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Transient Catalytic Reaction Analysis Through Signal Defragmentation

The Temporal Analysis of Products (TAP) pulse response technique provides valuable insights into catalytic function and reaction kinetics. However, complex fragmentation patterns in the TAP mass spectrometry signals can complicate precise quantification, particularly when analyzing transient gas flux data typical of TAP experiments. This work demonstrates a standard defragmentation method that deconvolves transient TAP signals while maintaining the temporal resolution of the experiment. First, the integrals of calibration gas fluxes are used to determine the fingerprint fragmentation pattern and construct a fragmentation matrix. This matrix is then used to defragment experimental flux data at each recorded time point via a non-negative least squares regression. The effectiveness of this method is demonstrated using virtual data and control experiments with a TAP reactor system. The defragmentation is then applied to the more complex propane dehydrogenation reaction on a chromia/alumina catalyst, which can contain up to ten significant gas species in the reactor outlet. Initial propane pulsing reveals an induction period during which propane is fully oxidized to CO2, followed by partial reduction to CO. Afterwards, there is a transition in chemistries towards coking and propylene production. Our example illustrates a practical method for the accurate determination of the time-dependent reactant/product concentrations and rates for a thorough analysis of the propane dehydrogenation kinetics. This approach can be broadly applied to any transient mass spectrometry experiment for a better understanding of catalyst-reaction dynamics.

36 - MATERIALS SCIENCE↗

Mass Spectrometer Transient Analysis

This software implements a complete preprocessing pipeline for transient mass spectrometry (MS) data collected during TAP (Temporal Analysis of Products) experiments. It is designed to extract chemically meaningful fluxes from overlapping ion signals by applying a calibrated defragmentation matrix and solving the resulting linear system using non-negative least squares (NNLS) regression. The core script, preprocess_mass_spec.py, performs the following operations: Gain correction: Applies amplifier gain scalars derived from inert-packed calibration pulses to normalize signal intensities across AMUs and acquisition settings. Background subtraction: Removes experiment baselines using user-defined time windows, ensuring compatibility with slow-diffusing species and preventing negative values that would interfere with NNLS. Options to subtract before and after defragmentation. Defragmentation: Constructs a fragmentation matrix A from zeroth moments of calibration pulses (equal molar gas:inert mixtures) and solves Ax=b at each time point, where b is the raw MS signal and x is the estimated species flux. The matrix is normalized to inert signals and accounts for instrument-specific fragmentation behavior. Pulse-mode handling: Supports both averaged and individual pulse modes, enabling statistical treatment of fluxes and calculation of standard deviations. Integration and output: Computes zeroth moments (integrated fluxes) and exports time-resolved and integrated data in CSV format, suitable for downstream kinetic modeling. The software is validated using both virtual TAP simulations (VTAP) and experimental data from propane dehydrogenation (PDH) on CrOx/Al2O3 catalysts. It preserves temporal resolution by applying NNLS point-by-point across the pulse duration (typically 6,000+ time slices per pulse), leveraging the linear superposition principle to reconstruct full flux profiles. The defragmented outputs are compatible with kinetic extraction methods such as the G and Y procedures, which are used to derive rate–concentration relationships from TAP data. The details of these validations are discussed in detail in the supporting manuscript and supporting information. Example data and output files are also included. The methodology is robust to experimental noise and drift, with calibration protocols that account for pulse size effects, MS aging, and inert gas normalization. The software is modular, reproducible, and tailored for high-throughput TAP-MS workflows in catalysis research.

Kristy, Stephen [Idaho National Laboratory (INL), ↗

A New Recycled Al–Si–Mg Alloy for Sustainable Structural Die Casting Applications

The use of secondary aluminum for structural components in the automotive industry is limited by the high Fe contents in recycled alloys which often result in the formation of brittle β-Al 5 FeSi phase which reduces the ductility of aluminum castings. In this study, a new secondary Al-Si-Mg alloy with high Fe content (about 0.44 wt.%) was developed for die casting applications. Based on thermodynamic modeling, manganese was added to obtain a designed Fe-to-Mn ratio of 2 which successfully suppressed the formation of β-Al 5 FeSi phase, by forming α-Al 15 (Fe,Mn) 3 Si 2 phase with rounded or hexagonal morphology. Additionally, a fine needle-like π-Al 8 FeMg 3 Si 2 phase was also formed within the eutectic regions. The new recycled alloy showed comparable mechanical properties in as-cast and heat treated (T5 and T6) conditions to three major primary die cast alloys (≤ 0.2 wt.% Fe) with similar composition. Ductility up to 7.4% from tensile elongation was achieved in as-cast recycled alloy due to the modification and refinement of α-Al 15 (Fe,Mn) 3 Si 2 by Mn and Sr additions. Tensile elongation was further improved to 9.1% after T6 treatment as a result of dissolution of π-Al 8 FeMg 3 Si 2 phase, defragmentation of α-Al 15 (Fe,Mn) 3 Si 2 and the spheroidization of Si phase. This new alloy provides a promising path for increasing usage of recycled aluminum with high Fe content in structural die castings for automotive and other applications.

36 MATERIALS SCIENCE↗