T2-OEHRH in CIE (1931) 2D chromaticity diagram and corresponding color representations obtained from CIELAB color space coordinates (L*, a*, b*) for (c) T2- ORH and (d) tunable with dyes based on T2-OEHRH films with active layers. Summary of photovoltaic parameters of slot-coated OPV devices on glass substrates at variable processing temperatures.
Introduction
Introduction to Solar Energy and Organic Photovoltaics
- Renewable Energy Source: Solar Energy and Solar Cells
- History of Organic Photovoltaics
- Working Principle of Organic Photovoltaics
- Characterization of Organic Photovoltaics
- Strategies to Achieve High Performance Organic Photovoltaics
In this structure, the exciton can be easily separated into an electron and a hole at the D/A interface. The degree of sensitivity can be evaluated by the spectral response (SR), which is defined as the ratio between the measured photocurrent at a certain wavelength (Iph()) and the light intensity of the same wavelength (Plight()).
Introduction to Printing Technology
- Background of Printing Technology
- Coating and Printing Techniques on Batch Process
- Coating Techniques on Continuous Process
This is when g is the gap difference between the blade and the substrate, c is the concentration of solute in the solution, ρ is the density of the material in the final film. It is noted that f is the flow rate of the solution, S is the coating speed (web), w is the coating width, c is the concentration of the solution and is the density of the material in the dried film.
Introduction to Machine Learning
- What is Machine Learning?
- Machine Learning: History and Perspective
- Basic Concept of Machine Learning
- Classification of Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Deep Learning
- Machine Learning: Consideration and Evaluation
- Consideration: Key Problem on Machine Learning
- Validation Techniques
- Evaluation: Model Performance Metrics
- Evaluation: Confusion Table
- Learning Curve
- Consideration: Programming Languages and Frameworks
It is an important feature of the Least Square method that the regressive linear function is unbiased, i.e. it is one of the simplest UL technology and, as shown in Figure 1.3.12, its principle can be stated in stages. It is worth mentioning that it can be used in complicated applications such as the game of Go, self-driving cars, personalized recommendations, bidding and advertising, etc.
18, it is possible to establish an algorithm composed of neuron (or node) and synapse (or edge). Back to the model diagram, the activation conditions for input data can be expressed as functions, it is called activation function (AF). Also, after calculating the loss once, IP is implemented in the direction from the output to the hidden layer, so it is called as backpropagation.
It is clear that an underperforming model, such as underfitting, is generated when it is lost to validation and test sets in a small number of data sets. It is noted that the relationship between variances that can be described through the regression model and total variance-dependent variables can be expressed as Eq. In this regard, it is important to accurately determine the performance of the model taking into account other parameters.
Score: It is a harmonic average of PPV and TPR and is used in unbalanced data sets. In ML, this is also called a learning curve and indicates the validation or training score of a model with a different amount of training data.
Exploiting Ternary Blends to Accurately Control the Coloration of Semi-Transparent, Non-
Research Backgrounds
Exploiting ternary mixtures to precisely control the coloration of semitransparent, non-fullerene organic photovoltaics. In the context of BIPVs, it is necessary to achieve a high level of control over the color of STOPVs in order for them to match the design of the buildings and be realistically considered as potential architectural design elements. To achieve the necessary level of color control, a subtle understanding of the composition of photoactive materials, including how the absorption bands and spectra of different components mix together to give specific colors, and a system for gap balancing wide/medium and narrow band. organic semiconductors are essential.
In this respect, NFA-based OPVs offer many opportunities due to their flexibility in color tuning, which results from their easily modulated energy bands (from wide to narrow bandgap materials) and absorption spectra of donor and acceptor materials;. In this work, we used PTB7-Th as narrow band gap donor (1.58 eV)119 and IEICO-4F as the acceptor material (1.25 eV)40 to form the basis for semi-transparent devices. Additionally, as wideband gap receivers (2.07 eV) T2-ORH120, 121 and T2-OEHRH122 were included as ternary components, which were used to obtain a diverse range of colors.
Colored STOPVs based on these ternary mixtures were fabricated using semi-transparent electrodes consisting of Sb2O3/Ag/Sb2O3 (SAS) stacks123-125 whose compositions were systematically formulated to obtain a range of colors including cyan, aqua, indigo , purple and red purple , with a champion PCE of 6.93% and AVT of 34.03%. To our knowledge, this study is the first to take advantage of ternary active layer mixtures to achieve precise control over the color of STOPVs. Our work shows that color modulation using ternary active layer mixtures is a convenient and efficient strategy to achieve STOPV colors tunable over a wide range, for applications where color and aesthetic appearance are primary considerations.
