•New method to correct the probe position in ptychography is proposed.•This method can correct the probe position with sub-pixel accuracy.•Less computationally expensive than other existing ...methods.•It is straightforward to implement.•Results with visible light experimental data are shown.
Ptychography, a form of Coherent Diffractive Imaging, is used with short wavelengths (e.g. X-rays, electron beams) to achieve high-resolution image reconstructions. One of the limiting factors for the reconstruction quality is the accurate knowledge of the illumination probe positions. Recently, many advances have been made to relax the requirement for the probe positions accuracy. Here, we analyse and demonstrate a straightforward approach that can be used to correct the probe positions with sub-pixel accuracy. Simulations and experimental results with visible light are presented in this work.
Luminescence imaging is widely used to identify spatial defects and extract key electrical parameters of photovoltaic devices. To reliably identify defects, high‐quality images are desirable; ...however, acquiring such images implies a higher cost or lower throughput as they require better imaging systems or longer exposure times. This study proposes a deep learning‐based method to effectively diminish the noise in luminescence images, thereby enhancing their quality for inspection and analysis. The proposed method eliminates the requirement for extra hardware expenses or longer exposure times, making it a cost‐effective solution for image enhancement. This approach significantly improves image quality by >30% and >39% in terms of the peak signal‐to‐noise ratio and the structural similarity index, respectively, outperforming state‐of‐the‐art classical denoising algorithms.
A state‐of‐the‐art U‐net model has been developed to effectively denoise luminescence images, resulting in a clearer image on the right from the noisy image on the left. The method outperforms conventional denoising techniques and has the potential to reduce solar cell production costs by increasing throughput and reducing the need for expensive imaging systems.
The current study is attempting to derive the reference to the hierarchy of Maslow, where the consumers were placed before the arrival of Covid-19 and during the lockdown time. Consumer behavior ...consists of cognitive, emotional or physical activities in which people pick, purchase, consume and dispose of products and services to satisfy their choices and expectations. Abraham Maslow defined hierarchy of needs in different forms viz Physiological, Safety,Social,Esteem and Self-actualization needs. A multiplicity of competing factors influences human behaviour and thereby needs and requirements.Recognition of needs is essential as the initial step for market participants in the supply chain. At the same time recognising where the needs of consumers will alter is parallelly significant for smooth functioning of market processes and securing profitability along with capturing the trend. In the present study with the help of primary survey need recognition or any sort of variation therein, pre and during the Covid-19 lockdown periodare traced within the conceptual framework of Maslow Hierarchy of needs theory.
Accurate and efficient characterisation techniques are essential to ensure the optimal performance and reliability of photovoltaic devices, especially given the large number of silicon solar cells ...produced each day. To unlock valuable insights from the amount of data generated during the characterisation process, researchers have increasingly turned to different machine learning (ML) techniques. In this review, advances in ML applications for silicon photovoltaic (PV) characterisation from 2018 to 2023, including device investigation, process optimisation, and manufacturing line assessment are examined. Additionally, studies on deep learning techniques for luminescence-based measurements, such as defect classification, detection, and segmentation, which can help manufacturers identify potential reliability issues are explored. Despite the abundance of ML applications, it is emphasised that the lack of both publicly available datasets and the uniform use of ML metrics poses a significant challenge for researchers to benchmark their frameworks and achieve consistent and accurate results. In advancing ML applications in PV, future research should focus on improving model interpretability, balancing speed and accuracy, understanding computational demands, and integrating niche applications into a unified framework. Lastly, industry involvement and interdisciplinary collaboration among experts in solar energy, data science, and engineering are vital in tailoring ML solutions and enhancing innovation in addressing various challenges in the PV field.
•Recent ML applications in Si PV characterisation from 2018 to 2023 are presented.•Key challenges include scarce public datasets and inconsistent evaluation metrics.•Future research should focus on improving model interpretability.•Crucial considerations are speed vs accuracy trade-off and integration to a unified framework.•Interdisciplinary collaboration and industry involvement will enhance ML applications in PV.
Luminescence imaging is a fast and non-destructive method to spatially resolve non-uniform electrical properties of solar cells. The spatial resolution of these images determines the smallest ...identifiable features. The higher the spatial resolution of an image, the better the capability to detect small defects. However, high-resolution cameras with high near-infrared light sensitivity that are suited to luminescence imaging of crystalline silicon are often expensive. In this study, we present a method, based on deep learning, that enhances the spatial resolution of luminescence images computationally with minimal cost. We also demonstrate the ability to overcome noise which is inevitable in any imaging system. This approach provides a simple and promising path toward reducing the cost of luminescence imaging systems and enhancing the capability of existing systems.
