Incorporation of Bi2O3 Residuals with Metallic Bi as High Performance Electrocatalyst toward Hydrogen Evolution Reaction
Abstract
:1. Introduction
2. Results
3. Materials and Methods
3.1. Materials
3.2. Fabrication of Oxidized Bi (Bi2O3)
3.3. Materials Characterizations
3.4. Electrochemical Evaluations
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Chen, H.; Heidari, A.A.; Chen, H.; Wang, M.; Pan, Z.; Gandomi, A.H. Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies. Futur. Gener. Comput. Syst. 2020, 111, 175–198. [Google Scholar] [CrossRef]
- Wang, M.; Chen, H. Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Appl. Soft Comput. 2020, 88. [Google Scholar] [CrossRef]
- Xu, Y.; Chen, H.; Luo, J.; Zhang, Q.; Jiao, S.; Zhang, X. Enhanced Moth-flame optimizer with mutation strategy for global optimization. Inf. Sci. 2019, 492, 181–203. [Google Scholar] [CrossRef]
- He, L.; Li, M.-X.; Chen, F.; Yang, S.-S.; Ding, J.; Ding, L.; Ren, N.-Q. Novel coagulation waste-based Fe-containing carbonaceous catalyst as peroxymonosulfate activator for pollutants degradation: Role of ROS and electron transfer pathway. J. Hazard. Mater. 2021, 417, 126113. [Google Scholar] [CrossRef] [PubMed]
- Li, Z.; Shi, Y.; Zhu, A.; Zhao, Y.; Wang, H.; Binks, B.P.; Wang, J. Light-Responsive, Reversible Emulsification and Demulsification of Oil-in-Water Pickering Emulsions for Catalysis. Angew. Chem. Int. Ed. 2020, 60, 3928–3933. [Google Scholar] [CrossRef]
- Huang, W.-Y.; Wang, G.-Q.; Li, W.-H.; Li, T.-T.; Ji, G.-J.; Ren, S.-C.; Jiang, M.; Yan, L.; Tang, H.-T.; Pan, Y.-M.; et al. Porous Ligand Creates New Reaction Route: Bifunctional Single-Atom Palladium Catalyst for Selective Distannylation of Terminal Alkynes. Chem 2020, 6, 2300–2313. [Google Scholar] [CrossRef]
- Duan, Y.; Liu, Y.; Chen, Z.; Liu, D.; Yu, E.; Zhang, X.; Fu, H.; Fu, J.; Zhang, J.; Du, H. Amorphous molybdenum sulfide nanocatalysts simultaneously realizing efficient upgrading of residue and synergistic synthesis of 2D MoS2 nanosheets/carbon hierarchical structures. Green Chem. 2019, 22, 44–53. [Google Scholar] [CrossRef]
- Xu, G.; Ling, R.; Deng, L.; Wu, Q.; Ma, W. Image Interpolation via Gaussian-Sinc Interpolators with Partition of Unity. Comput. Mater. Contin. 2020, 62, 309–319. [Google Scholar] [CrossRef]
- He, S.; Li, Z.; Tang, Y.; Liao, Z.; Li, F.; Lim, S.-J. Parameters Compressing in Deep Learning. Comput. Mater. Contin. 2020, 62, 321–336. [Google Scholar] [CrossRef]
- Guo, W.; Liu, T.; Dai, F.; Xu, P. An Improved Whale Optimization Algorithm for Feature Selection. Comput. Mater. Contin. 2020, 62, 337–354. [Google Scholar] [CrossRef]
- Vijayalakshmi, K.; Anandan, P. Global levy flight of cuckoo search with particle swarm optimization for effective cluster head selection in wireless sensor network. Intell. Autom. Soft Comput. 2020, 26, 303–311. [Google Scholar] [CrossRef]
- Huang, D.H.; Gu, P.; Feng, H.-M.; Lin, Y.; Zheng, L. Robust Visual Tracking Model Designs Through Kernelized Correlation Filters. Intell. Autom. Soft Comput. 2019, 26, 313–322. [Google Scholar] [CrossRef]
- Sezer, O.; Ozbayoglu, A. Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks. Intell. Autom. Soft Comput. 2020, 26, 323–334. [Google Scholar] [CrossRef]
