Altered serum interleukin-17A and interleukin-23A levels may be associated with the pathophysiology and development of generalized anxiety disorder.

In recent times, the pathogenesis of generalized anxiety disorder (GAD) and the influence of pro- and anti-inflammatory cytokines on it have garnered considerable interest. Cytokine research, especially Th-17 cytokine research on GAD patients, is limited. Here, we aim to assess the role of interleukin-17A (IL-17A) and interleukin-23A (IL-23A) in the pathophysiology and development of GAD. This investigation included 50 GAD patients and 38 age-sex-matched healthy controls (HCs). A psychiatrist diagnosed patients with GAD and assessed symptom severity using the DSM-5 and the GAD-7 scales. The serum concentrations of IL-17A and IL-23A were determined using commercially available ELISA kits. GAD patients exhibited elevated levels of IL-17A (77.14 ± 58.30 pg/ml) and IL-23A (644.90 ± 296.70 pg/ml) compared to HCs (43.50 ± 25.54 pg/ml and 334.40 ± 176.0 pg/ml). We observed a positive correlation between disease severity and cytokine changes (IL-23A: r = 0.359, p = 0.039; IL-17A: r = 0.397, p = 0.032). These findings indicate that IL-17A and IL-23A may be associated with the pathophysiology of GAD. ROC analysis revealed moderately higher AUC values (IL-23A: 0.824 and IL-17A: 0.710), demonstrating their potential to discriminate between patients and HCs. Also, the sensitivity values of both cytokines were relatively higher (IL-23A: 80.49% and IL-17A: 77.27%). According to the present findings, there may be an association between peripheral serum levels of IL-17A and IL-23A and the pathophysiology and development of GAD. These altered serum IL-17A and IL-23A levels may play a role in directing the early risk of developing GAD. We recommend further research to ascertain their exact role in the pathophysiology and their performance as risk assessment markers of GAD.

COVID-19 and Pneumonia detection and web deployment from CT scan and X-ray images using deep learning.

During the COVID-19 pandemic, pneumonia was the leading cause of respiratory failure and death. In addition to SARS-COV-2, it can be caused by several other bacterial and viral agents. Even today, variants of SARS-COV-2 are endemic and COVID-19 cases are common in many places. The symptoms of COVID-19 are highly diverse and robust, ranging from invisible to severe respiratory failure. Current detection methods for the disease are time-consuming and expensive with low accuracy and precision. To address such situations, we have designed a framework for COVID-19 and Pneumonia detection using multiple deep learning algorithms further accompanied by a deployment scheme. In this study, we have utilized four prominent deep learning models, which are VGG-19, ResNet-50, Inception V3 and Xception, on two separate datasets of CT scan and X-ray images (COVID/Non-COVID) to identify the best models for the detection of COVID-19. We achieved accuracies ranging from 86% to 99% depending on the model and dataset. To further validate our findings, we have applied the four distinct models on two more supplementary datasets of X-ray images of bacterial pneumonia and viral pneumonia. Additionally, we have implemented a flask app to visualize the outcome of our framework to show the identified COVID and Non-COVID images. The findings of this study will be helpful to develop an AI-driven automated tool for the cost effective and faster detection and better management of COVID-19 patients.

Ultra-high-performance liquid chromatography-quadrupole time-of-flight-mass spectrometry-characterized extract of Aerides odorata Lour alleviates paracetamol-induced hepatotoxicity in animal model evidenced by biochemical, molecular, and computational studies.

