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Herein, we report the synthesis and characterization of four homological branched polyynes (3a–3d). X-ray diffraction revealed that a propeller-shaped configuration was adopted by those polyynes. Subsequently, their ultraviolet-visible absorption and fluorescence spectra were studied in dichloromethane solutions. Interestingly, compared to its several analogues (peripheral methyl, phenyl, and pyridinyl substituted), the thiophene-substituted homologue 3d exhibited enhanced absorption and redshifted emission. Besides, strong and stable electrochemiluminescence signals were observed for all the above polyynes. Surprisingly, unlike the aforesaid photoluminescence, compound 3a with methyl substituted periphery exhibited the strongest electrochemiluminescence emission. Finally, 3a was selected as an electrode material to construct an ultrasensitive and stable dopamine sensor, validating the practical potential of those polyynes. This work provided new design concepts for regulating the photoelectric properties of branched polyynes.

A novel Boron-dipyrromethene derivative (BODIPY-3) was synthesized by functionalizing the meso position of the BODIPY core with an imidazole-containing moiety and evaluated as both a spectrophotometric and electrochemical sensor. The spectrophotometric sensing behavior of BODIPY-3 toward Fe²⁺, Fe³⁺, and Cu²⁺ ions was investigated. Fluorescence spectroscopy revealed a highly selective response to Fe²⁺, a moderate affinity for Fe³⁺, and a lower sensitivity toward Cu²⁺. Based on calibration studies, the limits of detection (LOD) and quantification (LOQ) were determined as 28.665 µM and 95.551 µM for Fe²⁺, 30.999 µM and 103.331 µM for Fe³⁺, and 26.216 µM and 87.386 µM for Cu²⁺, respectively, with low relative standard deviation values, indicating good repeatability. For electrochemical sensing, pencil graphite electrodes were modified with polypyrrole and BODIPY-3 (BODIPY-3/PPy/PGE) via cyclic voltammetry. Differential pulse voltammetry enabled sensitive Cu²⁺ detection, yielding LOD and LOQ values of 0.069 ppm and 0.231 ppm, respectively. The applicability of the developed sensor was demonstrated using real water samples, with recovery values ranging from 94.67% to 102.86%. Overall, the results highlight the potential of imidazole-functionalized BODIPY-based systems for selective metal ion sensing, with demonstrated electrochemical sensing of Cu²⁺ and spectrophotometric detection of Fe²⁺, suggesting their applicability in environmental monitoring.

Recent experimental studies suggest that various heterocyclic organic molecules have good anticorrosion properties on mild steel in aqueous HCl medium. However, screening a large number of such molecules for anticorrosion activities is experimentally challenging. To address this challenge, we have applied a Quantum Mechanics based Machine Learning (ML) approach on a set of benzothiazole (BT) derivatives to predict new molecules that might have better corrosion inhibition efficiency. To overcome the small number of BT derivatives with experimentally available data on corrosion inhibition, theoretical calculations using high-level density functional theory and molecular dynamics simulations on structurally similar molecules with various electron-donating and electron-withdrawing groups were performed and added to the dataset. Using data augmentation, we first expand the dataset in order to reduce the biasness of the dataset used in different ML models. Using correlation matrix, we find that few properties such as dipole moment, Molecular surface area, energy of the Frontier molecular orbitals etc. are highly correlated and contribute maximum to the adsorption energy property. We feed these data to three distinct ML models (Tabular Neural Network, Light Gradient Boosting Method, and Random Forest Regressor) and predict new BT derivatives that might have better corrosion inhibition properties without performing experiments.

The insertion reactions of α-diazo esters to sulfonic acids have been efficiently run in flow with visible light irradiation, leading to the practical synthesis of aryl and alkyl sulfonates under mild room temperature without using any catalyst. Besides the sustainable reaction conditions, the current method displays attractiveness for the broad substrate tolerance and practical scale-up synthesis.

Sb-doped p-type tin selenide (SnSe) chalcogenides with different Sb concentrations (x = 0.00, 0.05, 0.10, 0.15, and 0.20) were successfully synthesized using the direct vapor transport method in our earlier work, and their optical, morphological, and structural characteristics were carefully described. Building on these discoveries, the current work conducts additional research to assess their antibacterial and photocatalytic uses. While doping-induced peak shifts and the orthorhombic phase, as proven by X-ray diffraction (XRD), showed the successful integration of Sb, UV-visible spectroscopy had previously shown bandgap tunability (1.18–1.46 eV), which improved absorption of visible light. When Methyl Orange dye degradation was used to test the photocatalytic performance under visible light irradiation, Sb₀.₂₀Sn₀.₈₀Se had the greatest degradation efficiency of 94% in 240 min. Higher Sb doping concentrations improved zones of inhibition, demonstrating notable efficiency against Gram-positive Bacillus subtilis but little activity against Gram-negative Escherichia coli, according to an evaluation of antibacterial activity. These findings prove that Sb-doped SnSe is a bifunctional material with potential applications in microbial control and environmental cleanup driven by visible light. Additionally, the work highlights the wider implications of alloy-engineered SnSe chalcogenides, which are in line with Sustainable Development Goals (SDGs) such as SDGs 3, 6, 12, 13, and 9.

