2D MXene-Based Nanoscale Materials for Electrochemical Sensing Toward the Detection of Hazardous Pollutants: A Perspective.

MXenes (Mn+1XnTx), a subgroup of 2-dimensional (2D) materials, specifically comprise transition metal carbides, nitrides, and carbonitrides. They exhibit exceptional electrocatalytic and photocatalytic properties, making them well-suited for the detection and removal of pollutants from aqueous environments. Because of their high surface area and remarkable properties, they are being utilized in various applications, including catalysis, sensing, and adsorption, to combat pollution and mitigate its adverse effects. Different characterization techniques like XRD, SEM, TEM, UV-Visible spectroscopy, and Raman spectroscopy have been used for the structural elucidation of 2D MXene. Current responses against applied potential were measured during the electrochemical sensing of the hazardous pollutants in an aqueous system using a variety of electroanalytical techniques, including differential pulse voltammetry, amperometry, square wave anodic stripping voltammetry, etc. In this review, a comprehensive discussion on structural patterns, synthesis, properties of MXene and their application for electrochemical detection of lethal pollutants like hydroquionone, phenol, catechol, mercury and lead, etc. are presented. This review will be helpful to critically understand the methods of synthesis and application of MXenes for the removal of environmental pollutants.

Revealing the full potential of CsPbIBr2 perovskite solar cells: advancements towards enhanced performance.

Cesium lead iodide bromide (CsPbIBr2) perovskite solar cells (PSCs) have improved stability compared to other perovskite compositions. However, they still face significant challenges due to their poor photovoltaic performance parameters, which limit the devices' power conversion efficiencies (PCEs). This study proposes a novel device design to tailor the potential of CsPbIBr2 PSCs by improving their optoelectronic properties. An advanced 3D multiphysics approach was rigorously used to investigate the optics and electrical properties of the proposed CsPbIBr2 PSCs. This approach combines finite-difference time-domain (FDTD) and finite element method (FEM) techniques with the particle swarm optimization (PSO) algorithm. The outcome from the adapted numerical approach is in good agreement with the experimental results. The optimized CsPbIBr2 PSC demonstrates a promising power conversion efficiency (PCE) of over 16.4%, associated VOC of 1.53 V, FF of 80.6%, and JSC of 13.4 mA cm-2. Therefore, the potential of CsPbIBr2 perovskites could be further explored with continued research and development in material science and device physics.

Adaptation of complex interventions for people with long-term conditions: a scoping review.

Adaptation seeks to transfer and implement healthcare interventions developed and evaluated in one context to another. The aim of this scoping review was to understand current approaches to the adaptation of complex interventions for people with long-term conditions (LTCs) and to identify issues for studies performed in low- and middle-income countries (LMICs). Bibliographic databases were searched from 2000 to October 2022. This review involved five stages: (i) definition of the research question(s); (ii) identifying relevant studies; (iii) study selection; (iv) data charting; and (v) data synthesis. Extraction included an assessment of the: rationale for adaptation; stages and levels of adaptation; use of theoretical frameworks, and quality of reporting using a checklist based on the 2021 ADAPT guidance. Twenty-five studies were included from across 21 LTCs and a range of complex interventions. The majority (16 studies) focused on macro (national or international) level interventions. The rationale for adaptation included intervention transfer across geographical settings [high-income country (HIC) to LMIC: six studies, one HIC to another: eight studies, one LMIC to another: two studies], or transfer across socio-economic/racial groups (five studies), or transfer between different health settings within a single country (one study). Overall, studies were judged to be of moderate reporting quality (median score 23, maximum 46), and typically focused on early stages of adaptation (identification and development) with limited outcome evaluation or implementation assessment of the adapted version of the intervention. Improved reporting of the adaptation for complex interventions targeted at LTCs is needed. Development of future adaptation methods guidance needs to consider the needs and priorities of the LMIC context.

Limited finance and human capacity may reduce access to new treatments for people with long-term conditions. This is especially true in low- and middle-income countries. One solution is to transfer treatments developed in one place for use in other areas. This paper provides a current summary of international research on adapting treatments. We used a checklist to assess study reporting quality, based on published advice. Our findings showed the need for better conduct and reporting of adaptation. Future guidance should consider the specific needs of low- and middle-income countries.

