Structure evolution and specific effects for the catalysis of atomically ordered intermetallic compounds.

Atomically ordered intermetallic compounds (IMCs) have been extensively studied for exploring catalysts with high activity, selectivity, and longevity. Compared to random alloys, IMCs present a more pronounced geometric and electronic effect with desirable catalytic performance. Their well-defined structure makes IMCs ideal model catalysts for studying the catalytic mechanism. This review focuses especially on elemental composition, electron transfer, and structure/phase evolution under high temperature treatment conditions, providing direct evidence for the migration and rearrangement of metal atoms through electron microscopy. We then present the outstanding applications of IMCs in growing single-walled nanotubes, hydrogenation/dehydrogenation reactions, and electrocatalysis from the perspective of electronic, geometric, strain, and bifunctional effects of ordered IMCs. Finally, the current obstacles associated with the use of in situ techniques are proposed, as well as future research possibilities.

Muti-dimensional High-entropy Materials for Energy Conversion Reactions: Current State and Future Trends.

The high-entropy materials (HEMs), composed of five or more elements, have attracted significant attention in electrocatalysis due to their unique physicochemical properties arising from the existence of multi-elements compositions. Beyond chemical composition, microstructure significantly influences the catalytic performance and even the catalytic mechanism towards energy conversion reactions. Given the rapid proliferation of research on HEMs and the critical roles of microstructure in their catalytic performance, a timely and comprehensive review of recent advancements is imperative. This review meticulously examines the synthesis methods and physicochemical characteristics of HEMs with distinct one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) morphologies. By highlighting representative examples from the past five years, we elucidate the unique properties of HEMs with 1D, 2D and 3D microstructures, detailing their intricate influence on electrocatalytic performance, aiming to spur further advancements in this promising research area.

Effects of sludge retention time on sludge reduction by ultrasound treatment: Sludge characteristics, microbial community, and metabolism.

Both ultrasound and sludge retention time (SRT) enable the in-situ sludge reduction during wastewater treatment, but the influence of SRT on ultrasonic lysis - cryptic growth is unclear. This paper researched the influence of different SRTs on sludge lysis - cryptic growth using a sequential bio-reactor (SBR), then explained in details the changes of microorganisms in the SBR. The best SRT for sludge reduction was 30 d, and 47.29% reduction in sludge was achieved. The different SRTs changed the organic matter removal in the wastewater, and the removal rate decreased when SRT exceeded 60 d. The size of the sludge particles varied depending on the SRT, with the smallest size at SRT of 10d being 45.6 µm and the largest size at SRT of 90d being 110.0 µm. SEM showed that the sludge surface changed rough at longer SRT. FTIR and XPS showed notable effect in sludge functional group strength at SRT of 30 d. Extracellular polymeric substance (EPS) reduced the most at SRT of 30 d. The microbial communities of sludge varied with the SRT, and the unique main genus at SRT of 5, 15, 30 and 90 d were C10-SB1A, Lactococcus, Propioniciclava, Lactococcus, respectively. Furthermore, the SRT changed relative abundance of enzymes concerned with metabolism of carbon, nitrogen, and phosphorus. Similarly, SRT changed the metabolic rate, and the metabolic rate of carbon, nitrogen and phosphorus was best at SRT of 30 d.

A comprehensive study on the co-removal of Cr (VI) and ciprofloxacin via microbial-photocatalytic coupling: Mechanistic insights and performance evaluation.

The increasing contamination of water systems by antibiotics and heavy metals has become a growing concern. The intimately coupled photocatalysis and biodegradation (ICPB) approach offers a promising strategy for the effective removal of mixed pollutants. Despite some prior research on ICPB applications, the mechanism by which ICPB eliminates mixed pollutants remains unclear. In our current study, the ICPB approach achieved approximately 1.53 times the degradation rate of ciprofloxacin (CIP) and roughly 1.82 times the reduction rate of Cr (VI) compared to photocatalysis. Remarkably, after 30 days, the ICPB achieved a 96.1% CIP removal rate, and a 97.8% reduction in Cr (VI). Our investigation utilized three-dimensional fluorescence analysis and photo-electrochemical characterization to unveil the synergistic effects of photocatalysis and biodegradation in removal of CIP and Cr (VI). Incorporation of B-Bi3O4Cl (B-BOC) photocatalyst facilitated electron-hole separation, leading to production of ·O2-, ·OH, and h+ species which interacted with CIP, while electrons reduced Cr (VI). Subsequently, the photocatalytic products were biodegraded by a protective biofilm. Furthermore, we observed that CIP, acting as an electron donor, promoted the reduction of Cr (VI). The microbial communities revealed that the number of bacteria favoring pollutant removal increased during ICPB operation, leading to a significant enhancement in performance.

