The metal-assisted chemical etching (MACE) technique is commonly employed for texturing the wafer surfaces when fabricating black silicon (BSi) solar cells and is considered to be a potential technique to improve the efficiency of traditional Si-based solar cells. This article aims to review the MACE technique along with its mechanism for Ag-, Cu- and Ni-assisted etching. Primarily, several essential aspects of the fabrication of BSi are discussed, including chemical reaction, etching direction, mass transfer, and the overall etching process of the MACE method. Thereafter, three metal catalysts (Ag, Cu, and Ni) are critically analyzed to identify their roles in producing cost-effective and sustainable BSi solar cells with higher quality and efficiency. The conducted study revealed that Ag-etched BSi wafers are more suitable for the growth of higher quality and efficiency Si solar cells compared to Cu- and Ni-etched BSi wafers. However, both Cu and Ni seem to be more cost-effective and more appropriate for the mass production of BSi solar cells than Ag-etched wafers. Meanwhile, the Ni-assisted chemical etching process takes a longer time than Cu but the Ni-etched BSi solar cells possess enhanced light absorption capacity and lower activity in terms of the dissolution and oxidation process than Cu-etched BSi solar cells.

Black silicon (BSi) fabrication via surface texturization of Si-wafer in recent times has become an attractive concept regarding photon trapping and improved light absorption properties for photovoltaic applications. In this study, surface texturization has been conducted on mono-crystalline Si(100) wafer using a wet chemical anisotropic etching process with IPA:KOH solution to form micro-pyramidal surface structures. Moreover, the optimized properties of the fabricated BSi wafers are used for numerical simulation using PC1D software to analyze the performance of the solar cell and establish the correlation among relevant parameters. Effects such as doping concentration, texturization, passivation, and anti-reflection coating of BSi on the solar cell performance have numerically been investigated. Results show that textured surface obtained from the wet chemical anisotropic etching process has successfully reduced the reflectance of the BSi wafer and surpassed the solar cell efficiency by 2%, which is mainly attributed to the optical confinement of the textured pyramids on the surface with a height of 1–2 μm and angles of 70 degrees. Furthermore, the doping concentration of the p-type wafer and n-type emitter were optimized to be 1 × 1016 cm−3 and 1 × 1018 cm−3, respectively. In the case of device optimization, the SiO2 passivation layer with a refractive index of 1.48 and the Si3N4 ARC layer with a refractive index of 2.015 has been identified as the best combination for the solar cell performance. These optimized parameters eventually result in 23.14% conversion efficiency from numerical simulation for solar cells that use black silicon wafers as fabricated in this study.

The choice of this study has a significant impact on daily life. In various fields such as journalism, academia, business, and more, large amounts of text need to be processed quickly and efficiently. Text summarization is a technique used to generate a precise and shortened summary of spacious texts. The generated summary sustains overall meaning without losing any information and focuses on those parts that contain useful information. The goal is to develop a model that converts lengthy articles into concise versions. The task to be solved is to select an effective procedure to develop the model. Although the present text summarization models give us good results in many recognized datasets such as cnn/daily- mail, newsroom, etc. All the problems can not be resolved by these models. In this paper, a new text summarization method has been proposed: combining the Extractive and Abstractive Text Summarization technique. In the extractive-based method, the model generates a summary using Sentence Ranking Algorithm and passes this generated summary through an abstractive method. When using the sentence ranking algorithm, after rearranging the sentences, the relationship between one sentence and another sentence is destroyed. To overcome this situation, Pronoun to Noun conversion has been proposed with the new system. After generating the extractive summary, the generated summary is passed through the abstractive method. The proposed abstractive model consists of three pre-trained models: google/pegusus-xsum, face-book/bart-large-cnn model, and Yale-LILY/brio-cnndm-uncased, which generates a final summary depending on the maximum final score. The following results were obtained: experimental results on CNN/daily-mail dataset show that the proposed model obtained scores of ROUGE-1, ROUGE-2 and ROUGE-L are respectively 42.67 %, 19.35 %, and 39.57 %. Then, the result has been compared with three state-of-the-art methods: JEANS, DEATS and PGAN-ATSMT. The results outperform state-of-the-art models. Experimental results also show that the proposed model is qualitatively readable and can generate abstract summaries. Conclusion: In terms of ROUGE score, the model outperforms some art-of-the-state models for ROUGE-1 and ROUGE-L, but doesn’t achieve good result in ROUGE-2.