Biocompatible Gold Nanoparticles-Modified Fluorine Doped Tin Oxide Electrode for the Fabrication of Enzyme-Free Glucose Sensor.

This work demonstrates the use of jute stick extract as a reducing and stabilizing agent for the synthesis of spherical gold nanoparticles (AuNPs). In UV-Vis spectroscopy, peak at 550 nm was used to confirm the formation of AuNPs. The spherical surface morphology of AuNPs was determined through SEM and TEM analysis. While XRD investigation revealed the crystallinity of the prepared AuNPs. To ensure the biocompatibility of synthesized AuNPs, a bacterial investigation was conducted with negative results towards bacterial strain. The, modified FTO with AuNPs were able to detect glucose in CV analysis and the constructed sensor displayed a wide linear range of 50 µM to 40 mM with a detection limit of 20 µM. Scan rate analysis was performed to determine the charge transfer coefficient (0.42) and Tafel slope (102 mV/decade). Furthermore, the interfacial surface mechanism is illustrated to understand the interaction of glucose with the electrode surface in an alkaline medium and the product formation through the dehydrogenation and hydrolysis process. The prepared sensor also showed good stability, reproducibility, and anti-interference capabilities. In the case of real sample analysis, we used a blood serum sample. A low RSD value (<10 %) suggests the practical use of AuNPs/FTO in real-life applications.

AIPs-SnTCN: Predicting Anti-Inflammatory Peptides Using fastText and Transformer Encoder-Based Hybrid Word Embedding with Self-Normalized Temporal Convolutional Networks.

Inflammation is a biologically resistant response to harmful stimuli, such as infection, damaged cells, toxic chemicals, or tissue injuries. Its purpose is to eradicate pathogenic micro-organisms or irritants and facilitate tissue repair. Prolonged inflammation can result in chronic inflammatory diseases. However, wet-laboratory-based treatments are costly and time-consuming and may have adverse side effects on normal cells. In the past decade, peptide therapeutics have gained significant attention due to their high specificity in targeting affected cells without affecting healthy cells. Motivated by the significance of peptide-based therapies, we developed a highly discriminative prediction model called AIPs-SnTCN to predict anti-inflammatory peptides accurately. The peptide samples are encoded using word embedding techniques such as skip-gram and attention-based bidirectional encoder representation using a transformer (BERT). The conjoint triad feature (CTF) also collects structure-based cluster profile features. The fused vector of word embedding and sequential features is formed to compensate for the limitations of single encoding methods. Support vector machine-based recursive feature elimination (SVM-RFE) is applied to choose the ranking-based optimal space. The optimized feature space is trained by using an improved self-normalized temporal convolutional network (SnTCN). The AIPs-SnTCN model achieved a predictive accuracy of 95.86% and an AUC of 0.97 by using training samples. In the case of the alternate training data set, our model obtained an accuracy of 92.04% and an AUC of 0.96. The proposed AIPs-SnTCN model outperformed existing models with an ∼19% higher accuracy and an ∼14% higher AUC value. The reliability and efficacy of our AIPs-SnTCN model make it a valuable tool for scientists and may play a beneficial role in pharmaceutical design and research academia.

Indocyanine Green (ICG) Fluorescence Is Dependent on Monomer with Planar and Twisted Structures and Inhibited by H-Aggregation.

The optical properties of indocyanine green (ICG) as a near-infrared (NIR) fluorescence dye depend on the nature of the solvent medium and the dye concentration. In the ICG absorption spectra of water, at high concentrations, there were absorption maxima at 700 nm, implying H-aggregates. With ICG dilution, the main absorption peak was at 780 nm, implying monomers. However, in ethanol, the absorption maximum was 780 nm, and the shapes of the absorption spectra were identical regardless of the ICG concentration, indicating that ICG in ethanol exists only as a monomer without H-aggregates. We found that emission was due to the monomer form and decreased with H-aggregate formation. In the fluorescence spectra, the 820 nm emission band was dominant at low concentrations, whereas at high concentrations, we found that the emission peaks were converted to 880 nm, suggesting a new form via the twisted intramolecular charge transfer (TICT) process of ICG. The NIR fluorescence intensity of ICG in ethanol was approximately 12- and 9-times brighter than in water in the NIR-I and -II regions, respectively. We propose an energy diagram of ICG to describe absorptive and emissive transitions through the ICG structures such as the monomer, H-aggregated, and TICT monomer forms.