- Zhao, X.; Zhang, X.; Cai, Z.; Tian, X.; Wang, X.; Huang, Y.; Chen, H.; Hu, L. Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients. Comput. Biol. Chem. 2019, 78, 481–490. [Google Scholar] [CrossRef] [PubMed]
- Li, C.; Hou, L.; Sharma, B.Y.; Li, H.; Chen, C.; Li, Y.; Zhao, X.; Huang, H.; Cai, Z.; Chen, H. Developing a new intelligent system for the diagnosis of tuberculous pleural effusion. Comput. Methods Programs Biomed. 2018, 153, 211–225. [Google Scholar] [CrossRef]
- Wang, M.; Chen, H.; Yang, B.; Zhao, X.; Hu, L.; Cai, Z.; Huang, H.; Tong, C. Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses. Neurocomputing 2017, 267, 69–84. [Google Scholar] [CrossRef]
- Guan, H.; Huang, S.; Ding, J.; Tian, F.; Xu, Q.; Zhao, J. Chemical environment and magnetic moment effects on point defect formations in CoCrNi-based concentrated solid-solution alloys. Acta Mater. 2020, 187, 122–134. [Google Scholar] [CrossRef]
- Zhang, M.; Zhang, L.; Tian, S.; Zhang, X.; Guo, J.; Guan, X.; Xu, P. Effects of graphite particles/Fe3+ on the properties of anoxic activated sludge. Chemosphere 2020, 253, 126638. [Google Scholar] [CrossRef]
- Cheng, J.-Z.; Tan, Z.-R.; Xing, Y.-Q.; Shen, Z.-Q.; Zhang, Y.-J.; Liu, L.-L.; Yang, K.; Chen, L.; Liu, S.-Y. Exfoliated conjugated porous polymer nanosheets for highly efficient photocatalytic hydrogen evolution. J. Mater. Chem. A 2021, 9, 5787–5795. [Google Scholar] [CrossRef]
- Ren, S.; Ye, B.; Li, S.; Pang, L.; Pan, Y.; Tang, H. Well-defined coordination environment breaks the bottleneck of organic synthesis: Single-atom palladium catalyzed hydrosilylation of internal alkynes. Nano Res. 2021, 1–9. [Google Scholar] [CrossRef]
- Tümer, A.E.; Akkus, A. Application of radial basis function networks with feature selection for gdp per capita estimation based on academic parameters. Comput. Syst. Sci. Eng. 2019, 34, 145–150. [Google Scholar]
- Xue, Y.; Li, Q.; Ling, F. Teensensor: Gaussian processes for micro-blog based teen’s acute and chronic stress detection. Comput. Syst. Sci. Eng. 2019, 34, 151–164. [Google Scholar] [CrossRef]
- Xu, Z. Applications and Techniques in Cyber Intelligence. Comput. Syst. Sci. Eng. 2019, 34, 169–170. [Google Scholar] [CrossRef]
- Chubo, L.; Kenli, L.; Keqin, L. A Game Approach to Multi-Servers Load Balancing with Load-Dependent Server Availability Consideration. IEEE Trans. Cloud Comput. 2021, 9, 1–13. [Google Scholar]
- Chubo, L.; Kenli, L.; Keqin, L.; Rajkumar, B. A New Service Mechanism for Profit Optimizations of a Cloud Provider and Its Users. IEEE Trans. Cloud Comput. 2021, 9, 14–26. [Google Scholar]
- Hu, X.; Xie, J.; Cai, W.; Wang, R.; Davarpanah, A. Thermodynamic effects of cycling carbon dioxide injectivity in shale reservoirs. J. Pet. Sci. Eng. 2020, 195, 107717. [Google Scholar] [CrossRef]
- Guoqing, X.; Kenli, L.; Yuedan, C.; Wangquan, H.; Albert, Y.; Zomaya, T.L. CASpMV: A Customized and Accelerative SpMV Framework for the Sunway TaihuLight. IEEE Trans. Parallel Distributed Syst. 2021, 32, 131–146. [Google Scholar]
- Chu, S.; Majumdar, A. Opportunities and challenges for a sustainable energy future. Nature 2012, 488, 294–303. [Google Scholar] [CrossRef] [PubMed]
