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Mini-emulsion and nanoprecipitation techniques relied on large amounts of surfactants, and unresolved miscibility issues of heterojunction materials limited their efficiency and applicability in the past. Through our molecular design and developed surfactant-free precipitation method, we successfully fabricated the best miscible bulk-heterojunction-particles (BHJP) ever achieved, using donor ( PS ) and acceptor ( PSOS ) polymers. The structural similarity ensures optimal miscibility, as supported by the interaction parameter of the PS / PSOS blend is positioned very close to the binodal curve. Experimental studies and molecular dynamics simulations further revealed that surfactants hinder electron output sites and reduce the concentration of sacrificial agents at the interface, slowing polaron formation. Multiscale experiments verified that these BHJP, approximately 12 nm in diameter, further form cross-linked fractal networks of several hundred nanometers. Transient absorption spectroscopy showed that BHJP facilitates polaron formation and electron transfer. Our BHJP demonstrated a superior hydrogen evolution rate (HER) compared to traditional methods. The most active BHJP achieved an HER of 251.2 mmol h -1 g -1 and an apparent quantum yield of 26.2% at 500 nm. This work not only introduces a practical method for preparing BHJP but also offers a new direction for the development of heterojunction materials.

The quick and precise estimation of D-π-A Organic Dye absorption maxima in different solvents is an important challenge for the efficient design of novel chemical structures that could improve the performance of dye-sensitized solar cells (DSSCs) and related technologies. Time-Dependent Density Functional Theory (TD-DFT) has often been employed for these predictions, but it has limitations, including high computing costs and functional dependence, particularly for solvent interactions. In this study, we introduce a high-accuracy and rapid deep-learning ensemble method using daylight fingerprints as chemical descriptors to predict the absorption maxima (λ max ) of D-π-A organic dyes in 18 different solvent environments. This study introduces a novel approach leveraging advanced ensemble deep learning of 10 models of multiple neural architectures including convolutional networks to demonstrate exceptional predictive power in capturing complex relationships between molecular structures with solvent interaction and absorption maximum. Leveraging a comprehensive range of molecular descriptors from organic dye fingerprints, we developed a highly accurate ensemble model with an R 2 of 0.94 and a mean absolute error (MAE) of 8.6 nm, which enhances predictive accuracy and significantly reduces computational time. Additionally, we developed a user-friendly web-based platform that allows for quick prediction of absorption maxima including solvent effect. This tool, which directly uses SMILES representations and advanced deep learning techniques, offers significant potential for accelerating the discovery of efficient dye candidates for various applications, including solar energy, environmental solutions, and medical research. This research opens the door to more effective next-generation dye design, which will facilitate rapid testing in a variety of fields and design an efficient new material., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

This scientific paper presents a novel approach to explore and predict the potential of imidazole-based organic dyes for use in Dye-Sensitized Solar Cells (DSSCs) using a machine learning web application. The design of efficient and cost-effective organic dyes is critical to enhance the performance of DSSCs. Traditional experimental methods are time-consuming and resource-intensive, making it challenging to screen a large number of potential dyes. In this study, we propose a machine learning-based approach to accelerate the discovery process by predicting the photovoltaic performance of imidazole-based organic dyes. Machin learning predictions provide valuable insights into the expected PCE% and behaviors of the molecules toward DSSCs. Based on the RDKit library, several fingerprints such as Molecular ACCess System, Avalon, Daylight, Pharmacophore and Morgan with different radius (r2, r3, r4), were studied. In addition, more than 20 ML algorithms using different cross validation (3, 5, 7, 10) were also evaluated. Among of these, Deep Neural Network models of MLPRegressor algorithm based on the daylight fingerprint shows a significant coefficient of determination combined with the lowest errors. Utilize the trained ML models to screen of 50 million SMILE structure for identify promising imidazole and nitrogen-containing derivative as a doner group. By replacing the donor groups in the well-known MK2 dye structure with the top imidazole derivatives proposed by machine learning, significant improvements in PCE were observed, increasing from 7.70% to as high as 11.49%, representing nearly a 50% enhancement over the control. DFT calculations confirm the ML predictions and clarify the significantly higher oscillator strength and charge transfer properties of MK2-DM1, compared to MK2. This result provides a promising pathway for developing new dye materials that can push the efficiency limits of DSSCs, leading to more efficient solar energy conversion technologies in the future. In addition, a developed web application offers a user-friendly interface for researchers to input their molecular structures and obtain PCE% predictions toward DSSCs. This information can guide researchers in designing a new imidazole dye with high photovoltaic performance to validate and refine the predictions without time consuming., (© 2024. The Author(s).)

