Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation.

Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. Using deep learning and traditional machine learning, it presents advancements in metabolic data analysis, including novel algorithms for accurate peak identification, robust disease classification from metabolic profiles, and improved metabolite annotation. It also highlights multiomics integration, demonstrating machine learning's potential in elucidating biological phenomena and advancing disease diagnostics. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis.

The close association of micronutrients with COVID-19.

Objectives: The present study was conducted to explore the performance of micronutrients in the prediction and prevention of coronavirus disease 2019 (COVID-19). Methods: This is an observational case-control study. 149 normal controls (NCs) and 214 COVID-19 patients were included in this study. Fat-soluble and water-soluble vitamins were determined by liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis, and inorganic elements were detected by inductively coupled plasma-mass spectrometry (ICP-MS) analysis. A logistic regression model based on six micronutrients were constructed using DxAI platform. Results: Many micronutrients were dysregulated in COVID-19 compared to normal control (NC). 25-Hydroxyvitamin D3 [25(OH)D3], magnesium (Mg), copper (Cu), calcium (Ca) and vitamin B6 (pyridoxic acid, PA) were significantly independent risk factors for COVID-19. The logistic regression model consisted of 25(OH)D3, Mg, Cu, Ca, vitamin B5 (VB5) and PA was developed, and displayed a strong discriminative capability to differentiate COVID-19 patients from NC individuals [area under the receiver operating characteristic curve (AUROC) = 0.901]. In addition, the model had great predictive ability in discriminating mild/normal COVID-19 patients from NC individuals (AUROC = 0.883). Conclusions: Our study showed that micronutrients were associated with COVID-19, and our logistic regression model based on six micronutrients has potential in clinical management of COVID-19, and will be useful for prediction of COVID-19 and screening of high-risk population.

Uncovering Structural Evolution during the Dealloying Process in Pt-Based Oxygen-Reduction Catalyst.

The comprehensive understanding toward the dealloying process is crucial for designing alloy catalysts employed in the oxygen reduction reaction (ORR). However, the specific leaching procedure and subsequent reconstruction of the dealloyed catalyst still remain unclear. Herein, we employ in situ X-ray absorption fine structure spectroscopy to monitor the dealloying process of a two-dimensional PtTe ordered alloy, known for its enhanced ORR activity. Our findings reveal the unsynchronous evolutions of Pt and Te sites, wherein the Pt component undergoes a structural transformation prior to the complete leaching of Te, leading to the formation of a defect-rich Pt catalyst. This dealloyed catalyst exhibits a significant enhancement in ORR activity, featuring a half-wave potential of 0.90 V versus the reversible hydrogen electrode and a mass activity of 0.62 A mgPt-1, outperforming the performance of commercial Pt/C counterpart. This in-depth understanding of the dealloying mechanism enriches our knowledge for the development of high-performance Pt-based alloy catalysts.

Self-Assembly Intermetallic PtCu3 Core with High-Index Faceted Pt Shell for High-Efficiency Oxygen Reduction.

Rational design of well-defined active sites is crucial for promoting sluggish oxygen reduction reactions. Herein, leveraging the surfactant-oriented and solvent-ligand effects, we develop a facile self-assembly strategy to construct a core-shell catalyst comprising a high-index Pt shell encapsulating a PtCu3 intermetallic core with efficient oxygen-reduction performance. Without undergoing a high-temperature route, the ordered PtCu3 is directly fabricated through the accelerated reduction of Cu2+, followed by the deposition of the remaining Pt precursor onto its surface, forming high-index steps oriented by the steric hindrance of surfactant. This approach results in a high half-wave potential of 0.911 V versus reversible hydrogen electrode, with negligible deactivation even after 15000-cycle operation. Operando spectroscopies identify that this core-shell catalyst facilitates the conversion of oxygen-involving intermediates and ensures antidissolution ability. Theoretical investigations rationalize that this improvement is attributed to reinforced electronic interactions around high-index Pt, stabilizing the binding strength of rate-determining OHads species.

Improved RRT* Algorithm for Disinfecting Robot Path Planning.

