Document Type

Article

Publication Date

2026

DOI

10.1116/6.0005542

Publication Title

Journal of Vacuum Science & Technology B

Volume

44

Issue

4

Pages

044206 (Article No.)

Abstract

Pump-down pressure-time data are routinely fitted to infer the surface physics of adsorption, yet models built on different assumptions produce near-indistinguishable fits, so the inferred physics can be an artifact of the fitting choice rather than a property of the surface. This work provides two tools to close that gap: a model-selection criterion suited to autocorrelated pump-down data (a corrected Akaike information criterion with a generalized-least-squares first-order autocorrelation correction, AICcGLS), and an identifiability diagnostic that determines whether a given experimental protocol can constrain an adsorption isotherm at all. Applying both to 20 datasets from AISI 1020 low-carbon steel and 316L stainless steel chambers, under three protocols (isothermal pump-down, nonisothermal throughput with a bake, and N₂ vent/repump at 25 degrees C), shows that the protocol, the combination of thermal regime, pumping speed, and surface-area-to-pump-speed (A/S₀) ratio, is the primary factor controlling identifiability. Only the isothermal, high-pumping-speed protocol resolves the isotherm; even there the five candidates fit statistically indistinguishably (all R² ≥ 0.96), so goodness-of-fit cannot select among them and the apparent preference reduces to parsimony under the information criterion: the two-parameter Dubinin-Radushkevich model on most datasets, with the single-energy Langmuir model most parsimonious at 75 degrees C. The chamber-bake protocol is confounded by the thermal ramp and the N₂-vent protocol by the H₂ background, leaving the isotherm unidentifiable in both. The diagnostic identifies when isotherm parameters from pump-down data are physically meaningful rather than fitting artifacts.

Rights

© 2026 The Authors.

This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in 

Al-Allaq, A. H., Mamun, M. A., Poelker, M., & Elmustafa, A. (2026). Statistical identification of isotherm models from vacuum pump-down data. Journal of Vacuum Science and Technology B, 44(4), Article 044204. 

and may be found at https://doi.org/10.1116/6.0005542

Data Availability

Article states: "The processed pump-down datasets and the complete analysis code, the genuine-ODE isotherm fitter, the AICc and AR (1)-GLS model-selection pipeline, and the figure-generation scripts are available from the corresponding author upon reasonable request."

Original Publication Citation

Al-Allaq, A. H., Mamun, M. A., Poelker, M., & Elmustafa, A. (2026). Statistical identification of isotherm models from vacuum pump-down data. Journal of Vacuum Science and Technology B, 44(4), Article 044204. https://doi.org/10.1116/6.0005542

ORCID

0000-0002-0415-2795 (Al-Allaq), 0000-0001-5865-6035 (Elmustafa)

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