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. Combined strategies were implemented to achieve highly efficient sampling, including the decomposition of degrees of freedom, stratified sampling, Gaussian process regression, and well-designed constraint optimization. The MLAS framework was applied to the rate-determining step in NH3 synthesis, namely the N2 activation process. We calculated the produced population function, PA, which provides a comprehensive and intuitive understanding of active centers. The MLAS framework can be broadly applied to other more complicated catalyst materials and reaction networks.

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. In our proposed spectraformer multi-classification model, we successfully differentiated 24 barley varieties, 19 chickpea varieties, and ten sorghum varieties, with the highest accuracy rates reaching 85%, 95%, and 86%, respectively. These results demonstrate that small near-infrared spectroscopy instruments are cost-effective and efficient tools with the potential to advance research in various identification methods, but also underscore the value of transformer models in near-infrared spectroscopy classification. Furthermore, we delve into the discussion of the influence of data preprocessing on the performance of deep learning models compared to traditional machine learning models, providing valuable insights for future research in this field.

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). To deplete the gut microbiota, we administered a course of antibiotics for 8 weeks. The severity of OA was assessed through micro-CT scanning, X-rays, and immunohistochemical staining. Microbiome analysis was performed using PCR of 16S DNA on fecal samples, and the levels of serum lipopolysaccharide, interleukin 6, tumor necrosis factor-α (TNF-α), osteocalcin, and estrogen were measured using enzyme-linked immunosorbent assay. We found that in comparison to the OVX+OA group, the OVX+OA+ABT group exhibited increased bone mineral density (P < 0.0001), bone volume fraction (P = 0.0051), and trabecular number (P = 0.0023) in the metaphyseal bone. Additionally, cartilage injury and levels of matrix metalloproteinase 13 were reduced in the OVX+OA+ABT group compared to the OVX+OA group. Moreover, the OVX+OA+ABT group demonstrated decreased relative abundance of Bacteroidetes, serum lipopolysaccharide (P = 0.0005), TNF-α (P < 0.0001), CTX-1 (P = 0.0002), and increased expression of bone formation markers. These findings were further supported by correlation network analyses. Depletion of gut microbiota was shown to protect against bone loss and cartilage degradation by modulating the composition of the gut microbiota in osteoporosis and osteoarthritis.

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 (Zncus) 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 Zncus active sites on the surface of zinc silicate (HD-Zncus@ZS). Density functional theory (DFT) calculations further illustrated the reaction pathway and intermediates, supporting that the Zncus sites can efficiently activate the C-H bond of ethyl on ethylbenzene. Developing the high-density Zncus catalyst and exploring the catalytic mechanism laid a good foundation for designing practical non-noble metal catalysts.

Clustering-Evolved Frontier Orbital for Low-Temperature CO2 Dissociation.

In this study, single Ni2 clusters (two Ni atoms bridged by a lattice oxygen) are successfully synthesized on monolayered CuO. They exhibit a remarkable activity toward low-temperature CO2 thermal dissociation, in contrast to cationic Ni atoms that nondissociatively adsorb CO2 and metallic Ni ones that are chemically inert for CO2 adsorption. Density functional theory calculations reveal that the Ni2 clusters can significantly alter the spatial symmetry of their unoccupied frontier orbitals to match the occupied counterpart of the CO2 molecule and enable its low-temperature dissociation. This study may help advance single-cluster catalysis and exploit the unexcavated mechanism for low-temperature CO2 activation.

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. Raman spectroscopy has been widely used in blood analysis, and efficient and accurate analysis results can be obtained through the machine learning algorithm feature extraction. Recently, the transformer network structure was applied to Raman spectroscopy recognition. However, the multihead self-attention mechanism does not perform well in extracting local feature peaks, although it obtains global feature relations. This paper proposes a neural network based on the combination of one-dimensional convolution and multihead self-attention mechanism (Raman ConvMSANet) to identify 52 species of blood and semen Raman spectra. The network can achieve reliable identification effects in multiclassification and sample imbalance situations, and the average identification accuracy of blood and semen can reach more than 98.5%. The proposed network model can be applied not only to blood and semen identification but also to other biological fields.