In this paper, an improved APF-GFARRT* (artificial potential field-guided fuzzy adaptive rapidly exploring random trees) algorithm based on APF (artificial potential field) guided sampling and fuzzy adaptive expansion is proposed to solve the problems of weak orientation and low search success rate when randomly expanding nodes using the RRT (rapidly exploring random trees) algorithm for disinfecting robots in the dense environment of disinfection operation. Considering the inherent randomness of tree growth in the RRT* algorithm, a combination of APF with RRT* is introduced to enhance the purposefulness of the sampling process. In addition, in the context of RRT* facing dense and restricted environments such as narrow passages, adaptive step-size adjustment is implemented using fuzzy control. It accelerates the algorithm's convergence and improves search efficiency in a specific area. The proposed algorithm is validated and analyzed in a specialized environment designed in MATLAB, and comparisons are made with existing path planning algorithms, including RRT, RRT*, and APF-RRT*. Experimental results show the excellent exploration speed of the improved algorithm, reducing the average initial path search time by about 46.52% compared to the other three algorithms. In addition, the improved algorithm exhibits faster convergence, significantly reducing the average iteration count and the average final path cost by about 10.01%. The algorithm's enhanced adaptability in unique environments is particularly noteworthy, increasing the chances of successfully finding paths and generating more rational and smoother paths than other algorithms. Experimental results validate the proposed algorithm as a practical and feasible solution for similar problems.

Association of sleep characteristics with cardiovascular disease risk in adults over 40 years of age: a cross-sectional survey.

Background: The relationship between sleep characteristics and cardiovascular disease (CVD) risk has yet to reach a consistent conclusion, and more research needs to be carried out. This study aimed to explore the relationship between snoring, daytime sleepiness, bedtime, sleep duration, and high-risk sleep patterns with CVD risk. Methods: Data from the National Health and Nutrition Examination Survey (NHANES) 2015-2018 were collected and analyzed. Multivariable logistic regression was used to evaluate the relationship between snoring, daytime sleepiness, bedtime, sleep duration, high-risk sleep patterns, and CVD risk. Stratified analysis and interaction tests were carried out according to hypertension, diabetes and age. Results: The final analysis contained 6,830 participants, including 1,001 with CVD. Multivariable logistic regression suggested that the relationship between snoring [OR = 7.37,95%CI = (6.06,8.96)], daytime sleepiness [OR = 11.21,95%CI = (9.60,13.08)], sleep duration shorter than 7 h [OR = 9.50,95%CI = (7.65,11.79)] or longer than 8 h [OR = 6.61,95%CI = (5.33,8.19)], bedtime after 0:00 [OR = 13.20,95%CI = (9.78,17.80)] compared to 22:00-22:59, high-risk sleep patterns [OR = 47.73,95%CI = (36.73,62.04)] and CVD risk were statistically significant. Hypertension and diabetes interacted with high-risk sleep patterns, but age did not. Conclusions: Snoring, daytime sleepiness, excessive or short sleep duration, inappropriate bedtime, and high-risk sleep patterns composed of these factors are associated with the CVD risk. High-risk sleep patterns have a more significant impact on patients with hypertension and diabetes.

Association between different insulin resistance surrogates and all-cause mortality in patients with coronary heart disease and hypertension: NHANES longitudinal cohort study.

BACKGROUND: Studies on the relationship between insulin resistance (IR) surrogates and long-term all-cause mortality in patients with coronary heart disease (CHD) and hypertension are lacking. This study aimed to explore the relationship between different IR surrogates and all-cause mortality and identify valuable predictors of survival status in this population. METHODS: The data came from the National Health and Nutrition Examination Survey (NHANES 2001-2018) and National Death Index (NDI). Multivariate Cox regression and restricted cubic splines (RCS) were performed to evaluate the relationship between homeostatic model assessment of IR (HOMA-IR), triglyceride glucose index (TyG index), triglyceride glucose-body mass index (TyG-BMI index) and all-cause mortality. The recursive algorithm was conducted to calculate inflection points when segmenting effects were found. Then, segmented Kaplan-Meier analysis, LogRank tests, and multivariable Cox regression were carried out. Receiver operating characteristic (ROC) and calibration curves were drawn to evaluate the differentiation and accuracy of IR surrogates in predicting the all-cause mortality. Stratified analysis and interaction tests were conducted according to age, gender, diabetes, cancer, hypoglycemic and lipid-lowering drug use. RESULTS: 1126 participants were included in the study. During the median follow-up of 76 months, 455 participants died. RCS showed that HOMA-IR had a segmented effect on all-cause mortality. 3.59 was a statistically significant inflection point. When the HOMA-IR was less than 3.59, it was negatively associated with all-cause mortality [HR = 0.87,95%CI (0.78, 0.97)]. Conversely, when the HOMA-IR was greater than 3.59, it was positively associated with all-cause mortality [HR = 1.03,95%CI (1.00, 1.05)]. ROC and calibration curves indicated that HOMA-IR was a reliable predictor of survival status (area under curve = 0,812). No interactions between HOMA-IR and stratified variables were found. CONCLUSION: The relationship between HOMA-IR and all-cause mortality was U-shaped in patients with CHD and hypertension. HOMA-IR was a reliable predictor of all-cause mortality in this population.