Recent Progress on Perovskite-Based Electrocatalysts for Efficient CO2 Reduction.

An efficient carbon dioxide reduction reaction (CO2RR), which reduces CO2 to low-carbon fuels and high-value chemicals, is a promising approach for realizing the goal of carbon neutrality, for which effective but low-cost catalysts are critically important. Recently, many inorganic perovskite-based materials with tunable chemical compositions have been applied in the electrochemical CO2RR, which exhibited advanced catalytic performance. Therefore, a timely review of this progress, which has not been reported to date, is imperative. Herein, the physicochemical characteristics, fabrication methods and applications of inorganic perovskites and their derivatives in electrochemical CO2RR are systematically reviewed, with emphasis on the structural evolution and product selectivity of these electrocatalysts. What is more, the current challenges and future directions of perovskite-based materials regarding efficient CO2RR are proposed, to shed light on the further development of this prospective research area.

[Analysis of neural fragility in epileptic zone based on stereoelectroencephalography].

There are some limitations in the localization of epileptogenic zone commonly used by human eyes to identify abnormal discharges of intracranial electroencephalography in epilepsy. However, at present, the accuracy of the localization of epileptogenic zone by extracting intracranial electroencephalography features needs to be further improved. As a new method using dynamic network model, neural fragility has potential application value in the localization of epileptogenic zone. In this paper, the neural fragility analysis method was used to analyze the stereoelectroencephalography signals of 35 seizures in 20 patients, and then the epileptogenic zone electrodes were classified using the random forest model, and the classification results were compared with the time-frequency characteristics of six different frequency bands extracted by short-time Fourier transform. The results showed that the area under curve (AUC) of epileptic focus electrodes based on time-frequency analysis was 0.870 (delta) to 0.956 (high gamma), and its classification accuracy increased with the increase of frequency band, while the AUC by using neural fragility could reach 0.957. After fusing the neural fragility and the time-frequency characteristics of the γ and high γ band, the AUC could be further increased to 0.969, which was improved on the original basis. This paper verifies the effectiveness of neural fragility in identifying epileptogenic zone, and provides a theoretical reference for its further clinical application.

Dual function of magnetic field in enhancing antibiotic wastewater treatment by an integrated photocatalysis and fluidized bed biofilm reactor (FBBR).

The integrated photocatalysis and fluidized bed biofilm reactor (FBBR) is an attractive wastewater treatment technique for managing wastewater containing antibiotics. However, the fast recombination of photoinduced charge and low microbial activity limit the degradation and mineralization efficiency for antibiotics. To address this, we attempt to introduce magnetic field (MF) to the integrated system with B-doped Bi3O4Cl as the photocatalysts to effectively improve removal and mineralization of ciprofloxacin (CIP). As a consequence, the degradation rate reaches 96% after 40 d in integrated system with MF. The biofilm inside the integrated system with MF carrier can mineralize the photocatalytic products, thereby increasing the total organic carbon (TOC) degradation rate by more than 32%. The electrochemical experiment indicates the Lorentz force generated by MF can accelerate charge separation, increasing the electron concentration. Simultaneously, the increased amounts of electrons lead to the generation of more ·OH and ·O2-. MF addition also results in increased biomass, increased biological respiratory activity, microbial community evolution and accelerated microbial metabolism, enabling more members to biodegrade photocatalytic intermediates. Therefore, applied MF is an efficient method to enhance CIP degradation and mineralization by the integrated system.

Effects of lysed sludge reflux point on ultrasound lysis-cryptic growth in anaerobic/aerobic (A/O) wastewater treatment: Sludge reduction, microbial community, and metabolism.

