Application of a Multilayer Perceptron Artificial Neural Network for the Prediction and Optimization of the Andrographolide Content in Andrographis paniculata

Andrographolide, the principal secondary metabolite of Andrographis paniculata, displays a wide spectrum of medicinal activities. The content of andrographolide varies significantly in the species collected from different geographical regions. Therefore, this study aims at investigating the role of different abiotic factors and selecting suitable sites for the cultivation of A. paniculata with high andrographolide content using a multilayer perceptron artificial neural network (MLP-ANN) approach. A total of 150 accessions of A. paniculata collected from different regions of Odisha and West Bengal in eastern India showed a variation in andrographolide content in the range of 0.28–5.45% on a dry weight basis. The MLP-ANN was trained using climatic factors and soil nutrients as the input layer and the andrographolide content as the output layer. The best topological ANN architecture, consisting of 14 input neurons, 12 hidden neurons, and 1 output neuron, could predict the andrographolide content with 90% accuracy. The developed ANN model showed good predictive performance with a correlation coefficient (R²) of 0.9716 and a root-mean-square error (RMSE) of 0.18. The global sensitivity analysis revealed nitrogen followed by phosphorus and potassium as the predominant input variables influencing the andrographolide content. The andrographolide content could be increased from 3.38% to 4.90% by optimizing these sensitive factors. The result showed that the ANN approach is reliable for the prediction of suitable sites for the optimum andrographolide yield in A. paniculata.     

Volatile Profiling of Magnolia champaca Accessions by Gas Chromatography Mass Spectrometry Coupled with Chemometrics

Magnolia champaca (L.) Baill. ex Pierre of family Magnoliaceae, is a perennial tree with aromatic, ethnobotanical, and medicinal uses. The M. champaca leaf is reported to have a myriad of therapeutic activities, however, there are limited reports available on the chemical composition of the leaf essential oil of M. champaca. The present study explored the variation in the yield and chemical composition of leaf essential oil isolated from 52 accessions of M. champaca. Through hydrodistillation, essential oil yield was obtained, varied in the range of 0.06 ± 0.003% and 0.31 ± 0.015% (v/w) on a fresh weight basis. GC-MS analysis identified a total of 65 phytoconstituents accounting for 90.23 to 98.90% of the total oil. Sesquiterpene hydrocarbons (52.83 to 65.63%) constituted the major fraction followed by sesquiterpene alcohols (14.71 to 22.45%). The essential oils were found to be rich in β-elemene (6.64 to 38.80%), γ-muurolene (4.63 to 22.50%), and β-caryophyllene (1.10 to 20.74%). Chemometrics analyses such as PCA, PLS-DA, sPLS-DA, and cluster analyses such as hierarchical clustering, i.e., dendrogram and partitional clustering, i.e., K-means classified the essential oils of M. champaca populations into three different chemotypes: chemotype I (β-elemene), chemotype II (γ-muurolene) and chemotype III (β-caryophyllene). The chemical polymorphism analyzed in the studied populations would facilitate the selection of chemotypes with specific compounds. The chemotypes identified in the M. champaca populations could be developed as promising bio-resources for conservation and pharmaceutical application and further improvement of the taxa.     

Laser-Patterned Carbon-Supported Graphitic Carbon Nitride Quantum Dots for Flexible Nanozyme-Based Fluoride Sensor