The current bottleneck in the development of efficient photocatalysts for hydrogen evolution is the limited availability of high-performance acceptor units. Over the past nine years, dibenzo[b,d]thiophene sulfone (DBS) has been the preferred choice for the acceptor unit. Despite extensive exploration of alternative structures as potential replacements for DBS, a superior substitute remains elusive. In this study, a symmetry-breaking strategy was employed on DBS to develop a novel acceptor unit, BBTT-1SO. The asymmetric structure of BBTT-1SO proved beneficial for increasing multiple moment and polarizability. BBTT-1SO-containing polymers showed higher efficiencies for hydrogen evolution than their DBS-containing counterparts by up to 166 %. PBBTT-1SO exhibited an excellent hydrogen evolution rate (HER) of 222.03 mmol g -1  h -1 and an apparent quantum yield of 27.5 % at 500 nm. Transient spectroscopic studies indicated that the BBTT-1SO-based polymers facilitated electron polaron formation, which explains their superior HERs. PBBTT-1SO also showed 14 % higher HER in natural seawater splitting than that in deionized water splitting. Molecular dynamics simulations highlighted the enhanced water-PBBTT-1SO polymer interactions in salt-containing solutions. This study presents a pioneering example of a substitute acceptor unit for DBS in the construction of high-performance photocatalysts for hydrogen evolution., (© 2024 Wiley-VCH GmbH.)

In the present study, numerous acridine derivatives A1-A20 were synthesized via aromatic nucleophilic substitution (S NAr ) reaction of 9-chloroacridine with carbonyl hydrazides, amines, or phenolic derivatives depending upon facile, novel, and eco-friendly approaches (Microwave and ultrasonication assisted synthesis). The structures of the new compounds were elucidated using spectroscopic methods. The title products were assessed for their antimicrobial, antioxidant, and antiproliferative activities using numerous assays. Promisingly, the investigated compounds mainstream revealed promising antibacterial and anticancer activities. Thereafter, the investigated compounds' expected mode of action was debated by using an array of in silico studies. Compounds A2 and A3 were the most promising antimicrobial agents, while compounds A2, A5, and A7 revealed the most cytotoxic activities. Accordingly, RMSD, RMSF, Rg, and SASA analyses of compounds A2 and A3 were performed, and MMPBSA was calculated. Lastly, the ADMET (absorption, distribution, metabolism, excretion, and toxicity) analyses of the novel acridine derivatives were investigated. The tested compounds' existing screening results afford an inspiring basis leading to developing new compelling antimicrobial and anticancer agents based on the acridine scaffold., (© 2024 Wiley-VHCA AG, Zurich, Switzerland.)

Iron-doped Zinc oxide nanoparticles were produced by the sol-gel combustion method. This study aims to see how iron doping affects the structural, optical, and photocatalytic characteristics of ZnO composites. XRD examined all samples to detect the structural properties and proved that all active materials are a single hexagonal phase. The morphology and particle size were investigated by TEM. Computational Density functional theory (DFT) calculation of the band structure, density of state, and charge distributions for ZnO were investigated in comparison with ZnO dope iron. We reported the application results of ZnO doped Fe for Methylene blue dye removal under photocatalytic degradation effect. The iron concentrations affect the active material's band gap, producing higher photocatalytic performance. The acquired results could be employed to enhance the photocatalytic properties of ZnO.

Numerous scientific, health care, and industrial applications are showing increasing interest in developing optical pH sensors with low-cost, high precision that cover a wide pH range. Although serious efforts, the development of high accuracy and cost-effectiveness, remains challenging. In this perspective, we present the implementation of the machine learning technique on the common pH paper for precise pH-value estimation. Further, we develop a simple, flexible, and free precise mobile application based on a machine learning algorithm to predict the accurate pH value of a solution using an available commercial pH paper. The common light conditions were studied under different light intensities of 350, 200, and 20 Lux. The models were trained using 2689 experimental values without a special instrument control. The pH range of 1: 14 is covered by an interval of ~ 0.1 pH value. The results show a significant relationship between pH values and both the red color and green color, in contrast to the poor correlation by the blue color. The K Neighbors Regressor model improves linearity and shows a significant coefficient of determination of 0.995 combined with the lowest errors. The free, publicly accessible online and mobile application was developed and enables the highly precise estimation of the pH value as a function of the RGB color code of typical pH paper. Our findings could replace higher expensive pH instruments using handheld pH detection, and an intelligent smartphone system for everyone, even the chef in the kitchen, without the need for additional costly and time-consuming experimental work., (© 2022. The Author(s).)

