Distinct Diffusion and Recombination Dynamics of Atomic Carbon and Oxygen on Face-Centered Cubic and Body-Centered Cubic Iron Multilayers.

This study investigates the distinctly different dynamics of atomic carbon and oxygen diffusion, both on the surface and into the bulk of iron multilayer films with face-centered cubic (FCC) (100) and body-centered cubic (BCC) (110) structures, and how these processes impact the recombination behavior of carbon and oxygen, particularly at elevated temperatures. On FCC-iron (γ-iron), CO dissociation occurs around 300 K, leading to the formation of segregated carbide and oxide islands on the surface upon annealing. Above the onset temperature of 600 K, mobile oxygen atoms diffuse to the edge of the carbide islands, where they combine with carbon to form CO.

Enhanced immobilization of trace nickel by nanoplastic-Fe-Mn oxide complexes in sedimentary systems.

Fe/Mn oxides are widely distributed mineral components in marine sediments and act as significant scavengers of trace metals. The emergence of plastic-rock complexes has led to an increasing recognition that plastics may influence the environmental behavior of minerals. Plastics, especially nanoplastics, can affect the formation of Fe/Mn oxides and their ability to immobilize heavy metals.

Ponatinib exacerbate renal injury in systemic lupus erythematosus mouse model through PDGFR-PI3K/AKT pathway.

Lupus nephritis (LN) is a common clinical complication of systemic lupus erythematosus (SLE). Proliferative lupus nephritis represents the gravest form of LN, and since effective drugs for its treatment are still lacking, tyrosine kinase inhibitors (TKIs) find extensive clinical utility due to their notable impact on suppressing cell proliferation and may serve as potential drugs for LN treatment. However, previous studies on the effects of TKI on LN have been controversial.

Bird's-Eye View of the Activity Distribution on a Catalyst Surface via a Machine Learning-Driven Adequate Sampling Algorithm.

Rational design of catalysts relies on a deep understanding of the active centers. The structure and activity distribution of active centers on a surface are two of the central issues in catalysis and important targets of theoretical and experimental investigations. Herein, we report a machine learning-driven adequate sampling (MLAS) framework for obtaining a statistical understanding of the chemical environment near catalyst sites.

Spectraformer: deep learning model for grain spectral qualitative analysis based on transformer structure.

This study delves into the use of compact near-infrared spectroscopy instruments for distinguishing between different varieties of barley, chickpeas, and sorghum, addressing a vital need in agriculture for precise crop variety identification. This identification is crucial for optimizing crop performance in diverse environmental conditions and enhancing food security and agricultural productivity. We also explore the potential application of transformer models in near-infrared spectroscopy and conduct an in-depth evaluation of the impact of data preprocessing and machine learning algorithms on variety classification.

Microbiome dysbiosis by antibiotics protects cartilage degradation in OAOP mice.

The role of this study was to evaluate the impact of gut microbiota depletion on the progression of osteoarthritis (OA) and osteoporosis (OP). We conducted an experimental mouse model of OA and OP over an 8-week period. The model involved destabilization of the medial meniscus and bilateral ovariectomy (OVX).

High-Density Coordinatively Unsaturated Zn Catalyst for Efficient Alkane Dehydrogenation.

The exploration of non-noble metal catalysts for alkane dehydrogenation and their catalytic mechanisms is the priority in catalysis research. Here, we report a high-density coordinatively unsaturated Zn cation (Zn) catalyst for the direct dehydrogenation (DDH) of ethylbenzene (EB) to styrene (ST). The catalyst demonstrated good catalytic performance (∼40% initial EB conversion rate and >98% ST selectivity) and excellent regeneration ability in the reaction, which is attributed to the high-density (HD) distribution and high-stability structure of Zn active sites on the surface of zinc silicate (HD-Zn@ZS).

Raman ConvMSANet: A High-Accuracy Neural Network for Raman Spectroscopy Blood and Semen Identification.