- Hatami, E.; Toghraei, A.; Darband, G.B. Electrodeposition of Ni–Fe micro/nano urchin-like structure as an efficient electrocatalyst for overall water splitting. Int. J. Hydrogen Energy 2021, 46, 9394–9405. [Google Scholar] [CrossRef]
- Xia, J.; Chen, H.; Li, Q.; Zhou, M.; Chen, L.; Cai, Z.; Fang, Y.; Zhou, H. Ultrasound-based differentiation of malignant and benign thyroid Nodules: An extreme learning machine approach. Comput. Methods Programs Biomed. 2017, 147, 37–49. [Google Scholar] [CrossRef]
- Chen, H.; Wang, G.; Ma, C.; Cai, Z.-N.; Liu, W.-B.; Wang, S.-J. An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson’s disease. Neurocomputing 2016, 184, 131–144. [Google Scholar] [CrossRef] [Green Version]
- Shen, L.; Chen, H.; Yu, Z.; Kang, W.; Zhang, B.; Li, H.; Yang, B.; Liu, D. Evolving support vector machines using fruit fly optimization for medical data classification. Knowl.-Based Syst. 2016, 96, 61–75. [Google Scholar] [CrossRef]
- Chen, L.; Xu, J.; Zhang, M.; Rong, T.; Jiang, Z.; Li, P. Systematic study on mechanical and electronic properties of ternary VAlN, TiAlN and WAlN systems by first-principles calculations. Ceram. Int. 2020, 47, 7511–7520. [Google Scholar] [CrossRef]
- Wang, X.; Feng, Z.; Xiao, B.; Zhao, J.-X.; Ma, H.; Tian, Y.; Pang, H.; Tan, L. Polyoxometalate-based metal–organic framework-derived bimetallic hybrid materials for upgraded electrochemical reduction of nitrogen. Green Chem. 2020, 22, 6157–6169. [Google Scholar] [CrossRef]
- Zhang, Y.; Hao Nan, L.I.; Changhe, L.I.; Chuanzhen, H.; Hafiz Muhammad, A.; Xuefeng, X.; Cong, M.; Wenfeng, D.; Xin, C.; Min, Y.; et al. Nano-enhanced biolubricant in sustainable manufacturing: From processability to mechanisms. Friction 2021, in press. [Google Scholar] [CrossRef]
- Shi, C.; Zhang, X.; Zhang, X.; Chen, P.; Xu, L. Ultrasonic desulfurization of amphiphilic magnetic-Janus nanosheets in oil-water mixture system. Ultrason. Sonochemistry 2021, 76, 105662. [Google Scholar] [CrossRef] [PubMed]
- Mingxing, D.; Kenli, L.; Keqin, L.; Qi, T. A Novel Multi-task Tensor Correlation Neural Network for Facial Attribute Prediction. ACM Trans. Intell. Syst. Technol. 2021, 12, 1–22. [Google Scholar]
- Cen, C.; Kenli, L.; Sin, G.; Xiaofeng, Z.T.; Keqin, L.; Zeng, Z. Citywide Traffic Flow Prediction Based on Multiple Gated Spatio-temporal Convolutional Neural Networks. ACM Trans. Knowl. Discov. Data 2020, 14, 1–23. [Google Scholar]
- Zhou, X.; Kenli, L.; ZhiBang, Y.; Gao, Y.; Keqin, L. Efficient Approaches to k Representative G-Skyline Queries. ACM Trans. Knowl. Discov. Data 2020, 14, 1–27. [Google Scholar] [CrossRef]
- Davarpanah, A.; Mirshekari, B. Experimental Investigation and Mathematical Modeling of Gas Diffusivity by Carbon Dioxide and Methane Kinetic Adsorption. Ind. Eng. Chem. Res. 2019, 58, 12392–12400. [Google Scholar] [CrossRef]
- Valizadeh, K.; Farahbakhsh, S.; Bateni, A.; Zargarian, A.; Davarpanah, A.; Alizadeh, A.; Zarei, M. A parametric study to simulate the non-Newtonian turbulent flow in spiral tubes. Energy Sci. Eng. 2020, 8, 134–149. [Google Scholar] [CrossRef] [Green Version]