•A neural-network based approach (ESRGAN) was used to solve the issue of poor spatial resolution in luminescence imaging.•ESRGAN outperforms the traditional bicubic interpolation algorithm, for enhancing the spatial resolution of EL images.•New image features, which are not present in the training dataset, were successfully enhanced and image noise was supressed.
This study assesses and improves the accuracy of commonly used expressions for the fill factor (FF). Parameters that could affect the accuracy of the revised expressions are investigated. Empirical ...coefficients of the commonly used analytical expressions are first recalculated using a modified fitting approach. Although the predictions of the revised expressions perfectly match the results of theoretical one-diode model simulations, gaps are observed when compared with actual measurements. The different impacts of unaccounted factors in the expressions are then explored. It is shown that adjusting the ideality factor or considering edge recombination improves the accuracy of the predictions. Moreover, the expressions can slightly overestimate the FF of cells with non-uniform implied open-circuit voltage distribution. As methods to extract electrical parameters from luminescence images continuously improve, the findings of this study can aid in developing techniques for extracting FF from luminescence images of industrial solar cells.
•The fill factor (FF) of solar cells can be derived from empirical expressions.•The expressions are improved for modern industrial solar cells.•With ideality factor or edge recombination, FF predictions are more accurate.•Non-uniform implied open-circuit voltage tends to overestimate FF.
The internal quantum efficiency (IQE) is given as the ratio between the externally collected electron current and the photon current absorbed by the device. Spectral analysis of IQE measurements is a ...powerful method to identify performance‐limiting mechanisms in solar cells. It also enables the extraction of key electrical and optical parameters. However, the potential of IQE measurements is only rarely fully utilized, presumably due to the significant complexity associated with the fitting process and its sensitivity to noise. In this study, machine learning is proposed as an efficient method to extract quantitative information from IQE measurements. The extraction method is automated and easy to use, providing an array of specific device parameters. By simplifying the analytical process, the developed machine learning algorithms also extract the parasitic absorption of the antireflection coating, a key parameter that is difficult to obtain by traditional methods. Although the method has been developed for and tested on silicon solar cells, it can be adapted and applied to other types of solar cells.
This study presents a unique approach to analyzing the internal quantum efficiency (IQE) of solar cells. Machine learning chain regressors were trained to extract the key electrical and optical parameters from IQE measurements, achieving extremely low error scores. Upon testing on different cell structures, the trained models achieved better performance than traditional fitting methods. The method can be adapted to a wide range of solar cell materials and technologies.
Gemcitabine (2′, 2′-difluorodeoxycytidine) is a deoxycytidine analog with significant antitumor activity against variety of cancers including non-small cell lung cancer. However, rapid metabolism and ...shorter half-life of drug mandate higher dose and frequent dosing schedule which subsequently results into higher toxicity. Therefore, there is a need to design a vector which can reduce the burden of frequent dosing and higher toxicity associated with the use of gemcitabine. In this study, we investigated the possibility of improving the targeting potential by employing the surface modification on Chitosan/poly(ethylene glycol) (CTS/PEG) Nanoparticles. We demonstrate formulation and characterization of chitosan/poly(ethylene glycol)-anisamide (CTS/PEG-AA) and compared its efficiency with CTS/PEG and free gemcitabine. Our results reveal its sizeable compatibility, comparatively less organ toxicity and higher antitumor activity in vitro as well as in vivo. This wealth of information surfaces the potential of CTS/PEG-AA nanoparticles as a potent carrier for drug delivery. In brief, this novel carrier opens new avenues for drug delivery which better meets the needs of anticancer research.
Electroluminescence (EL) imaging is one of the most common characterisation techniques for photovoltaic cells and modules. EL images contain information on both the open-circuit voltage (Voc) and ...series resistance (Rs) of the device. However, separating the two effects and identifying features related to each parameter can be challenging. In this study, a novel approach for decomposing EL images into Voc and Rs maps using a convolutional neural network architecture is presented. A deep learning model was first trained on paired EL and photoluminescence images that were generated using a simulation tool. Results obtained using the validation set show that the trained model is able to accurately differentiate between features related to Voc and Rs in EL images, thus replacing the need for multiple types of measurements. The proposed method presents a unique approach to analyse EL images, unlocking new capabilities that have the potential to advance solar cell characterisation.
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