Graphitic carbon nitride quantum dots (g-CNQDs)-based colorimetric sensors are typically solution-based and hence incompatible with wearable electronics. Today's competitive technology demands safe and reliable, high-performance sensors suitable for integration with sophisticated electronics—all at a low cost. Herein, a flexible and reusable solid-state fluoride ion sensor manufactured by combining the intriguing surface properties of laser-patterned carbon (LP-C) with the sensitivity of g-CNQDs is reported. LP-C is obtained by direct IR-laser writing onto polyimide films, and g-CNQDs are synthesized via a solvent-free and zero-waste green process. The hybrid of LP-C and g-CNQDs (g-CNQDs/LP-C), mimics the natural enzyme horseradish peroxidase and oxidizes the chromogenic substrate 3,3’,5,5’-tetramethylbenzidine in the presence of H₂O₂ in acidic media. The highly selective and user-friendly nanozyme sensors feature a lower limit of detection of 0.568 ± 0.006  × 10⁻⁶ m (23.8 ± 1.5 µg L⁻¹) with linearity in the range of 0.5 × 10⁻⁶ to 100  × 10⁻⁶ m . A sensing mechanism based on the electronic transitions of g-CNQDs and LP-C, the two variants of nitrogen-containing carbon used in this work, is established. Finally, the device is tested for fluoride ion sensing in natural water samples collected from the Uhl river in Mandi, India.     

A Zwitterionic Tetrastanna(II) Cyclic Crown

A 12-membered zwitterionic tetrastanna(II) cycle 1 having a crown ether-like topology has been isolated from the deprotonation of 1,1′-methylenediimidazole ( B ) with two equivalents of Sn[N(SiMe₃)₂]₂ ( A ). The solid-state structure and NMR analysis confirms the tetrastanna(II) cycle 1 to be comprised of two stannate(II) and two stannyliumylidene ion pairs in alternating positions of the heterocycle. Computational analysis shows greater nucleophilicity at the proximally located stannate(II) centers. Nonetheless, the tetrastanna(II) cycle 1 remains poorly reactive due to engagement of Sn^II lone pair electrons in intramolecular donor-acceptor interactions. Simple deprotonation reaction between Sn[N(SiMe₃)₂]₂ ( A ) and N-(diisopropylphenyl)imidazole ( C ) in equimolar ratio has led to a stannylene 2 , involving the formation of a Sn−C covalent bond with the anionic imidazol-2-yl carbon center along with the release of NH(SiMe₃)₂. Compound 2 exists as a dimer, where the unsubstituted ring nitrogen atom coordinated intermolecularly to the other stannylene center.     

A Luminescent Zinc(II) Coordination Polymer for Selective Detection of Fe3+ and Cr2O72− in Water and Catalytic CO2 Fixation

Fluorescence-based detection technique using coordination polymer has been considered an attractive alternative over conventional approaches. Herein, a new luminescent zinc(II) coordination polymer, [Zn(4-ABPT)(NIPA)(H₂O)], SSICG-5 , is synthesized by using a Lewis acidic Zn(II) ion, aromatic nitro group containing ligand 5-nitroisophthalic acid (H₂NIPA), and basic −NH₂ rich ligand 3,5-di(pyridine-4-yl)-4H-1,2,4-triazol-4-amine (4-ABPT). SSICG-5 can detect Fe³⁺ and Cr₂O₇²⁻ selectively with a LOD of 0.16 μM and 1.94 μM, respectively. Additionally, carbon dioxide (CO₂) fixation via one-pot CO₂ cycloaddition reaction has significant importance for reduced waste formation, minimizing reaction time and lowering chemical usage. Zn metal centre of SSICG-5 possesses a replaceable coordinated water molecule. The active metal sites combined with the Lewis acidic and basic sites of the ligands make SSICG-5 an ideal bifunctional heterogeneous catalyst for efficient CO₂ cycloaddition reaction under room temperature (RT), solvent-free conditions. Notably, SSICG-5 exhibits near quantitative conversion (turnover number (TON) of 198) of propylene oxide to its carbonate compound under mild reaction conditions.     

Facile soft solution synthesis of delafossite structured Cu(Cr1-xFex)O2 (x = 0, 0.5, and 1) materials and their supercapacitor application

Journal of Solid State Electrochemistry - The investigation of metal oxides with delafossite-type crystal structure for electrochemical energy storage applications is an emerging area....     

