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Tang Z, Shi L, Zhang K, Zhang F, Sun Y, Wang X, Yao Y, Liu X, Wang D, Xie J, Yang Z, Yan YM. Modulating the d-Band Center of Palladium via Ethylene Glycol Modification: Accelerating H ad Desorption for Enhanced Formate Electrooxidation. J Phys Chem Lett 2024:3354-3362. [PMID: 38498427 DOI: 10.1021/acs.jpclett.4c00127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
This study addresses the critical challenge in alkaline direct formate fuel cells (DFFCs) of slow formate oxidation reaction (FOR) kinetics as a result of strong hydrogen intermediate (Had) adsorption on Pd catalysts. We developed WO3-supported Pd nanoparticles (EG-Pd/WO3) via an organic reduction method using ethylene glycol (EG), aiming to modulate the d-band center of Pd and alter Had adsorption dynamics. Cyclic voltammetry demonstrated significantly improved Had desorption kinetics in EG-Pd/WO3 catalysts. Density functional theory (DFT) calculations revealed that the presence of EG reduces the d-band center of Pd, leading to weaker Pd-H bonds and enhanced Had desorption during the FOR. This research provides a new approach to optimize catalyst efficiency in DFFCs, highlighting the potential for more effective and sustainable energy solutions through advanced material engineering.
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Affiliation(s)
- Zheng Tang
- State Key Laboratory of Organic-Inorganic Composites, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
| | - Lanlan Shi
- State Key Laboratory of Organic-Inorganic Composites, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
| | - Kaixin Zhang
- State Key Laboratory of Organic-Inorganic Composites, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
| | - Feike Zhang
- State Key Laboratory of Organic-Inorganic Composites, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
| | - Yanfei Sun
- State Key Laboratory of Organic-Inorganic Composites, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
| | - Xiaoxuan Wang
- State Key Laboratory of Organic-Inorganic Composites, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
| | - Yebo Yao
- State Key Laboratory of Organic-Inorganic Composites, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
| | - Xia Liu
- State Key Laboratory of Organic-Inorganic Composites, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
| | - Dewei Wang
- State Key Laboratory of Organic-Inorganic Composites, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
| | - Jiangzhou Xie
- School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Zhiyu Yang
- State Key Laboratory of Organic-Inorganic Composites, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
| | - Yi-Ming Yan
- State Key Laboratory of Organic-Inorganic Composites, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
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Verma AK, Singh J, Nguyen-Tri P. Gold-Deposited Graphene Nanosheets for Self-Cleaning Graphene Surface-Enhanced Raman Spectroscopy with Superior Charge-Transfer Contribution. ACS APPLIED MATERIALS & INTERFACES 2024; 16:10969-10983. [PMID: 38355426 DOI: 10.1021/acsami.3c17303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2024]
Abstract
The interaction of graphene with metals initiates charge-transfer interaction-induced chemical enhancements, which critically depend on the doping effect from deposited metallic configurations. In this paper, we have explored the gold nanoparticle-decorated monolayer graphene nanosheets for the large graphene-induced Raman enhancement of adsorbed analytes, indicating the surface-enhanced Raman spectroscopy (SERS) capabilities of metal-doped graphene (G-SERS). Here, the systematically sputtered Au thickness optimization procedure revealed noticeable modifications in the graphene Raman spectra and photoluminescence (PL) background quenching, which indicated favorable charge transfer through n-type doping of chemical vapor deposition-grown graphene nanosheets. The highly consistent, individually distributed morphology of the gold nanoislands over graphene nanosheets depicted a reproducibly uniform G-SERS signal with excellent relative standard deviation values (<5%), resulting in the strongest Raman intensity enhancement factors of ∼108 (MB) (methylene blue) and 107 (DPA) (2,6-pyridinedicarboxylic acid) composed of the weakest PL background. The combined charge-transfer-induced chemical enhancement and electromagnetic enhancement from individual Au nanoislands result in a lowering of detectability down to 10-16 M (MB) and 10-11 M (DPA) concentrations with efficient time-dependent signal stability. Additionally, the GAu demonstrated its effective (∼94.4%) photocatalytic degradation capabilities by decomposing MB dye molecules from a concentration of 1 μM to 2.52 fM within 60 min. Therefore, the prominent charge-transfer contribution through controlled Au decoration over graphene nanosheets provides a potential strategy for fabricating superior SERS sensors and photocatalysts exhibiting adequate signal consistency, stability, and photodegradation efficiency through overcoming the limitations of the traditional sensing platforms.