- Hu, L.; Hong, G.; Ma, J.; Wang, X.; Chen, H. An efficient machine learning approach for diagnosis of paraquat-poisoned patients. Comput. Biol. Med. 2015, 59, 116–124. [Google Scholar] [CrossRef]
- Xu, X.; Chen, H.-L. Adaptive computational chemotaxis based on field in bacterial foraging optimization. Soft Comput. 2013, 18, 797–807. [Google Scholar] [CrossRef]
- Zhang, Y.; Liu, R.; Wang, X.; Chen, H.; Li, C. Boosted binary Harris hawks optimizer and feature selection. Eng. Comput. 2020, 1–30. [Google Scholar] [CrossRef]
- Karbakhshzadeh, A.; Derakhshande, M.; Farhami, M.; Hosseinian, A.; Ebrahimiasl, S.; Ebadi, A. Study the adsorption of letrozole drug on the silicon doped graphdiyne monolayer: A DFT investigation. Silicon 2021, 10, 1–8. [Google Scholar]
- Sun, H.; Ebadi, A.G.; Toughani, M.; Nowdeh, S.A.; Naderipour, A.; Abdullah, A. Designing Framework of Hybrid Photovoltaic-Biowaste Energy System with Hydrogen Storage Considering Economic and Technical Indices Using Whale Optimization Algorithm. Energy 2021, 238, 121555. [Google Scholar] [CrossRef]
- Sabeen, M.; Mahmood, Q.; Ebadi, A.G.; Bhatti, Z.A.; Irshad, M.; Kakar, A.; Bilal, M.; Arshad, H.M.; Shahid, N. Health risk assessment consequent to wastewater irrigation in Pakistan. Soil Environ. 2020, 39, 67–76. [Google Scholar] [CrossRef]
- Renbing, Q.; Shujun, W.; Fengjuan, X. Structural Changes and in vitro Enzymatic Diges tibility of Starch-Lipid Complexes Altered by High Hydrostatic Pressure. J. Food Res. Dev. 2021, 42, 25–30. [Google Scholar]
- Nejad, R.M.; Liu, Z.; Ma, W.; Berto, F. Reliability analysis of fatigue crack growth for rail steel under variable amplitude service loading conditions and wear. Int. J. Fatigue 2021, 106450. [Google Scholar] [CrossRef]
- Nejad, R.M.; Liu, Z.; Ma, W.; Berto, F. Fatigue reliability assessment of a pearlitic Grade 900A rail steel subjected to multiple cracks. Eng. Fail. Analysis 2021, 105625. [Google Scholar] [CrossRef]
- Wang, J.; Gao, Y.; Yin, X.; Li, F.; Kim, H.-J. An Enhanced PEGASIS Algorithm with Mobile Sink Support for Wireless Sensor Networks. Wirel. Commun. Mob. Comput. 2018, 2018, 1–9. [Google Scholar] [CrossRef]
- Liao, Z.; Wang, J.; Zhang, S.; Cao, J.; Min, G. Minimizing Movement for Target Coverage and Network Connectivity in Mobile Sensor Networks. IEEE Trans. Parallel Distrib. Syst. 2014, 26, 1971–1983. [Google Scholar] [CrossRef]
- Wang, J.; Gao, Y.; Liu, W.; Sangaiah, A.K.; Kim, H.-J. An intelligent data gathering schema with data fusion supported for mobile sink in wireless sensor networks. Int. J. Distrib. Sens. Netw. 2019, 15. [Google Scholar] [CrossRef] [Green Version]
- Zhang, J.; Jin, X.; Sun, J.; Wang, J.; Sangaiah, A.K. Spatial and semantic convolutional features for robust visual object tracking. Multimedia Tools Appl. 2018, 79, 15095–15115. [Google Scholar] [CrossRef]
- Yu, F.; Liu, L.; Xiao, L.; Li, K.; Cai, S. A robust and fixed-time zeroing neural dynamics for computing time-variant nonlinear equation using a novel nonlinear activation function. Neurocomputing 2019, 350, 108–116. [Google Scholar] [CrossRef]
- Zhang, Y.; Liu, R.; Heidari, A.A.; Wang, X.; Chen, Y.; Wang, M.; Chen, H. Towards augmented kernel extreme learning models for bankruptcy prediction: Algorithmic behavior and comprehensive analysis. Neurocomputing 2020, 430, 185–212. [Google Scholar] [CrossRef]