Impact analysis of hydraulic residence time and dissolved oxygen on performance efficiency and microbial community in N, N-dimethylformamide wastewater treated by an AnSBR-ASBR.

Suitable operating parameters are one of the key factors to efficient and stable biological wastewater treatment of N, N-dimethylformamide (DMF) wastewater. In this study, an improved AnSBR-ASBR reactor (anaerobic sequencing batch reactor, AnSBR, and aerobic SBR, ASBR, run in series) was used to investigated the effects of operating conditions such as hydraulic residence time (HRT), AnSBR stirring speed and ASBR dissolved oxygen (DO) for DMF wastewater treatment. When HRT decreased from 24 h to 12 h, the average removal rates of COD by the AnSBR were 34.59% and 39.54%, respectively. Meanwhile, the removal rate of NH4+-N by ASBR decreased from 88.38% to 62.81%. The DMF removal rate reached the best at 18 h and the expression of dehydrogenase was the highest in the AnSBR. The abundance of Megasphaera, the dominant sugar-degrading bacteria in the AnSBR, continued to decline due to the decrease of HRT. The relative abundance of Methanobacterium gradually increased to 80.2% with the decrease of HRT and that hydrotrophic methanogenesis dominated the methanogenic process. The HRT decrease promoted butyrate and pyruvate metabolism in anaerobic sludge, but the proportion of glycolysis and methane metabolism decreased. The AnSBR-ASBR reactor had the best operation performance when HRT was 18 h, AnSBR speed was 220 r/min, and ASBR DO content was 3-4 mg/L. This study provided an effective reference for the reasonable selection of operating parameters in the treatment of DMF-containing wastewater by the AnSBR-ASBR.

Vector-borne disease models with Lagrangian approach.

We develop a multi-group and multi-patch model to study the effects of population dispersal on the spatial spread of vector-borne diseases across a heterogeneous environment. The movement of host and/or vector is described by Lagrangian approach in which the origin or identity of each individual stays unchanged regardless of movement. The basic reproduction number [Formula: see text] of the model is defined and the strong connectivity of the host-vector network is succinctly characterized by the residence times matrices of hosts and vectors. Furthermore, the definition and criterion of the strong connectivity of general infectious disease networks are given and applied to establish the global stability of the disease-free equilibrium. The global dynamics of the model system are shown to be entirely determined by its basic reproduction number. We then obtain several biologically meaningful upper and lower bounds on the basic reproduction number which are independent or dependent of the residence times matrices. In particular, the heterogeneous mixing of hosts and vectors in a homogeneous environment always increases the basic reproduction number. There is a substantial difference on the upper bound of [Formula: see text] between Lagrangian and Eulerian modeling approaches. When only host movement between two patches is concerned, the subdivision of hosts (more host groups) can lead to a larger basic reproduction number. In addition, we numerically investigate the dependence of the basic reproduction number and the total number of infected hosts on the residence times matrix of hosts, and compare the impact of different vector control strategies on disease transmission.

Discovery of Insect Attractants Based on the Functional Analyses of Female-Biased Odorant Receptors and Their Orthologs in Two Closely Related Species.

Olfaction plays an instrumental role in host plant selection by phytophagous insects. Helicoverpa assulta and Helicoverpa armigera are two closely related moth species with different host plant ranges. In this study, we first comparatively analyzed the function of 11 female-biased odorant receptors (ORs) and their orthologs in the two species by the Drosophila T1 neuron expression system and then examined the electroantennography responses of the two species to the most effective OR ligands. Behavioral assays using a Y-tube olfactometer indicate that guaiene, the primary ligand of HassOR21-2 and HarmOR21-2, only attracts the females, while benzyl acetone, the main ligand of HassOR35 and HarmOR35, attracts both sexes of the two species. Oviposition preference experiments further confirm that guaiene and benzyl acetone are potent oviposition attractants for the mated females of both species. These findings deepen our understanding of the olfactory coding mechanisms of host plant selection in herbivorous insects and provide valuable attractants for managing pest populations.