Realization of Z2 Topological Metal in Single-Crystalline Nickel Deficient NiV2Se4

Temperature-dependent electronic and magnetic properties are reported for nickel-deficient NiV₂Se₄. Single-crystal X-ray diffraction shows it to crystallize in the monoclinic Cr₃S₄ structure type with space group $I2/m$ and vacancies on the Ni site, resulting in the composition Ni_0.85V₂Se₄ in agreement with our electron-probe microanalysis. Structural distortions are not observed down to 1.5 K. Nevertheless, the electrical resistivity shows metallic behavior with a broad anomaly around 150–200 K that is also observed in the heat capacity data. This anomaly indicates a change of state of the material below 150 K. It is believed that this anomaly could be due to spin fluctuations or charge-density-wave fluctuations, where the lack of long-range order is caused by vacancies at the Ni site of Ni_0.85V₂Se₄. The non-linear temperature dependence of the resistivity as well as an enhanced value of the Sommerfeld coefficient $\gamma =104.0(1)$ mJ mol⁻¹ K⁻² suggest strong electron–electron correlations in this material. First-principles calculations performed for NiV₂Se₄, which are also applicable to Ni_0.85V₂Se₄, classify this material as a topological metal with $Z_2=(1;110)$ and coexisting electron and hole pockets at the Fermi level. The phonon spectrum lacks any soft phonon mode, consistent with the absence of periodic lattice distortion in the present experiments.     

Compact stars admitting Finch-Skea symmetry in the presence of various matter fields

In the present study, we investigate the anisotropic stellar solutions admitting Finch-Skea symmetry (viable and non-singular metric potentials) in the presence of some exotic matter fields, such as Bose-Einstein Condensate (BEC) dark matter, the Kalb-Ramond fully anisotropic rank-2 tensor field from the low-energy string theory effective action, and the gauge field imposing \begin{document}$ U(1) $\end{document} symmetry. Interior spacetime is matched with both Schwarzchild and Reissner-N?rdstrom vacuum spacetimes for BEC, KB, and gauge fields. In addition, we study the energy conditions, Equation of State (EoS), radial derivatives of energy density and anisotropic pressures, Tolman-Oppenheimer-Volkoff equilibrium condition, relativistic adiabatic index, sound speed, and surface redshift. Most of the aforementioned conditions are satisfied. Therefore, the solutions derived in the current study lie in the physically acceptable regime.     

Three Component syn-1,2-Arylmethylation of Internal Alkynes**

A Pd-catalyzed three-component syn-1,2-arylmethylation of internal alkynes (ynamides/yne-acetates/alkynes) is described. The readily available and bench stable coupling partners iodo-arenes, and methyl boronic acid are successfully used in this coupling strategy to access the methyl-containing tetra-substituted olefins; the scope is broad showing excellent functional-group tolerance. Notably, the transformation is regio- as well as stereoselective. The biologically relevant motifs (BRM) bearing iodo-arenes and ynamides are also used for the late-stage syn-1,2-arylmethylation of alkynes. Aryl-alkylation, aryl-trideuteriomethylation, alkynyl-methylation, and alkenyl-methylation of ynamides are also presented. The Me-substituted alkenes are further transformed into synthetically important β-amino-indenones and α-fluoro-α′-methyl ketones.     

Harnessing Deep Neural Networks to Analyze Multi-Channel Anion Sensing Characteristics of a Ru(II)-Pyrazolyl-Bis(Benzimidazole) Complex

In this work, the anion-responsive conduct of a Ru(II)-bipyridine complex incorporating pyrazolyl-bis (benzimidazole) ligand is thoroughly investigated in acetonitrile and water via absorption and emission spectroscopy as well as by square-wave voltammetry (SWV). Substantial alteration of the photo-redox behavior of the complex is observed in the presence of the selected anions. The free form of the complex exhibits emission indicating the “on-state”, while inclusion of anions leads to quenching of emission and represents the “off-state”. The restoration of the initial state of the complex is feasible in the presence of acid and the process is reversible and can be recycled. In essence, the complex functions as anion- and acid-responsive molecular switches. Additionally, we applied herein neural network based deep learning methodologies, viz. Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS)} for thorough analysis and fully understand the multi-channel anion sensing behavior of the complex.