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Sofiah AGN, Pasupuleti J, Samykano M, Kadirgama K, Koh SP, Tiong SK, Pandey AK, Yaw CT, Natarajan SK. Harnessing Nature's Ingenuity: A Comprehensive Exploration of Nanocellulose from Production to Cutting-Edge Applications in Engineering and Sciences. Polymers (Basel) 2023; 15:3044. [PMID: 37514434 PMCID: PMC10385464 DOI: 10.3390/polym15143044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 06/07/2023] [Accepted: 06/16/2023] [Indexed: 07/30/2023] Open
Abstract
Primary material supply is the heart of engineering and sciences. The depletion of natural resources and an increase in the human population by a billion in 13 to 15 years pose a critical concern regarding the sustainability of these materials; therefore, functionalizing renewable materials, such as nanocellulose, by possibly exploiting their properties for various practical applications, has been undertaken worldwide. Nanocellulose has emerged as a dominant green natural material with attractive and tailorable physicochemical properties, is renewable and sustainable, and shows biocompatibility and tunable surface properties. Nanocellulose is derived from cellulose, the most abundant polymer in nature with the remarkable properties of nanomaterials. This article provides a comprehensive overview of the methods used for nanocellulose preparation, structure-property and structure-property correlations, and the application of nanocellulose and its nanocomposite materials. This article differentiates the classification of nanocellulose, provides a brief account of the production methods that have been developed for isolating nanocellulose, highlights a range of unique properties of nanocellulose that have been extracted from different kinds of experiments and studies, and elaborates on nanocellulose potential applications in various areas. The present review is anticipated to provide the readers with the progress and knowledge related to nanocellulose. Pushing the boundaries of nanocellulose further into cutting-edge applications will be of particular interest in the future, especially as cost-effective commercial sources of nanocellulose continue to emerge.
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Affiliation(s)
| | - Jagadeesh Pasupuleti
- Institute of Sustainable Energy, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia
| | - Mahendran Samykano
- Centre for Research in Advanced Fluid and Processes, Universiti Malaysia Pahang, Gambang 26300, Pahang, Malaysia
| | - Kumaran Kadirgama
- Centre for Research in Advanced Fluid and Processes, Universiti Malaysia Pahang, Gambang 26300, Pahang, Malaysia
| | - Siaw Paw Koh
- Institute of Sustainable Energy, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia
| | - Sieh Kieh Tiong
- Institute of Sustainable Energy, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia
| | - Adarsh Kumar Pandey
- Research Centre for Nano-Materials and Energy Technology (RCNMET), School of Science and Technology, Sunway University, No. 5, Bandar Sunway, Petaling Jaya 47500, Selangor, Malaysia
- Center for Transdiciplinary Research (CFTR), Saveetha University, Chennai 602105, India
| | - Chong Tak Yaw
- Institute of Sustainable Energy, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia
| | - Sendhil Kumar Natarajan
- Solar Energy Laboratory, Department of Mechanical Engineering, National Institute of Technology Puducherry, University of Puducherry, Karaikal 609609, India
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Yaw CT, Koh SP, Sandhya M, Ramasamy D, Kadirgama K, Benedict F, Ali K, Tiong SK, Abdalla AN, Chong KH. An Approach for the Optimization of Thermal Conductivity and Viscosity of Hybrid (Graphene Nanoplatelets, GNPs: Cellulose Nanocrystal, CNC) Nanofluids Using Response Surface Methodology (RSM). Nanomaterials (Basel) 2023; 13:nano13101596. [PMID: 37242013 DOI: 10.3390/nano13101596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 05/02/2023] [Accepted: 05/05/2023] [Indexed: 05/28/2023]
Abstract
Response surface methodology (RSM) is used in this study to optimize the thermal characteristics of single graphene nanoplatelets and hybrid nanofluids utilizing the miscellaneous design model. The nanofluids comprise graphene nanoplatelets and graphene nanoplatelets/cellulose nanocrystal nanoparticles in the base fluid of ethylene glycol and water (60:40). Using response surface methodology (RSM) based on central composite design (CCD) and mini tab 20 standard statistical software, the impact of temperature, volume concentration, and type of nanofluid is used to construct an empirical mathematical formula. Analysis of variance (ANOVA) is applied to determine that the developed empirical mathematical analysis is relevant. For the purpose of developing the equations, 32 experiments are conducted for second-order polynomial to the specified outputs such as thermal conductivity and viscosity. Predicted estimates and the experimental data are found to be in reasonable arrangement. In additional words, the models could expect more than 85% of thermal conductivity and viscosity fluctuations of the nanofluid, indicating that the model is accurate. Optimal thermal conductivity and viscosity values are 0.4962 W/m-K and 2.6191 cP, respectively, from the results of the optimization plot. The critical parameters are 50 °C, 0.0254%, and the category factorial is GNP/CNC, and the relevant parameters are volume concentration, temperature, and kind of nanofluid. From the results plot, the composite is 0.8371. The validation results of the model during testing indicate the capability of predicting the optimal experimental conditions.