A Huffman code LSB based image steganography technique using multi-level encryption and achromatic component of an image.

In the recent couple of years, due to the accelerated popularity of the internet, various organizations such as government offices, military, private companies, etc. use different transferring methods for exchanging their information. The Internet has various benefits and some demerits, but the primary bad mark is security of information transmission over an unreliable network, and widely uses of images. So, Steganography is the state of the art of implanting a message in the cover objects, that nobody can suspect or identify it. Therefore, in the field of cover steganography, it is very critical to track down a mechanism for concealing data by utilizing different blends of compression strategies. Amplifying the payload limit, and robustness, and working on the visual quality are the vital factors of this research to make a reliable mechanism. Different cover steganography research strategies have been recommended, and each adores its benefits and impediments but there is a need to foster some better cover steganography implements to accomplish dependability between the essential model of cover steganography. To handle these issues, in this paper we proposed a method in view of Huffman code, Least Significant Bits (LSB) based cover steganography utilizing Multi-Level Encryption (MLE) and colorless part (HC-LSBIS-MLE-AC) of the picture. It also used different substitution and flicking concepts, MLE, Magic matrix, and achromatic concepts for proving the proficiency, and significance of the method. The algorithm was also statistically investigated based on some Statistical Assessment Metrics (SAM) such as Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR), Normalized Cross Correlation (NCC), Structural Similarity Index Metric (SSIM), etc. and different perspectives. The observational outcomes show the likelihood of the proposed algorithm and the capacity to give unwavering quality between security, payload, perception, computation, and temper protection.

Synthesis of a novel hydrazone-based compound applied as a fluorescence turn-on chemosensor for iron(iii) and a colorimetric sensor for copper(ii) with antimicrobial, DFT and molecular docking studies.

Hydrazone-hydrazide-based linkers perform a crucial role in environmental as well as biological fields. Such linkers are employed to detect exact metal ions at a minute level; hence, numerous probes are available. Even though thiophene-based molecules have a unique position in the medicinal arena, only very few chemosensors are reported based on such a moiety. In this current work, a novel hydrazide-hydrazone-based fluorogenic molecule 5-bromo-2-hydroxy-N'-[(1E)-1-(thiophen-2-yl)ethylidene]benzohydrazide (L) has been successfully designed and synthesized. The sensing studies of L demonstrated a ratio metric as well as turn-on-enhanced fluorescence and colorimetric response toward Fe3+ and Cu2+ ions, respectively and it was observed to be insensitive toward various metal ions. The Job plots revealed that the binding stoichiometry of L and metal ions is 2 : 1. In addition, density functional theory (DFT) results strongly suggested that L can be used as a powerful colorimetric sensor for the detection of Cu2+ ions. In vitro antimicrobial activities of L were evaluated by disk diffusion and results revealed good antibacterial activities against E. coli. Further, molecular docking was executed with DNA gyrase (PDB ID: 1KZN) of E. coli and the calculated interaction energy value was found to be -7.7 kcal mol-1. Finally, molecular docking, fluorescence, colorimetry and the HOMO-LUMO energy gap of the compound can provide new insights into developing drugs and detecting metals in biomolecules.

Interaction of mercury species with proteins: towards possible mechanism of mercurial toxicology.

The nature of the binding of mercurials (organic and inorganic) and their subsequent transformations in biological systems is a matter of great debate as several different hypotheses have been proposed and none of them has been conclusively proven to explain the characteristics of Hg binding with the proteins. Thus, the chemical nature of Hg-protein binding through the possible transportation mechanism in living tissues is critically reviewed herein. Emphasis is given to the process of transportation, and binding of Hg species with selenol-containing biomolecules that are appealing for toxicological studies as well as the advancement of environmental and biological research.

Rhamnopyranoside-Based Fatty Acid Esters as Antimicrobials: Synthesis, Spectral Characterization, PASS, Antimicrobial, and Molecular Docking Studies.

