| 1. |
- Ali Ahmad, Syed Ossama, et al.
(författare)
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Application of two-dimensional materials in perovskite solar cells: recent progress, challenges, and prospective solutions
- 2021
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Ingår i: Journal of Materials Chemistry C. - : ROYAL SOC CHEMISTRY. - 2050-7526 .- 2050-7534. ; 9:40, s. 14065-14092
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Forskningsöversikt (refereegranskat)abstract
- Perovskite solar cells (per-SCs) with high performance and cost-effective solution processing have been the center of interest for researchers in the past decade. Power conversion efficiencies (PCEs) have been gradually improved up to 25.2% with relatively improved stability, which is an unparalleled progress in all generations of solar cell (SC) technology. However, there are still some prevailing challenges regarding the stability and upscaling of these promising devices. Recently, 2D layered materials (LMs) have been extensively explored to overcome the prevailing challenges of poor stability (under moisture, light soaking and high temperature), halide segregation, hysteresis, involvement of toxic materials (i.e., lead), and upscaling of devices. A critical review addressing the recent developments in the use of 2D materials, especially transition metal dichalcogenides (TMDCs), is hence necessary. The development of novel synthesis and deposition techniques including liquid-metal synthesis and ultrasonic assisted spray pyrolysis has offered more efficient fabrication of 2D-LMs with controlled thickness and morphology. Effective functionalization approaches to increase the dispersability of 2D-LMs in non-polar solvents has boosted their potential application in solar cell technology as well. Moreover, compositing 2D TMDCs with suitable organic/inorganic compounds has enabled superior charge kinetics in all functional parts of per-SCs. In addition, newly developed materials such as graphyne and graphdyine along with 2D metal organic frameworks (MOFs) and covalent organic frameworks (COFs) have been employed in per-SCs to achieve PCEs up to 20%. This review summarizes the recent progress and challenges in the application of 2D-LMs in per-SCs and outlines the future pathways to further extend the PCE of per-SCs beyond 25%. This review particularly focuses on 2D-LMs as electrode materials and additives, the underlying charge (electron-hole) transport phenomenon in the functional layers, and their chemical and structural stability.
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| 2. |
- Iftikhar, Bawar, et al.
(författare)
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Experimental study on the eco-friendly plastic-sand paver blocks by utilising plastic waste and basalt fibers
- 2023
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Ingår i: Heliyon. - : Elsevier. - 2405-8440. ; 9:6
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Tidskriftsartikel (refereegranskat)abstract
- Plastic waste poses a significant hazard to the environment as a result of its high production rates, which endanger both the environment and its inhabitants. Similarly, another concern is the production of cement, which accounts for roughly 8% of global CO2 emissions. Thus, recycling plastic waste as a replacement for cementitious materials may be a more effective strategy for waste minimisation and cement elimination. Therefore, in this study, plastic waste (low-density polyethylene) is utilised in the production of plastic sand paver blocks without the use of cement. In addition to this, basalt fibers which is a green industrial material is also added in the production of eco-friendly plastic sand paver blocks to satisfy the standard of ASTM C902-15 of 20 N/mm2 for the light traffic. In order to make the paver blocks, the LDPE waste plastic was melted outside in the open air and then combined with sand. Variations were made to the ratio of LDPE to sand, the proportion of basalt fibers, and sand particle size. Paver blocks were evaluated for their compressive strength, water absorption, and at different temperatures. Including 0.5% percent basalt fiber of length 4 mm gives us the best result by enhancing compressive strength by 20.5% and decreasing water absorption by 50.5%. The best results were obtained with a ratio of 30:70 LDPE to sand, while the finest sand provides the greatest compressive strength. Moreover, the temperature effect was also studied from 0 to 60 °C, and the basalt fibers incorporated in plastic paver blocks showed only a 20% decrease in compressive strength at 60 °C. This research has produced eco-friendly paver blocks by removing cement and replacing it with plastic waste, which will benefit the environment, save money, reduce carbon dioxide emissions, and be suitable for low-traffic areas, all of which contribute to sustainable development.
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| 3. |
- Javed, Muhammad Faisal, et al.
(författare)
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Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants
- 2024
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Ingår i: Scientific Reports. - : Nature Research. - 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- The escalation of global urbanization and industrial expansion has resulted in an increase in the emission of harmful substances into the atmosphere. Evaluating the effectiveness of titanium dioxide (TiO2) in photocatalytic degradation through traditional methods is resource-intensive and complex due to the detailed photocatalyst structures and the wide range of contaminants. Therefore in this study, recent advancements in machine learning (ML) are used to offer data-driven approach using thirteen machine learning techniques namely XG Boost (XGB), decision tree (DT), lasso Regression (LR2), support vector regression (SVR), adaBoost (AB), voting Regressor (VR), CatBoost (CB), K-Nearest Neighbors (KNN), gradient boost (GB), random Forest (RF), artificial neural network (ANN), ridge regression (RR), linear regression (LR1) to address the problem of estimation of TiO2 photocatalytic degradation rate of air contaminants. The models are developed using literature data and different methodical tools are used to evaluate the developed ML models. XGB, DT and LR2 models have high R2 values of 0.93, 0.926 and 0.926 in training and 0.936, 0.924 and 0.924 in test phase. While ANN, RR and LR models have lowest R2 values of 0.70, 0.56 and 0.40 in training and 0.62, 0.63 and 0.31 in test phase respectively. XGB, DT and LR2 have low MAE and RMSE values of 0.450 min-1/cm2, 0.494 min-1/cm2 and 0.49 min-1/cm2 for RMSE and 0.263 min-1/cm2, 0.285 min-1/cm2 and 0.29 min-1/cm2 for MAE in test stage. XGB, DT, and LR2 have 93% percent errors within 20% error range in training phase. XGB has 92% and DT, and LR2 have 94% errors with 20% range in test phase. XGB, DT, LR2 models remained the highest performing models and XGB is the most robust and effective in predictions. Feature importances reveal the role of input parameters in prediction made by developed ML models. Dosage, humidity, UV light intensity remain important experimental factors. This study will impact positively in providing efficient models to estimate photocatalytic degradation rate of air contaminants using TiO2.
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