BACKGROUND: Many kinds of orchids have significant health benefits although adequate research on their biological functions is yet to be carried out. This study investigated the paracetamol-induced liver damage-protecting effect of epiphytic Aerides odorata methanol extract (AODE). METHODS: The protective effects of AODE were studied by analyzing its effect on liver function parameters, messenger RNA (mRNA) expression, and tissue histopathological architecture. The results were confirmed by ligand-receptor interaction of molecular docking and multitarget interaction of network pharmacological analyses. RESULTS: AODE significantly (p < 0.05) minimized the dose-dependent increase in acid phosphatase, aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, γ-glutamyl transferase, lactate dehydrogenase, and total bilirubin compared to the reference drug silymarin. Malondialdehyde level decreased, and the antioxidant genes catalase (CAT), superoxide dismutase (SOD), ß-actin, paraoxonase-1 (PON1), and phosphofructokinase-1 (PFK-1) were upregulated in AODE-treated paracetamol-intoxicated rats. A total of 376 compounds comprising phenols and flavonoids were identified using ultra-high-performance liquid chromatography-quadrupole time-of-flight-mass spectrometry (UPLC-qTOF-MS). The online toxicity assessment using SwissADME and admetSAR exhibited drug-like, nontoxic, and potential pharmacological properties. Additionally, in silico analysis showed that isoacteoside, one of the identified compounds, exhibited the best docking score (-11.42) with the liver protein human pituitary adenylate cyclase-1 (Protein Data Bank ID: 3N94). Furthermore, network pharmacology analysis identified the top 10 hub genes, namely AKT1 (protein kinase B), CTNNB1 (catenin beta-1), SRC (proto-oncogene c-Src), TNF (tumor necrosis factor), EGFR (epidermal growth factor receptor), HSP90AA1 (heat shock protein 90α), MAPK3 (mitogen-activated protein kinase 3), STAT3 (signal transducer and activator of transcription 3), CASP3 (caspase protein), and ESR1 (estrogen receptor 1), which are responsible for hepatoprotective activity. CONCLUSION: The findings demonstrate that AODE could be a novel hepatoprotective target in drug-induced liver damage with a further single compound-based animal study.

Diagnostic and Prognostic Models Based on Electrocardiograms for Rapid Clinical Applications.

Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECG) has the potential to transform diagnosis and estimate the prognosis of not only cardiac but, increasingly, non-cardiac conditions. In this review, we summarize clinical studies and AI-enhanced ECG-based clinical applications in the early detection, diagnosis, and estimating prognosis of cardiovascular diseases (CVD) in the last five years (2019-2023). With advancements in deep learning and the rapid increased use of ECG technologies, a large number of clinical studies have been published. However, a majority of these studies are single-center, retrospective, proof-of-concept studies that lack external validation. Prospective studies that progress from development toward deployment in clinical settings account for <15% of the studies. Successful implementations of ECG-based AI applications that have received approval from the Food and Drug Administration (FDA) have been developed through commercial collaborations, with about half of them being for mobile or wearable devices. The field is in its early stages, and overcoming several obstacles is essential, such as prospective validation in multi-center large datasets, addressing technical issues, bias, privacy, data security, model generalizability, and global scalability. This review concludes with a discussion of these challenges and potential solutions. By providing a holistic view of the state of AI in ECG analysis, this review aims to set a foundation for future research directions, emphasizing the need for comprehensive, clinically integrated, and globally deployable AI solutions in CVD management.

Endophyte mediated biocontrol mechanisms of phytopathogens in agriculture.

The global human population is growing and demand for food is increasing. Global agriculture faces numerous challenges, including excessive application of synthetic pesticides, emergence of herbicide-and pesticide-resistant pathogenic microbes, and more frequent natural disasters associated with global warming. Searches for valuable endophytes have increased, with the aim of making agriculture more sustainable and environmentally friendly. Endophytic microbes are known to have a variety of beneficial effects on plants. They can effectively transfer nutrients from the soil into plants, promote plant growth and development, increase disease resistance, increase stress tolerance, prevent herbivore feeding, reduce the virulence of pathogens, and inhibit the growth of rival plant species. Endophytic microbes can considerably minimize the need for agrochemicals, such as fertilizers, fungicides, bactericides, insecticides, and herbicides in the cultivation of crop plants. This review summarizes current knowledge on the roles of endophytes focusing on their mechanisms of disease control against phytopathogens through the secretion of antimicrobial substances and volatile organic compounds, and the induction of systemic resistance in plants. Additionally, the beneficial roles of these endophytes and their metabolites in the control of postharvest diseases in plants have been summarized.