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 (8.8 mol·gIr-1·h-1) and high butene selectivity (95.6%) at low temperature (450 °C). Significantly, we correlate the BDH activity with the Ir species from nanoscale to sub-nanoscale, to reveal the nature of structure-dependence of catalyst. Moreover, we compare Ir single atoms with Pt single atoms and Pd single atoms for in-depth understanding the nature of metal-dependence at the atomic level. From experimental and theoretical calculations results, the isolated Ir site is suitable for both reactant adsorption/activation and product desorption. Its remarkable dehydrogenation capacity and moderate adsorption behavior are the key to the outstanding catalytic activity and selectivity.

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. The proposed ELN provides a unified model for a large class of error loss functions, which includes some information-theoretic learning (ITL) loss functions as special cases. The activation function, weight parameters, and network size of the ELN can be predetermined or learned from the error samples. On this basis, we propose a new machine learning paradigm where the learning process is divided into two stages: first, learning a loss function using an ELN; second, using the learned loss function to continue to perform the learning. Experimental results are presented to demonstrate the desirable performance of the new method.

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 O2 dissociation at room temperature. The adsorbed O atoms are active to oxidize incoming CO molecules from the gas phase into CO2, which follows the Eley-Rideal mechanism, in contrast to the Mars-van Krevelen mechanism on CuO-monolayer-supported metallic Ni atoms as well as our previously reported Au and Pt model catalysts. This study helps understand the chemistry of a supported single-metal cation, which is of great importance in heterogeneous catalysis.

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 (BDEH-O =5.1 eV). Many approaches have been explored to stimulate water for hydrogenative reactions, but the efficiency and productivity still require significant enhancement. Here, we show that the surface hydroxylated graphitic carbon nitride (gCN-OH) only requires 2.25 eV to activate H-O bonds in water, enabling abstraction of hydrogen atoms via dehydrogenation of pure water into hydrogen peroxide under visible light irradiation. The gCN-OH presents a stable catalytic performance for hydrogenative N-N coupling, pinacol-type coupling and dehalogenative C-C coupling, all with high yield and efficiency, even under solar radiation, featuring extensive impacts in using renewable energy for a cleaner process in dye, electronic, and pharmaceutical industries.

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. Evolutionary strategies also apply parameter perturbations, which makes network architecture robust and diverse. Our main concern is whether the notion of synaptic perturbation introduced in a spiking neural network (SNN) is biologically relevant or if novel frameworks and components are desired to account for the perturbation properties of artificial neural systems. In this work, we first review part of the locality-sensitive hashing (LSH) of similarity search, the FLY algorithm, as recently published in Science, and propose an improved architecture, time-shifted spiking LSH (TS-SLSH), with the consideration of temporal perturbations of the firing moments of spike pulses. Experiment results show promising performance of the proposed method and demonstrate its generality to various spiking neuron models. Therefore, we expect temporal perturbation to play an active role in SNN performance.

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. In both correntropy and MC, the center of the kernel function is, however, always located at zero. In the present work, to further improve the learning performance, we propose the concept of multikernel correntropy (MKC), in which each component of the mixture Gaussian kernel can be centered at a different location. The properties of the MKC are investigated and an efficient approach is proposed to determine the free parameters in MKC. Experimental results show that the learning algorithms under the maximum MKC criterion (MMKCC) can outperform those under the original maximum correntropy criterion (MCC) and the maximum MC criterion (MMCC).

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. CoNi alloy is protected by ultrathin graphene shell from oxidation and therefore modulates the electronic property of Pt-graphene interface via electron penetration effect. This catalyst can achieve near 100% CO conversion at room temperature, while there are limited conversions over Pt/C and Pt/CoNiOx catalysts. Experiments and theoretical calculations indicate that CO will saturate Pt sites, but O2 can adsorb at the Pt-graphene interface without competing with CO, which facilitate the O2 activation and the subsequent surface reaction. This graphene-isolated system is distinct from the classical metal-metal oxide interface for catalysis, and it provides a new thought for the design of heterogeneous catalysts.

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-N2, Co-N3, and Co-N4) 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. Among the Co SACs, the best catalyst was Co-N2 with two coordinated nitrogen atoms, and the ammonia yield was 181 mg·h-1·gcat-1. The experimental and theoretical calculations were consistent in that a low Co-N coordination number was beneficial to the adsorption and dissociation of N2, thereby enhancing the reduction activity of N2 and promoting the increase of ammonia production.

