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In this study, the proposed method involves the confident insertion of π-spacer fragments between donor and acceptor parts of a newly designed (A1-D-A2) molecule into the reference molecule (PP2). Frontier molecular orbitals study using MPW1PW91/6-31G(d,p) level of DFT demonstrates that all designed molecules possess a lower band gap (2.62–3.35 eV) in comparison to R (3.77 eV). The absorption properties clearly show that all the designed molecules (DD1–DD8) have higher absorption values (434.44–566.40 nm) in the gas phase and (498.65–624.01 nm) in the solvent phase compared to R values of 380.32 nm in the gas phase and 415.61 nm in the solvent phase. Significant LHE values and the lowest λ e values (0.0062–0.0110 eV) are observed in the designed molecules. This study will help researchers to design molecules for the development of efficient PSCs devices.

A combination of high open-circuit voltage (V oc ) and short-circuit current density (Jsc) typically creates effective organic solar cells (OSCs). To enhance the open-circuit voltage, we have designed three new fullerene-free acceptor molecules with elongated π-conjugation in the end-capped units. Y-series-based newly designed molecules (CPSS-4F, CPSS-4Cl, CPSS-4CN) exhibited a narrow energy bandgap with high electron mobility. Red shift in the absorption spectrum with high intensities is also noted for designed molecules. Low binding and excitation energies of designed molecules favor easy excitation of exciton in the excited state. Further, CPSS-4F, CPSS-4Cl, and CPSS-4CN exhibited better open-circuit voltage with favorable molecular orbitals contributions. Transition density analysis (TDM) was also performed to locate the total transitions in the designed molecules. Outcomes of all analyses suggested that designed molecules are effective contributors to the active layer of organic solar cells. Graphical abstract

A rapid growth in the development of power generation and transportation sectors would result in an increase in the carbon dioxide (CO 2 ) concentration in the atmosphere. As it will continue to play a vital role in meeting current and future needs, significant efforts have been made to address this problem. Over the past few years, extensive studies on the development of heterogeneous catalysts for CO 2 methanation have been investigated and reported in the literatures. In this paper, a comprehensive overview of methanation research studies over lanthanide oxide catalysts has been reviewed. The utilisation of lanthanide oxides as CO 2 methanation catalysts performed an outstanding result of CO 2 conversion and improvised the conversion of acidity from CO 2 gas to CH 4 gas. The innovations of catalysts towards the reaction were discussed in details including the influence of preparation methods, the structure-activity relationships as well as the mechanism with the purpose of outlining the pathways for future development of the methanation process.

Context Various innovative molecules have been designed and explored for use in organic photovoltaics. In this study, we devised novel molecules (KZ1–KZ7) specifically for organic solar cells (OSCs). The newly formulated acceptor compounds possess a lower bandgap ( E g = 1.85–2.02), along with bathochromic shift ( λ max = 713–788 nm) compared to the reference ( E g = 2.04 eV and λ max = 774 nm). Moreover, the FMO results identified the distinct charge transfer from HOMO to LUMO, which was strongly corroborated by the TDM maps. Similarly, the new designed molecules show less excitation energy ( E x = 1.31–1.54(gas)) than reference ( E x = 1.72). Likewise, all designed molecules (KZ1–KZ7) have demonstrated an analogous open circuit voltage ( V oc ) with the donor polymer PTB7-Th. All seven designed molecules (KZ1–KZ7) exhibited more fill factor ranging from 97.08 to 97.29 than reference 95.25 and PCE of between 8 and 20% at short circuit current densities of 9, 12, and 15 mA cm −2 . Overall, the findings support that designed molecules can be potential molecules for future practical applications. Methods Geometric calculations were conducted with Gaussian 09W software, and the findings were visualized using Gauss View software. DFT and TD-DFT were employed to evaluate various parameters for R and designed molecules (KZ1–KZ7). Firstly, four functionals including B3LYP, CAM-B3LYP, MPW1PW91, and ωB97XD with 6-31G(d,p) DFT level were applied to R to decide the best level for results. After appropriate analysis, the MPW1PW91/6-31G(d,p) was selected for further examination by comparing the experimental and DFT-based absorption graphs of R. External and internal reorganization energy are the two main factors contributing to reorganization energy. External energy refers to changes in external environment, while internal energy deals with information related to internal geometrical symmetry or the internal environment. The effect of outside factors or external reorganizational energy is omitted because it creates too little change.

Hydrogen is clean energy source that can replace fossil fuels, considering the great need for clean and environmentally friendly energy. Researchers are busy developing new materials for efficient hydrogen storage. Herein, we present the detailed analysis of hydrogen adsorption on pristine B 12 N 12 as well as alkali metals (Li, Na and K) centered B 12 N 12 . B3LYP/6-31G(d,p) basis set of DFT has been used in this investigation. These parameters are carried out to analyze the structure, stability, and reactivity of centered nano-cage towards hydrogen. Firstly, we optimized alkali metals (Li, N,a and K) centered nanocage. And then these nano-clusters are analyzed for hydrogen adsorption. Adsorption energies, bond lengths, HOMO–LUMO gap, molecular electrostatic potential, charge density, and PDOS analysis are performed by using the B3LYP/6-31(d,p) basis set of DFT. All centered nano-cages offer better adsorption of hydrogen as compared to pure B 12 N 12 . Dipole moment analysis indicates that a high charge density exists when H 2 is adsorbed on metals centered in B 12 N 12 . MEP shows that charge separation occurred when hydrogen is adsorbed on a metals-centered nano-cage. HOMO–LUMO energy gap investigation shows that LUMO shows stabilization while HOMO is destabilized when metal or hydrogen are in contact with BN nano-cage. Alkali metals encapsulation can improve the chemical and physical properties of B 12 N 12 . A novel type of system for developing hydrogen storage materials was finally proposed. Graphical Abstract

