arXiv subjects

Swellam W. Sharshir

Publications and source records attributed to Swellam W. Sharshir.

6 recordsLinked to original sources

arXiv2025

Pioneering Advancements of 2D Graphene: Energy and Electronics applications

This review explores the synthesis, characterization, and potential applications of graphene, a two-dimensional material with exceptional properties. Graphene's versatility in energy and electronics applications is highlighted, with its high conductivity and huge surface area facilitating improved energy storage capabilities in supercapacitors and batteries. In electronics, graphene is revolutionizing the industry by enabling the development of flexible displays, high-speed transistors, and enhanced thermal management systems. The integration of graphene into composite materials presents opportunities for stronger, lighter, and more conductive materials. The study provides a comprehensive overview of graphene's current and future impact on technology, emphasizing its transformative potential in energy solutions and electronic advancements. In the energy sector, graphene's integration into batteries, energy storage systems, capacitors, fuel cells, and renewable energy technologies signifies a leap forward in efficiency, capacity, and sustainability. In the electronics sector, graphene's unique characteristics are utilized in RFID, sensors, and EMI shielding, leading to communication, security, and device miniaturization advancements. The study underscores graphene's potential to spearhead future innovations, reinforcing its status as a pivotal material in the ongoing technological evolution.

cond-mat.mes-hall↗

arXiv2024

The effect of dataset size and the process of big data mining for investigating solar-thermal desalination by using machine learning

Machine learning's application in solar-thermal desalination is limited by data shortage and inconsistent analysis. This study develops an optimized dataset collection and analysis process for the representative solar still. By ultra-hydrophilic treatment on the condensation cover, the dataset collection process reduces the collection time by 83.3%. Over 1,000 datasets are collected, which is nearly one order of magnitude larger than up-to-date works. Then, a new interdisciplinary process flow is proposed. Some meaningful results are obtained that were not addressed by previous studies. It is found that Radom Forest might be a better choice for datasets larger than 1,000 due to both high accuracy and fast speed. Besides, the dataset range affects the quantified importance (weighted value) of factors significantly, with up to a 115% increment. Moreover, the results show that machine learning has a high accuracy on the extrapolation prediction of productivity, where the minimum mean relative prediction error is just around 4%. The results of this work not only show the necessity of the dataset characteristics' effect but also provide a standard process for studying solar-thermal desalination by machine learning, which would pave the way for interdisciplinary study.

physics.app-ph↗

arXiv2021

A compact flat solar still with high performance

Solar still is a convenient off-grid device for desalination, which can provide fresh water for families, ships, islands and so on. The conventional inclined solar still (ISS) suffers from low efficiency and low productivity. To improve the performance of solar still, a flat solar still (FSS) is proposed, which has a working principle similar to the solar cell. The condensate water in FSS is collected by the capillary grid attached under the ultra-hydrophilic glass cover, instead of by gravity. Therefore, FSS avoids the inclined structure and is much more compact than ISS. The daily productivity of FSS reaches up to 4.3 kg/m2. Theoretical analysis shows that the enhanced mass transfer in FSS by the compact structure is an important factor for high performance. More interestingly, FSS can also be easily extended to more stage for latent heat recovery. The results show that the daily productivity of a double-stage FSS reaches up to 7 kg/m2, which is much higher than the conventional solar still. FSS paves a new way in designing and optimizing of solar still.

physics.app-ph↗

arXiv2020

Prediction of tubular solar still performance by machine learning integrated with Bayesian optimization algorithm

Presented is a new generation prediction model of a tubular solar still (TSS) productivity utilizing two machine learning (ML) techniques, namely:Random forest (RF) and Artificial neural network (ANN). Prediction models were conducted based on experimental data recorded under Egyptian climate. Meteorological and operational thermal parameters were utilized as input layers. Moreover, Bayesian optimization algorithm (BOA) was used to obtain the optimal performance of RF and ANN models. In addition, these models results were compared to those of a multilinear regression (MLR) model. As resulted, experimentally, the average value accumulated productivity was 4.3 L/(m2day). For models results, RF was less sensitive to hyper parameters than ANN as ANN performance could be significantly improved by BOA more than RF. In addition, RF achieved better prediction performance of TSS on the current dataset. The determination coefficients (R2) of RF and ANN were 0.9964 and 0.9977, respectively, which were much higher than MLR models, 0.9431. Based on the robustness performance and high accuracy, RF is recommended as a stable method for predicting the productivity of TSS.

physics.app-ph↗

arXiv2019

Micro/nanomaterials for improving solar still and solar evaporation -- A review

In last decades, solar stills, as one of the solar desalination technologies, have been well studied in terms of their productivity, efficiency and economics. Recently, to overcome the bottleneck of traditional solar still, improving solar still by optimizing the solar evaporation process based on micro/nanomaterials have been proposed as a promising strategy. In this review, the recent development for achieving high-performance of solar still and solar evaporation are discussed, including materials as well as system configurations. Meanwhile, machine learning was used to analyze the importance of different factors on solar evaporation, where thermal design was founded to be the most significant parameter that contributes in high-efficiency solar evaporation. Moreover, several important points for the further investigations of solar still and solar evaporation were also discussed, including the temperature of the air-water interface, salt rejecting and durability, the effect of solid-liquid interaction on water phase change.

physics.app-ph↗

arXiv2018

High efficient solar evaporation by airing multifunctional textile

Solar evaporation is important for many applications such as desalination, power generation and industrial drying. Recently, some studies on evaporation reported obtaining high energy efficiency and evaporation rate, which are based on floating evaporation setup (FES) with nanomaterials. Here, we proposed a new cheap and simple setup, named as airing evaporation setup (AES). It shows that the energy efficiency of AES reaches up to 87 % under 1 kW/m2 of solar irradiation, which is 14% higher than that of FES. Meanwhile, the total evaporation rate of AES is about 20% higher than that of FES. The theoretical analysis reveals that the main reason for a better performance of AES is the increasing evaporation area. More interesting, AES could be used for designing portable systems due to its simplicity and flexibility. Furthermore, we show that AES and the corresponding wick material can be used in solar desalination, textile quick-drying and warm-keeping.

physics.app-ph↗