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Affiliation(s)
- Ashwani Kumar Verma
- Department of Physics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Jaspal Singh
- Laboratory of Advanced Materials for Energy and Environment, Université Du Québec à Trois-Rivières (UQTR), 3351, Boul. des Forges, C.P. 500, Trois-Rivières, Québec G9A 5H7, Canada
| | - Phuong Nguyen-Tri
- Laboratory of Advanced Materials for Energy and Environment, Université Du Québec à Trois-Rivières (UQTR), 3351, Boul. des Forges, C.P. 500, Trois-Rivières, Québec G9A 5H7, Canada
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Zhang P, Liu B, Mu X, Xu J, Du B, Wang J, Liu Z, Tong Z. Performance of Classification Models of Toxins Based on Raman Spectroscopy Using Machine Learning Algorithms. Molecules 2023; 29:197. [PMID: 38202780 PMCID: PMC10780255 DOI: 10.3390/molecules29010197] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 12/21/2023] [Accepted: 12/26/2023] [Indexed: 01/12/2024] Open
Abstract
Rapid and accurate detection of protein toxins is crucial for public health. The Raman spectra of several protein toxins, such as abrin, ricin, staphylococcal enterotoxin B (SEB), and bungarotoxin (BGT), have been studied. Multivariate scattering correction (MSC), Savitzky-Golay smoothing (SG), and wavelet transform methods (WT) were applied to preprocess Raman spectra. A principal component analysis (PCA) was used to extract spectral features, and the PCA score plots clustered four toxins with two other proteins. The k-means clustering results show that the spectra processed with MSC and MSC-SG methods have the best classification performance. Then, the two data types were classified using partial least squares discriminant analysis (PLS-DA) with an accuracy of 100%. The prediction results of the PCA and PLS-DA and the partial least squares regression model (PLSR) perform well for the fingerprint region spectra. The PLSR model demonstrates excellent classification and regression ability (accuracy = 100%, Rcv = 0.776). Four toxins were correctly classified with interference from two proteins. Classification models based on spectral feature extraction were established. This strategy shows excellent potential in toxin detection and public health protection. These models provide alternative paths for the development of rapid detection devices.
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Affiliation(s)
| | | | | | | | | | | | | | - Zhaoyang Tong
- State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China; (P.Z.); (B.L.); (X.M.); (J.X.); (B.D.); (J.W.); (Z.L.)
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Yu X, Pu H, Sun DW. Developments in food neonicotinoids detection: novel recognition strategies, advanced chemical sensing techniques, and recent applications. Crit Rev Food Sci Nutr 2023:1-19. [PMID: 38149655 DOI: 10.1080/10408398.2023.2290698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
Neonicotinoid insecticides (NEOs) are a new class of neurotoxic pesticides primarily used for pest control on fruits and vegetables, cereals, and other crops after organophosphorus pesticides (OPPs), carbamate pesticides (CBPs), and pyrethroid pesticides. However, chronic abuse and illegal use have led to the contamination of food and water sources as well as damage to ecological and environmental systems. Long-term exposure to NEOs may pose potential risks to animals (especially bees) and even human health. Consequently, it is necessary to develop effective, robust, and rapid methods for NEOs detection. Specific recognition-based chemical sensing has been regarded as one of the most promising detection tools for NEOs due to their excellent selectivity, sensitivity, and robust interference resistance. In this review, we introduce the novel recognition strategies-enabled chemical sensing in food neonicotinoids detection in the past years (2017-2023). The properties and advantages of molecular imprinting recognition (MIR), host-guest recognition (HGR), electron-catalyzed recognition (ECR), immune recognition (IR), aptamer recognition (AR), and enzyme inhibition recognition (EIR) in the development of NEOs sensing platforms are discussed in detail. Recent applications of chemical sensing platforms in various food products, including fruits and vegetables, cereals, teas, honey, aquatic products, and others are highlighted. In addition, the future trends of applying chemical sensing with specific recognition strategies for NEOs analysis are discussed.
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Affiliation(s)
- Xinru Yu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
- Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland
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Cai Y, Li S, Yao Z, Li T, Wang Q. Online detection of concentrate grade in the antimony flotation process based on in situ Raman spectroscopy combined with a CNN-GRU hybrid model. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 301:122909. [PMID: 37302195 DOI: 10.1016/j.saa.2023.122909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/22/2023] [Accepted: 05/22/2023] [Indexed: 06/13/2023]
Abstract
Froth flotation is the most critical process for separating stibnite from raw ore. Concentrate grade is a vital production indicator in the antimony flotation process. It is a direct reflection of the product quality of the flotation process and an essential basis for the dynamic adjustment of its operating parameters. Existing methods of measuring concentrate grades suffer from expensive measurement equipment, difficult maintenance of complex sampling systems, and extended testing times. This paper presents a nondestructive and fast methodology to quantify the concentrate grade in the antimony flotation process based on in situ Raman spectroscopy. A particular Raman spectroscopic measuring system is designed for on-line measurement of the Raman spectra of the mixed minerals from the froth layer during the antimony flotation process. To obtain representative Raman spectra that better characterize the concentrate grades, a traditional Raman spectroscopic system has been redesigned to account for the different interferences during actual flotation field acquisition. A one-dimensional convolutional neural network (1D-CNN) is combined with a gated recurrent unit (GRU) and applied to construct a model for online prediction of concentrate grades based on continuously collected Raman spectra of mixed minerals in the froth layer. With an average prediction error of 4.37% and a maximum prediction deviation of 10.56%, the quantitative analysis of concentrate grade by the model demonstrates that our method is distinguished by high accuracy, low deviation, and in situ analysis, and it essentially satisfies the requirements for online quantitative determination of concentrate grade in the antimony flotation site.
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Affiliation(s)
- Yaoyi Cai
- College of Engineering and Design, Hunan Normal University, Changsha, Hunan 410081, PR China; Xiangji Haidun Technology Co., Ltd., Changsha, Hunan 410199, PR China.
| | - Shiwen Li
- College of Engineering and Design, Hunan Normal University, Changsha, Hunan 410081, PR China
| | - Zekai Yao
- College of Engineering and Design, Hunan Normal University, Changsha, Hunan 410081, PR China
| | - Tian Li
- College of Engineering and Design, Hunan Normal University, Changsha, Hunan 410081, PR China
| | - Qingya Wang
- School of Earth Sciences, East China University of Technology, Nanchang, Jiangxi 330013, PR China
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