- Zhao, D.; Liu, L.; Yu, F.; Heidari, A.A.; Wang, M.; Liang, G.; Muhammad, K.; Chen, H. Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2D Kapur entropy. Knowl.-Based Syst. 2020, 216, 106510. [Google Scholar] [CrossRef]
- Tu, J.; Chen, H.; Liu, J.; Heidari, A.A.; Zhang, X.; Wang, M.; Ruby, R.; Pham, Q.-V. Evolutionary biogeography-based whale optimization methods with communication structure: Towards measuring the balance. Knowl.-Based Syst. 2020, 212, 106642. [Google Scholar] [CrossRef]
- Deng, Z.; Liu, C.; Zhu, Z. Inter-hours rolling scheduling of behind-the-meter storage operating systems using electricity price forecasting based on deep convolutional neural network. Int. J. Electr. Power Energy Syst. 2020, 125, 106499. [Google Scholar] [CrossRef]
- Deng, Z.; Wang, B.; Xu, Y.; Xu, T.; Liu, C.; Zhu, Z. Multi-Scale Convolutional Neural Network With Time-Cognition for Multi-Step Short-Term Load Forecasting. IEEE Access 2019, 7, 88058–88071. [Google Scholar] [CrossRef]
- Wang, J.; Gu, X.; Liu, W.; Sangaiah, A.K.; Kim, H.-J. An empower hamilton loop based data collection algorithm with mobile agent for WSNs. Human-centric Comput. Inf. Sci. 2019, 9, 18. [Google Scholar] [CrossRef]
- Li, W.; Chen, Z.; Gao, X.; Liu, W.; Wang, J. Multimodel Framework for Indoor Localization Under Mobile Edge Computing Environment. IEEE Internet Things J. 2018, 6, 4844–4853. [Google Scholar] [CrossRef]
- Xiang, L.; Shen, X.; Qin, J.; Hao, W. Discrete Multi-graph Hashing for Large-Scale Visual Search. Neural Process. Lett. 2019, 49, 1055–1069. [Google Scholar] [CrossRef]
- Zhang, J.; Wang, W.; Lu, C.; Wang, J.; Sangaiah, A.K. Lightweight deep network for traffic sign classification. Annals Telecom. 2019, 75, 369–379. [Google Scholar] [CrossRef]
- Zhou, S.-R.; Yin, J.-P.; Zhang, J.-M. Local binary pattern (LBP) and local phase quantization (LBQ) based on Gabor filter for face representation. Neurocomputing 2013, 116, 260–264. [Google Scholar] [CrossRef]
- Hsieh, T.H.; Lin, H.; Liu, J.; Duan, W.; Bansil, A.; Fu, L. Topological crystalline insulators in the SnTe material class. Nat. Commun. 2012, 3, 982. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Khiarak, B.N.; Hasanzadeh, M.; Mojaddami, M.; Far, H.S.; Simchi, A. In situ synthesis of quasi-needle-like bimetallic organic frameworks on highly porous graphene scaffolds for efficient electrocatalytic water oxidation. Chem. Commun. 2020, 56, 3135–3138. [Google Scholar]
- Huang, J.; Duan, T.; Zhang, Y.; Liu, J.; Zhang, J.; Lei, Y. Predicting the Permeability of Pervious Concrete Based on the Beetle Antennae Search Algorithm and Random Forest Model. Adv. Civ. Eng. 2020, 2020, 1–11. [Google Scholar] [CrossRef]
- Huang, J.; Kumar, G.S.; Ren, J.; Sun, Y.; Li, Y.; Wang, C. Towards the potential usage of eggshell powder as bio-modifier for asphalt binder and mixture: Workability and mechanical properties. Int. J. Pavement Eng. 2021, 1–13. [Google Scholar] [CrossRef]