Ultrasonication allows sludge reduction to be performed in situ during wastewater treatment, and the reflux point of the lysed sludge affects this performance. This study investigated the effects of reflux point (anaerobic stage, carbon/nitrogen (C/N) lowest stage, and aerobic stage) on sludge lysis-cryptic growth in an anaerobic/aerobic reactor and variations in the sludge and microbial community. The best reflux point occurred at the lowest C/N ratio stage, and a 50.96% reduction in excess sludge was achieved. The reflux of the lysed sludge to the aerobic stage reduced nitrogen and phosphorus removal. The reflux of the lysed sludge decreased the average sludge size, reaching 29.2 µm when reflux to the aerobic stage. Scanning electron microscopy showed that the sludge surface was unaffected by the reflux point. The Fourier-transform infrared spectrometry and X-ray photoelectron spectroscopy results showed that the most prominent variation in the intensity of the sludge functional groups occurred when the reflux was at the lowest C/N stage. The amount of extracellular polymeric substances decreased the most during reflux to the anaerobic stage. The sludge microbial communities varied with the reflux point, and the dominant phyla during reflux to the anaerobic, lowest C/N, and aerobic stages were Bacteroidetes, Firmicutes, and Bacteroidetes, respectively. Furthermore, the reflux point did not alter the metabolic pathway of sludge microorganisms but increased the number of enzymes in metabolic pathways.

Insights into the underpinning effect of graphene in Cu1Mn10 on enhancing the low-temperature catalytic activity for CO oxidation.

CO emission is a critical issue of industrial processes such as steel-smelting, cement manufacturing, and waste incineration. Catalytic oxidation based on Cu-Mn binary catalysts shows great potential for efficient removal of CO, whereas their practical applicability is limited by the inferior low-temperature catalytic activity and the high catalyst cost owing to a substantial quantity of Cu. In this study, doping graphene is designed to adjust the electron transfer capability to improve the low-temperature catalytic activity as well as reduce the amount of Cu, and thereby Cu1Mn10 catalysts doped with slight amounts of graphene (x%G-Cu1Mn10, x is 1∼5) were fabricated. It was found that the introduction of graphene could form effective electron transport channels to enhance the intermetallic interaction and oxygen vacancy generation, thus improving the low-temperature catalytic performance of the Cu1Mn10 catalyst. Among all the catalysts, 4%G-Cu1Mn10 exhibited the highest activity, achieving CO conversion of 92% at 110 °C at a weight hourly space velocity of 120,000 mL/(g∙h). The introduction of graphene also enabled the catalyst with excellent catalytic activity and stability at a relative humidity of 70%. Attractively, 4%G-Cu1Mn10 can be further loaded into the polyester fabric, presenting great application potentials in the effective elimination of CO during the dust removal process since the flue gas temperature in the dust collector is just around the T90% and the catalyst that is inside of fabric fiber rather than on the fabric surface can be rarely influenced by the dust. In general, doping graphene provides a facile method to enhance the low-temperature activities of the Cu-Mn binary catalysts and cut down the use of valuable Cu, showing great application potential.

Association of greenness with the disease burden of lower respiratory infections and mediation effects of air pollution and heat: a global ecological study.

Exposure to greenness is increasingly linked to beneficial health outcomes, but the associations between greenness and the disease burden of lower respiratory infections (LRIs) are unclear. We used the normalized difference vegetation index (NDVI) and the leaf area index (LAI) to measure greenness and incidence, death, and disability-adjusted life years (DALYs) due to LRIs to represent the disease burden of LRIs. We applied a generalized linear mixed model to evaluate the association between greenness and LRI disease burden and performed a stratified analysis, after adjusting for covariates. Additionally, we assessed the potential mediating effects of fine particulate matter (PM2.5), ozone (O3), nitrogen dioxide (NO2), and heat on the association between greenness and the disease burden of LRIs. In the adjusted model, one 0.1 unit increase of NDVI and 0.5 increase in LAI were significantly inversely associated with incidence, death, and DALYs due to LRIs, respectively. Greenness was negatively correlated with the disease burden of LRIs across 15-65 age group, both sexes, and low SDI groups. PM2.5, O3, and heat mediated the effects of greenness on the disease burden of LRIs. Greenness was significantly negatively associated with the disease burden of LRIs, possibly by reducing exposure to air pollution and heat.