Potential data for feasibility assessment and deployment of a 6.7 MW floating solar PV plant in Hatirjheel Lake, Dhaka, Bangladesh.

Floating solar photovoltaic has emerged as a highly sustainable and environmentally friendly solution worldwide from the various clean energy generation technologies. However, the installation of floating solar differs from rooftop or ground-mounted solar due to the significant consideration of the availability of water bodies and suitable climatic conditions. Therefore, conducting a feasibility analysis of the suitable climate is essential for installing a floating solar plant on water bodies. These data are evaluated for the viability of installing a 6.7 MW floating solar power plant on Hatirjheel Lake in Dhaka, Bangladesh. The feasibility analysis incorporated various climatic data, such as temperature, humidity, rainfall, sunshine hours, solar radiation, and windspeed, obtained from Meteonorm 8.1 software and the archive of the Bangladesh Meteorological Department. Besides, this study gathered and analyzed the energy demands of the local grid substation operated by Dhaka Power Distribution Company, to determine the appropriate capacity and architecture of the power plant. The power plant design was conducted using the PVsyst 7.3 software, which determined the necessary equipment quantities, DC energy generation capacity, and the energy injected into the grid in MWh. The study also calculated the Levelized Cost of Energy per kilowatt-hour and the payback period for the system, which indicates the economic viability of installing the system. Furthermore, the acquired dataset possesses significant potential and can be utilized for the establishment of all sorts of solar power plants, including floating solar plants, in any location or body of water within the Dhaka Metropolitan area.

Possible involvement of sialidase and sialyltransferase activities in a stage-dependent recycling of sialic acid in some organs of type 1 and type 2 diabetic rats.

Background: Type 1 (T1D) and type 2 (T2D) diabetes lead to an aberrant metabolism of sialoglycoconjugates and elevated free serum sialic acid (FSSA) level. The present study evaluated sialidase and sialyltranferase activities in serum and some organs relevant to diabetes at early and late stages of T1D and T2D. Methods: Sialic acid level with sialidase and sialyltransferase activities were monitored in the serum, liver, pancreas, skeletal muscle and kidney of diabetic animals at early and late stages of the diseases. Results: The FSSA and activity of sialidase in the serum were significantly increased at late stage of both T1D and T2D while sialic acid level in the liver was significantly decreased in the early and late stages of T1D and T2D, respectively. Furthermore, the activity of sialidase was significantly elevated in most of the diabetes-relevant organs while the activity of sialyltransferase remained largely unchanged. A multiple regression analysis revealed the contribution of the liver to the FSSA while pancreas and kidney contributed to the activity of sialidase in the serum. Conclusions: We concluded that the release of hepatic sialic acid in addition to pancreatic and renal sialidase might (in)directly contribute to the increased FSSA during both types of diabetes mellitus.

Experience and side effects of COVID-19 vaccine uptake among university students: a cross-sectional survey study.

Introduction: Many people expressed concern over coronavirus vaccinations' reliability and side effects. This research aimed to assess university students' perceptions and experiences regarding the side effects of the COVID-19 vaccines in Bangladesh. Method: We conducted an online cross-sectional survey to collect responses from university students vaccinated with any vaccines administered in Bangladesh between November 2021 to April 2022. Bangladeshi university students over the age of 18 and having an internet connection was included in the study. A binary logistic regression analysis along with Pearson's Chi-square test were used to identify COVID-19 vaccine-related side effects predictors after receiving the first dose. Results: A total of 1,176 participants responded voluntarily to the online study, and most were vaccinated. More than half of the participants received the Sinopharm vaccine (56.5%), while others received Covishield (8.9%), Moderna (7.3%), and Pfizer (5.8%) vaccine. Around 32% of the participants reported side effects after receiving the first dose of the vaccine, including pain and edema (78.4%), body temperature (20.3%), and headache (14.5%), while a few experienced allergy, anxiety, and uneasy feelings. About 17% of the participants reported experiencing side effects after the second dose of the vaccine, including pain and edema (7.5%), body temperature (8.8%), and headache (7.3%). Most side effects were significantly associated with the Moderna vaccine (p < 0.001). Female students and those previously infected with COVID-19 were significantly associated with the side effects after taking the first dose of the vaccine. Conclusion: We found that side effects are mild and did not pose a significant challenge to Bangladesh's effort in managing and reducing the risk associated with the COVID-19 pandemic.

