Material Selection for Dye Sensitized Solar Cells Using Multiple Attribute Decision Making Approach

Baghel, Sarita; Jha, Ranjana; Jindal, Nikhil

doi:10.1155/2014/506216

Cited by 5 publications

(6 citation statements)

References 61 publications

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“…The results of research that have been carried out on the performance of DSSC solar cells from papaya leaf chlorophyll, the highest efficiency was obtained at the grain size of the substrate ITO 44.46, namely 0.00071% [24]. 7 research flow, which is as above.…”

Section: Literature Reviewmentioning

confidence: 77%

“…ITO grain size associated with TiO2 grains can be diffused in the ITO grain gap. The results revealed that TiO2 is the best semiconductor material for applications in DSSC and its efficiency is related to its properties such as band gap, electron mobility, electron injection rate, and static dielectric constant [24]. Semiconductor thin layers play an important role in the work of DSSC.…”

Section: Figure 10 Ito Thin Layer Xrd Resultsmentioning

confidence: 99%

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Plant Leaf Chlorophyll Based DSSC Solar Cell with ITO Transparent Nanoparticle Alloy

Muslimin¹,

Napitupulu²,

Rahman³

2023

IJHT

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The optimization of the combination of the optical properties of transparent ITO nanoparticles with the optical and morphological properties of TiO2 as well as the absorbing properties of chlorophyll dyes plays a very important role in the performance of DSSC solar cells. Therefore, it is necessary to characterize these properties to produce maximum DSSC solar performance. The stages of research are fabrication, characterization, and performance test. TiO2 film was made by coating TiO2 on ITO and soaking in chlorophyll dye. Alloy DSSC solar cells are characterized by optical, electrical and morphological properties. Characterization of optical properties, reflectance, absorption of chlorophyll dye using UV-Vis spectrometer. Electrical properties were characterized by measuring the current-voltage in the coating, DSSC solar cell performance and grain size morphology with XRD. Performance optimization of DSSC solar cells was obtained at the grain size of the transparent nanoparticle layer 54.87 at annealing temperature of 50℃ and chlorophyll absorption power of 20.900 mg/l efficiency of 0.038%. The grain size is 48.71, annealing temperature is 60℃ and chlorophyll absorption is 73.20 mg/l, the efficiency is 0.005%. The grain size is 44.46 the annealing temperature is 50℃ and the chlorophyll absorption is 34.6 mg/l and the efficiency is 0.020%. The conclusion is that the grain size of transparent nanoparticles, annealing temperature and chlorophyll absorption affect the efficiency of DSSC solar cells.

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Section: Literature Reviewmentioning

confidence: 77%

Section: Figure 10 Ito Thin Layer Xrd Resultsmentioning

confidence: 99%

Plant Leaf Chlorophyll Based DSSC Solar Cell with ITO Transparent Nanoparticle Alloy

Muslimin¹,

Napitupulu²,

Rahman³

2023

IJHT

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“…They claimed that DC classifiers attain the best accuracy of 98%, whereas the DT shows the least accuracy of 84%. These models are also used to predict the best suitable material for designing a solar cell, 79 the optimum range of wavelength of sunlight, temperature, and humidity to obtain the highest efficiency of a solar cell. As per the discussion given in, 80 ANN reports better accuracy than the statistical, conventional, linear, non‐linear, and fuzzy logic models.…”

Section: Role Of Ann In Designing Dsscmentioning

confidence: 99%

Role of artificial neural networks in predicting design and efficiency of dye sensitized solar cells

Tomar

Dhaka

Surolia

2022

Intl J of Energy Research

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Photovoltaic technology attracts researchers from industry and academia due to its potential in producing electricity directly from the sunlight. Among all the photovoltaic devices, the dye-sensitized solar cell has gained preference due to its low-cost fabrication and versatility in electrolytes, dye, substrate, and catalyst. The optical, electrical, and structural properties of the materials determine the power conversion efficiency of a solar cell. But, conducting experiments in the laboratory to identify the suitable materials for the fabrication of an efficient solar cell requires much time, cost, and human effort.

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“…Chakrabotry et al [13] investigated the influence of characteristics loading on the ranking effectiveness of three frequently used MCDM techniques and concluded that the most important criteria with the greatest weight and higher influence on material selection outcomes. Baghel et al [14] utilized the MCDM for selecting the suitable material for Dye-Sensitized Solar Cells and suggested that the most essential performance factors for a semi-conductor material in DSCs are the budget, static dielectric constant, mobility, band gap and electron injection rate. Zhao et al [15] used GRA and AHP to develop a material selection analytical framework in the context of green design.…”

Section: Bakground Literaturementioning

confidence: 99%

Development of Application Specific Intelligent Framework for the Optimized Selection of Industrial Grade Magnetic Material

Rizwan

2021

Polymers

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This article attempts to introduce a simple and robust way for the classification of soft magnetic material by using multivariate statistics. The six magnetic properties including coercive magnetic field, relative magnetic permeability, electrical resistivity magnetic inductions, i.e., remanence and saturation along with Curie temperature are used for the classification of 16 soft magnetic materials. Descriptive statistics have been used for defining the prioritization order of the mentioned magnetic characteristics with coercive magnetic field and Curie temperature as the most and least important characteristics for classification of soft magnetic material. Moreover, it has also justified the usage of cluster analysis and principal component analysis for classifying the enlisted materials. After descriptive statistics, cluster analysis is used for classification of materials into four groups, i.e., excellent, good, fair and poor while defining the prioritization order of materials on a relative scale. Principal component analysis reveals that the relative permeability is responsible for defining 99.69% of total variance and is also negatively correlated with the coercive magnetic field. Therefore, these two characteristics are considered the responsible factors for categorically placing the enlisted materials into four clusters. Furthermore, principal component analysis also helps in figuring out the fact that a combined influential consequence of relative permeability, coercive magnetic field, electrical resistivity and critical temperature are responsible for defining prioritization ordering of materials within the clusters. The material’s suitability index is identified while making use of adjacency and decision matrices obtained from material assessment graph and relative importance of magnetic properties, respectively. Afterward this material suitability index is used to rank the enlisted materials based on selected attributes. According to the suitability index, the best choice among enlisted soft magnetic materials is Supermalloy, Magnifer 7904 which is present in group 1 labeled as excellent by multivariate analysis. Therefore, the results of graph theory are in accordance with cluster analysis and principal component analysis, thus confirming the potential of this intelligent approach for the selection application specific magnetic materials.

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Material Selection for Dye Sensitized Solar Cells Using Multiple Attribute Decision Making Approach

Cited by 5 publications

References 61 publications

Plant Leaf Chlorophyll Based DSSC Solar Cell with ITO Transparent Nanoparticle Alloy

Plant Leaf Chlorophyll Based DSSC Solar Cell with ITO Transparent Nanoparticle Alloy

Role of artificial neural networks in predicting design and efficiency of dye sensitized solar cells

Development of Application Specific Intelligent Framework for the Optimized Selection of Industrial Grade Magnetic Material

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