Percutaneous Transhepatic Sphincterotome-Guided Management of Post-Living Donor Liver Transplant Biliary Anastomotic Stricture: An Innovative Approach.

Post-liver transplantation biliary complications remain a serious concern and are associated with reduced patient and graft survival. Among various biliary complications, anastomotic stricture (AS) is the most frequent and challenging one. The frequency of AS after living donor liver transplantation (LDLT) is higher as compared to deceased donor liver transplantation. The management involves endoscopic retrograde cholangiopancreatography and/or percutaneous transhepatic biliary drainage, but refractory cases necessitate surgical revision. We present a case of complex biliary AS in a 63-year-old man after LDLT. The conventional approaches including endoscopic retrograde cholangiopancreatography, percutaneous transhepatic cholangiography, and cholangioscope-guided interventions remained unsuccessful. An innovative approach using a wire-guided sphincterotome through percutaneous transhepatic route successfully managed the complex post-LDLT AS. This is perhaps the first reported case of novel utilization of sphincterotome through transhepatic route for the management of AS in LDLT, averting major surgical interventions with related morbidity and mortality.

Unravelling the nexus between energy prices and exchange rate in Malaysia: Fresh insights from a non-linear perspective using threshold cointegration analysis.

This paper proposes a nonlinear threshold cointegration framework to study how energy prices affect Malaysia's nominal exchange rate, considering the money supply, income, and interest rate. The study employs a threshold cointegration approach utilizing threshold autoregressive and momentum threshold autoregressive models. The momentum threshold vector error correction model determines the short-run adjustment of exchange rate deviation from the long-run equilibrium level. The findings reveal that the nonlinear adjustment process to capture the short-run deviation in the long-run equilibrium path is primarily influenced by energy prices, money supply, and interest rates. These results highlight the importance of considering the impact of energy prices on exchange rate policies when formulating and implementing economic policies in Malaysia. The findings can also be valuable for decision-makers to comprehend the future dynamics of exchange rates and make well-informed decisions.

Impact of induced magnetic field on Darcy-Forchheimer nanofluid flows comprising carbon nanotubes with homogeneous-heterogeneous reactions.

The appealing traits of carbon nanotubes (CNTs) encompassing mechanical and chemical steadiness, exceptional electrical and thermal conductivities, lightweight, and physiochemical reliability make them desired materials in engineering gadgets. Considering such stimulating characteristics of carbon nanotubes, our goal in the current study is to scrutinize the comparative analysis of Darcy-Forchheimer nanofluid flows containing CNTs of both types of multi and single-wall carbon nanotubes (MWCNTs, SWCNTs) immersed into two different base fluids over a stretched surface. The originality of the model being presented is the implementation of the induced magnetic field that triggers the electric conductivity of carbon nanotubes. Moreover, the envisioned model is also analyzed with homogeneous-heterogeneous (h-h) chemical reactions and heat source/sink. The second-order slip constraint is assumed at the boundary of the surface. The transmuted high-nonlinearity ordinary differential equations (ODEs) are attained from the governing set of equations via similarity transformations. The bvp4c scheme is engaged to get the numerical results. The influence of different parameters is depicted via graphs. For both CNTs, the rate of heat flux and the surface drag coefficient are calculated using tables. It is highlighted that an increase in liquid velocity is witnessed for a varied counts volume fraction of nanoparticles. Also, Single-wall water-based carbon nanotube fluid has comparatively stronger effects on concentration than the multi-walled carbon nanotubes in water-based liquid. The analysis also indicates that the rate of heat flux and the surface drag coefficient are augmented for both SWCNTs and MWCNTs for different physical parameters. The said model is also validated by comparing it with a published result.

Correction: Gastrointestinal tract disorders classification using ensemble of InceptionNet and proposed GITNet based deep feature with ant colony optimization.

[This corrects the article DOI: 10.1371/journal.pone.0292601.].

Serum metallomics reveals insights into the associations of elements with the progression of preleukemic diseases toward acute leukemia.