A Novel Algorithmic Structure of EEG Channel Attention Combined With Swin Transformer for Motor Patterns Classification.

With the development of brain-computer interfaces (BCI) technologies, EEG-based BCI applications have been deployed for medical purposes. Motor imagery (MI), applied to promote neural rehabilitation for stroke patients, is among the most common BCI paradigms that. The Electroencephalogram (EEG) signals, encompassing an extensive range of channels, render the training dataset a high-dimensional construct. This high dimensionality, inherent in such a dataset, tends to challenge traditional deep learning approaches, causing them to potentially disregard the intrinsic correlations amongst these channels. Such an oversight often culminates in erroneous data classification, presenting a significant drawback of these conventional methodologies. In our study, we propose a novel algorithmic structure of EEG channel-attention combined with Swin Transformer for motor pattern recognition in BCI rehabilitation. Effectively, the self-attention module from transformer architecture could captures temporal-spectral-spatial features hidden in EEG data. The experimental results verify that our proposed methods outperformed other state-of-art approaches with the average accuracy of 87.67%. It is implied that our method can extract high-level and latent connections among temporal-spectral features in contrast to traditional deep learning methods. This paper demonstrates that channel-attention combined with Swin Transformer methods has great potential for implementing high-performance motor pattern-based BCI systems.

Graph-Structure-Based Multigranular Belief Fusion for Human Activity Recognition.

The belief functions (BFs) introduced by Shafer in the mid of 1970s are widely applied in information fusion to model epistemic uncertainty and to reason about uncertainty. Their success in applications is however limited because of their high-computational complexity in the fusion process, especially when the number of focal elements is large. To reduce the complexity of reasoning with BFs, we can envisage as a first method to reduce the number of focal elements involved in the fusion process to convert the original basic belief assignments (BBAs) into simpler ones, or as a second method to use a simple rule of combination with potentially a loss of the specificity and pertinence of the fusion result, or to apply both methods jointly. In this article, we focus on the first method and propose a new BBA granulation method inspired by the community clustering of nodes in graph networks. This article studies a novel efficient multigranular belief fusion (MGBF) method. Specifically, focal elements are regarded as nodes in the graph structure, and the distance between nodes will be used to discover the local community relationship of focal elements. Afterward, the nodes belonging to the decision-making community are specially selected, and then the derived multigranular sources of evidence can be efficiently combined. To evaluate the effectiveness of the proposed graph-based MGBF, we further apply this new approach to combine the outputs of convolutional neural networks + attention (CNN + Attention) in the human activity recognition (HAR) problem. The experimental results obtained with real datasets prove the potential interest and feasibility of our proposed strategy with respect to classical BF fusion methods.

Magnetic-field-induced simultaneous charge separation and oxygen transfer in photocatalytic oxygen activation for algae inactivation.

Photocatalytic oxygen activation is an excellent strategy for algae control in water. However, the fast recombination of photogenerated charge and slow rate of oxygen transfer limit the reactive oxygen species generation efficiency for algae inactivation. Herein, to solve above issues, magnetic field was introduced to the BiO2-x/Bi3NbO7 system to effectively covert oxygen into reactive radicals. The electrochemical experiment and DFT calculation results indicated the charge separation could be accelerated by the Lorentz force generated by the magnetic field, resulting in increase of electron concentration. Meanwhile, the value of volumetric gas-liquid mass transfer coefficient was increased by 59.79 % with magnetic field, thus more oxygen could be reduced to superoxide radical. Photocatalytic algae inactivation rate by BiO2-x/Bi3NbO7 with magnetic field could be increased by 2.07 times than that without magnet filed. This work further extends the strategy of using magnetic field to simultaneously facilitate the charge separation and oxygen transfer rate.

Enhanced antibiotic wastewater degradation by intimately coupled B-Bi3O4Cl photocatalysis and biodegradation reactor: Elucidating degradation principle systematically.