Development and Enhancement of PCF-based Sensors for Terahertz-frequency Region Breast Cancer Cell Detection.

In today's medical research, breast cancer is a severe problem, so it is imperative to develop a reliable and efficient approach for identifying cancerous breast cells. PCF, with its exceptional sense-making abilities, simplifies and distinguishes that procedure. The research presents a unique structural hybrid PCF for detecting breast cancer cells using sensors based on PCF that are specifically built for the terahertz-frequency range. The improvement in sensor sensitivity and specificity in identifying cancer cells at these frequencies is a notable progress compared to conventional approaches, which could potentially result in earlier and more precise diagnosis. In our analysis, we discovered the most common malignancies in breast cancer. We investigate the features of the cancerous cell detector using the COMSOL-Multiphysics 5.6 software. This PCF detector achieves a Confinement Loss of 4.75 × 10-12 and 3.42 × 10-13 dB/m for Type-1 and Type-2 cancer cells, respectively, at 1.2 THz, as well as about 99.946% and 99.969% relative sensitivity. This sensor ensures the highest level of sensitivity for the identification of cancerous breast cells. This sensor's physical architecture is quite straightforward, making it simple to build using current manufacturing techniques. Therefore, it seems that this sensor will pave a new path for identifying and treating cancerous cells.

A microbiome-directed therapeutic food for children recovering from severe acute malnutrition.

Severe acute malnutrition (SAM), defined anthropometrically as a weight-for-length z-score more than 3 standard deviations below the mean (WLZ<-3), affects 19 million children under 5-years-old worldwide. Complete anthropometric recovery after standard inventions is rare with children often left with moderate acute malnutrition (MAM; WLZ -2 to -3). Here we conduct a randomized controlled trial (RCT), involving 12-18-month-old Bangladeshi children from urban and rural sites, who after hospital-based treatment for SAM received a 3-month intervention with a microbiota-directed complementary food (MDCF-2) or a ready-to-use supplementary food (RUSF) as they transitioned to MAM. The rate of WLZ improvement was significantly greater with MDCF-2 than the more calorically-dense RUSF, as we observed in a previous RCT of Bangladeshi children with MAM without antecedent SAM. A correlated meta-analysis of aptamer-based measurements of 4,520 plasma proteins in this and the prior RCT revealed 215 proteins positively-associated with WLZ (prominently those involved in musculoskeletal and CNS development) and 44 negatively-associated proteins (related to immune activation), with a significant enrichment in levels of the positively WLZ-associated proteins in the MDCF-2 arm. Characterizing changes in 754 bacterial metagenome-assembled genomes in serially collected fecal samples disclosed the effects of acute rehabilitation for SAM on the microbiome, its transition as each child achieves a state of MAM, and how specific strains of Prevotella copri function at the intersection between MDCF-2 glycan metabolism and the rescue of growth faltering. These results provide a rationale for further testing the generalizability of the efficacy of MDCF and identify biomarkers for defining treatment responses.

Mechanical and biological behavior of double network hydrogels reinforced with alginate versus gellan gum.