The most widely used and accessible monosaccharides have a number of stereogenic centers that have been hydroxylated and are challenging to chemically separate. As a result, the task of regioselective derivatization of such structures is particularly difficult. Considering this fact and to get novel rhamnopyranoside-based esters, DMAP-catalyzed di-O-stearoylation of methyl α-l-rhamnopyranoside (3) produced a mixture of 2,3-di-O- (4) and 3,4-di-O-stearates (5) (ratio 2:3) indicating the reactivity of the hydroxylated stereogenic centers of rhamnopyranoside as 3-OH > 4-OH > 2-OH. To get novel biologically active rhamnose esters, di-O-stearates 4 and 5 were converted into six 4-O- and 2-O-esters 6-11, which were fully characterized by FT-IR, 1H, and 13C NMR spectral techniques. In vitro antimicrobial assays revealed that fully esterified rhamnopyranosides 6-11 with maximum lipophilic character showed better antifungal susceptibility than antibacterial activity. These experimental findings are similar to the results found from PASS analysis data. Furthermore, the pentanoyl derivative of 2,3-di-O-stearate (compound 6) showed better antifungal functionality against F. equiseti and A. flavus, which were found to be better than standard antibiotics. To validate the better antifungal results, molecular docking of the rhamnose esters 4-11 was performed with lanosterol 14α-demethylase (PDB ID: 3LD6), including the standard antifungal antibiotics ketoconazole and fluconazole. In this instance, the binding affinities of 10 (-7.6 kcal/mol), 9 (-7.5 kcal/mol), and 7 (-6.9 kcal/mol) were better and comparable to fluconazole (-7.3 kcal/mol), indicating the likelihood of their use as non-azole type antifungal drugs in the future.

AFP-SPTS: An Accurate Prediction of Antifreeze Proteins Using Sequential and Pseudo-Tri-Slicing Evolutionary Features with an Extremely Randomized Tree.

The development of intracellular ice in the bodies of cold-blooded living organisms may cause them to die. These species yield antifreeze proteins (AFPs) to live in subzero temperature environments. Additionally, AFPs are implemented in biotechnological, industrial, agricultural, and medical fields. Machine learning-based predictors were presented for AFP identification. However, more accurate predictors are still highly desirable for boosting the AFP prediction. This work presents a novel approach, named AFP-SPTS, for the correct prediction of AFPs. We explored the discriminative features with four schemes, namely, dipeptide deviation from the expected mean (DDE), reduced amino acid alphabet (RAAA), grouped dipeptide composition (GDPC), and a novel representative method, called pseudo-position-specific scoring matrix tri-slicing (PseTS-PSSM). Considering the advantages of ensemble learning strategy, we fused each feature vector into different combinations and trained the models with five machine learning algorithms, i.e., multilayer perceptron (MLP), extremely randomized tree (ERT), decision tree (DT), random forest (RF), and AdaBoost. Among all models, PseTS-PSSM + RAAA with an extremely randomized tree attained the best outcomes. The proposed predictor (AFP-SPTS) boosted the accuracies of AFPs in the literature by 1.82 and 4.1%.

Copper oxide nanostructured thin films processed by SILAR for optoelectronic applications.

The lack of high-functioning p-type semiconductor oxide material is one of the critical challenges that face the widespread performance of transparent and flexible electronics. Cu x O nanostructured thin films are potentially appealing materials for such applications because of their innate p-type semi-conductivity, transparency, non-toxicity, abundant availability, and low-cost fabrication. This review summarizes current research on Cu x O nanostructured thin films deposited by the SILAR technique. After a brief introduction to the advantages of Cu x O semiconductor material, diverse approaches for depositing and growing such thin films are discussed. SILAR is one of the simplest deposition techniques in terms of better flexibility of the substrate choice, the capability of large-area fabrication, budget-friendly, deposition of stable and adherent film, low processing temperature for the film fabrication as well as reproducibility. In addition, various fabrication parameters such as types of copper salts, pH of precursors, number of cycles during immersion, annealing of as-deposited films, doping by diverse dopants, and growth temperature affect the rate of fabrication with the structural, electrical, and optical properties of Cu x O nanostructured thin films, which led the technique unique to study extensively. This review will include the recent progress that has recently been made in different aspects of Cu x O processed by the SILAR. It will describe the theory, mechanism, and factors affecting SILAR-deposited Cu x O. Finally, conclusions and perspectives concerning the use of Cu x O materials in optoelectronic devices will be visualized.

