Versatile recognition of graphene layers from optical images under controlled illumination through green channel correlation method.

In this study, a simple yet versatile method is proposed for identifying the number of exfoliated graphene layers transferred on an oxide substrate from optical images, utilizing a limited number of input images for training, paired with a more traditional number of a few thousand well-published Github images for testing and predicting. Two thresholding approaches, namely the standard deviation-based approach and the linear regression-based approach, were employed in this study. The method specifically leverages the red, green, and blue color channels of image pixels and creates a correlation between the green channel of the background and the green channel of the various layers of graphene. This method proves to be a feasible alternative to deep learning-based graphene recognition and traditional microscopic analysis. The proposed methodology performs well under conditions where the effect of surrounding light on the graphene-on-oxide sample is minimum and allows rapid identification of the various graphene layers. The study additionally addresses the functionality of the proposed methodology with nonhomogeneous lighting conditions, showcasing successful prediction of graphene layers from images that are lower in quality compared to typically published in literature. In all, the proposed methodology opens up the possibility for the non-destructive identification of graphene layers from optical images by utilizing a new and versatile method that is quick, inexpensive, and works well with fewer images that are not necessarily of high quality.

Feasibility study of Mg storage in a bilayer silicene anode via application of an external electric field.

With the goal of developing a Si-based anode for Mg-ion batteries (MIBs) that is both efficient and compatible with the current semiconductor industry, the current research utilized classical Molecular Dynamics (MD) simulation in investigating the intercalation of a Mg2+ ion under an external electric field (E-field) in a 2D bilayer silicene anode (BSA). First principles density functional theory calculations were used to validate the implemented EDIP potentials. Our simulation shows that there exists an optimum E-field value in the range of 0.2-0.4 V Å-1 for Mg2+ intercalation in BSA. To study the effect of the E-field on Mg2+ ions, an exhaustive spread of investigations was carried out under different boundary conditions, including calculations of mean square displacement (MSD), interaction energy, radial distribution function (RDF), and trajectory of ions. Our results show that the Mg2+ ions form a stable bond with Si in BSA. The effects of E-field direction and operating temperature were also investigated. In the X-Y plane in the 0°-45° range, 15° from the X-direction was found to be the optimum direction for intercalation. The results of this work also suggest that BSA does not undergo drastic structural changes during the charging cycles with the highest operating temperature being ∼300 K.

Machine Learning Approach to Delineate the Impact of Material Properties on Solar Cell Device Physics.

In this research, solar cell capacitance simulator-one-dimensional (SCAPS-1D) software was used to build and probe nontoxic Cs-based perovskite solar devices and investigate modulations of key material parameters on ultimate power conversion efficiency (PCE). The input material parameters of the absorber Cs-perovskite layer were incrementally changed, and with the various resulting combinations, 63,500 unique devices were formed and probed to produce device PCE. Versatile and well-established machine learning algorithms were thereafter utilized to train, test, and evaluate the output dataset with a focused goal to delineate and rank the input material parameters for their impact on ultimate device performance and PCE. The most impactful parameters were then tuned to showcase unique ranges that would ultimately lead to higher device PCE values. As a validation step, the predicted results were confirmed against SCAPS simulated results as well, highlighting high accuracy and low error metrics. Further optimization of intrinsic material parameters was conducted through modulation of absorber layer thickness, back contact metal, and bulk defect concentration, resulting in an improvement in the PCE of the device from 13.29 to 16.68%. Overall, the results from this investigation provide much-needed insight and guidance for researchers at large, and experimentalists in particular, toward fabricating commercially viable nontoxic inorganic perovskite alternatives for the burgeoning solar industry.

Synthesis, Characterization, and Studies on Photophysical Properties of Rhodamine Derivatives and Metal Complexes in Dye-Sensitized Solar Cells.

Rhodamine 6G dyes are low-cost, highly soluble fluorescent dyes frequently utilized as laser dyes, chemical sensors, and as tracer dyes in the determination of the direction and rate of flow of water. In this study, the photophysical properties of three rhodamine 6G dyes, bearing phenyl (P15), furan (P41), and 5-hydroxymethyl furan (P45), and their metal complexes were investigated using ultraviolet-visible (UV-vis) spectroscopy, fluorescence spectroscopy, fluorescence lifetime, and Fourier transform infrared (FTIR) measurements. Rhodamine 6G dyes and their complexes were subsequently applied as sensitizing dyes in the fabrication of dye-sensitized solar cells, and the solar to electric power efficiency and electrochemical impedance spectroscopy measurements were performed. The solar to electric power efficiency values of the metal complexes of the rhodamine 6G dyes were higher than those of the devices fabricated with only rhodamine dyes without copper (II). The most significant change was observed in rhodamine P41 with a 30% increase in solar to electric power efficiency when the dye was conjugated to the copper ion.

Supervised Machine Learning-Aided SCAPS-Based Quantitative Analysis for the Discovery of Optimum Bromine Doping in Methylammonium Tin-Based Perovskite (MASnI3-xBrx).

