Insights into the Acidic Site in Manganese Oxide in Terms of the Sulfur and Water Tolerance of Low-Temperature NH3 Selective Catalytic Reduction.

A critical constraint impeding the utilization of Mn-based oxide catalysts in NH3 selective catalytic reduction (NH3-SCR) is their inadequate resistance to water and sulfur. This vulnerability primarily arises from the propensity of SO2 to bind to the acidic site in manganese oxide, resulting in the formation of metal sulfate and leading to the irreversible deactivation of the catalyst. Therefore, gaining a comprehensive understanding of the detrimental impact of SO2 on the acidic sites and elucidating the underlying mechanism of this toxicity are of paramount importance for the effective application of Mn-based catalysts in NH3-SCR. Herein, we strategically modulate the acidity of the manganese oxide catalyst surface through the incorporation of Ce and Nb. Comprehensive analyses, including thermogravimetry, NH3 temperature-programmed desorption, in situ diffused reflectance infrared Fourier transform spectroscopy, and density functional theory calculations, reveal that SO2 exhibits a propensity for adsorption at strongly acidic sites. This mechanistic understanding underscores the pivotal role of surface acidity in governing the sulfur resistance of manganese oxide.

Engineering of Pore Design and Oxygen Vacancy on High-Entropy Oxides by a Microenvironment Tailoring Strategy.

High-entropy oxides (HEOs) exhibit abundant structural diversity due to cationic and anionic sublattices with independence, rendering them superior in catalytic applications compared to monometallic oxides. Nevertheless, the conventional high-temperature calcination approach undermines the porosity and reduces the exposure of active sites (such as oxygen vacancies, OVs) in HEOs, leading to diminished catalytic efficiency. Herein, we fabricate a series of HEOs with a large surface area utilizing a microenvironment modulation strategy (m-NiMgCuZnCo: 86 m2/g, m-MnCuCoNiFe: 67 m2/g, and m-FeCrCoNiMn: 54 m2/g). The enhanced porosity in m-NiMgCuZnCo facilitates the presentation of numerous OVs, exhibiting an exceptional catalytic performance. This tactic creates inspiration for designing HEOs with rich porosity and active species with vast potential applications.

Constructing an efficient electrocatalyst for water oxidation: an Fe-doped CoO/Co catalyst enabled by in situ MOF growth and a solvent-free strategy.

The development of non-precious metal electrocatalysts with high activity for the oxygen evolution reaction (OER) is a crucial and challenging task. In this work, we proposed a solvent-free in situ metal-organic framework (MOF) growth strategy for the fabrication of an Fe-doped CoO/Co electrocatalyst. This approach not only partially granted the MOF's porous structure to the catalyst but also resulted in a tighter combination between the Co metal and CoO, thereby enhancing its electrical conductivity. Furthermore, this method enabled the Fe species to be more uniformly dispersed on CoO/Co, which significantly exposed more active sites for efficient electrocatalysis. The entire synthesis process was solvent-free, except for a small amount of water and ethanol used during catalyst washing. The as-synthesized Fe-CoO/Co electrocatalyst exhibited superior OER activity on a glass carbon electrode, with η = 276 mV at a current density of 10 mA cm-2, even higher than that of the commercial precious IrO2/C catalyst. Additionally, it was also extended to prepare a Ni-doped CoO/Co electrocatalyst by the same procedure with satisfactory OER performance. This work presents a new preparation approach for MOF-derived catalysts with potential applications in energy conversion and beyond.

Unsupervised Phonocardiogram Analysis With Distribution Density Based Variational Auto-Encoders.

This paper proposes an unsupervised way for Phonocardiogram (PCG) analysis, which uses a revised auto encoder based on distribution density estimation in the latent space. Auto encoders especially Variational Auto-Encoders (VAEs) and its variant ß-VAE are considered as one of the state-of-the-art methodologies for PCG analysis. VAE based models for PCG analysis assume that normal PCG signals can be represented by latent vectors that obey a normal Gaussian Model, which may not be necessary true in PCG analysis. This paper proposes two methods DBVAE and DBAE that are based on estimating the density of latent vectors in latent space to improve the performance of VAE based PCG analysis systems. Examining the system performance with PCG data from the a single domain and multiple domains, the proposed systems outperform the VAE based methods. The representation of normal PCG signals in the latent space is also investigated by calculating the kurtosis and skewness where DBAE introduces normal PCG representation following Gaussian-like models but DBVAE does not introduce normal PCG representation following Gaussian-like models.