Prediction of antifreeze proteins using machine learning.

Living organisms including fishes, microbes, and animals can live in extremely cold weather. To stay alive in cold environments, these species generate antifreeze proteins (AFPs), also referred to as ice-binding proteins. Moreover, AFPs are extensively utilized in many important fields including medical, agricultural, industrial, and biotechnological. Several predictors were constructed to identify AFPs. However, due to the sequence and structural heterogeneity of AFPs, correct identification is still a challenging task. It is highly desirable to develop a more promising predictor. In this research, a novel computational method, named AFP-LXGB has been proposed for prediction of AFPs more precisely. The information is explored by Dipeptide Composition (DPC), Grouped Amino Acid Composition (GAAC), Position Specific Scoring Matrix-Segmentation-Autocorrelation Transformation (Sg-PSSM-ACT), and Pseudo Position Specific Scoring Matrix Tri-Slicing (PseTS-PSSM). Keeping the benefits of ensemble learning, these feature sets are concatenated into different combinations. The best feature set is selected by Extremely Randomized Tree-Recursive Feature Elimination (ERT-RFE). The models are trained by Light eXtreme Gradient Boosting (LXGB), Random Forest (RF), and Extremely Randomized Tree (ERT). Among classifiers, LXGB has obtained the best prediction results. The novel method (AFP-LXGB) improved the accuracies by 3.70% and 4.09% than the best methods. These results verified that AFP-LXGB can predict AFPs more accurately and can participate in a significant role in medical, agricultural, industrial, and biotechnological fields.

Development of Stable Liposomal Drug Delivery System of Thymoquinone and Its In Vitro Anticancer Studies Using Breast Cancer and Cervical Cancer Cell Lines.

Thymoquinone, a well-known phytoconstituent derived from the seeds of Nigella sativa, exhibits unique pharmacological activities However, despite the various medicinal properties of thymoquinone, its administration in vivo remains challenging due to poor aqueous solubility, bioavailability, and stability. Therefore, an advanced drugdelivery system is required to improve the therapeutic outcome of thymoquinone by enhancing its solubility and stability in biological systems. Therefore, this study is mainly focused on preparing thymoquinone-loaded liposomes to improve its physicochemical stability in gastric media and its performance in different cancer cell line studies. Liposomes were prepared using phospholipid extracted from egg yolk. The liposomal nano preparations were evaluated in terms of hydrodynamic diameter, zeta potential, microscopic analysis, and entrapment efficiency. Cell-viability measurements were conducted using breast and cervical cancer cell lines. Optimized liposomal preparation exhibited polygonal, globule-like shape with a hydrodynamic diameter of less than 260 nm, PDI of 0.6, and zeta potential values of -23.0 mV. Solid-state characterizations performed using DSC and XRPD showed that the freeze-dried liposomal preparations were amorphous in nature. Gastric pH stability data showed no physical changes (precipitation, degradation) or significant growth in the average size of blank and thymoquinone-loaded liposomes after 24 h. Cell line studies exhibited better performance for thymoquinone-loaded liposomal drug delivery system compared with the thymoquinone-only solution; this finding can play a critical role in improving breast and cervical cancer treatment management.

Transmission of intelligence, working memory, and processing speed from parents to their seven-year-old offspring is function specific in families with schizophrenia or bipolar disorder.

BACKGROUND: Prior studies have shown high heritability estimates regarding within-function transmission of neurocognition, both in healthy families and in families with schizophrenia but it remains an open question whether transmission from parents to offspring is function specific and whether the pattern is the same in healthy families and families with schizophrenia or bipolar disorder. We aimed to characterize the transmission of intelligence, processing speed, and verbal working memory functions from both biological parents to their 7-year-old offspring in families with parental schizophrenia, bipolar disorder, and population-based control parents. METHODS: The population-based cohort consists of 7-year-old children with one parent diagnosed with schizophrenia (n = 186), bipolar disorder (n = 114), and of parents without schizophrenia or bipolar disorder (n = 192). Children and both parents were assessed using identical, age-relevant neurocognitive tests of intelligence, verbal working memory, and processing speed. RESULTS: In multiple regression analyses children's intelligence, verbal working memory, and processing speed scores were significantly associated with the corresponding parental cognitive function score. All associations from parents to offspring across functions were non-significant. No significant parental cognitive function by group interaction was observed. CONCLUSION: Transmissions of intelligence, processing speed, and verbal working memory from parents to offspring are function specific. The structure of transmission is comparable between families with schizophrenia, families with bipolar disorder and families without these disorders.

