Predicting moisture outgassing dynamics of vacuum materials from temperature-programmed desorption energy distributions

H2O outgassing from ambient-exposed technical materials often dominates the residual gas load in vacuum systems, requiring thermally accelerated removal (bakeout) to achieve low-contaminant environments needed for applications like semiconductor manufacturing. Determining bakeout conditions that achieve a target outgassing rate remains difficult, especially under practical time, thermal-budget, or pumping-speed constraints, because available outgassing-rate data are typically tied to specific measurement conditions and are, therefore, difficult to generalize to the limits relevant for a given vacuum system. Here, a dynamic transfer-function model for time- and temperature-dependent H2O outgassing flux is developed from temperature-programmed desorption (TPD) measurements using a semiempirical first-order desorption framework with a single fitted Arrhenius parameter for 304 stainless steel, 6061 aluminum, and molybdenum. For each material, a single measured TPD spectrum is converted into a desorption-energy distribution and used to predict H2O outgassing flux under arbitrary time-temperature trajectories. Model parameters are fit and validated by reproducing the remaining distributions probed by TPD reramp experiments after 200 °C holds of 30 min, 6 h, and 48 h, and after extended, 96 h pumping of Mo at ambient temperature; the consistency across hold durations indicates that a single hold experiment is sufficient to develop a predictive model. The validated framework further predicts dynamic H2O outgassing rates, including approximate power-law decay with time, shows especially good agreement with compiled literature values for degreased samples, and generates practical bakeout tradeoffs indicating that increasing maximum bakeout temperature by ∼15–30 °C is equivalent to increasing pumping time tenfold for the materials examined.