework in which equilibrium and dynamics emerge from microscopic reversibility and statistical mechanics. Applied to argon dimer formation—a chemically inert system with no free parameters, the model reproduces both transient and equilibrium behavior without approximation. Post hoc computation of $\eta$ reveals that it becomes negative and collapses to zero at equilibrium, exposing a structural failure in the parameter when reversibility is restored. This result is not merely theoretical. Climate models rely on molecular clustering to simulate black carbon and secondary organic aerosol formation, key drivers of radiative forcing. Models using $\eta$ inherit its inconsistencies. This framework offers a thermodynamically grounded and general replacement. Physical sciences/Chemistry/Physical chemistry/Thermodynamics Physical sciences/Chemistry/Theoretical chemistry/Statistical mechanics microscopic reversibility emergent kinetics sticking efficiency argon dimer molecular clustering statistical mechanics reversible dynamics Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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