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.

Rough set based information theoretic approach for clustering uncertain categorical data.

MOTIVATION: Many real applications such as businesses and health generate large categorical datasets with uncertainty. A fundamental task is to efficiently discover hidden and non-trivial patterns from such large uncertain categorical datasets. Since the exact value of an attribute is often unknown in uncertain categorical datasets, conventional clustering analysis algorithms do not provide a suitable means for dealing with categorical data, uncertainty, and stability. PROBLEM STATEMENT: The ability of decision making in the presence of vagueness and uncertainty in data can be handled using Rough Set Theory. Though, recent categorical clustering techniques based on Rough Set Theory help but they suffer from low accuracy, high computational complexity, and generalizability especially on data sets where they sometimes fail or hardly select their best clustering attribute. OBJECTIVES: The main objective of this research is to propose a new information theoretic based Rough Purity Approach (RPA). Another objective of this work is to handle the problems of traditional Rough Set Theory based categorical clustering techniques. Hence, the ultimate goal is to cluster uncertain categorical datasets efficiently in terms of the performance, generalizability and computational complexity. METHODS: The RPA takes into consideration information-theoretic attribute purity of the categorical-valued information systems. Several extensive experiments are conducted to evaluate the efficiency of RPA using a real Supplier Base Management (SBM) and six benchmark UCI datasets. The proposed RPA is also compared with several recent categorical data clustering techniques. RESULTS: The experimental results show that RPA outperforms the baseline algorithms. The significant percentage improvement with respect to time (66.70%), iterations (83.13%), purity (10.53%), entropy (14%), and accuracy (12.15%) as well as Rough Accuracy of clusters show that RPA is suitable for practical usage. CONCLUSION: We conclude that as compared to other techniques, the attribute purity of categorical-valued information systems can better cluster the data. Hence, RPA technique can be recommended for large scale clustering in multiple domains and its performance can be enhanced for further research.

Energy Harvesting by Mesoporous Reduced Graphene Oxide Enhanced the Mediator-Free Glucose-Powered Enzymatic Biofuel Cell for Biomedical Applications.

Harnessing electrochemical energy in an engineered electrical circuit from biochemical substrates in the human body using biofuel cells is gaining increasing research attention in the current decade due to the wide range of biomedical possibilities it creates for electronic devices. In this report, we describe and characterize the construction of just such an enzymatic biofuel cell (EBFC). It is simple, mediator-free, and glucose-powered, employing only biocompatible materials. A novel feature is the two-dimensional mesoporous thermally reduced graphene oxide (rGO) host electrode. An additionally novelty is that we explored the potential of using biocompatible, low-cost filter paper (FP) instead of carbon paper, a conductive polymer, or gold as support for the host electrode. Using glucose (C6H12O6) and molecular oxygen (O2) as the power-generating fuel, the cell consists of a pair of bioelectrodes incorporating immobilized enzymes, the bioanode modified by rGO-glucose oxidase (GOx/rGO), and the biocathode modified by rGO-laccase (Lac/rGO). Scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM/EDX), transmission electron microscopy, and Raman spectroscopy techniques have been employed to investigate the surface morphology, defects, and chemical structure of rGO, GOx/rGO, and Lac/rGO. N2 sorption, SEM/EDX, and powder X-ray diffraction revealed a high Brunauer-Emmett-Teller surface area (179 m2 g-1) mesoporous rGO structure with the high C/O ratio of 80:1 as well. Results from the Fourier transform infrared spectroscopy, UV-visible spectroscopy, and electrochemical impedance spectroscopy studies indicated that GOx remained in its native biochemical functional form upon being embedded onto the rGO matrix. Cyclic voltammetry studies showed that the presence of mesoporous rGO greatly enhanced the direct electrochemistry and electrocatalytic properties of the GOx/rGO and Lac/rGO nanocomposites. The electron transfer rate constant between GOx and rGO was estimated to be 2.14 s-1. The fabricated EBFC (GOx/rGO/FP-Lac/rGO/FP) using a single GOx/rGO/FP bioanode and a single Lac/rGO/FP biocathode provides a maximum power density (Pmax) of 4.0 nW cm-2 with an open-circuit voltage (VOC) of 0.04 V and remains stable for more than 15 days with a power output of ∼9.0 nW cm-2 at a pH of 7.4 under ambient conditions.