Clinical prediction of HBV-associated cirrhosis using machine learning based on platelet and bile acids.

OBJECTIVES: The present study was conducted to evaluate the performance of serum bile acids in the prediction of cirrhosis in chronic hepatitis B (CHB) population. METHODS: Dysregulated metabolites were explored using untargeted and targeted metabolomic analyses. A machine learning model based on platelet (PLT) and several bile acids was constructed using light gradient boosting machine (LightGBM), to differentiate HBV-associated cirrhosis (BAC) from CHB patients. RESULTS: Serum bile acids were dysregulated in BAC compared to CHB patients. The LightGBM model consisted of PLT, TUDCA, UDCA, TLCA, LCA and CA. The model demonstrated a strong discrimination ability in the internal test subset of the training cohort to diagnose BAC from CHB patients (AUC = 0.97). The high diagnostic accuracy of the model was further validated in an independent validation cohort. In addition, the model had high predictive efficacy in discriminating compensated BAC from CHB patients (AUC = 0.89). The performance of the model was better than AST/ALT ratio and the gradient boosting (GB)-based model reported in previous studies. CONCLUSIONS: Our study showed that this LightGBM model based on PLT and 5 bile acids has potential in clinical assessments of CHB progression and will be useful for early detection of cirrhosis in CHB patients.

Multimodal imaging and photothermal/chemodynamic therapy of cervical cancer using GSH-responsive MoS2@MnO2 theranostic nanoparticles.

The development of nanoparticles capable of inducing reactive oxygen species (ROS) formation has become an important strategy for cancer therapy. Simultaneously, the preparation of multifunctional nanoparticles that respond to the tumor microenvironment is crucial for the diagnosis and treatment of tumors. In this study, we designed a Molybdenum disulfide (MoS2) core coated with Manganese dioxide (MnO2), which possessed a good photothermal effect and could produce Fenton-like Mn2+ in response to highly expressed glutathione (GSH) in the tumor microenvironment, thereby generating a chemodynamic therapy (CDT). The nanoparticles were further modified with Methoxypoly(Ethylene Glycol) 2000 (mPEG-NH2) to improve their biocompatibility, resulting in the formation of MoS2@MnO2-PEG. These nanoparticles were shown to possess significant Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) imaging capabilities, making them useful in tumor diagnosis. In vitro and in vivo experiments demonstrated the antitumor ability of MoS2@MnO2-PEG, with a significant killing effect on tumor cells under combined treatment. These nanoparticles hold great potential for CDT/photothermal therapy (PTT) combined antitumor therapy and could be further explored in biomedical research.

Research on the Preparation and Application of Fixed-Abrasive Tools Based on Solid-Phase Reactions for Sapphire Wafer Lapping and Polishing.

Single-crystal sapphire specimen (α-Al2O3) have been widely applied in the semiconductor industry, microelectronics, and so on. In order to shorten the production time and improve the processing efficiency of sapphire processing, an integrated fixed-abrasive tool (FAT) based on solid-phase reactions is proposed in this article. The optimal FAT composition is determined using a preliminary experiment and orthogonal experiments. The mass fraction of the abrasives is chosen as 55 wt%, and the mass ratio of SiO2/Cr2O3 is 2. Surface roughness Ra decreased from 580.4 ± 52.7 nm to 8.1 ± 0.7 nm after 150 min, and the average material removal rate was 14.3 ± 1.2 nm/min using the prepared FAT. Furthermore, FAT processing combined with chemical mechanical polishing (CMP) was shortened by 1.5 h compared to the traditional sapphire production process in obtaining undamaged sapphire surfaces with a roughness of Ra < 0.4 nm, which may have the potential to take the place of the fine lapping and rough polishing process.

Method for Denoising the Vibration Signal of Rotating Machinery through VMD and MODWPT.