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Affiliation(s)
- Chong Tak Yaw
- Institute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Malaysia
| | - Siaw Paw Koh
- Institute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Malaysia
| | - Madderla Sandhya
- College of Engineering, Universiti Malaysia Pahang, Gambang 26300, Malaysia
- Advanced Nano Coolant-Lubricant (ANCL), College of Engineering, Universiti Malaysia Pahang, Pekan 26600, Malaysia
| | - Devarajan Ramasamy
- College of Engineering, Universiti Malaysia Pahang, Gambang 26300, Malaysia
- Advanced Nano Coolant-Lubricant (ANCL), College of Engineering, Universiti Malaysia Pahang, Pekan 26600, Malaysia
| | - Kumaran Kadirgama
- Advanced Nano Coolant-Lubricant (ANCL), College of Engineering, Universiti Malaysia Pahang, Pekan 26600, Malaysia
- Faculty of Mechanical and Automotive Engineering Technology, Universiti Malaysia Pahang, Pekan 26600, Malaysia
- Automotive Engineering Centre, Universiti Malaysia Pahang, Pekan 26600, Malaysia
| | - Foo Benedict
- Enhance Track Sdn. Bhd., No. 9, Jalan Meranti Jaya 12, Meranti Jaya Industrial Park, Puchong 47120, Malaysia
| | - Kharuddin Ali
- Faculty of Electrical and Automation Engineering Technology, University College TATI, Teluk Kalong, Kemaman 24000, Malaysia
| | - Sieh Kiong Tiong
- Institute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Malaysia
| | - Ahmed N Abdalla
- Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai'an 223025, China
| | - Kok Hen Chong
- College of Engineering, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Malaysia
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Mohammed Alsumaidaee YA, Yaw CT, Koh SP, Tiong SK, Chen CP, Yusaf T, Abdalla AN, Ali K, Raj AA. Detection of Corona Faults in Switchgear by Using 1D-CNN, LSTM, and 1D-CNN-LSTM Methods. Sensors (Basel) 2023; 23:3108. [PMID: 36991819 PMCID: PMC10059847 DOI: 10.3390/s23063108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 02/28/2023] [Accepted: 03/01/2023] [Indexed: 06/19/2023]
Abstract
The damaging effects of corona faults have made them a major concern in metal-clad switchgear, requiring extreme caution during operation. Corona faults are also the primary cause of flashovers in medium-voltage metal-clad electrical equipment. The root cause of this issue is an electrical breakdown of the air due to electrical stress and poor air quality within the switchgear. Without proper preventative measures, a flashover can occur, resulting in serious harm to workers and equipment. As a result, detecting corona faults in switchgear and preventing electrical stress buildup in switches is critical. Recent years have seen the successful use of Deep Learning (DL) applications for corona and non-corona detection, owing to their autonomous feature learning capability. This paper systematically analyzes three deep learning techniques, namely 1D-CNN, LSTM, and 1D-CNN-LSTM hybrid models, to identify the most effective model for detecting corona faults. The hybrid 1D-CNN-LSTM model is deemed the best due to its high accuracy in both the time and frequency domains. This model analyzes the sound waves generated in switchgear to detect faults. The study examines model performance in both the time and frequency domains. In the time domain analysis (TDA), 1D-CNN achieved success rates of 98%, 98.4%, and 93.9%, while LSTM obtained success rates of 97.3%, 98.4%, and 92.4%. The most suitable model, the 1D-CNN-LSTM, achieved success rates of 99.3%, 98.4%, and 98.4% in differentiating corona and non-corona cases during training, validation, and testing. In the frequency domain analysis (FDA), 1D-CNN achieved success rates of 100%, 95.8%, and 95.8%, while LSTM obtained success rates of 100%, 100%, and 100%. The 1D-CNN-LSTM model achieved a 100%, 100%, and 100% success rate during training, validation, and testing. Hence, the developed algorithms achieved high performance in identifying corona faults in switchgear, particularly the 1D-CNN-LSTM model due to its accuracy in detecting corona faults in both the time and frequency domains.
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Affiliation(s)
- Yaseen Ahmed Mohammed Alsumaidaee
- College of Graduate Studies (COGS), Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Selangor, Malaysia
| | - Chong Tak Yaw
- Institute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Selangor, Malaysia
| | - Siaw Paw Koh
- Institute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Selangor, Malaysia
- Department Electrical and Electronics Engineering, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Selangor, Malaysia
| | - Sieh Kiong Tiong
- Institute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Selangor, Malaysia
- Department Electrical and Electronics Engineering, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Selangor, Malaysia
| | - Chai Phing Chen
- Department Electrical and Electronics Engineering, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Selangor, Malaysia
| | - Talal Yusaf
- School of Engineering and Technology, Central Queensland University, Brisbane, QLD 4009, Australia
| | - Ahmed N Abdalla
- Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an 223003, China
| | - Kharudin Ali
- Faculty of Electrical and Automation Engineering Technology, UC TATI, Teluk Kalong, Kemaman 24000, Terengganu, Malaysia
| | - Avinash Ashwin Raj
- Tenaga National Berhard Research Sdn. Bhd., No. 1, Kawasan Institusi Penyelidikan, Jln Ayer Hitam, Kajang 43000, Selangor, Malaysia
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