Animal blood and semen analysis plays a significant role in national biological resource management, wildlife conservation, and customs security quarantine. Traditional blood analysis methods have disadvantages, such as complex sample preparation, time consumption, and false positives. Therefore, proposing a rapid and highly accurate analysis method is highly valuable.

Structure-dependence and metal-dependence on atomically dispersed Ir catalysts for efficient n-butane dehydrogenation.

Single-site pincer-ligated iridium complexes exhibit the ability for C-H activation in homogeneous catalysis. However, instability and difficulty in catalyst recycling are inherent disadvantages of the homogeneous catalyst, limiting its development. Here, we report an atomically dispersed Ir catalyst as the bridge between homogeneous and heterogeneous catalysis, which displays an outstanding catalytic performance for n-butane dehydrogenation, with a remarkable n-butane reaction rate (8.

Error Loss Networks.

A novel model called error loss network (ELN) is proposed to build an error loss function for supervised learning. The ELN is similar in structure to a radial basis function (RBF) neural network, but its input is an error sample and output is a loss corresponding to that error sample. That means the nonlinear input-output mapper of the ELN creates an error loss function.

Single-Cation Catalyst: Ni Cation in Monolayered CuO for CO Oxidation.

It is vital to differentiate catalytic properties between cationic and metallic single atoms at the atomic level. To achieve this, we fabricated well-defined cationic Ni atoms snugged in and metallic Ni atoms supported on monolayered CuO. The Ni cations are chemically inert for CO adsorption even at 70 K but highly active toward O dissociation at room temperature.

Photocatalytic Abstraction of Hydrogen Atoms from Water Using Hydroxylated Graphitic Carbon Nitride for Hydrogenative Coupling Reactions.

Employing pure water, the ultimate green source of hydrogen donor to initiate chemical reactions that involve a hydrogen atom transfer (HAT) step is fascinating but challenging due to its large H-O bond dissociation energy (BDE =5.1 eV). Many approaches have been explored to stimulate water for hydrogenative reactions, but the efficiency and productivity still require significant enhancement.

Synergistic Catalysis of the Synthesis of Ammonia with Co-Based Catalysts and Plasma: From Nanoparticles to a Single Atom.

In this study, a series of Co nanoparticles (NPs) with different sizes and Co single-atom catalysts (SACs) with different cobalt-nitrogen coordination numbers (Co-N, Co-N, and Co-N) were synthesized and applied to the synthesis of ammonia catalyzed by plasma at low temperatures and atmospheric pressures. Under the same reaction conditions, the yield of nitrogen obtained from the reduction to ammonia over a series of Co NP catalysts varies with the Co particle size. The smaller the size of the Co NPs, the greater the number of exposed active centers, and the catalytic activity is higher.

Electron penetration triggering interface activity of Pt-graphene for CO oxidation at room temperature.

Achieving CO oxidation at room temperature is significant for gas purification but still challenging nowadays. Pt promoted by 3d transition metals (TMs) is a promising candidate for this reaction, but TMs are prone to be deeply oxidized in an oxygen-rich atmosphere, leading to low activity. Herein we report a unique structure design of graphene-isolated Pt from CoNi nanoparticles (PtǀCoNi) for efficiently catalytic CO oxidation in an oxygen-rich atmosphere.

Multikernel Correntropy for Robust Learning.

As a novel similarity measure that is defined as the expectation of a kernel function between two random variables, correntropy has been successfully applied in robust machine learning and signal processing to combat large outliers. The kernel function in correntropy is usually a zero-mean Gaussian kernel. In a recent work, the concept of mixture correntropy (MC) was proposed to improve the learning performance, where the kernel function is a mixture Gaussian kernel, namely, a linear combination of several zero-mean Gaussian kernels with different widths.

Settling velocity of irregularly shaped microplastics under steady and dynamic flow conditions.

