Amine N‐alkylation is a process involved in the production of a wide range of chemicals. Here we describe the synthesis of well‐defined (Ni0.5Cu0.5)Fe2O4 magnetic nanoparticles by plasma induction, and their successful application to amine N‐alkylation using alcohols as coupling agents through a borrowing hydrogen pathway. Plasma induction allows precise morphology and size control over nanoparticle synthesis, while allowing the one‐pot production of decagram quantities of material. Up to date, such nanoparticles have never been applied for organic reactions. By coupling high‐end characterization techniques with catalytic optimization, we showed that small Cu(0) satellite nanoparticles played an essential role in alcohol oxidation, whereas both Ni and Cu were required for the last step of the reaction. Using elemental mapping, we demonstrated that catalyst deactivation occurred through a leaching/re‐deposition mechanism of Cu and Ni. The reactions were conducted under microwave conditions, which exerted a positive effect on catalytic activity. Finally, the catalyst was active at low metal loadings (2 mol%) even on the gram‐scale, and affording unprecedented TON for this reaction catalyzed by Ni/Cu bimetallic systems (19).
With improved performance of TEMs and spectrometers, the limiting step for better materials characterization is now the ability to translate large datasets into meaningful information. Here, we present a new, semi-supervised method for retrieving spectral components and their respective spatial contribution that is efficient when weak spectral features are scattered in the spectrum-image (SI).The sample is made of nanoparticles (NPs) produced by injecting an equimolar ratio of Cu and Fe nitrates solution in an induction Ar/O2 plasma reactor. XRD analyses indicated the presence of CuFe2O4 tetragonal phase (33% wt), Cu0.5Fe2.5O4 (47% wt) and (Cu,Fe)O (20% wt). The NPs were dispersed in ethanol on a lacey carbon TEM grid. The analyses were performed with an objective lens aberration corrected FEI Titan TEM operated at 300 kV and equipped with an annular detector and a Gatan Quantum image filter. Fig. 1a shows nanoparticles decorated with a secondary phase. X-ray diffraction (not shown) combined with the HREM confirms that the NPs adopt the cubic and tetragonal phase of Cu ferrites (Fig. 1b). However, the low crystallinity of the secondary phase ( Fig. 1c) makes it difficult to identify it using HREM. Wet tried using the Cu L2,3-Fe L2,3 signal to generate a bivariate histogram to isolate the Cu-rich regions of the SI, but a good compromise between the signal-to-noise ratio (SNR) and the "purity" of the metallic Cu character could not be achieved. Given the scarcity of pure Cu pixels, various versions of Independent Component Analysis [1] failed to produce physically sound components although the Bayesian Linear Unmixing with a NFINDR initialization [2] showed potential.We used a Multivariate Curve Resolution (MCR) algorithm to extract physically meaningful spectra ( ) and composition maps ( ) from an image ( ). The loglikelihood variant (MCR-LLM) has been shown to perform particularly well in low SNR datasets [3]. In this work, a hierarchical version of MCR-LLM was used to deal with local composition gradients and faulty pixels. Fig. 1d illustrates the methodology in which spectral features are extracted dyadically with each step. The 137×93 spectra were thus reduced to 331 significant ones from which we selected 4 ( Fig. 1e): Cu (red), Cu ferrites (green), carbon (blue: featureless exponential background) and the hole (yellow: noise). The maps (Fig. 1f) associated with each spectrum were generated by finding the set coefficients that maximizes the loglikelihood [3]. This method is especially suited for datasets with low signal-to-noise levels contaminated with Poisson noise.The EELS Fe:Cu:O intensity ratio of the Cu ferrite phase is consistent with CuxFe3-xO4 (0 ≤ ≤ 1). The Cu content of the metallic Cu approached 70%, but was found along Fe (10% at) and O (20% at). The Cu L2,3 edge profile is mostly metallic and the shape of the O K edge is indicative of the presence of (Cu,Fe)O, demonstrating that metallic Cu is mixed with the monoxyde. Comparing the spatial distribution of Cu clusters (Fig. 1f) with the Sobel-fi...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.