- Shan, W.; Qiao, Z.; Heidari, A.A.; Chen, H.; Turabieh, H.; Teng, Y. Double adaptive weights for stabilization of moth flame optimizer: Balance analysis, engineering cases, and medical diagnosis. Knowl.-Based Syst. 2020, 214, 106728. [Google Scholar] [CrossRef]
- Yu, C.; Chen, M.; Cheng, K.; Zhao, X.; Ma, C.; Kuang, F.; Chen, H. SGOA: Annealing-behaved grasshopper optimizer for global tasks. Eng. Comput. 2021, 1–28. [Google Scholar] [CrossRef]
- Hu, J.; Chen, H.; Heidari, A.A.; Wang, M.; Zhang, X.; Chen, Y.; Pan, Z. Orthogonal learning covariance matrix for defects of grey wolf optimizer: Insights, balance, diversity, and feature selection. Knowl.-Based Syst. 2020, 213, 106684. [Google Scholar] [CrossRef]
- Huang, J.; Zhang, Y.; Sun, Y.; Ren, J.; Zhao, Z.; Zhang, J. Evaluation of pore size distribution and permeability reduction behavior in pervious concrete. Constr. Build. Mater. 2021, 290, 123228. [Google Scholar] [CrossRef]
- Zhou, J.; Shen, X.; Qiu, Y.; Li, E.; Rao, D.; Shi, X. Improving the efficiency of microseismic source locating using a heuristic algorithm-based virtual field optimization method. Geomech. Geophys. Geo-Energy Geo-Resour. 2021, 7, 1–18. [Google Scholar] [CrossRef]
- Zhou, J.; Chen, C.; Wang, M.; Khandelwal, M. Proposing a novel comprehensive evaluation model for the coal burst liability in underground coal mines considering uncertainty factors. Int. J. Min. Sci. Technol. 2021, 1–15. [Google Scholar] [CrossRef]
- Zhou, J.; Qiu, Y.; Khandelwal, M.; Zhu, S.; Zhang, X. Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations. Int. J. Rock Mech. Min. Sci. 2021, 145, 104856. [Google Scholar] [CrossRef]
- Zhou, J.; Li, X.; Mitri, H.S. Classification of Rockburst in Underground Projects: Comparison of Ten Supervised Learning Methods. J. Comput. Civ. Eng. 2016, 30, 04016003. [Google Scholar] [CrossRef]
- Xiong, B.; Yang, K.; Zhao, J.; Li, W.; Li, K. Performance evaluation of OpenFlow-based software-defined networks based on queueing model. Comput. Netw. 2016, 102, 172–185. [Google Scholar] [CrossRef]
- Wang, J.; Yang, Y.; Wang, T.; Sherratt, R.S.; Zhang, J. Big data service architecture: A survey. J. Internet Technol. 2020, 21, 393–405. [Google Scholar]
- He, S.; Xie, K.; Xie, K.; Xu, C.; Wang, J. Interference-Aware Multisource Transmission in Multiradio and Multichannel Wireless Network. IEEE Syst. J. 2019, 13, 2507–2518. [Google Scholar] [CrossRef]
- Zhang, D.; Yin, T.; Yang, G.; Xia, M.; Li, L.; Sun, X. Detecting image seam carving with low scaling ratio using multi-scale spatial and spectral entropies. J. Vis. Commun. Image Represent. 2017, 48, 281–291. [Google Scholar] [CrossRef]
- Long, M.; Peng, F.; Li, H.-Y. Separable reversible data hiding and encryption for HEVC video. J. Real-Time Image Process. 2017, 14, 171–182. [Google Scholar] [CrossRef]
- Ortel, E.; Reier, T.; Strasser, P.; Kraehnert, R. Mesoporous IrO2 films templated by PEO-PB-PEO block-copolymers: Self-assembly, crystallization behavior, and electrocatalytic performance. Chem. Mater. 2011, 23, 3201–3209. [Google Scholar] [CrossRef]
- Zhang, J.; Zhong, S.; Wang, T.; Chao, H.C.; Wang, J. Blockchain-based systems and applications: A survey. J. Internet Technol. 2020, 21, 1–14. [Google Scholar]