Experimental Details
Results and Discussion
- Optical and Electrochemical Properties
- STOPV Application
- Optical Characterization
- Optical Calculation
Furthermore, the HOMO and LUMO shift between PTB7-Th and T2-ORH or T2-OEHRH was sufficient for charge transfer. The color of the ternary blend films can be changed dramatically by increasing the relative ratios of T2-ORH and T2-OEHRH. The addition of T2-ORH or T2-OEHRH to the IEICO-4F binary mixtures did not have a significant beneficial effect on the photovoltaic properties.
Summary of photovoltaic parameters for T2-ORH and T2-OEHRH based STOPV with variable active layer thickness. J–V characteristics of (a) T2-ORH and (c) T2-OEHRH-based color tunable opaque and semi-transparent SAS electrode devices. Both T2-ORH and T2-OEHRH-based films show blue-shifted absorption compared to PTB7-Th (shown in Fig. 2. 1b).
The IPCE contribution of all the ternary OPVs in the 500–550 nm region increased with the incorporation of T2-ORH or T2-OEHRH to the IEICO-4F binary mixture, consistent with the absorption spectra. The same effect is observed in the T2-ORH or T2-OEHRH ternary systems, where the ternary components show negligible absorption at wavelengths greater than 600 nm compared to the PTB7-Th and IEICO-4F components in the mixture. As the content of T2-ORH and T2-OEHRH increases, it decreases the transmission in the 500−565 nm region.
Conclusion
Machine Learning-Assisted Development of Organic Photovoltaics via High-Throughput
Research Backgrounds
We chose the PM6:Y6:IT-4F ternary system, one of the best performing ternary systems158, and then fabricated >2000 unique devices to create training data for the ML system. An RF regression model was used to find compositions for the highest efficiency and also for printability, that is, further experiments were carried out based on the predicted compositions, resulting in a PCE of more than 10%, which is the highest efficiency is of R2R slot-die-coated OPVs to date.
Experimental Details
All printing experiments and sample preparation were also performed under atmospheric conditions, and the top electrodes were deposited in the same manner as the rigid devices. The solutions were deposited in the range of 8.5120 μl min-1 (WTF: 2.1830.8 μm) using a programmable syringe pump (NEMESYS, CETONI GmbH) so that the corresponding total deposition densities (TDD) increased linearly. Experimental validations for BPF (best printable formulation) and BEF were performed in the same way.
Dead volume of the tubing from the Y-junction and the slot die was calculated to be approximately 60 μl for our typical setup. The composition of the film at each position was calculated based on the relative flow rate and deposition displacement resulting from the dead volume of the deposition system. The solutions were mixed and deposited in the same way as type (ii) experiment at several.
The film composition at each position was also calculated considering the relative flow velocity and deposition offset same as experiment type (ii). Light was illuminated through a glove box quartz window and the intensity was calibrated and monitored using a secondary reference cell (Hamamatsu S1133, with KG-5 mm2 photosensitive area filter) which was pre-calibrated by a certified reference cell ( PV measurements, certified by NREL) under 1000 W m−2 AM 1.5G illumination from an Oriel AAA solar simulator equipped with a 1000 W Xe lamp. We followed a general data splitting method, the 80/20 rule (split 80% of the data selected in a training set and the remaining 20% in the sample.
Results and Discussion
- Material Selection, Device Application and Characterization
- Machine Learning-Assisted Optimization and Prediction
- Experimental Validation of the Model Prediction
The J–V curves and corresponding IPCE spectra of the devices with the corresponding compositions are shown in Figure 3. However, we found minor differences in the physical thickness of the films depending on the composition, as shown in Figure 3. The upper x-axis represents the physical positions of each device relative to the start of deposition covers.
Photovoltaic parameters of R2R produced OPVs depending on thickness changes of preblended mixtures at different D:A ratios (fixed Y6:IT-4F ratio of 4:1, w/w). Photovoltaic parameters of R2R produced OPVs depending on Y6:IT-4F ratio at different D:A ratios (fixed TDD at ∼28.2 μg cm-2). Photovoltaic parameters of R2R produced OPVs depending on D:A ratio at different thicknesses (fixed Y6:IT-4F ratio of 4:1, w/w).
The data planes in the 3D plot were converted to 2D plots as shown in Figure 3. The 2D plots clearly show different optimal compositions for different thicknesses. For BEF, the relationship between physical thickness and TDD was calibrated and found to be thickness = 6.78 * TDD as shown in Figure 3. The device results, along with the estimated thicknesses, are shown in Figure 3. Although the highest position TDD is not an exact match, devices around 28.25 μg cm-2 TDD show similar performance with about 10% PCE. As shown in Figure 3.14, the datasets still only cover a small part of the composition space, and the prediction accuracy would be improved with more datasets covering the unexplored space. a) PCE relative to TDD for BPF and BEF.
Conclusion
Summary
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