Intimately coupled photocatalysis and biodegradation (ICPB) is an emerging technology that has potential applications in the degradation of bio-recalcitrant pollutants. However, the interaction principles between photocatalysts and biofilms in ICPB have not been well developed. This article covers a cooperative degradation scheme coupling photocatalysis and biodegradation for efficient degradation and mineralization of ciprofloxacin (CIP) using ICPB with B-doped Bi3O4Cl as the photocatalyst. In consequence, a removal rate of ∼95 % is reached after 40 d. The biofilms inside the ICPB carriers can mineralize the photocatalytic products, thus improving the removal rate of total organic carbon (TOC) by more than 20 %. Interior biofilms are not destroyed by CIP or photocatalysis, and they adapt to ICPB of CIP by enriching in Pseudoxanthomonas, Ferruginibacter, Clostridium, Stenotrophomonas and Comamonas and reconstructing their microbial communities using energy produced by the light-excited photoelectrons. Furthermore, this research gives new opinion into the degradation principles of the ICPB system.

Genetic Algorithm Based on a New Similarity for Probabilistic Transformation of Belief Functions.

Recent studies of alternative probabilistic transformation (PT) in Dempster-Shafer (DS) theory have mainly focused on investigating various schemes for assigning the mass of compound focal elements to each singleton in order to obtain a Bayesian belief function for decision-making problems. In the process of such a transformation, how to precisely evaluate the closeness between the original basic belief assignments (BBAs) and transformed BBAs is important. In this paper, a new aggregation measure is proposed by comprehensively considering the interval distance between BBAs and also the sequence inside the BBAs. Relying on this new measure, we propose a novel multi-objective evolutionary-based probabilistic transformation (MOEPT) thanks to global optimizing capabilities inspired by a genetic algorithm (GA). From the perspective of mathematical theory, convergence analysis of EPT is employed to prove the rationality of the GA used here. Finally, various scenarios in evidence reasoning are presented to evaluate the robustness of EPT.

A Novel Deep Learning Method Based on an Overlapping Time Window Strategy for Brain-Computer Interface-Based Stroke Rehabilitation.

Globally, stroke is a leading cause of death and disability. The classification of motor intentions using brain activity is an important task in the rehabilitation of stroke patients using brain-computer interfaces (BCIs). This paper presents a new method for model training in EEG-based BCI rehabilitation by using overlapping time windows. For this aim, three different models, a convolutional neural network (CNN), graph isomorphism network (GIN), and long short-term memory (LSTM), are used for performing the classification task of motor attempt (MA). We conducted several experiments with different time window lengths, and the results showed that the deep learning approach based on overlapping time windows achieved improvements in classification accuracy, with the LSTM combined vote-counting strategy (VS) having achieved the highest average classification accuracy of 90.3% when the window size was 70. The results verified that the overlapping time window strategy is useful for increasing the efficiency of BCI rehabilitation.

Identification and Exploration of Serine Peptidase Inhibitor Kazal Type I (SPINK1) as a Potential Biomarker Correlated with the Progression of Non-Small Cell Lung Cancer.

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. Although significant advances have been achieved in the treatment of NSCLC during the past two decades, the 5-year survival rate of patients with NSCLC remains <20%. Thus, there is an urgent requirement to further understand the molecular mechanisms that promote NSCLC development and to identify novel therapeutic targets. In the present study, the gene expression profiles of patients with NSCLC from The Cancer Genome Atlas database were carefully analyzed and SPINK1 was identified as a tumor-inducing factor. SPINK1 expression level was found to be increased in both NSCLC tissues and cell lines. Moreover, SPINK1 promoted cell proliferation in A549 and H1299 cells. Knockdown of SPINK1 could activate cell autophagy and apoptosis. Mechanistically, SPINK1 was demonstrated to induce the proliferation of NSCLC via activating the MEK/ERK signaling pathway. In conclusion, these findings suggested that SPINK1 may serve as a potential biomarker in NSCLC.

An Effective Fusing Approach by Combining Connectivity Network Pattern and Temporal-Spatial Analysis for EEG-Based BCI Rehabilitation.