- Tang, Q.; Yang, K.; Zhou, D.; Luo, Y.-S.; Yu, F. A Real-Time Dynamic Pricing Algorithm for Smart Grid With Unstable Energy Providers and Malicious Users. IEEE Internet Things J. 2015, 3, 554–562. [Google Scholar] [CrossRef]
- He, S.; Zeng, W.; Xie, K.; Yang, H.; Lai, M.; Su, X. PPNC: Privacy preserving scheme for random linear network coding in smart grid. KSII Transac. Internet Inform. Syst. 2017, 11, 1510–1532. [Google Scholar]
- Liang, Y.; Li, Y.; Wang, H.; Zhou, J.; Wang, J.; Regier, T.; Dai, H. Co3O4 nanocrystals on graphene as a synergistic catalyst for oxygen reduction reaction. Nature Mater. 2011, 10, 780–786. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhao, X.; Li, D.; Yang, B.; Ma, C.; Zhu, Y.; Chen, H. Feature selection based on improved ant colony optimization for online detection of foreign fiber in cotton. Appl. Soft Comput. 2014, 24, 585–596. [Google Scholar] [CrossRef]
- Yu, H.; Li, W.; Chen, C.; Liang, J.; Gui, W.; Wang, M.; Chen, H. Dynamic Gaussian bare-bones fruit fly optimizers with abandonment mechanism: Method and analysis. Eng. Comput. 2020, 1–29. [Google Scholar] [CrossRef]
- Zheng, L.; Zheng, S.; Wei, H.; Du, L.; Zhu, Z.; Chen, J.; Yang, D. Palladium/bismuth/copper hierarchical nano-architectures for efficient hydrogen evolution and stable hydrogen detection. ACS Appl. Mater. Interfaces 2019, 11, 6248–6256. [Google Scholar] [CrossRef]
- Wang, S.; Xue, W.; Fang, Y.; Li, Y.; Yan, L.; Wang, W.; Zhao, R. Bismuth activated succulent-like binary metal sulfide heterostructure as a binder-free electrocatalyst for enhanced oxygen evolution reaction. J. Colloid Interface Sci. 2020, 573, 150–157. [Google Scholar] [CrossRef] [PubMed]
- Khiarak, B.N.; Golmohammad, M.; Shahraki, M.M.; Simchi, A. Facile synthesis and self-assembling of transition metal phosphide nanosheets to microspheres as a high-performance electrocatalyst for full water splitting. J. Alloys Compd. 2021, 875, 160049. [Google Scholar] [CrossRef]
- Al-Odail, F.A.; Anastasopoulos, A.; Hayden, B.E. Hydrogen evolution and hydrogen oxidation on palladium bismuth alloys. Top. Catal. 2011, 54, 77–82. [Google Scholar] [CrossRef]
- Zhou, L.; Yang, T.; Chen, S.; Gao, J.; Wang, X.; He, P.; Lei, H.; Yang, D.; Dong, F.; Jia, L.; et al. Tunably fabricated nanotremella-like Bi2S3/MoS2: An excellent and highly stable electrocatalyst for alkaline hydrogen evolution reaction. Int. J. Hydrogen Energy 2020, 45, 9535–9545. [Google Scholar] [CrossRef]
- Chen, S.; Qiao, S.-Z. Hierarchically porous nitrogen-doped graphene–NiCo2O4 hybrid paper as an advanced electrocatalytic water-splitting material. ACS Nano 2013, 7, 10190–10196. [Google Scholar] [CrossRef]
- Khiarak, B.N.; Hasanzadeh, M.; Simchi, A. Electrocatalytic hydrogen evolution reaction on graphene supported transition metal-organic frameworks. Inorg. Chem. Commun. 2021, 127, 108525. [Google Scholar] [CrossRef]
- Li, Y.; Zhou, W.; Wang, H.; Xie, L.; Liang, Y.; Wei, F.; Idrobo, J.-C.; Pennycook, S.J.; Dai, H. An oxygen reduction electrocatalyst based on carbon nanotube–graphene complexes. Nat. Nanotechnol. 2012, 7, 394–400. [Google Scholar] [CrossRef]