Acute leukemia (AL) is a critical neoplasm of white blood cells with two main subtypes: acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). This study is focused on understanding the association of the preleukemic disease aplastic anemia (APA) with ALL and AML at metallomic level, using healthy subjects as a control. In this study, a validated and efficient inductively coupled plasma-mass spectrometry/MS-based workflow was employed to profile a total of 13 metallomic features. The study encompassed 41 patients with AML, 62 patients with ALL, 46 patients with APA, and 55 age-matched healthy controls. The metallomic features consisted of eight essential elements (Ca, Co, Cu, Fe, Mg, Mn, Se, and Zn) and five non-essential/toxic elements (Ag, Cd, Cr, Ni, and Pb). Six out of the 13 elements were found to be substantially different (P < .05) using absolute concentrations between serum samples of AL (ALL and AML) and preleukemia (APA) patients in comparison with healthy subjects. Elements including magnesium, calcium, iron, copper, and zinc were upregulated and only one element (chromium) was downregulated in serum samples of disease when compared with healthy subjects. Through the utilization of both univariate tests and multivariate classification modeling, it was determined that chromium exhibited a progressive behavior among the studied elements. Specifically, chromium displayed a sequential upregulation from healthy individuals to preleukemic disease (APA), and ultimately in patients diagnosed with ALL. Overall, metallomic-based biomarkers may have the utility to predict the association of APA with ALL.

Neurokinin B Administration Induces Dose Dependent Proliferation of Seminal Vesicles in Adult Rats.

BACKGROUND: Neurokinin B; an endogenous decapeptide, mediates its reproductive physiological actions through gonadotropin releasing hormone. Despite the potential role of Neurokinin B on seminal vesicles, its effects on seminal vesicles in adult male mammals remain elusive. We aimed to investigate the potentials of variable doses of Neurokinin B, its agonist and antagonist on histomorphology and expression of NK3R on seminal vesicles, and secretory activity of seminal vesicles in adult male rats. METHODS: Adult male Sprague Dawley rats (n=10 in each group) were administered intraperitoneally with Neurokinin B in three variable doses: 1 µg, 1 ηg and 10 ρg while, Senktide (Neurokinin B agonist) and SB222200 (Neurokinin B antagonist) in 1 µg doses consecutively for 12 days. After 12 days of peptide treatment, half of the animals (n=05) in each group were sacrificed while remaining half (n=05) were kept for another 12 days without any treatment to investigate treatment reversal. Seminal vesicles were dissected and excised tissue was processed for light microscopy, immunohistochemistry and estimation of seminal fructose levels. RESULTS: Treatment with Neurokinin B and Senktide significantly increased while SB222200 slightly decrease the seminal vesicles weight, epithelial height and seminal fructose levels as compared to control. Light microscopy revealed increased epithelial height and epithelial folding as compared to control in all Neurokinin B and Senktide treated groups while decreased in SB222200. Effects of various doses of Neurokinin B, Senktide and SB222200 on seminal vesicles weight, epithelial height, seminal fructose levels and histomorphology were reversed when rats were maintained without treatments. Immuno-expression of Neurokinin B shows no change in treatment and reversal groups Conclusion: Continuous administration of Neurokinin B and Senktide effect positively while SB222200 have detrimental effects on cellular morphology, epithelial height and seminal fructose levels in seminal vesicles. Effects of peptide treatments depicted a reversal towards control group when rats were kept without any treatment.

Development of Data-driven Machine Learning Models and their Potential Role in Predicting Dengue outbreak.

ABSTRACT: Dengue fever is one of the most widespread vector-borne viral infections in the world, resulting in increased socio-economic burdens. The WHO has reported that 2.5 billion people are infected with dengue fever across the world, resulting in high mortalities in tropical and subtropical regions. The current article endeavors to present an overview of predicting dengue outbreaks through data-based machine-learning models. This artificial intelligence model uses real-world data such as dengue surveillance, climatic variables, and epidemiological data and combines big data with machine learning algorithms to forecast dengue. Monitoring and predicting dengue incidences have been significantly enhanced through innovative approaches. This involves gathering data on various climatic factors, including temperature, rainfall, relative humidity, and wind speed, along with monthly records of dengue cases. The study functions as an efficient warning system, enabling the anticipation of dengue outbreaks. This early warning system not only alerts communities but also aids relevant authorities in implementing crucial preventive measures.

Revealing Melatonin's Mysteries: Receptors, Signaling Pathways, and Therapeutics Applications.

