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In an epoch dominated by escalating concerns over climate change and looming energy crises, the imperative to design highly efficient catalysts that can facilitate the sequestration and transformation of carbon dioxide (CO2) into beneficial chemicals is paramount. This research presents the successful synthesis of nanofiber catalysts, incorporating monometallic nickel (Ni) and cobalt (Co) and their bimetallic blend, NiCo, via a facile electrospinning technique, with precise control over the Ni/Co molar ratios. Application of an array of advanced analytical methods, including SEM, TGA–DSC, FTIR-ATR, XRD, Raman, XRF, and ICP-MS, validated the effective integration and homogeneous distribution of active Ni/Co catalysts within the nanofibers. The catalytic performance of these mono- and bimetallic Ni/Co nanofiber catalysts was systematically examined under ambient pressure conditions for CO2 hydrogenation reactions. The bimetallic NiCo nanofiber catalysts, specifically with a Ni/Co molar ratio of 1:2, and thermally treated at 1050 °C, demonstrated a high CO selectivity (98.5%) and a marked increase in CO2 conversion rate—up to 16.7 times that of monometallic Ni nanofiber catalyst and 10.8 times that of the monometallic Co nanofiber catalyst. This significant enhancement in catalytic performance is attributed to the improved accessibility of active sites, minimized particle size, and the strong Ni–Co–C interactions within these nanofiber structures. These nanofiber catalysts offer a unique model system that illuminates the fundamental aspects of supported catalysis and accentuates its crucial role in addressing pressing environmental challenges. 

The catalytic conversion of CO2 to value-added chemicals and fuels has been long regarded as a promising approach to the mitigation of CO2 emissions if green hydrogen is used. Light olefins, particularly ethylene and propylene, as building blocks for polymers and plastics, are currently produced primarily from CO2-generating fossil resources. The identification of highly efficient catalysts with selective pathways for light olefin production from CO2 is a high-reward goal, but it has serious technical challenges, such as low selectivity and catalyst deactivation. In this review, we first provide a brief summary of the two dominant reaction pathways (CO2-Fischer-Tropsch and MeOH-mediated pathways), mechanistic insights, and catalytic materials for CO2 hydrogenation to light olefins. Then, we list the main deactivation mechanisms caused by carbon deposition, water formation, phase transformation and metal sintering/agglomeration. Finally, we detail the recent progress on catalyst development for enhanced olefin yields and catalyst stability by the following catalyst functionalities: (1) the promoter effect, (2) the support effect, (3) the bifunctional composite catalyst effect, and (4) the structure effect. The main focus of this review is to provide a useful resource for researchers to correlate catalyst deactivation and the recent research effort on catalyst development for enhanced olefin yields and catalyst stability. 

Abstract Machining-induced residual stresses (MIRS) are a main driver for distortion of thin-walled monolithic aluminum workpieces. Before one can develop compensation techniques to minimize distortion, the effect of machining on the MIRS has to be fully understood. This means that not only an investigation of the effect of different process parameters on the MIRS is important. In addition, the repeatability of the MIRS resulting from the same machining condition has to be considered. In past research, statistical confidence of MIRS of machined samples was not focused on. In this paper, the repeatability of the MIRS for different machining modes, consisting of a variation in feed per tooth and cutting speed, is investigated. Multiple hole-drilling measurements within one sample and on different samples, machined with the same parameter set, were part of the investigations. Besides, the effect of two different clamping strategies on the MIRS was investigated. The results show that an overall repeatability for MIRS is given for stable machining (between 16 and 34% repeatability standard deviation of maximum normal MIRS), whereas instable machining, detected by vibrations in the force signal, has worse repeatability (54%) independent of the used clamping strategy. Further experiments, where a 1-mm-thick wafer was removed at the milled surface, show the connection between MIRS and their distortion. A numerical stress analysis reveals that the measured stress data is consistent with machining-induced distortion across and within different machining modes. It was found that more and/or deeper MIRS cause more distortion. 

Abstract Motivation Two-dimensional [15N-1H] separated local field solid-state nuclear magnetic resonance (NMR) experiments of membrane proteins aligned in lipid bilayers provide tilt and rotation angles for α-helical segments using Polar Index Slant Angle (PISA)-wheel models. No integrated software has been made available for data analysis and visualization. Results We have developed the PISA-SPARKY plugin to seamlessly integrate PISA-wheel modeling into the NMRFAM-SPARKY platform. The plugin performs basic simulations, exhaustive fitting against experimental spectra, error analysis and dipolar and chemical shift wave plotting. The plugin also supports PyMOL integration and handling of parameters that describe variable alignment and dynamic scaling encountered with magnetically aligned media, ensuring optimal fitting and generation of restraints for structure calculation. Availability and implementation PISA-SPARKY is freely available in the latest version of NMRFAM-SPARKY from the National Magnetic Resonance Facility at Madison (http://pine.nmrfam.wisc.edu/download_packages.html), the NMRbox Project (https://nmrbox.org) and to subscribers of the SBGrid (https://sbgrid.org). The pisa.py script is available and documented on GitHub (https://github.com/weberdak/pisa.py) along with a tutorial video and sample data. Supplementary information Supplementary data are available at Bioinformatics online.