The vibration signals from rotating machinery are constantly mixed with other noises during the acquisition process, which has a negative impact on the accuracy of signal feature extraction. For vibration signals from rotating machinery, the conventional linear filtering-based denoising method is ineffective. To address this issue, this paper suggests an enhanced signal denoising method based on maximum overlap discrete wavelet packet transform (MODWPT) and variational mode decomposition (VMD). VMD decomposes the vibration signal of rotating machinery to produce a set of intrinsic mode functions (IMFs). By computing the composite weighted entropy (CWE), the phantom IMF component is then removed. In the end, the sensitive component is obtained by computing the value of the degree of difference (DID) after the high-frequency noise component has been decomposed through MODWPT. The denoised signal reconstructs the signal's intrinsic characteristics as well as the denoised high-frequency IMF component. This technique was used to analyze the simulated and real-world signals of gear faults and it was compared to wavelet threshold denoising (WTD), empirical mode decomposition reconstruction denoising (EMD-RD), and ensemble empirical mode decomposition wavelet threshold denoising (EEMD-WTD). The outcomes demonstrate that this method can accurately extract the signal feature information while filtering out the noise components in the signal.

Compounding warning letters to 503A facilities between 2017 and 2021.

BACKGROUND: The growing population demand and the epidemic lead of coronavirus disease 2019 have highlighted the critical importance of patient access to compounded formulations, including for special purposes such as pediatrics, geriatrics, and other uses. However, there are many potential risks, including quality issues and 503A facilities have not received valid prescriptions for individually-identified patients for a portion of the drug products they produce. OBJECTIVE: The aim is to analyze the (503A facilities) warning letters and identify the problem of compounding medicines not meeting the United States Pharmacopoeia specifications. METHODS: Content analysis and descriptive statistics methods were used to analyze the violations of compounding warning letters from 2017 to 2021. The content of warning letter violations was analyzed in terms of both the compounding environment and 503A facilities that did not received valid prescriptions for individually-identified patients for a portion of the drug products they produced. RESULTS: A total of 113 compounding warning letters (503A facilities, N = 112) from 2017 to 2021 were analyzed in this study. The percentage of 503A facilities involved in sterile compounding environmental issues was 79.46%, with facility design and environmental controls (73/89, 82.02%), cleaning and disinfecting the compounding area (59/89, 66.29%), and personnel cleansing and garbing (44/89, 49.44%) being the top 3 issues. Seventy-two (72/112, 64.29%) 503A facilities that did not received valid prescriptions for individually-identified patients for a portion of the drug products they produced. Fifty-one (51/72, 70.83%) of these warning letters were related to sterile environment issues, and 28 warning letters identified specific drugs that did not qualify for Section 503A exemptions. CONCLUSION: The warning letter of compounding drugs issued by Food and Drug Administration can be used as a learning tool for compounders. Compounders can learn from the experience and lessons, improve compounding operations and reduce mistakes.

Residue Lys219 of CpxR is critical in the regulation of the antibiotic resistance of Escherichia coli.

BACKGROUND: CpxR is a critical regulator in bacterial adaptation to various harmful stresses, and is known to regulate bacterial resistance to commonly used antibiotics, such as aminoglycosides, ß-lactams and polypeptides. However, the detailed study of functional residues of CpxR remains insufficient. OBJECTIVES: To investigate the contribution of Lys219 to CpxR's function in regulating antibiotic resistance of Escherichia coli. METHODS: We performed sequence alignment and conservative analysis of the CpxR protein and constructed mutant strains. We then performed electrophoretic mobility shift assay, real-time quantitative PCR assay, determination of reactive oxygen species (ROS) levels, molecular dynamics simulation, conformational analysis and circular dichroism. RESULTS: All mutant proteins (K219Q, K219A and K219R) lost the cpxP DNA-binding ability. Additionally, the three complemented strains eK219A, eK219Q, and eK219R exhibited lower resistance to copper toxicity and alkaline pH toxicity than eWT. Molecular dynamics analysis revealed that mutation of Lys219 leads to looser and more unstable conformation of CpxR, leading to its decreased binding affinity with downstream genes. Moreover, the Lys219 mutation resulted in the down-regulation of efflux pump genes (acrD, tolC, mdtB and mdtA), leading to the accumulation of antibiotics inside the cells and an increase in ROS production, which significantly reduces antibiotic resistance. CONCLUSIONS: The mutation of the key residue Lys219 causes a conformational change that results in the loss of regulatory ability of CpxR, which may potentially reduce to antibiotic resistance. Therefore, this study suggests that targeting the highly conserved sequence of CpxR could be a promising strategy for the development of new antibacterial drugs.

