Synergistic Effect of Alkali Na and K Promoter on Fe-Co-Cu-Al Catalysts for CO2 Hydrogenation to Light Hydrocarbons

Zheng, Yuhao; Xu, Chenghua; Zhang, Xia; Wu, Qiong; Liu, Jie

doi:10.3390/catal11060735

Cited by 16 publications

(6 citation statements)

References 37 publications

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“…The electron-rich Fe@Cu (110) and Fe-Co@Cu (200) crystal phases were claimed to be active centers for subsequent dissociative adsorption of H 2 and formation of O-C-Fe intermediates after CO adsorption (from RWGS). This was beneficial for the carbon chain growth of C 2 + hydrocarbons, including olefins and alkanes, and exhibited the highest CO 2 conversion and C 2 + selectivity of ~53 mol.% and ~90 mol.%, respectively [130].…”

Section: Effect Of the Catalyst Preparation Methodsmentioning

confidence: 99%

“…Recently, using ZnO-ZrO2/SAPO-34 and In2O3/ZSM-5 bifunctional catalysts, Gao et al successfully converted ~20% of CO2 to light olefins via MTO mechanisms [7,39]. Zheng et al synthesized K-and/or Na-promoted FeCoCuAl catalysts by precipitation and impregnation [130]. The best results in terms of activity and selectivity were shown by the FeCoCuAl catalyst prepared by simultaneous modification with K and Na.…”

Section: Effect Of the Catalyst Preparation Methodsmentioning

confidence: 99%

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Catalysts for the Conversion of CO2 to Low Molecular Weight Olefins—A Review

et al. 2021

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There is a large worldwide demand for light olefins (C2=–C4=), which are needed for the production of high value-added chemicals and plastics. Light olefins can be produced by petroleum processing, direct/indirect conversion of synthesis gas (CO + H2) and hydrogenation of CO2. Among these methods, catalytic hydrogenation of CO2 is the most recently studied because it could contribute to alleviating CO2 emissions into the atmosphere. However, due to thermodynamic reasons, the design of catalysts for the selective production of light olefins from CO2 presents different challenges. In this regard, the recent progress in the synthesis of nanomaterials with well-controlled morphologies and active phase dispersion has opened new perspectives for the production of light olefins. In this review, recent advances in catalyst design are presented, with emphasis on catalysts operating through the modified Fischer–Tropsch pathway. The advantages and disadvantages of olefin production from CO2 via CO or methanol-mediated reaction routes were analyzed, as well as the prospects for the design of a single catalyst for direct olefin production. Conclusions were drawn on the prospect of a new catalyst design for the production of light olefins from CO2.

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Section: Effect Of the Catalyst Preparation Methodsmentioning

confidence: 99%

Section: Effect Of the Catalyst Preparation Methodsmentioning

confidence: 99%

Catalysts for the Conversion of CO2 to Low Molecular Weight Olefins—A Review

et al. 2021

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“…ITR and computation time reflect the speed of classification and are crucial factors in many studies. Notably, six studies improve ITR, with the maximum ITR reaching 170.67 bit/min in Zhang K. et al (2021). Seven studies achieve time cost reduction, reflecting the importance of computational efficiency.…”

Section: Indicators Of Eegmentioning

confidence: 96%

“…A detailed table with information on the directions and performance of each paper can be found in the Supplementary material. The following reviewed papers are presented in ascending order of their published date (Aellen et al, 2021;Asheri et al, 2021;Ashwini and Nagaraj, 2021;Awais et al, 2021;Cai et al, 2021;Dagdevir and Tokmakci, 2021;De Venuto and Mezzina, 2021;Du et al, 2021;Fan et al, 2021Fan et al, , 2022Ferracuti et al, 2021;Gao N. et al, 2021;Gaur et al, 2021;Lashgari et al, 2021;Lian et al, 2021;Liu and Jin, 2021;Liu and Yang, 2021;Qi et al, 2021;Rashid et al, 2021;Sun et al, 2021;Varsehi and Firoozabadi, 2021;Vega et al, 2021;Vorontsova et al, 2021;Wahid and Tafreshi, 2021;Wang and Quan, 2021;Xu C. et al, 2021;Xu F. et al, 2021;Yin et al, 2021;Zhang K. et al, 2021;Algarni et al, 2022;Ali et al, 2022;Asadzadeh et al, 2022;Ayoobi and Sadeghian, 2022;Bagchi and Bathula, 2022;Chang et al, 2022;Chen J. et al, 2022;Chen L. et al, 2022;Cui et al, 2022;Geng et al, 2022;Islam et al, 2022;…”

Section: Reviewed Papersmentioning

confidence: 99%

Survey on the research direction of EEG-based signal processing

Sun

Mou

2023

Front. Neurosci.