Melatonin (5-methoxy-acetyl tryptamine) is a sleep-inducing hormone, and the pineal gland produces it in response to the circadian clock of darkness. In the body, MT1 and MT2 receptors are mostly found, having an orthosteric pocket and ligand binding determinants. Melatonin acts by binding on melatonin receptors, intracellular proteins, and orphan nuclear receptors. It inhibits adenyl cyclase and activates phospholipase C, resulting in gene expression and an intracellular alteration environment. Melatonin signaling pathways are also associated with other intracellular signaling pathways, i. e., cAMP/PKA and MAPK/ERK pathways. Relative expression of different proteins depends on the coupling profile of G protein, accounting pharmacology of the melatonin receptor bias system, and mediates action in a Gi-dependent manner. It shows antioxidant, antitumor, antiproliferative, and neuroprotective activity. Different types of melatonin agonists have been synthesized for the treatment of sleeping disorders. Researchers have developed therapeutics that target melatonin signaling, which could benefit a wide range of medical conditions. This review focuses on melatonin receptors, pharmacology, and signaling cascades; it aims to provide basic mechanical aspects of the receptor's pharmacology, melatonin's functions in cancer and neurodegenerative diseases, and any treatments and drugs designed for these diseases. This will allow a basic comparison between the receptors in question, highlighting any parallels and differences that may exist and providing fundamental knowledge about these receptors to future researchers.

BRICS and the climate challenge: navigating the role of factor productivity and institutional quality in CO2 emissions.

The BRICS countries are important contributors to global efforts aimed at preventing a climate catastrophe. These countries account for half of the total emissions generated by the G20 nations. In this context, this paper examines the relationship between total factor productivity (TFP) and CO2 emissions (CE) in BRICS countries from 1996 to 2022, with institutional quality serving as a moderating factor. Moreover, a diverse range of methodologies was employed to address the problem of cross-sectional dependence; i.e., the CS-ARDL technique is used to analyze the relationship between variables in both the long and short-run. The AMG and CCEMG methods are employed for robustness analysis, while the Dumitrescu-Hurlin causality test is used to assess causality. Our empirical analysis demonstrates that TFP is positively associated with CE. Conversely, we find that institutional quality has a negative impact on CE. Furthermore, the study confirms that the interaction between TFP and institutional quality has a negative effect on CE. This implies that an improvement in institutional quality leads to a decrease in CE, as it strengthens the regulatory system governing CE and reduces pollution. Environmental policy must include economic flexibility and policy unpredictability in order to meet CO2 reduction targets. In addition, the study has identified bidirectional causal links between CE and variables such as TFP, institutional quality, and other control variables. According to our study, the BRICS countries should encourage digitalization and renewable energy production while preserving a reasonable standard of institutional quality since they have significant resource advantages in the renewable energy sector.

Gastrointestinal tract disorders classification using ensemble of InceptionNet and proposed GITNet based deep feature with ant colony optimization.

Computer-aided classification of diseases of the gastrointestinal tract (GIT) has become a crucial area of research. Medical science and artificial intelligence have helped medical experts find GIT diseases through endoscopic procedures. Wired endoscopy is a controlled procedure that helps the medical expert in disease diagnosis. Manual screening of the endoscopic frames is a challenging and time taking task for medical experts that also increases the missed rate of the GIT disease. An early diagnosis of GIT disease can save human beings from fatal diseases. An automatic deep feature learning-based system is proposed for GIT disease classification. The adaptive gamma correction and weighting distribution (AGCWD) preprocessing procedure is the first stage of the proposed work that is used for enhancing the intensity of the frames. The deep features are extracted from the frames by deep learning models including InceptionNetV3 and GITNet. Ant Colony Optimization (ACO) procedure is employed for feature optimization. Optimized features are fused serially. The classification operation is performed by variants of support vector machine (SVM) classifiers, including the Cubic SVM (CSVM), Coarse Gaussian SVM (CGSVM), Quadratic SVM (QSVM), and Linear SVM (LSVM) classifiers. The intended model is assessed on two challenging datasets including KVASIR and NERTHUS that consist of eight and four classes respectively. The intended model outperforms as compared with existing methods by achieving an accuracy of 99.32% over the KVASIR dataset and 99.89% accuracy using the NERTHUS dataset.

Strained Monolayer MoTe2 as a Photon Absorber in the Telecom Range.