Resistance mechanism to Notch inhibition and combination therapy in human T-cell acute lymphoblastic leukemia.

Gain-of-function mutations in NOTCH1 are among the most frequent genetic alterations in T-cell acute lymphoblastic leukemia (T-ALL), highlighting the Notch signaling pathway as a promising therapeutic target for personalized medicine. Yet, a major limitation for long-term success of targeted therapy is relapse due to tumor heterogeneity or acquired resistance. Thus, we performed a genome-wide CRISPR-Cas9 screen to identify prospective resistance mechanisms to pharmacological NOTCH inhibitors and novel targeted combination therapies to efficiently combat T-ALL. Mutational loss of phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1) causes resistance to Notch inhibition. PIK3R1 deficiency leads to increased PI3K/AKT signaling, which regulates cell cycle and the spliceosome machinery, both at the transcriptional and posttranslational level. Moreover, several therapeutic combinations have been identified, in which simultaneous targeting of the cyclin-dependent kinases 4 and 6 (CDK4/6) and NOTCH proved to be the most efficacious in T-ALL xenotransplantation models.

Self-Optimized Ligand Effect of Single-Atom Modifier in Ternary Pt-Based Alloy for Efficient Hydrogen Oxidation.

Modifying the atomic and electronic structure of platinum-based alloy to enhance its activity and anti-CO poisoning ability is a vital issue in hydrogen oxidation reaction (HOR). However, the role of foreign modifier metal and the underlying ligand effect is not fully understood. Here, we propose that the ligand effect of single-atom Cu can dynamically modulate the d-band center of Pt-based alloy for boosting HOR performance. By in situ X-ray absorption spectroscopy, our research has identified that the potential-driven structural rearrangement into high-coordination Cu-Pt/Pd intensifies the ligand effect in Pt-Cu-Pd, leading to enhanced HOR performance. Thereby, modulating the d-band structure leads to near-optimal hydrogen/hydroxyl binding energies and reduced CO adsorption energies for promoting the HOR kinetics and the CO-tolerant capability. Accordingly, PtPdCu1/C exhibits excellent CO tolerance even at 1,000 ppm impurity.

Natural compounds from botanical drugs targeting mTOR signaling pathway as promising therapeutics for atherosclerosis: A review.

Atherosclerosis (AS) is a chronic inflammatory disease that is a major cause of cardiovascular diseases (CVDs), including coronary artery disease, hypertension, myocardial infarction, and heart failure. Hence, the mechanisms of AS are still being explored. A growing compendium of evidence supports that the activity of the mechanistic/mammalian target of rapamycin (mTOR) is highly correlated with the risk of AS. The mTOR signaling pathway contributes to AS progression by regulating autophagy, cell senescence, immune response, and lipid metabolism. Various botanical drugs and their functional compounds have been found to exert anti- AS effects by modulating the activity of the mTOR signaling pathway. In this review, we summarize the pathogenesis of AS based on the mTOR signaling pathway from the aspects of immune response, autophagy, cell senescence, and lipid metabolism, and comb the recent advances in natural compounds from botanical drugs to inhibit the mTOR signaling pathway and delay AS development. This review will provide a new perspective on the mechanisms and precision treatments of AS.

Understanding Synergistic Catalysis on Cu-Se Dual Atom Sites via Operando X-ray Absorption Spectroscopy in Oxygen Reduction Reaction.

The construction and understanding of synergy in well-defined dual-atom active sites is an available avenue to promote multistep tandem catalytic reactions. Herein, we construct a dual-hetero-atom catalyst that comprises adjacent Cu-N4 and Se-C3 active sites for efficient oxygen reduction reaction (ORR) activity. Operando X-ray absorption spectroscopy coupled with theoretical calculations provide in-depth insights into this dual-atom synergy mechanism for ORR under realistic device operation conditions. The heteroatom Se modulator can efficiently polarize the charge distribution around symmetrical Cu-N4 moieties, and serve as synergistic site to facilitate the second oxygen reduction step simultaneously, in which the key OOH*-(Cu1 -N4 ) transforms to O*-(Se1 -C2 ) intermediate on the dual-atom sites. Therefore, this designed catalyst achieves satisfied alkaline ORR activity with a half-wave potential of 0.905 V vs. RHE and a maximum power density of 206.5 mW cm-2 in Zn-air battery.