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Abstract Reactive capture of carbon dioxide (CO2) offers an electrified pathway to produce renewable carbon monoxide (CO), which can then be upgraded into long-chain hydrocarbons and fuels. Previous reactive capture systems relied on hydroxide- or amine-based capture solutions. However, selectivity for CO remains low (

Abstract Electrosynthesis of acetate from CO offers the prospect of a low-carbon-intensity route to this valuable chemical––but only once sufficient selectivity, reaction rate and stability are realized. It is a high priority to achieve the protonation of the relevant intermediates in a controlled fashion, and to achieve this while suppressing the competing hydrogen evolution reaction (HER) and while steering multicarbon (C2+) products to a single valuable product––an example of which is acetate. Here we report interface engineering to achieve solid/liquid/gas triple-phase interface regulation, and we find that it leads to site-selective protonation of intermediates and the preferential stabilization of the ketene intermediates: this, we find, leads to improved selectivity and energy efficiency toward acetate. Once we further tune the catalyst composition and also optimize for interfacial water management, we achieve a cadmium-copper catalyst that shows an acetate Faradaic efficiency (FE) of 75% with ultralow HER (

Abstract Renewable CH4 produced from electrocatalytic CO2 reduction is viewed as a sustainable and versatile energy carrier, compatible with existing infrastructure. However, conventional alkaline and neutral CO2-to-CH4 systems suffer CO2 loss to carbonates, and recovering the lost CO2 requires input energy exceeding the heating value of the produced CH4. Here we pursue CH4-selective electrocatalysis in acidic conditions via a coordination method, stabilizing free Cu ions by bonding Cu with multidentate donor sites. We find that hexadentate donor sites in ethylenediaminetetraacetic acid enable the chelation of Cu ions, regulating Cu cluster size and forming Cu-N/O single sites that achieve high CH4 selectivity in acidic conditions. We report a CH4 Faradaic efficiency of 71% (at 100 mA cm−2) with

Precise synthesis at the atomic scale is a highly desirable and controllable route for the preparation of heterogeneous catalysts with the desired structure and properties, which promotes the rational design of highly efficient catalysts and facilitates the understanding of structure-properties relationship. The precise construction of the active sites of the catalysts provides important opportunities for atomic insight into the correlation between structure and catalytic performance. In this review, the atomic-level tuning strategies for the precise synthesis of heterogeneous catalysts are summarized with the emphasis on the precise control of the structure of active sites, including single atom sites, dual atom sites and complex active sites. Furthermore, we illustrate the crucial role of atomic-level regulation of structure in determining the catalytic performance by providing typical catalysis examples in different reactions. In the end, some perspectives on the further development of precise synthesis of catalysts at the atomic level are presented.

Solar radiation prediction research is a key area of interest in the realm of solar energy utilization and has garnered significant attention in recent times. In order to realize accurate prediction of solar radiation and make solar radiation prediction better serve photovoltaic (PV) power generation, this study proposes a solar radiation prediction method based on sequence model, which integrates two kinds of neural networks, namely, temporal convolutional network (TCN) and neural basis expansion analysis (N-BEATS). First, the dataset is preprocessed using Pearson’s correlation coefficient, outlier detection, and normalized to obtain valid and relevant data; second, the features of TCN feature extraction and N-BEATS flexible extension are integrated to predict the solar radiation; then, the model’s hyperparameters are fine-tuned using the grid search algorithm to ensure precise predictions; and last, the correctness of the method is verified by comparing the error metrics and the running time. Empirical findings indicate that the TCN-N-BEATS sequence model has high prediction accuracy and short time overhead, and it has certain application value in solar radiation prediction, and the model could offer valuable insights for predicting solar radiation.

Summary: The excessive CO2 emission has resulted in climate changes, which has threatened human existence. Photocatalytic and electrocatalytic CO2 reduction, driven by wind electricity and solar energy, are feasible ways of tackling carbon dioxide emission, as both energies are clean and renewable. Single-atom catalyst (SAC) is a candidate owing to excellent electrocatalytic and photocatalytic performance. Methods for preparing an SAC by using metal-organic frameworks (MOFs) as support or precursors are summarized. Also, applications in energy conversion are exhibited. However, the real challenge is to improve the selectivity of catalytic reactions to yield higher value products, which is to be discussed.

Development of fuel cells and metal-air batteries is hindered by electrocatalyst performance, which can be enhanced with uniform and atomically dispersed active sites. Here the authors report an iron-based electrocatalyst for oxygen reduction in cathodes for a zinc-air battery and a hydrogen-air fuel cell.

A nonlinear train-track-bridge system (TTBS) considering the random track irregularity and mass of train is discussed. Based on the Karhunen–Loéve theory, the track irregularity is expressed and input into the TTBS, and the result of random response is calculated using the point estimation method. Two cases are used to compare and validate the applicability of the proposed method, which show that the proposed method has a high precision and efficiency. Then, taking a 7-span bridge and a high-speed train as an example, the calculation results of random response of the nonlinear and linear wheel-rail model are compared, and the results show that for the bridge and rail response, the nonlinear and linear models are almost the same. Finally, comparing the calculated probability distribution results with the test results, it shows that the method can be applied to the prediction of actual response range.