This study successfully synthesized a well-defined Rh-Nd complex and systematically evaluated its urate-lowering and renal protective effects in a rat model of hyperuricemia with renal injury. The results demonstrated that Rh-Nd complex not only significantly reduced serum uric acid levels and improved renal function indicators but also exhibited superior activity compared to rhein alone, demonstrating a synergistic enhancement of rhein's efficacy by the rare-earth element neodymium. Mechanistic studies revealed that Rh-Nd complex markedly promoted intestinal uric acid excretion by upregulating ABCG2 protein expression in the jejunum and ileum and downregulating GLUT9 protein expression in the ileum and colon, thereby exerting its extra-renal uric acid clearance effect. Molecular docking further elucidated the high-affinity binding mode of Rh-Nd with ABCG2 and GLUT9. This study provides an important material basis and theoretical foundation for the development of efficient and low-toxicity intestinal uric acid excretion agents.

Oxidative stress and hyperactivation of the renin‒angiotensin‒aldosterone system (RAAS) accelerates the progression of chronic kidney disease (CKD), a global health burden. Angiotensin-converting enzyme (ACE) is a key therapeutic target for CKD. This study uses a computational approach to evaluate Nypa fruticans (NF) as a natural ACE inhibitor. HPLC and GC‒MS analysis identified 36 compounds in NF extracts, with quercetin, rosmarinic acid, and caffeic acid as key bioactive compounds. Pharmacokinetic and toxicity evaluations via SwissADME, pkCSM, and ProTox 3.0, confirmed their drug-likeness and safety profiles. Molecular docking studies revealed that quercetin and rosmarinic acid exhibited binding affinities of −8.28 and −8.95 kcal/mol with ACE (PDB: 1O86), respectively, outperforming the control, enalapril (−8.1 kcal/mol). Density functional theory (DFT) analysis revealed that rosmarinic acid (0.1342 eV) presented an energy gap similar to that of enalapril (0.1488 eV). In contrast, quercetin presented greater electrophilicity (0.2166 vs. 0.0853 eV), indicating a stronger electron-accepting potential. Additionally, 100-ns molecular dynamics simulations and MM-GBSA analysis confirmed the stable binding of quercetin and rosmarinic acid, supporting their potential as natural antihypertensive agents for CKD management. This study demonstrates the utility of computational drug discovery in identifying plant-derived candidates for cardiovascular and renal therapy, warranting further mechanistic validation.

Cs₂AgBiBr₆ is a lead-free double perovskite material with a 3D crystal structure, characterized by a long carrier lifetime, low effective mass, excellent thermal stability, and minimal toxicity. Despite these advantages, Cs₂AgBiBr₆-based solar cells still face significant challenges, primarily due to their wide indirect bandgap, which limits power conversion efficiency. In this study, the performance of double perovskite solar cells (DPSCs) based on a Cs₂AgBiBr₆ absorber was systematically investigated using the 1D Solar Cell Capacitance Simulator (SCAPS-1D). Tin(IV) oxide (SnO₂) and nickel oxide (NiO) were employed as the electron transport layer (ETL) and hole transport layer (HTL), respectively. The effects of key device parameters including absorber layer thickness, defect density, doping concentration, operating temperature, and parasitic resistances were thoroughly analyzed. After optimization, the proposed device achieved excellent photovoltaic performance, with a power conversion efficiency (PCE) of 23.29%, a short-circuit current density (Jsc) of 22.03 mA/cm², a fill factor (FF) of 86.31%, and an open-circuit voltage (Voc) of 1.23 V. These results provide valuable insights and suggest promising strategies for the development of efficient, environmentally friendly, and stable Cs₂AgBiBr₆-based perovskite solar cells.

The integration of machine learning (ML) with density functional theory (DFT) is transforming the landscape of catalysis research, offering new avenues for the design and optimization of catalytic materials. This review explores the application of ML techniques in both nanoscale and macroscale catalyst design, highlighting how these methods predict catalyst performance, optimize reaction conditions, and accelerate high-throughput screening. This review discusses the role of DFT in providing detailed insights into electronic structures and reaction mechanisms, which, when combined with ML, enhance the efficiency and precision of catalyst development. Case studies, including methanation reactions and selective catalytic reduction, illustrate the successful implementation of ML-driven models in real-world applications. Furthermore, the challenges and limitations of this combined approach were addressed, such as data quality, descriptor selection, model interpretability, and experimental validation. This review underscores the potential of ML and DFT to advance energy and environmental catalysis, while also identifying key areas for future research.

A disposable pencil graphite electrode (PGE) was coated with a poly-amino-1,3,4-thiadiazole-2-thiol (p-AMT) film generated by the electropolymerization of AMT using cyclic voltammetry (CV). This polymeric layer was subsequently used as a platform for anchoring thiolated HBV-DNA probes through thiol–disulfide interactions. The aim of the study was to develop a highly sensitive, selective, and repeatable biosensor for DNA hybridization. Surface alterations following the electrode modification were examined via scanning electron microscopy, confirming successful film formation. Immobilization efficiency on the p-AMT modified electrode was compared to that on an unmodified PGE, revealing approximately a 2.4-fold enhancement in the adenine oxidation response. The sensor achieved a detection limit of 56.5 nM (351 ppb). Selectivity was evaluated by comparing responses to the complementary target, a single-base mismatched sequence, and a non-complementary sequence. The findings confirmed the strong preference of the sensor toward the fully complementary DNA strand.