Alginate and gellan gum have both been used by researchers as reinforcing networks to create tough and biocompatible polyethylene glycol (PEG) based double network (DN) hydrogels; however, the relative advantages and disadvantages of each approach are not understood. This study directly compares the mechanical and biological properties of polyethylene glycol di-methacrylate (PEGDMA) hybrid DN hydrogels reinforced with either gellan gum or sodium alginate using PEGDMA concentrations from 10 to 20 wt% and reinforcing network concentrations of 1 and 2 wt%. The findings demonstrate that gellan gum reinforcement is more effective at increasing the strength, stiffness, and toughness of PEGDMA DN hydrogels. In contrast, alginate reinforcement yields DN hydrogels with greater stretchability compared to gellan gum reinforced PEGDMA. Furthermore, separate measurements of toughness via unnotched work of rupture testing and notched fracture toughness testing showed a strong correlation of these two properties for a single reinforcing network type, but not across the two types of reinforcing networks. This suggests that additional notched fracture toughness experiments are important for understanding the full mechanical response when comparing different tough DN hydrogel systems. Regarding the biological response, after conjugation of matrix protein to the surface of both materials robust cell attachment and spreading was supported with higher yes associated protein (YAP) nuclear expression observed in populations adhering to the stiffer gellan gum-PEGDMA material. This study provides valuable insights regarding how to design double network hydrogels for specific property requirements, e.g., for use in biomedical devices, as scaffolding for tissue engineering, or in soft robotic applications.

From Gut Microbiomes to Infectious Pathogens: Neurological Disease Game Changers.

Gut microbiota and infectious diseases affect neurological disorders, brain development, and function. Compounds generated in the gastrointestinal system by gut microbiota and infectious pathogens may mediate gut-brain interactions, which may circulate throughout the body and spread to numerous organs, including the brain. Studies shown that gut bacteria and disease-causing organisms may pass molecular signals to the brain, affecting neurological function, neurodevelopment, and neurodegenerative diseases. This article discusses microorganism-producing metabolites with neuromodulator activity, signaling routes from microbial flora to the brain, and the potential direct effects of gut bacteria and infectious pathogens on brain cells. The review also considered the neurological aspects of infectious diseases. The infectious diseases affecting neurological functions and the disease modifications have been discussed thoroughly. Recent discoveries and unique insights in this perspective need further validation. Research on the complex molecular interactions between gut bacteria, infectious pathogens, and the CNS provides valuable insights into the pathogenesis of neurodegenerative, behavioral, and psychiatric illnesses. This study may provide insights into advanced drug discovery processes for neurological disorders by considering the influence of microbial communities inside the human body.

Paddy insect identification using deep features with lion optimization algorithm.

Pests are a significant challenge in paddy cultivation, resulting in a global loss of approximately 20 % of rice yield. Early detection of paddy insects can help to save these potential losses. Several ways have been suggested for identifying and categorizing insects in paddy fields, employing a range of advanced, noninvasive, and portable technologies. However, none of these systems have successfully incorporated feature optimization techniques with Deep Learning and Machine Learning. Hence, the current research provided a framework utilizing these techniques to detect and categorize images of paddy insects promptly. Initially, the suggested research will gather the image dataset and categorize it into two groups: one without paddy insects and the other with paddy insects. Furthermore, various pre-processing techniques, such as augmentation and image filtering, will be applied to enhance the quality of the dataset and eliminate any unwanted noise. To determine and analyze the deep characteristics of an image, the suggested architecture will incorporate 5 pre-trained Convolutional Neural Network models. Following that, feature selection techniques, including Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), Linear Discriminant Analysis (LDA), and an optimization algorithm called Lion Optimization, were utilized in order to further reduce the redundant number of features that were collected for the study. Subsequently, the process of identifying the paddy insects will be carried out by employing 7 ML algorithms. Finally, a set of experimental data analysis has been conducted to achieve the objectives, and the proposed approach demonstrates that the extracted feature vectors of ResNet50 with Logistic Regression and PCA have achieved the highest accuracy, precisely 99.28 %. However, the present idea will significantly impact how paddy insects are diagnosed in the field.

A practical solution to improve the field of view in circular scanning-based photoacoustic tomography.

One of the primary challenges in ring single-element photoacoustic tomography systems is the low image quality in areas away from the center of the ring. This is mainly due to the limited field of view (FOV) of each transducer, which in turn reduces the imaging FOV. To address this shortcoming, we have put forward a practical and straightforward solution to enhance the FOV of circular scanning-based photoacoustic tomography (CS-PAT). This is accomplished by placing transducers at different angles instead of using a single transducer placed at a normal angle to the imaging target. We also modified the ring scanner inner wall surface to significantly reduce photoacoustic reverberation. By imaging several phantoms, we show a significant improvement in the images generated by our system imaging from 4.1 to over 7 for the signal-to-noise ratio and structural similarity index increased from 41% to 70%.

