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Analyzing mechanisms in Co(i) redox catalysis using a pattern recognition platform.

ABSTRACT: Redox catalysis has been broadly utilized in electrochemical synthesis due to its kinetic advantages over direct electrolysis. The appropriate choice of redox mediator can avoid electrode passivation and overpotential, which strongly inhibit the efficient activation of substrates in electrolysis. Despite the benefits brought by redox catalysis, establishing the precise nature of substrate activation remains challenging. Herein, we determine that a Co(i) complex bearing two N,N,N-tridentate ligands acts as a competent redox catalyst for the reduction of benzyl bromide substrates. Kinetic studies combining electroanalytical techniques with multivariable linear-regression analysis were conducted, disclosing an outer-sphere electron-transfer mechanism, which occurs in concert with C-Br bond cleavage. Furthermore, we apply a pattern recognition platform to distinguish between mechanisms in the activation of benzyl bromides, found to be dependent on the ligation state of the cobalt(i) center and ligand used.

SUBMITTER: Tang T

PROVIDER: S-EPMC8179645 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Analyzing mechanisms in Co(i) redox catalysis using a pattern recognition platform.

Tang Tianhua T Sandford Christopher C Minteer Shelley D SD Sigman Matthew S MS

Chemical science 20210217 13

Redox catalysis has been broadly utilized in electrochemical synthesis due to its kinetic advantages over direct electrolysis. The appropriate choice of redox mediator can avoid electrode passivation and overpotential, which strongly inhibit the efficient activation of substrates in electrolysis. Despite the benefits brought by redox catalysis, establishing the precise nature of substrate activation remains challenging. Herein, we determine that a Co(i) complex bearing two N,N,N ...[more]

PMID: 34168755

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Project description:BackgroundPattern recognition algorithms are useful in bioimage informatics applications such as quantifying cellular and subcellular objects, annotating gene expressions, and classifying phenotypes. To provide effective and efficient image classification and annotation for the ever-increasing microscopic images, it is desirable to have tools that can combine and compare various algorithms, and build customizable solution for different biological problems. However, current tools often offer a limited solution in generating user-friendly and extensible tools for annotating higher dimensional images that correspond to multiple complicated categories.ResultsWe develop the BIOimage Classification and Annotation Tool (BIOCAT). It is able to apply pattern recognition algorithms to two- and three-dimensional biological image sets as well as regions of interest (ROIs) in individual images for automatic classification and annotation. We also propose a 3D anisotropic wavelet feature extractor for extracting textural features from 3D images with xy-z resolution disparity. The extractor is one of the about 20 built-in algorithms of feature extractors, selectors and classifiers in BIOCAT. The algorithms are modularized so that they can be "chained" in a customizable way to form adaptive solution for various problems, and the plugin-based extensibility gives the tool an open architecture to incorporate future algorithms. We have applied BIOCAT to classification and annotation of images and ROIs of different properties with applications in cell biology and neuroscience.ConclusionsBIOCAT provides a user-friendly, portable platform for pattern recognition based biological image classification of two- and three- dimensional images and ROIs. We show, via diverse case studies, that different algorithms and their combinations have different suitability for various problems. The customizability of BIOCAT is thus expected to be useful for providing effective and efficient solutions for a variety of biological problems involving image classification and annotation. We also demonstrate the effectiveness of 3D anisotropic wavelet in classifying both 3D image sets and ROIs.

Project description:Macroalgae have high potential to be an efficient, and sustainable feedstock for the production of biofuels and other more valuable chemicals. Attempts have been made to enable the co-fermentation of alginate and mannitol by Saccharomyces cerevisiae to unlock the full potential of this marine biomass. However, the efficient use of the sugars derived from macroalgae depends on the equilibrium of cofactors derived from the alginate and mannitol catabolic pathways. There are a number of strong metabolic limitations that have to be tackled before this bioconversion can be carried out efficiently by engineered yeast cells. An analysis of the redox balance during ethanol fermentation from alginate and mannitol by Saccharomyces cerevisiae using metabolic engineering tools was carried out. To represent the strain designed for conversion of macroalgae carbohydrates to ethanol, a context-specific model was derived from the available yeast genome-scale metabolic reconstructions. Flux balance analysis and dynamic simulations were used to determine the flux distributions. The model indicates that ethanol production is determined by the activity of 4-deoxy-l-erythro-5-hexoseulose uronate (DEHU) reductase (DehR) and its preferences for NADH or NADPH which influences strongly the flow of cellular resources. Different scenarios were explored to determine the equilibrium between NAD(H) and NADP(H) that will lead to increased ethanol yields on mannitol and DEHU under anaerobic conditions. When rates of mannitol dehydrogenase and DehRNADH tend to be close to a ratio in the range 1-1.6, high growth rates and ethanol yields were predicted. The analysis shows a number of metabolic limitations that are not easily identified through experimental procedures such as quantifying the impact of the cofactor preference by DEHU reductase in the system, the low flux into the alginate catabolic pathway, and a detailed analysis of the redox balance. These results show that production of ethanol and other chemicals can be optimized if a redox balance is achieved. A possible methodology to achieve this balance is presented. This paper shows how metabolic engineering tools are essential to comprehend and overcome this limitation.

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Analyzing mechanisms in Co(i) redox catalysis using a pattern recognition platform.

Publications

Analyzing mechanisms in Co(i) redox catalysis using a pattern recognition platform.

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