In this investigation, supervised machine learning (ML) was utilized to accurately predict the optimum bromine doping concentration in single-junction MASnI3-xBrx devices. Data-driven optimizations were carried out on 42 000 unique devices built utilizing a solar cell capacitance simulator (SCAPS). The devices were investigated through variations of bromine doping %, bandgap, electron affinity, series resistance, back-contact metal, and acceptor concentration─parameters that were specifically chosen because of their tunable nature and ability to be modified through facile experimental fabrication techniques of the device. Five different algorithms were utilized to explore feature engineering. The first step before bromine doping within the device included validation studies of a pure tin-based system, MASnI3: a power conversion efficiency (PCE) of 6.71% was achieved, having close congruence with experimental data. ML analyses for optimal bromine doping resulted in the discovery of two devices with bromine concentrations of 22.43% (Br22) and 25.63% (Br25), with the latter being a more fine-tuned value obtained through extra rigorous analysis. To understand the total and relative impact of each feature on power conversion efficiency (PCE), Br22 and Br25 were analyzed with a state-of-the-art algorithm, namely, the SHapley Additive exPlanations (SHAP) algorithm. Focusing on the two discovered devices, further device optimizations were carried out utilizing SCAPS. Modulations of absorber thickness, bulk and interfacial defect density, and choice of electron transport layer (ETL) and hole transport layer (HTL) materials were tried. Device stability was analyzed through carrier lifetime studies. Following these optimization steps, Br22 and Br25 demonstrated final high PCE values of 20.72 and 17.37%, respectively. The ML-assisted quantitative analysis of the current work provides significant confidence for optimal bromine-doped tin-based devices to be considered as viable and competitive nontoxic alternatives to traditional technologies.

Fabrication and characterization of a water purification system using activated carbon and graphene nanoplatelets: Toward the development of a nanofiltration matrix.

Researchers are trying to tackle water scarcity in numerous ways. One of those ways is the use of nanotechnology in water processing and purification. The current work involves the fabrication and optimization of activated carbon and graphene-based hybrid water purification system. Five different concentrations of methylene blue and deionized water (DI) dye solutions were used, and they were filtered in three different cycles. For the potential usage on the consumer side, a small-scale, low-cost water filter is developed using activated carbon, commercial filter paper, and graphene nanoplatelets. The filter paper is used to hold mixtures of the activated carbon and graphene nanoplatelets within the water filter. The conductivity, TDS, and pH are measured for the feed water and the processed water using an Oakton EcoTestr and Apera Instruments PH60 Premium Pocket pH meter, respectively. A UV-Vis spectrometer is used to measure the absorption of solutions. The distribution and adsorption of the dye particles were observed by scanning electron microscopy. PRACTITIONER POINTS: These results show the effectiveness of the system in the removal of dye particles above a given particle size. The concentration of the dye solution decreased after every cycle. The GnPs filtration system more effectively dye particles as compared to the filtration system containing only Activated carbon. UV-Vis spectroscopy results showed that the methylene blue dye particles decreased after every cycle. This research can open a broad area of projects toward waste/wastewater practice for particles above a certain particle size.

Near-IR absorbing solar cell sensitized with bacterial photosynthetic membranes.

Current interest in natural photosynthesis as a blueprint for solar energy conversion has led to the development of a biohybrid photovoltaic cell in which bacterial photosynthetic membrane vesicles (chromatophores) have been adsorbed to a gold electrode surface in conjunction with biological electrolytes (quinone [Q] and cytochrome c; Magis et al. [2010] Biochim. Biophys. Acta 1798, 637-645). Since light-driven current generation was dependent on an open circuit potential, we have tested whether this external potential could be replaced in an appropriately designed dye-sensitized solar cell (DSSC). Herein, we show that a DSSC system in which the organic light-harvesting dye is replaced by robust chromatophores from Rhodospirillum rubrum, together with Q and cytochrome c as electrolytes, provides band energies between consecutive interfaces that facilitate a unidirectional flow of electrons. Solar I-V testing revealed a relatively high I(sc) (short-circuit current) of 25 µA cm(-2) and the cell was capable of generating a current utilizing abundant near-IR photons (maximum at ca 880 nm) with greater than eight-fold higher energy conversion efficiency than white light. These studies represent a powerful demonstration of the photoexcitation properties of a biological system in a closed solid-state device and its successful implementation in a functioning solar cell.

Self-assembled TiO2 with increased photoelectron production, and improved conduction and transfer: enhancing photovoltaic performance of dye-sensitized solar cells.

Highly crystalline mesoporous anatase TiO(2) is prepared through supramolecular self-assembly and by utilizing cetyltrimethylammonium bromide (CTAB) as templating material. Photoanodes of dye-sensitized solar cells (DSSCs) made from these TiO(2) nanoparticles are found to have a high specific surface area of 153 m(2)/g and high surface roughness. Optical absorption spectroscopy studies reveal that the photoanode films adsorb four times more dye than films made of commercial P25 TiO(2). Mercury porosimetry and field emission scanning electron microscope (FESEM) studies show hierarchical macro- and meso-porosity of the photoanode films leading to better dye and electrolyte percolation, combined with improved electron conduction pathways compared to P25 films. Electrochemical impedance studies confirm lower impedance and higher electron lifetime in the synthesized mesoporous TiO(2) films compared to P25 films. Higher photovoltaic efficiency was recorded of cells made from the synthesized mesoporous TiO(2) in comparison to the corresponding cells made from P25. Incident-photon-to-current efficiency data provided critical understanding of recombination kinetics, and provided proof of Mie scattering by the self-assembled submicrometer sized TiO(2) aggregates and the macropores in their structure. The scattering phenomenon was further corroborated by diffused reflectance studies. An in-depth analysis of CTAB-templated mesoporous TiO(2) has been conducted to show how it can be a good candidate photoanode material for enhancing the performance of DSSCs.