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. Approximately 1270 MP particles with irregular shapes (near-sphere, polygonal ellipsoid, and fragment) were selected for the settling experiments. The experimental results show that the main factors affecting the settling velocity of MPs were shape irregularity, density, and particle size. The settling velocity of irregular MPs was significantly lower than that of perfectly spherical MPs. We proposed a model that predicts the correlation between the settling velocity of MPs and their shape, density, particle size, and water density.

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. The results showed that the abundance of MPs in the influent water and primary, secondary, and tertiary treatment discharges was 16.0, 10.3, 4.5, and 2.9 MP/L, respectively, and the total removal rate of MPs from the influent to the final effluent reached 81.9%. The MPs in the WWTP were mainly small-sized (0.08-0.55 mm), followed by medium-sized (0.55-1.7 mm). Fibers were the dominant MP shape in both the water and sediment samples. Black (36%) and red (23%) were the dominant MP colors. Six different polymer types of MPs were detected, which were mainly polypropylene followed by polyethylene. In general, for the MPs in the WWTP, the removal rate of fragments can reach 97.08%, which is better than that of fibers (70.50%); the removal rate of small-sized can reach 95.86%, which is better than that of medium-sized (83.53%) and large-sized (70.00%). In this study, primary treatment has better effects in eliminating fragments and large-sized MPs; secondary treatment has better effects in eliminating fibers and small-sized MPs. Although WWTPs have a very good removal effect on MPs, 870 million MP/d are still discharged into nearby rivers from WWTPs with a treatment scale of 300,000 m3/day. Graphical Abstract.

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

RuO2 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. Herein, robust interface Ru centers between RuO2 and graphene, via a controllable oxidation of graphene encapsulating Ru nanoparticles, are presented to efficiently enhance both the activity and stability of the acidic OER. Through precisely controlling the reaction interface, a much lower OER overpotential of only 227 mV at 10 mA cm-2 in acidic electrolyte, compared with that of 290 mV for commercial RuO2 , but a significantly higher durability than the commercial RuO2 , are achieved. Density functional theory (DFT) calculations reveal that the interface Ru centers between the RuO2 and the graphene can break the classic scaling relationships between the free energies of HOO* and HO* to reduce the limiting potential, rendering an enhancement in the intrinsic OER activity and the resistance to over-oxidation and corrosion for RuO2 .

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 CO2 emissions are the two major challenges of the world. Conversion of CO2 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. The prepared electrocatalyst has shown remarkable performance for CO2RR, ORR, OER, and HER. The practical utilization of the catalyst is mansifested by a dual model metal CO2/air battery and water electrolyzer. An excellent CO2RR with FE of 99% is achieved in 0.5 M KHCO3 medium. The catalyst exhibits more positive onset (0.98 V) and half wave potential (0.86 V) than Pt/C for ORR, extremely low overpotential (η10) of 250 mV for OER, and thus the lowest ORR/OER potential gap of 0.62 V. In alkaline medium, the catalyst also shows excellent HER performance with η10 of 49 mV, resulting in the smallest cell bias of 1.57 V for overall water splitting to date. This work provides a new pathway to design more stellar multifunctional electrocatalyst for sustainable and clean renewable energy technology.

Solid Base Bi24 O31 Br10 (OH)δ with Active Lattice Oxygen for the Efficient Photo-Oxidation of Primary Alcohols to Aldehydes.

The selective oxidation of primary alcohols to aldehydes by O2 instead of stoichiometric oxidants (for example, MnVII , CrVI , and OsIV ) 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 Bi24 O31 Br10 (OH)δ photocatalyst is very efficient in the selective oxidation of a series of aliphatic (carbon chain from C1 to C10 ) 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. High quantum efficiencies (71 % and 55 % under 410 and 450 nm irradiation) are reached in a representative reaction, the oxidation of isopropanol. We propose that the outstanding performance of the Bi24 O31 Br10 (OH)δ photocatalyst is associated with basic surface sites and active lattice oxygen that boost the dehydrogenation step in the photo-oxidation of alcohols.

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 Au25 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. For example, in Au25, we find that features based on the distribution of Ag atoms relative to the CO adsorption site are the most important in predicting adsorption energies. Our machine-learning model is easily extended to other Au-based nanoclusters, and we demonstrate predictions about CO adsorption on Ag-alloyed Au36 and Au133 nanoclusters.