To achieve the atomistic control of two-dimensional materials for emerging technological applications, such as valleytronics, spintronics, and single-photon emission, it is of paramount importance to gain an in-depth understanding of their structure-property relationships. In this work, we present a systematic analysis, carried out in the framework of density-functional theory, on the influence of uniaxial strain on the electronic and optical properties of monolayer MoTe2. By spanning a ±10% range of deformation along the armchair and zigzag direction of the two-dimensional sheet, we inspect how the fundamental gap, the dispersion of the bands, the frontier states, and the charge distribution are affected by strain. Under tensile strain, the system remains a semiconductor but a direct-to-indirect band gap transition occurs above 7%. Compressive strain, instead, is highly direction-selective. When it is applied along the armchair edge, the material remains a semiconductor, while along the zigzag direction a semiconductor-to-metal transition happens above 8%. The characteristics of the fundamental gap and wave function distribution are also largely dependent on the strain direction, as demonstrated by a thorough analysis of the band structure and of the charge density. Additional ab initio calculations based on many-body perturbation theory confirm the ability of strained MoTe2 to absorb radiation in the telecom range, thus suggesting the application of this material as a photon absorber upon suitable strain modulation.

Moderating role of green innovation and fiscal expenditure towards achieving the Sustainable Development Agenda 2030 at provincial-level in China: policy implication from green total factor productivity.

Amidst resource loss and environmental protection constraints, achieving green development necessitates enhancing green total factor productivity (GTFP) as a means of promoting rational and efficient resource allocation, thereby balancing economic growth and environmental preservation. Meanwhile, literature on the subject matter of GTFP from a sustainability viewpoint is minimal. As a result, this study employs the panel dataset from 30 provinces of China spanning the period 2005 to 2020 and utilizes the method of moments quantile regression (MMQR) developed by Machado and Santos Silva (2019) to analyze the heterogeneous role of green innovation, environmental regulations, and fiscal expenditure on GTFP. Moreover, the controlling variable for this study includes renewable energy and economic growth. Furthermore, this study investigates the heterogeneous combined impact of green innovation and fiscal expenditure (GTE*FSE) on GTFP. The findings of the MMQR reveal that green innovation has a positive impact on GTFP, while fiscal expenditure, environmental regulations, and renewable energy consumption have a negative impact. GTE*FSE has a positive and significant effect on GTFP, indicating that FSE can reinforce and increase the positive impact of GTE on GTFP in the long run. The study also reveals that economic growth has a mixed effect on GTFP, depending on the quantiles. Furthermore, environmental regulation has a significant and negative impact on GTFP, contradicting the Porter hypothesis. Likewise, the robustness of the findings is confirmed by the results of the fully modified OLS (FMOLS) and dynamic OLS (DOLS) estimations, which indicate a similar impact of the determinants on GTFP as observed in the MMQR analysis. This reinforces the validity of the findings and suggests that the observed relationships are robust to different estimation techniques. Furthermore, the findings of the Dumitrescu and Hurlin (D-H) panel causality test reveal significant bidirectional causality between renewable energy consumption and GTFP and fiscal expenditure and GTFP. Policy-makers need to channel a large chuck of their fiscal spending into green innovation so as to boost sustainability.

Analysis of Liquid Chromatography Considering a Linear Single-Component Heterogeneous-Type Reactive General Rate Model.

This paper elaborates on the significance of liquid chromatography for a single-component reactive linear general rate model. The model equations consist of a set of two coupled partial differential equations, which include diffusion, interfacial mass transfer, axial dispersion, external and intraparticle pore diffusivity, and heterogeneous chemical reaction of the first order with two sets of boundary conditions. The model equations are solved by the Laplace transformation. The actual time domain solution is obtained by numerical Laplace inversion, as analytical inversion cannot be obtained. The graphical sketch of different physical parameters is presented to analyze the dynamics of the elution profiles. The result indicates that the chromatographic reactor works more efficiently on increasing the value of the heterogeneous-type first-order reaction. To check the analytical results, a second-order high-resolution finite volume scheme is used. Both results are in good agreement and indicate the correctness of the numerical scheme. The current work is also compared with the previously available numerical schemes, which shows that the proposed numerical scheme is better for elaborating the chromatographic reactor performance. A comparison table is also presented to compute error analysis and computational run time for analyzing the efficiency of the reactor. A graphical sketch of the numerical temporal moment analysis is also presented, which gives significant information about the performance and the shape of the concentration profiles.

Role of institutional quality in debt-growth relationship in Pakistan: An econometric inquiry.

This study attempts to investigate the mediating role of institutional quality on the relationship between public debt and economic growth in Pakistan spanning 1996-2020. Time series data on all six World Bank World Governance indicators of institutional quality is used in the empirical analysis. Findings of the autoregressive distributed lag (ARDL) bounds testing technique and error correction method (ECM) confirmed the existence of cointegration among variables of interest. The short-run results indicate that public debt has a favorable association with economic growth, while the relationship is found to be detrimental in the long run. Furthermore, the combined effect of public debt and institutional quality indicators revealed the significant positive association with economic growth, suggesting that better institutional quality can contribute to mitigate the negative impact of public debt on economic growth in Pakistan.