Folate deficiency in pregnancy and the risk of preterm birth: A nested case-control study.

Background: Preterm birth (PTB) and its complications are important public health problems. Its aetiology is multifactorial and involves both modifiable and non-modifiable factors. Among the modifiable risk factors, micronutrient deficiencies, including maternal folate deficiency, are increasingly being studied in PTB. In this study, we estimated the prevalence of folate deficiency during pregnancy and examined its association with PTB among rural Bangladeshi women. Methods: We conducted a nested case-control study using data from a population-based cohort of 3000 pregnant women who were enrolled between 8 and 19 weeks of gestation following ultrasound confirmation of gestational age. Sociodemographic, epidemiologic, clinical, and pregnancy outcomes data were collected through home visits, while blood samples were collected at enrolment and 24-28 weeks of gestation during pregnancy. We included all women who delivered preterm (defined as live births <37 weeks of gestation) as cases (n = 235) and a random sample of women having a term birth as controls (n = 658). The main exposure was folate concentrations in maternal serum during 24-28 weeks of pregnancy. We categorised women into folate deficient (<3 ng/mL) and not deficient (≥3 ng/mL). We then performed multivariable logistic regression analysis to examine the association between maternal folate levels and PTB, adjusting for relevant covariates. Results: Thirty-eight per cent of the enrolled pregnant women were folate deficient. Maternal serum folate deficiency was significantly associated with PTB (adjusted OR (aOR) = 1.73; 95% confidence interval (CI) = 1.27-2.36). The risk of PTB was also higher among women who were of short stature (aOR = 1.83; 95% CI = 1.27-2.63), primiparous (aOR = 1.60; 95% CI = 1.15-2.22), and had exposure to passive smoking (aOR = 1.54; 95% CI = 1.02-2.31). Conclusions: The prevalence of folate deficiency was high among pregnant women in rural Bangladesh, and folate deficiency was significantly associated with an increased risk of preterm birth.

Efficiency and Mechanism of Catalytic Siloxane Exchange in Vitrimer Polymers: Modeling and Density Functional Theory Investigations.

Of late, siloxane-containing vitrimers have gained significant interest due to their fast dynamic characteristics over a reasonable temperature range (180-220 °C), making them well-suited for diverse applications. The exchange reaction pathway in the siloxane vitrimers is accountable for the covalent adaptive network, with the reaction's effectiveness being regulated by either organic or organometallic catalysts. However, directly studying the exchange reaction pathway in the bulk phase using experimental approaches is challenging because of the intricate and interconnected structure of these vitrimers. Here, we perform comprehensive density functional theory (DFT) and experimental investigations to discover the detailed catalytic efficacy of siloxane exchange and provide direction for the reaction process using a 1,5,7-triazabicyclo[4.4.0]dec-5-ene (TBD) catalyst. The calculated transition barrier energy and catalytic efficiency of hexamethyldisiloxane and dihydroxy-dimethylsilane exchange derived from the nudged elastic band with transition-state calculations strongly agree with the experimental findings. In addition, Fukui indices, along with partial charges, are employed to evaluate the nucleophilic and electrophilic behaviors of silanol and siloxane molecules. Our analysis revealed that by utilizing the Fukui indices of both the acid and the base, we can make an approximate estimation of the respective kinetics of the SN2 process in the siloxane exchange reaction mechanism. These findings establish a foundation for comprehending a crucial aspect of the exchange mechanism in siloxane vitrimer systems and could aid in the development of novel catalysts.

Synthesis, Antimicrobial, Molecular Docking Against Bacterial and Fungal Proteins and In Silico Studies of Glucopyranoside Derivatives as Potent Antimicrobial Agents.