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Electroencephalography (EEG) is increasingly important in Brain-Computer Interface (BCI) systems due to its portability and simplicity. In this paper, we provide a comprehensive review of research on EEG signal processing techniques since 2021, with a focus on preprocessing, feature extraction, and classification methods. We analyzed 61 research articles retrieved from academic search engines, including CNKI, PubMed, Nature, IEEE Xplore, and Science Direct. For preprocessing, we focus on innovatively proposed preprocessing methods, channel selection, and data augmentation. Data augmentation is classified into conventional methods (sliding windows, segmentation and recombination, and noise injection) and deep learning methods [Generative Adversarial Networks (GAN) and Variation AutoEncoder (VAE)]. We also pay attention to the application of deep learning, and multi-method fusion approaches, including both conventional algorithm fusion and fusion between conventional algorithms and deep learning. Our analysis identifies 35 (57.4%), 18 (29.5%), and 37 (60.7%) studies in the directions of preprocessing, feature extraction, and classification, respectively. We find that preprocessing methods have become widely used in EEG classification (96.7% of reviewed papers) and comparative experiments have been conducted in some studies to validate preprocessing. We also discussed the adoption of channel selection and data augmentation and concluded several mentionable matters about data augmentation. Furthermore, deep learning methods have shown great promise in EEG classification, with Convolutional Neural Networks (CNNs) being the main structure of deep neural networks (92.3% of deep learning papers). We summarize and analyze several innovative neural networks, including CNNs and multi-structure fusion. However, we also identified several problems and limitations of current deep learning techniques in EEG classification, including inappropriate input, low cross-subject accuracy, unbalanced between parameters and time costs, and a lack of interpretability. Finally, we highlight the emerging trend of multi-method fusion approaches (49.2% of reviewed papers) and analyze the data and some examples. We also provide insights into some challenges of multi-method fusion. Our review lays a foundation for future studies to improve EEG classification performance.

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“…A wide range of promoters, such as noble metals (e.g., Ru, Pt and Re) [113][114][115][116], alkali metals (Na, K, Rb and Li) [117][118][119], transition metal oxides (La, Cu, Gd, Ni, Zr and Mn ) [1, [120][121][122], as well as alkaline earth metals including Mg, Ba, and Ca [123][124][125]; have been researched and applied on FTS, but, there is still controversy on the their effect regarding catalyst performance, selectivity, and deactivation. [62].…”

Section: Support Promotionmentioning

confidence: 99%

Advances on Catalyst Support Modification and their Effect on Fischer Tropsch Synthesis: A Review

Thibanyane,

Gorimbo,

Yao

2024

Preprint

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One important goal of research on heterogeneous catalysis for Fischer Tropsch synthesis (FTS) is to understand the connection linking catalyst morphology and its activity. Development of these effective catalysts highlights the potential for understanding the effectiveness of promoted supported catalysts and revealing their complex and dynamic effects in FTS. The metal-support interaction (MSI) is influenced by the physicochemical and textural features provided by the modified support and can directly influence the sizes of active metal crystallites, reactants and/or products mass transfer, dispersion, mechanical strength, stability and consequently the adsorption-desorption characteristics of the catalyst. Herein, this article explores the general strategies for modifying silica supports (achieved through doping, coating, or promoting the support or support precursor) and how these strategies can be manipulated to further alter catalyst performance and product selectivity by enhancing the amount of active metal species and improving the intrinsic turnover frequency (TOF). Furthermore, the review emphasises a new strategy (the inverse model of the silica support modification) aiming at overcoming the challenges posed by active metal coating or coverage, as well as pore blockages by promoters that occur during conventional preparation of promoted silica supported FT catalysts.

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Synergistic Effect of Alkali Na and K Promoter on Fe-Co-Cu-Al Catalysts for CO2 Hydrogenation to Light Hydrocarbons

Cited by 16 publications

References 37 publications

Catalysts for the Conversion of CO2 to Low Molecular Weight Olefins—A Review

Catalysts for the Conversion of CO2 to Low Molecular Weight Olefins—A Review

Survey on the research direction of EEG-based signal processing

Advances on Catalyst Support Modification and their Effect on Fischer Tropsch Synthesis: A Review

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