The behavior of microplastics (MPs) in aquatic environments can vary significantly according to their composition, shape, and physical and chemical properties. To predict the settling trajectory of MPs in aquatic environments, this study investigates the settlement law of MPs under static and dynamic conditions. Four types of materials were analyzed, namely polystyrene, polyamide, polyethylene terephthalate, and polyvinyl chloride.

Perturbation of Spike Timing Benefits Neural Network Performance on Similarity Search.

Perturbation has a positive effect, as it contributes to the stability of neural systems through adaptation and robustness. For example, deep reinforcement learning generally engages in exploratory behavior by injecting noise into the action space and network parameters. It can consistently increase the agent's exploration ability and lead to richer sets of behaviors.

Abundance and removal characteristics of microplastics at a wastewater treatment plant in Zhengzhou.

The widespread use of synthetic polymers has made microplastic (MP) a new type of contaminant that has attracted worldwide attention. Studies have shown that wastewater treatment plants (WWTPs) are an important source of MP collection in the natural environment. This study investigated the removal efficiency and migration characteristics of MPs by sampling the sewage from each treatment section of a WWTP in Zhengzhou, China.

Robust Interface Ru Centers for High-Performance Acidic Oxygen Evolution.

RuO is considered as the state-of-the-art electrocatalyst for the oxygen evolution reaction (OER) in acidic media. However, its practical application is largely hindered by both the high reaction overpotential and severe electrochemical corrosion of the active centers. To overcome these limitations, innovative design strategies are necessary, which remains a great challenge.

Efficient Tetra-Functional Electrocatalyst with Synergetic Effect of Different Active Sites for Multi-Model Energy Conversion and Storage.

Energy crisis and global warming due to excessive CO emissions are the two major challenges of the world. Conversion of CO into useful fuels along with rechargeable metal air batteries and water splitting is one way to combat the energy crisis, which is bottlenecked due to the lack of multifunctional electrocatalyst. Herein simple but multifunctional electrocatalyst, which combined CoNi nanoalloy, N-doped carbon nanotubes, and single atomic Ni sites together is reported.

Solid Base Bi O Br (OH) with Active Lattice Oxygen for the Efficient Photo-Oxidation of Primary Alcohols to Aldehydes.

The selective oxidation of primary alcohols to aldehydes by O instead of stoichiometric oxidants (for example, Mn , Cr , and Os ) is an important but challenging process. Most heterogeneous catalytic systems (thermal and photocatalysis) require noble metals or harsh reaction conditions. Here we show that the Bi O Br (OH) photocatalyst is very efficient in the selective oxidation of a series of aliphatic (carbon chain from C to C ) and aromatic alcohols to their corresponding aldehydes/ketones under visible-light irradiation in air at room temperature, which would be challenging for conventional thermal and light-driven processes.

Machine-Learning Prediction of CO Adsorption in Thiolated, Ag-Alloyed Au Nanoclusters.

We propose a machine-learning model, based on the random-forest method, to predict CO adsorption in thiolate protected nanoclusters. Two phases of feature selection and training, based initially on the Au nanocluster, are utilized in our model. One advantage to a machine-learning approach is that correlations in defined features disentangle relationships among the various structural parameters.

FeC nanoparticles as low-cost HER electrocatalyst: the importance of Co substitution.

Constructing and understanding the doping effect of secondary metal in transition metal carbide (TMC) catalysts is pivotal for the design of low-cost hydrogen evolution reaction (HER) electrocatalysts. In this work, we developed a wet-chemistry strategy for synthesizing Co-modified FeC nanoparticles ((FeCo)C NPs) as highly active HER electrocatalysts in basic solution. The structure of (FeCo)C NPs was characterized by X-ray diffraction (XRD), extended X-ray absorption fine structure spectra (EXAFS) and scanning/transmission electron microscopy (S/TEM), indicating that the isomorphous substitution of cobalt in the lattice of FeC.