Perovskite materials have received a lot of attention due to their distinctive structural, electronic, and optical characteristics, particularly in photocatalytic water splitting applications. This work aims to explore structural, electronic, optical, elastic, and mechanical properties of cubic-phase A 2 LiBiI 6 (A = Na, K, Rb) double perovskites using DFT within the GGA-PBE framework. All compounds exhibit negative formation energies (−1.45, −1.14, and −1.07 eV) along with the tolerance factors ranging from 0.820 to 0.896, which fall within the perovskite stability domain. In the phonon dispersion analysis of all compounds, no imaginary frequencies appeared, confirming their dynamic stability. The materials possess a bandgap 1.891–2.014 eV (GGA-PBE) and 1.896–2.038 eV (YS-PBE0), appropriate for visible light absorption, with band gap energies increasing systematically with A-site ionic radius (Na < K < Rb). Dielectric function analysis reveals stable dispersion across the visible spectrum, with static dielectric constants ranging from 3.9 to 4.7, indicating a high degree of polarizability. Mechanical assessments, including positive shear modulus (G) values and Pugh’s ratio (B/G > 1.75), confirm their ductile and stable nature. This work lays the groundwork for designing cost-effective photocatalytic materials for hydrogen evolution reaction (HER).

Smart cities require the development of information and communication technology to become a reality (ICT). A “smart city” is built on top of a “smart grid”. The implementation of numerous smart systems that are advantageous to the environment and improve the quality of life for the residents is one of the main goals of the new smart cities. In order to improve the reliability and sustainability of the transportation system, changes are being made to the way electric vehicles (EVs) are used. As EV use has increased, several problems have arisen, including the requirement to build a charging infrastructure, and forecast peak loads. Management must consider how challenging the situation is. There have been many original solutions to these problems. These heavily rely on automata models, machine learning, and the Internet of Things. Over time, there have been more EV drivers. Electric vehicle charging at a large scale negatively impacts the power grid. Transformers may face additional voltage fluctuations, power loss, and heat if already operating at full capacity. Without EV management, these challenges cannot be solved. A machine-learning (ML)-based charge management system considers conventional charging, rapid charging, and vehicle-to-grid (V2G) technologies while guiding electric cars (EVs) to charging stations. This operation reduces the expenses associated with charging, high voltages, load fluctuation, and power loss. The effectiveness of various machine learning (ML) approaches is evaluated and compared. These techniques include Deep Neural Networks (DNN), K-Nearest Neighbors (KNN), Long Short-Term Memory (LSTM), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT) (DNN). According to the results, LSTM might be used to give EV control in certain circumstances. The LSTM model’s peak voltage, power losses, and voltage stability may all be improved by compressing the load curve. In addition, we keep our billing costs to a minimum, as well.

Artificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment.

Intricacy of the supply chains for deteriorating products, involving multiple retailers with unequal lot sizes and multiple deliveries is simplified in this article by optimizing the replenishment cycle, investment in preservation technology, and number of deliveries. This study proposes a multi-tier supply chain model consisting of a single manufacturer and multiple retailers. A single-setup multiple deliveries (SSMD) policy is adopted considering the synchronized cycle time of manufacturers with that of retailers and the delivery of unequal lot size for each retailer. Preservation technology is used at retailers to minimize the effects of deterioration in a way that the magnitude of decrease in deterioration reduces for additional investment in preservation technology. A centralized supply chain model is proposed by defining a nonlinear mathematical model for maximizing total profit through an analytical optimization technique and an algorithm. Numerical experiments are exhibited to validate the applications of the provided model. The results exhibit that the proposed preservation policy increases the product’s lifetime and the total profit by reducing the number of shipments/transportation and increasing the lot size. The SSMD policy helps to reduce the preservation cost and increase the total profit. Some managerial insights are provided for the decision makers for applying the proposed model.

The demand for modern electronics and semiconductors has increased throughout the years, which has enabled the innovation and exploration of solution-processed deposition. Solution-based processes have gained a lot of interest due to the low-cost fabrication and the large fabrication areas without the need for high-vacuum equipment. In this study, we utilized the ZnO ink for inkjet printer ink to fabricate a thin film via Electrohydrodynamic printing. Three different ink solutions were prepared for experimentation. The EHD printing technique demonstrated the ink’s compatibility with and without the modifications. The outcomes of the EHD printed materials were comparable with the spin-coated thin films. The EHD-printed films demonstrated better results in comparison to spin-coated films. Ra and Rq of the EHD film measured at 3.651 nm and 4.973 nm, respectively. It improved the absorbance up to two-fold at 360 nm wavelength and electrical conductivity up to 40% compared to the spin-coated films. Furthermore, the optimization of the printing parameters can lead to the improved morphology and thickness of the EHD thin films.