Motor-modality-based brain computer interface (BCI) could promote the neural rehabilitation for stroke patients. Temporal-spatial analysis was commonly used for pattern recognition in this task. This paper introduced a novel connectivity network analysis for EEG-based feature selection. The network features of connectivity pattern not only captured the spatial activities responding to motor task, but also mined the interactive pattern among these cerebral regions. Furthermore, the effective combination between temporal-spatial analysis and network analysis was evaluated for improving the performance of BCI classification (81.7%). And the results demonstrated that it could raise the classification accuracies for most of patients (6 of 7 patients). This proposed method was meaningful for developing the effective BCI training program for stroke rehabilitation.

Associations of ambient temperature with mortality for ischemic and hemorrhagic stroke and the modification effects of greenness in Shandong Province, China.

BACKGROUND: Evidence is scant on the relative and attributable contributions of ambient temperature on stroke subtypes mortality. Few studies have examined modification effects of multiple greenness indicators on such contributions, especially in China. We quantified the associations between ambient temperature and overall, ischemic, and hemorrhagic stroke mortality; further examined whether the associations were modified by greenness. METHODS: We conducted a multicenter time-series analysis from January 1, 2013 to December 31, 2019. we adopted a distributed lag non-linear model to evaluate county-specific temperature-stroke mortality associations. We then applied a random-effects meta-analysis to pool county-specific effects. Attributable mortality was calculated for cold and heat, defined as temperatures below and above the minimum mortality temperature (MMT). Finally, We conducted a multivariate meta-regression to determine associations between greenness and stroke mortality risks for cold and heat, using normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) as quantitative indicators of greenness exposure. RESULTS: In the study period, 138,749 deaths from total stroke were reported: 86,873 ischemic and 51,876 hemorrhagic stroke. We observed significant W-shaped relationships between temperature and stroke mortality, with substantial differences among counties and regions. With MMT as the temperature threshold, 17.16 % (95 % empirical CI, 13.38 %-19.75 %) of overall, 20.05 % (95 % eCI, 16.46 %-22.70 %) of ischemic, and 12.55 % (95 % eCI, 5.59 %-16.24 %) of hemorrhagic stroke mortality were attributable to non-optimum temperature (combining cold and heat), more mortality was caused by cold (14.94 %; 95 % eCI, 11.57 %-17.34 %) than by heat (2.22 %; 95 % eCI, 1.54 %-2.72 %). Higher levels of NDVI, SAVI and EVI were related to mitigated effects of non-optimum temperatures-especially heat. CONCLUSIONS: Exposure to non-optimum temperatures aggravated stroke mortality risks; increasing greenness could alleviate that risks. This evidence has important implications for local communities in developing adaptive strategies to minimize the health consequences of adverse temperatures.

Study on the synergy effect of MnOx and support on catalytic ozonation of toluene.

MnOx has received widespread attention in low-temperature catalytic oxidation of VOCs, however, the synergy effect of MnOx and support on the VOCs catalytic ozonation were rarely studied. In this study, five different MnOx/X (X: MCM-41, 13X, ZSM-5, HY, USY) were synthesized and found their support greatly affect the catalytic oxidation activity. MnOx/MCM-41 presents the largest specific surface area, pore volume and unique surface morphology, and thereby provides more sites for MnOx loading and VOCs adsorption. Moreover, MnOx/MCM-41 presents a high proportion of Mn3+, which helps to enhance the ion exchange capability, and thus promotes the regeneration of oxygen vacancies. Furthermore, a part of Mn was proved to be introduced into the MCM-41 lattice, which can promote the electron transfer between the active components and the support, and thereby effectively improve the surface electronic properties of the catalyst. The toluene catalytic experiments showed that MnOx/MCM-41 exhibited the best catalytic activity, presenting complete degradation of O3 and VOCs at room temperature. In addition, 5 wt%MnOx/MCM-41 exhibited better catalytic activity than other loading, and its higher surface oxygen species endowed it with strong water resistance and stability. In-situ DRIFTs indicated that toluene was initially oxidized into benzyl alcohol during the adsorption process, and then decomposed to intermediate products (benzaldehyde, phenolate, etc.) during the catalytic ozonation process, and finally oxidized to carbon dioxide. In conclusion, the supply of loading sites and the improvement of interfacial electron transfer are the manifestations of the synergy between the support and MnOx, leading to the promotion of the catalytic ozonation of VOCs.