The emergence of multidrug-resistant microbes and the propagation of cancer cells are global health issues. The unique properties of chitosan and its derivatives make it an important candidate for therapeutic applications. Herein, a new thiadiazole derivative, 4-((5-(butylthio)-1,3,4-thiadiazol-2-yl) amino)-4-oxo butanoic acid (BuTD-COOH) was synthesized and used to modify the chitosan through amide linkages, forming a new thiadiazole chitosan derivative (BuTD-CH). The formation of thiadiazole and the chitosan derivative was confirmed by FT-IR, 1 H/ 13 C-NMR, GC-MS, TGA, Elemental analysis, and XPS. The BuTD-CH showed a high antimicrobial effect against human pathogens Escherichia coli, Pseudomonas aeruginosa, Bacillus subtilis, Staphylococcus aureus, and Candida albicans with low MIC values of 25-50 μg ml -1 compared to unmodified chitosan. The in-vitro cytotoxicity of BuTD-CH was evaluated against two cancer cell lines (MCF-7 and HepG2) and one normal cell (HFB4) using the MTT method. The newly synthesized derivatives showed high efficacy against cancerous cells and targeted them at low concentrations (IC 50 was 178.9 ± 9.1 and 147.8 ± 10.5 μg ml -1 for MCF-7 and HepG2, respectively) compared with normal HFB4 cells (IC 50 was 335.7 ± 11.4 μg ml -1 ). Thus, low concentrations of newly synthesized BuTD-CH could be safely used as an antimicrobial and pharmacological agent for inhibiting the growth of human pathogenic microbes and hepatocellular and adenocarcinoma therapy., (© 2022. The Author(s).)

We report here on the synthesis, crystal structure, optoelectronic and vibrational properties, as well as the DFT calculations of the novel trimethylsulfonium tin trichloride (CH 3 ) 3 SSnCl 3 . The air-stable compound is prepared by reacting the (CH 3 ) 3 SCl and SnCl 2 solid precursors in evacuated silica tubes at 100 °C. According to powder X-ray diffraction and Rietveld refinement, it crystallizes at room temperature in the orthorhombic space group Pbca (No. 61) with isolated pyramids of [SnCl 3 ] - and (CH 3 ) 3 S + units. UV-vis reflectance and photoluminescence spectroscopies reveal a direct energy band gap of 3.85 eV, accompanied by a broad Stokes-shifted luminescence signal. Photoexcitation of the compound at room temperature and at -196 °C results in broadband luminescence with weak magenta emission centered at 400 nm using an excitation at 250 nm. First principal calculations provide insight into the physical properties through the electron and phonon density of states. Multitemperature Raman spectroscopy and differential scanning calorimetry reveal a reversible phase transition at ca. 70 °C that affects the vibrational modes of the [SnCl 3 ] - . By dissolving (CH 3 ) 3 SSnCl 3 in dimethylformamide in ambient air for a week, oxidation of tin occurs in the "defect" perovskite ((CH 3 ) 3 S) 2 SnCl 6 . The crystal structure of ((CH 3 ) 3 S) 2 SnCl 6 is also determined with high accuracy via single-crystal X-ray diffraction (cubic space group Pa -3 (No. 205)) and compared with (CH 3 ) 3 SSnCl 3 via Hirshfeld surface analysis.

The necessity to resolve the issue of rapid charge carrier recombination for boosting photocatalytic performance is a vigorous and challenging research field. To address this, the construction of a binary system of step-scheme (S-scheme) CuO@TiO 2 heterostructure composite has been demonstrated through a facile solid-state route. The remarkably enhanced photocatalytic performance of CuO@TiO 2 , compared with single TiO 2 , which can consequence in the more efficient separation of photoinduced charge carriers, reduced the band gap of TiO 2 , improved the electrical transport performance, and improved the lifetimes, thus donating it with the much more powerful oxidation and reduction capability. A photocatalytic mechanism was proposed to explain the boosted photocatalytic performance of CuO@TiO 2 on a complete analysis of physicochemical, DFT calculations, and electrochemical properties. In addition, this work focused on the investigation of the stability and recyclability of CuO@TiO 2 in terms of efficiency and its physical origin using XRD, BET, and XPS. It is found that the removal efficiency diminishes 4.5% upon five recycling runs. The current study not only promoted our knowledge of the binary system of S-scheme CuO@TiO 2 heterojunction composite photocatalyst but also shed new light on the design of heterostructure photocatalysts with high-performance and high stability., (© 2022. The Author(s).)

The photovoltaic efficiency and stability challenges encountered in perovskite solar cells (PSCs) were addressed by an innovative interface engineering approach involving the utilization of the organic chromophore (E)-3-(5-(4-(bis(2',4'-dibutoxy-[1,1'-biphenyl]-4-yl)amino)phenyl)thiophen-2-yl)-2-cyanoacrylic acid (D35) as an interlayer between the perovskite absorber and the hole transporter (HTM) of mesoporous PSCs. The organic D-π-A interlayer primarily improves the perovskite's crystallinity and creates a smoother perovskite/HTM interface, while reducing the grain boundary defects and inducing an energy level alignment with the adjacent layers. Champion power conversion efficiencies (PCE) as high as 18.5% were obtained, clearly outperforming the reference devices. Interestingly, the D35-based solar cells present superior stability since they preserved 83% of their initial efficiency after 37 days of storage under dark and open circuit (OC) conditions. The obtained results consolidate the multifunctional role of organic D-π-A molecules as perovskite interface modifiers towards performance enhancement and scale-up fabrication of robust PSCs.