Carbohydrate derivatives play a crucial role in biochemical and medicinal research. Therefore, the present study was designed to explore the synthesis of methyl α-D-glucopyranoside derivatives (1, MDG), focusing on their efficacy against bacterial and fungal infections. The structure of the synthesized compounds was ascertained using spectral and elemental analyses. Antimicrobial screening revealed strong antifungal properties and exhibited MIC values of 16-32 µg/L and MBC 64-128 µg/L. Structure-activity relationship (SAR) analysis indicated that adding nonanoyl and decanoyl groups to ribose moiety enhanced potency against both bacterial and fungal strains. Compounds 6 and 7, presented nonanoyl and decanoyl substituents and demonstrated greater efficacy. In addition, DFT studies identified compound 8 as possessing ideal electronic properties. Molecular docking revealed that compound 8 exhibits exceptional binding affinities to bacterial proteins, conferring potent antibacterial and antifungal activities. In addition, pharmacokinetic optimization via POM analysis highlighted compounds 1 and 2 as promising bioavailable drugs with minimal toxicity. Molecular dynamics simulations confirmed the stability of the 2-S. aureus complex, revealing the therapeutic potential of compounds 2 and 8. The integration of in vitro and in silico methods, including DFT anchoring dynamics and molecular dynamics simulations, provides a solid framework for the advancement of effective anti-infective drugs.

Paramagnetic Properties of [AnIV(NO3)6]2- Complexes (An = U, Np, Pu) Probed by NMR Spectroscopy and Quantum Chemical Calculations.

Actinide +IV complexes with six nitrates [AnIV(NO3)6]2- (An = Th, U, Np, and Pu) have been studied by 15N and 17O NMR spectroscopy in solution and first-principles calculations. Magnetic susceptibilities were evaluated experimentally using the Evans method and are in good agreement with the ab initio values. The evolution in the series of the crystal field parameters deduced from ab initio calculations is discussed. The NMR paramagnetic shifts are analyzed based on ab initio calculations. Because the cubic symmetry of the complex quenches the dipolar contribution, they are only of Fermi contact origin. They are evaluated from first-principles based on a complete active space/density functional theory (DFT) strategy, in good accordance with the experimental one. The ligand hyperfine coupling constants are deduced from paramagnetic shifts and calculated using unrestricted DFT. The latter are decomposed in terms of the contribution of molecular orbitals. It highlights two pathways for the delocalization of the spin density from the metallic open-shell 5f orbitals to the NMR active nuclei, either through the valence 5f hybridized with 6d to the valence 2p molecular orbitals of the ligands, or by spin polarization of the metallic 6p orbitals which interact with the 2s-based molecular orbitals of the ligands.

Tailoring Mott-Schottky RuO2/MgFe-LDH Heterojunctions in Electrospun Microfibers: A Bifunctional Electrocatalyst for Water Electrolysis.

Hydrogen is a fuel of the future that has the potential to replace conventional fossil fuels in several applications. The quickest and most effective method of producing pure hydrogen with no carbon emissions is water electrolysis. Developing highly active electrocatalysts is crucial due to the slow kinetics of oxygen and hydrogen evolution, which limit the usage of precious metals in water splitting. Interfacial engineering of heterostructures has sparked widespread interest in improving charge transfer efficiency and optimizing adsorption/desorption energetics. The emergence of a built-in-electric field between RuO2 and MgFe-LDH improves the catalytic efficiency toward water splitting reaction. However, LDH-based materials suffer from poor conductivity, necessitating the design of 1D materials by integration of RuO2/ MgFe-LDH to enhance catalytic properties through large surface areas and high electronic conductivity. Experimental results demonstrate lower overpotentials (273 and 122 mV at 10 mA cm-2) and remarkable stability (60 h) for the RuO2/MgFe-LDH/Fiber heterostructure in OER (1 m KOH) and HER (0.5 m H2SO4) reactions. Density functional theory (DFT) unveils a synergistic mechanism at the RuO2/MgFe-LDH interface, leading to enhanced catalytic activity in OER and improved adsorption energy for hydrogen atoms, thereby facilitating HER catalysis.