Facile synthesis of Cu2O nanorods in the presence of NaCl by successive ionic layer adsorption and reaction method and its characterizations.

Cuprous oxide (Cu2O) nanorods have been deposited on soda-lime glass substrates by the modified successive ionic layer adsorption and reaction technique by varying the concentration of NaCl electrolyte into the precursor complex solution. The structural, electrical and optical properties of synthesized Cu2O nanorod films have been studied by a variety of characterization tools. Structural analyses by X-ray diffraction confirmed the polycrystalline Cu2O phase with (111) preferential growth. Raman scattering spectroscopic measurements conducted at room temperature also showed characteristic peaks of the pure Cu2O phase. The surface resistivity of the Cu2O nanorod films decreased from 15 142 to 685 Ω.cm with the addition of NaCl from 0 to 4 mmol and then exhibited an opposite trend with further addition of NaCl. The optical bandgap of the synthesized Cu2O nanorod films was observed as 1.88-2.36 eV, while the temperature-dependent activation energies of the Cu2O films were measured as about 0.14-0.21 eV. Scanning electron microscope morphologies demonstrated Cu2O nanorods as well as closely packed spherical grains with the alteration of NaCl concentration. The Cu2O phase of nanorods was found stable up to 230°C corroborating the optical bandgap results of the same. The film fabricated in presence of 4 mmol of NaCl showed the lowest resistivity and activation energy as well as comparatively uniform nanorod morphology. Our studies demonstrate that the nominal presence of NaCl electrolytes in the precursor solutions has a significant impact on the physical properties of Cu2O nanorod films which could be beneficial in optoelectronic research.

Synthesis of Novel Tritopic Hydrazone Ligands: Spectroscopy, Biological Activity, DFT, and Molecular Docking Studies.

Polytopic organic ligands with hydrazone moiety are at the forefront of new drug research among many others due to their unique and versatile functionality and ease of strategic ligand design. Quantum chemical calculations of these polyfunctional ligands can be carried out in silico to determine the thermodynamic parameters. In this study two new tritopic dihydrazide ligands, N'2, N'6-bis[(1E)-1-(thiophen-2-yl) ethylidene] pyridine-2,6-dicarbohydrazide (L1) and N'2, N'6-bis[(1E)-1-(1H-pyrrol-2-yl) ethylidene] pyridine-2,6-dicarbohydrazide (L2) were successfully prepared by the condensation reaction of pyridine-2,6-dicarboxylic hydrazide with 2-acetylthiophene and 2-acetylpyrrole. The FT-IR, 1H, and 13C NMR, as well as mass spectra of both L1 and L2, were recorded and analyzed. Quantum chemical calculations were performed at the DFT/B3LYP/cc-pvdz/6-311G+(d,p) level of theory to study the molecular geometry, vibrational frequencies, and thermodynamic properties including changes of ∆H, ∆S, and ∆G for both the ligands. The optimized vibrational frequency and (1H and 13C) NMR obtained by B3LYP/cc-pvdz/6-311G+(d,p) showed good agreement with experimental FT-IR and NMR data. Frontier molecular orbital (FMO) calculations were also conducted to find the HOMO, LUMO, and HOMO−LUMO gaps of the two synthesized compounds. To investigate the biological activities of the ligands, L1 and L2 were tested using in vitro bioassays against some Gram-negative and Gram-positive bacteria and fungus strains. In addition, molecular docking was used to study the molecular behavior of L1 and L2 against tyrosinase from Bacillus megaterium. The outcomes revealed that both L1 and L2 can suppress microbial growth of bacteria and fungi with variable potency. The antibacterial activity results demonstrated the compound L2 to be potentially effective against Bacillus megaterium with inhibition zones of 12 mm while the molecular docking study showed the binding energies for L1 and L2 to be −7.7 and −8.8 kcal mol−1, respectively, with tyrosinase from Bacillus megaterium.