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A multimodal analytical toolkit to resolve correlated reaction pathways: the case of nanoparticle formation in zeolites† † Electronic supplementary information (ESI) available: Details of multimodal measurements and conventional analyses. See DOI: 10.1039/d1sc04232g

ABSTRACT: Unraveling the complex, competing pathways that can govern reactions in multicomponent systems is an experimental and technical challenge. We outline and apply a novel analytical toolkit that fully leverages the synchronicity of multimodal experiments to deconvolute causal from correlative relationships and resolve structural and chemical changes in complex materials. Here, simultaneous multimodal measurements combined diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and angular dispersive X-ray scattering suitable for pair distribution function (PDF), X-ray diffraction (XRD) and small angle X-ray scattering (SAXS) analyses. The multimodal experimental data was interpreted via multi-level analysis; conventional analyses of each data series were integrated through meta-analysis involving non-negative matrix factorization (NMF) as a dimensional reduction algorithm and correlation analysis. We apply this toolkit to build a cohesive mechanistic picture of the pathways governing silver nanoparticle formation in zeolite A (LTA), which is key to designing catalytic and separations-based applications. For this Ag-LTA system, the mechanisms of zeolite dehydration, framework flexing, ion reduction, and cluster and nanoparticle formation and transport through the zeolite are elucidated. We note that the advanced analytical approach outline here can be applied generally to multimodal experiments, to take full advantage of the efficiencies and self-consistencies in understanding complex materials and go beyond what can be achieved by conventional approaches to data analysis. Multimodal in situ experimental data probing a complex reaction have been integrated via a multi-level analysis involving non-negative matrix factorization and correlation analysis. This strategy can be applied generally to multimodal experiments.

SUBMITTER: O'Nolan D

PROVIDER: S-EPMC8549813 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundConstraint-based modeling of reconstructed genome-scale metabolic networks has been successfully applied on several microorganisms. In constraint-based modeling, in order to characterize all allowable phenotypes, network-based pathways, such as extreme pathways and elementary flux modes, are defined. However, as the scale of metabolic network rises, the number of extreme pathways and elementary flux modes increases exponentially. Uniform random sampling solves this problem to some extent to study the contents of the available phenotypes. After uniform random sampling, correlated reaction sets can be identified by the dependencies between reactions derived from sample phenotypes. In this paper, we study the relationship between extreme pathways and correlated reaction sets.ResultsCorrelated reaction sets are identified for E. coli core, red blood cell and Saccharomyces cerevisiae metabolic networks respectively. All extreme pathways are enumerated for the former two metabolic networks. As for Saccharomyces cerevisiae metabolic network, because of the large scale, we get a set of extreme pathways by sampling the whole extreme pathway space. In most cases, an extreme pathway covers a correlated reaction set in an 'all or none' manner, which means either all reactions in a correlated reaction set or none is used by some extreme pathway. In rare cases, besides the 'all or none' manner, a correlated reaction set may be fully covered by combination of a few extreme pathways with related function, which may bring redundancy and flexibility to improve the survivability of a cell. In a word, extreme pathways show strong complementary relationship on usage of reactions in the same correlated reaction set.ConclusionBoth extreme pathways and correlated reaction sets are derived from the topology information of metabolic networks. The strong relationship between correlated reaction sets and extreme pathways suggests a possible mechanism: as a controllable unit, an extreme pathway is regulated by its corresponding correlated reaction sets, and a correlated reaction set is further regulated by the organism's regulatory network.

Dataset Information

A multimodal analytical toolkit to resolve correlated reaction pathways: the case of nanoparticle formation in zeolites† † Electronic supplementary information (ESI) available: Details of multimodal measurements and conventional analyses. See DOI: 10.1039/d1sc04232g

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