| 1. |
- Ahmad, Zubair, et al.
(författare)
-
Fine-tuning of redox-ability, optical, and electrical properties of Bi2MoO6 ceramics via lanthanide doping and rGO integration for photo-degradation of Methylene Blue and Ciprofloxacin
- 2024
-
Ingår i: Journal of Alloys and Compounds. - : Elsevier BV. - 0925-8388 .- 1873-4669. ; 1002
-
Tidskriftsartikel (refereegranskat)abstract
- Herein, lanthanide ion (Gd+3) doped Bismuth Molybdate (Bi2MoO6) integrated on the rGO sheets has been prepared as a novel photocatalyst (Gd@Bi2MoO6/rGO) for the photocatalytic treatment of toxic pollutants. Different physiochemical, optical, electrical, thermal, and electrochemical properties of Gd@Bi2MoO6/rGO, along with its counterparts (Bi2MoO6 and Gd@Bi2MoO6) were studied through XRD, SEM/TEM, FT-IR, UV/Vis, I-V, TGA, Mott-Schottky, and EIS measurements. Photocatalytic experiments revealed that Gd@Bi2MoO6/rGO exhibited significantly enhanced photocatalytic activity, achieving 96.2 % photo-degradation of Methylene Blue with 120 min of irradiation, which is 6.5 and 3.1 times higher compared to Bi2MoO6 (40.9 %) and Gd@Bi2MoO6 (64.8 %), respectively. Moreover, Gd@Bi2MoO6/rGO demonstrated a notable photocatalytic efficiency of 81.7 % towards Ciprofloxacin, significant as per the existing literature benchmark. The enhanced photocatalytic activity is ascribed to the in-built Gd+3 redox centers, high electrical conductivity (7.35 × 10−3 S/m), favorable flat band potential (-0.81 V), and low semiconductor impedance (Rct = 51.71 Ω and Rs = 0.90 Ω). Additionally, the electron-capturing ability of lanthanide dopant ions and S-C heterojunction of Gd@Bi2MoO6/rGO facilitates the separation of photo-generated e-/h+ pairs and favors high concentrations of ROS. The results obtained highlight the potential of Gd@Bi2MoO6/rGO for applications in photocatalysis and wastewater treatment.
|
|
| 2. |
- Munir, M. Adeel, et al.
(författare)
-
Blockchain Adoption for Sustainable Supply Chain Management : Economic, Environmental, and Social Perspectives
- 2022
-
Ingår i: Frontiers in Energy Research. - : Frontiers Media S.A.. - 2296-598X. ; 10
-
Tidskriftsartikel (refereegranskat)abstract
- Due to the rapid increase in environmental degradation and depletion of natural resources, the focus of researchers is shifted from economic to socio-environmental problems. Blockchain is a disruptive technology that has the potential to restructure the entire supply chain for sustainable practices. Blockchain is a distributed ledger that provides a digital database for recording all the transactions of the supply chain. The main purpose of this research is to explore the literature relevant to blockchain for sustainable supply chain management. The focus of this review is on the sustainability of the blockchain-based supply chain concerning environmental conservation, social equality, and governance effectiveness. Using a systematic literature review, a total of 136 articles were evaluated and categorized according to the triple bottom-line aspects of sustainability. Challenges and barriers during blockchain adoption in different industrial sectors such as aviation, shipping, agriculture and food, manufacturing, automotive, pharmaceutical, and textile industries were critically examined. This study has not only explored the economic, environmental, and social impacts of blockchain but also highlighted the emerging trends in a circular supply chain with current developments of advanced technologies along with their critical success factors. Furthermore, research areas and gaps in the existing research are discussed, and future research directions are suggested. The findings of this study show that blockchain has the potential to revolutionize the entire supply chain from a sustainability perspective. Blockchain will not only improve the economic sustainability of the supply chain through effective traceability, enhanced visibility through information sharing, transparency in processes, and decentralization of the entire structure but also will help in achieving environmental and social sustainability through resource efficiency, accountability, smart contracts, trust development, and fraud prevention. The study will be helpful for managers and practitioners to understand the procedure of blockchain adoption and to increase the probability of its successful implementation to develop a sustainable supply chain network.
|
|
| 3. |
- Saif-Ul-Allah, Muhammad Waqas, et al.
(författare)
-
Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant
- 2022
-
Ingår i: Frontiers in Energy Research. - : FRONTIERS MEDIA SA. - 2296-598X. ; 10
-
Tidskriftsartikel (refereegranskat)abstract
- Coal-fired power plants have been used to meet the energy requirements in countries where coal reserves are abundant and are the key source of NOx emissions. Owing to the serious environmental and health concerns associated with NOx emissions, much work has been carried out to reduce NOx emissions. Sophisticated artificial intelligence (AI) techniques have been employed during the past few decades, such as least-squares support vector machine (LSSVM), artificial neural networks (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU), to develop the NOx prediction model. Several studies have investigated deep neural networks (DNN) models for accurate NOx emission prediction. However, there is a need to investigate a DNN-based NOx prediction model that is accurate and computationally inexpensive. Recently, a new AI technique, convolutional neural network (CNN), has been introduced and proven superior for image class prediction accuracy. According to the best of the author's knowledge, not much work has been done on the utilization of CNN on NOx emissions from coal-fired power plants. Therefore, this study investigated the prediction performance and computational time of one-dimensional CNN (1D-CNN) on NOx emissions data from a 500 MW coal-fired power plant. The variations of hyperparameters of LSTM, GRU, and 1D-CNN were investigated, and the performance metrics such as RMSE and computational time were recorded to obtain optimal hyperparameters. The obtained optimal values of hyperparameters of LSTM, GRU, and 1D-CNN were then employed for models' development, and consequently, the models were tested on test data. The 1D-CNN NOx emission model improved the training efficiency in terms of RMSE by 70.6% and 60.1% compared to LSTM and GRU, respectively. Furthermore, the testing efficiency for 1D-CNN improved by 10.2% and 15.7% compared to LSTM and GRU, respectively. Moreover, 1D-CNN (26 s) reduced the training time by 83.8% and 50% compared to LSTM (160 s) and GRU (52 s), respectively. Results reveal that 1D-CNN is more accurate, more stable, and computationally inexpensive compared to LSTM and GRU on NOx emission data from the 500 MW power plant.
|
|