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disease that causes the decline of some cognitive impairments. The present study aimed to identify the corpus callosum (CC) radiomic features related to the diagnosis of AD and build and evaluate a classification model.Methods: Radiomics analysis was applied to the three-dimensional T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) images of 78 patients with AD and 44 healthy controls (HC). The CC, in each subject, was segmented manually and 385 features were obtained after calculation. Then, the feature selection were carried out. The logistic regression model was constructed and evaluated according to identified features. Thus, the model can be used for distinguishing the AD from HC subjects.Results: Eleven features were selected from the three-dimensional T1-weighted MPRAGE images using the LASSO model, following which, the logistic regression model was constructed. The area under the receiver operating characteristic curve values (AUC), sensitivity, specificity, accuracy, precision, and positive and negative predictive values were 0.720, 0.792, 0.500, 0.684, 0.731, 0.731, and 0.583, respectively.Conclusion: The results demonstrated the potential of CC texture features as a biomarker for the diagnosis of AD. This is the first study showing that the radiomics model based on machine learning was a valuable method for the diagnosis of AD.

Abstract Background The level of tumor abnormal protein (TAP) level has a significant impact on tumor growth, recurrence, and metastasis. Previous studies have highlighted the influence of the mutations in exons 19 and 21 of the epidermal growth factor receptor (EGFR), particularly the sensitivity displayed by tumor cells to epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) therapy. Our study is centered on exploring the clinical relevance of TAP and EGFR mutations in patients with non-small cell lung cancer (NSCLC). Material and methods In this study, tissue samples were collected from a total of 176 patients diagnosed with non-small cell lung cancer (NSCLC). Real-time PCR technology was utilized to detect mutations within exons 19 and 21 of the epidermal growth factor receptor (EGFR) gene in these samples. This approach enables precise identification of EGFR mutations associated with NSCLC. Furthermore, the study investigated the impact of various tumor markers, including tumor abnormal protein (TAP) and carcinoembryonic antigen (CEA), on EGFR mutation status. Established assays were employed to evaluate TAP and CEA levels, aiming to ascertain their potential correlation with EGFR mutation in NSCLC patients. Results EGFR exhibited mutation rates of 23.86% and 12.50% in exons 19 and 21, respectively. EGFR mutations were more prevalent in younger women ( 6.5 ng/mL, and TAP > 228 µm2 for both genders. Increased TAP levels independently predicted EGFR mutations (P = 0.001 for males; P = 0.000 for females). An area under the curve (AUC) of 0.833 indecated EGFR mutation prediction with sensitivity and specificity of 79.7% and 87.0%, respectively. For females, the sensitivity increased to 89.7% and specificity increased to 93.8%. Conclusions TAP effectively predicts EGFR mutations in NSCLC patients with moderate accuracy, particularly benefiting diagnosis in females with high sensitivity and specificity. Integrating TAP assessment into EGFR mutation testing can significantly enhance diagnostic precision, especially in female NSCLC cases.

The hydrosilylation reaction is one of the largest-scale application of homogeneous catalysis and is widely used to enable the commercial manufacture of silicon products. However, considerable issues including disposable platinum consumption, undesired side reactions and unacceptable catalyst residues still remain. Here, we synthesize a heterogeneous partially charged single-atom platinum supported on anatase TiO2 (Pt1 δ+/TiO2) catalyst via an electrostatic-induction ion exchange and twodimensional confinement strategy, which can catalyze hydrosilylation reaction with almost complete conversion and produce exclusive adduct. Density functional theory calculations reveal that unexpected property of Pt1 δ+/TiO2 originates from atomic dispersion of active species and unique partially positive charge Ptδ+ electronic structure that conventional nanocatalysts do not possess. The fabrication of single-atom Pt1 δ+/TiO2 catalyst accomplishes a reasonable use of Pt through recycling and maximum atom-utilized efficiency, indicating the potential to achieve a green hydrosilylation industry. [ABSTRACT FROM AUTHOR]

Background: Esophageal squamous cell carcinoma (ESCC) acts as a fatal malignant tumor among human beings and is marked by late-stage diagnosis, frequent recurrence, metastasis, and therapy resistance. Tumor abnormal protein (TAP) remarkably affects cancer development and progression of human cancers. TAP has been shown to be a biomarker for gastric and lung cancer progression. Nevertheless, the clinical value exhibited by TAP for ESCC has not been well-explained in the current literature.Methods: The present study included 183 ESCC cases who received surgical resection and 183 cases who had normal physical checkup from March 2013 to January 2015 at the People's Hospital of Chizhou, and used the TAP detection agent for evaluating the TAP relative level.Results: As found, ESCC patients presented an obviously higher TAP expression relative to cases who had normal physical checkup. Moreover, TAP expression was significantly downregulated after surgery. Furthermore, the TAP expression was correlated with gender, smoking, pathologic differentiation, and pN stage, but not with age, tumor location, surgical type, pT stage, and vascular invasion. High expression of TAP was significantly correlated with poorer overall survival (OS) rate in ESCC patients. TAP was an independent prognostic predictor in ESCC patients, based on the multivariate survival analysis.Conclusion: The study reveals how TAP upregulation promotes ESCC malignant progression, and concludes that TAP acts as the therapeutic target and potential biomarker specific to ESCC.

The dimethyl ether to olefins process is very important for converting coal to chemicals. A SAPO-34 molecular sieve with a particle size up to 50 μm has been synthesized with morpholine as the template. The cold model fluidization experiment indicates that the SAPO-34 catalyst with average size of 45–50 μm is of good fluidization properties. After high temperature steam treatment at 1073 K for 8 h, this large fluidizable catalyst showed good reactivity and selectivity for the dimethyl ether to olefins (DTO) reaction in a micro-reactor. X-ray diffraction characterization showed that the structure of the catalyst was maintained well. A fluidized reactor of 50 mm inner diameter was used to carry out the DTO reaction, which showed that the fluidizable SAPO-34 is a selective catalyst for light olefin production. [ABSTRACT FROM AUTHOR]