- Erlin, T.; Ebadi, A.G.; Mavaluru, D.; Alshehri, M.; Mohamed, A.A.; Sobhani, B. Parameter derivation of a proton exchange membrane fuel cell based on coevolutionary ribonucleic acid genetic algorithm. Comput. Intell. 2019, 35, 1021–1041. [Google Scholar] [CrossRef]
- Zhao, S.; Li, C.; Liu, J.; Liu, N.; Qiao, S.; Han, Y.; Huang, H.; Liu, Y.; Kang, Z. Carbon quantum dots/SnO2–Co3O4 composite for highly efficient electrochemical water oxidation. Carbon 2015, 92, 64–73. [Google Scholar] [CrossRef]
- Jiang, N.; Ebadi, A.G.; Kishore, K.H.; Yousif, Q.A.; Salmani, M. Thermomechanical reliability assessment of solder joints in a photovoltaic module operated in a hot climate. IEEE Trans. Compon. Packag. Manuf. Technol. 2019, 10, 160–167. [Google Scholar] [CrossRef]
- Tian, M.W.; Ebadi, A.G.; Jermsittiparsert, K.; Kadyrov, M.; Ponomarev, A.; Javanshir, N.; Nojavan, S. Risk-based stochastic scheduling of energy hub system in the presence of heating network and thermal energy management. Appl. Therm. Eng. 2019, 159, 113825. [Google Scholar] [CrossRef]
- Ebadi, A.G.; Hisoriev, H. Gasification of algal biomass (Cladophora glomerata L.) with CO2/H2O/O2 in a circulating fluidized bed. Environ. Technol. 2019, 40, 749–755. [Google Scholar] [CrossRef]
- Ebadi, A.G.; Hisoriev, H.; Zarnegar, M.; Ahmadi, H. Hydrogen and syngas production by catalytic gasification of algal biomass (Cladophora glomerata L.) using alkali and alkaline-earth metals compounds. Environ. Technol. 2019, 40, 1178–1184. [Google Scholar] [CrossRef] [PubMed]
- Ebadi, A.G.; Hisoriev, H. Metal pollution status of Tajan River–Northern Iran. Toxicol. Environ. Chem. 2017, 99, 1358–1367. [Google Scholar] [CrossRef]
- Ebadi, A.G.; Hisoriev, H. Physicochemical characterization of sediments from Tajan river basin in the northern Iran. Toxicol. Environ. Chem. 2018, 100, 540–549. [Google Scholar] [CrossRef]
- Ebadi, A.G.; Hisoriev, H. The prevalence of heavy metals in Cladophora glomerata L. from Farahabad Region of Caspian Sea–Iran. Toxicol. Environ.Chem. 2017, 99, 883–891. [Google Scholar] [CrossRef]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Syah, R.; Ahmad, A.; Davarpanah, A.; Elveny, M.; Ramdan, D.; Albaqami, M.D.; Ouladsmane, M. Incorporation of Bi2O3 Residuals with Metallic Bi as High Performance Electrocatalyst toward Hydrogen Evolution Reaction. Catalysts 2021, 11, 1099. https://doi.org/10.3390/catal11091099
Syah R, Ahmad A, Davarpanah A, Elveny M, Ramdan D, Albaqami MD, Ouladsmane M. Incorporation of Bi2O3 Residuals with Metallic Bi as High Performance Electrocatalyst toward Hydrogen Evolution Reaction. Catalysts. 2021; 11(9):1099. https://doi.org/10.3390/catal11091099
Chicago/Turabian StyleSyah, Rahmad, Awais Ahmad, Afshin Davarpanah, Marischa Elveny, Dadan Ramdan, Munirah D. Albaqami, and Mohamed Ouladsmane. 2021. "Incorporation of Bi2O3 Residuals with Metallic Bi as High Performance Electrocatalyst toward Hydrogen Evolution Reaction" Catalysts 11, no. 9: 1099. https://doi.org/10.3390/catal11091099
APA StyleSyah, R., Ahmad, A., Davarpanah, A., Elveny, M., Ramdan, D., Albaqami, M. D., & Ouladsmane, M. (2021). Incorporation of Bi2O3 Residuals with Metallic Bi as High Performance Electrocatalyst toward Hydrogen Evolution Reaction. Catalysts, 11(9), 1099. https://doi.org/10.3390/catal11091099