Unlocking the secrets of exchange rate determination in Malaysia: A Game-Changing hybrid model.

Nominal exchange rate determination is a puzzling phenomenon throughout the literature. Thus, the study aims to analyze the nominal exchange rate determination with a hybrid approach of macroeconomic and microstructure determinants, i.e., interest rate differential, oil price, order flow, and bid-ask spread over the long- and short-run horizons in the context of Malaysia. The dataset consists of high-frequency daily data from 2010 to 2017, employing a nonlinear ARDL approach. The results indicate that the bid-ask spread and interest rate differential were found to be key determinants of exchange rate dynamics in the long and short run. The findings show strong evidence of long-run asymmetry in the interest rate differential, while short-run asymmetry effects exist between microstructure determinants and the exchange rate. In addition, it indicates that the bid-ask spread holds informative content to explain the dynamics of the exchange rate in Malaysia. Additionally, the negative changes in the oil price could potentially act as macroeconomic news announcements and the bid-ask spread as liquidity determinants in Malaysia, which play a significant role in exchange rate determination. The study concluded that the prominent short-run asymmetry effects captured in cumulative order flow and bid-ask spread While a long-run asymmetry exists between the oil price and exchange rate in Malaysia. The empirical results allow for long-run and short-run asymmetric pricing impacts of a hybrid approach on the nominal exchange rate in Malaysia. This study is helpful in providing policy direction and practical implications for monetary authorities and market dealers. The bid-ask spread and oil price could be considered influential exchange rate determinants in the short run in Malaysia.

Exploring the relationships between attitudes toward emission trading schemes, artificial intelligence, climate entrepreneurship, and sustainable performance.

The research objective of this research paper is to examine the relationships between organizational commitment to emission trading schemes, artificial intelligence, and climate entrepreneurship, as well as their impact on sustainable performance, i.e., environmental and organizational performance in organizations. This study aims to identify the key drivers and barriers to the adoption of these factors and to understand how they influence environmental and organizational performance. The study utilizes a cause-and-effect design on the sampled size of 387 subjects and employs the Smart PLS version 4.0 statistical tool to estimate the interactions between all constructs in the structural equation modeling. The research questions aim to explore the key drivers and barriers to the adoption of these factors in organization and their impact on sustainable performance, i.e., environmental and organizational performance. The study hypothesizes that organizational commitment to emission trading schemes, AI, and climate entrepreneurship has a positive impact on sustainable performances such as environmental and organizational performance. The findings suggest that attitudes toward AI and emission trading schemes have a direct impact on climate entrepreneurship, environmental performance, and organizational performance. By considering these factors together, the study seeks to uncover the synergistic effects and potential interactions between them and sheds light on their combined influence on environmental and organizational performance. Organizations can enhance environmental and organizational performance by prioritizing their attitudes toward emission trading schemes, AI, and climate entrepreneurship through resource allocation, technology investment, and fostering a climate entrepreneurship mindset. The research concludes that businesses that demonstrate high organizational commitment, positive attitude toward the ETS, and a focus on climate entrepreneurship experiences improved environmental and organizational performance.

Comparative evaluation of cell-mediated immune response in calves immunized with live-attenuated and killed Theileria annulata vaccines.

Tropical theileriosis is a tick-borne disease caused by the protozoan Theileria annulata and transmitted by numerous species of Ixodid ticks of the genus Hyalomma. The main clinical signs are fever, lymphadenopathy, and anemia responsible for heavy economic losses, including mortality, morbidity, vaccination failure, and treatment cost. Development of poor cell-mediated immunity (CMI) has been observed in the case of many bovine pathogens (bacteria, viruses, and parasites). Quantification of CMI is a prerequisite for evaluating vaccine efficacy against theileriosis caused by T. annulata. The current study evaluated the CMI in calves administered with two types of T. annulata vaccine (live attenuated and killed). We prepared a live attenuated T. annulata vaccine by attenuation in a rabbit model and also prepared killed vaccine from non-attenuated T. annulata. For the evaluation of immune response in experimental groups including control, 20 calves were divided into four different groups (A, B, C, and D). They were either inoculated subcutaneously with live rabbit-propagated-Theileria-infected RBCs (5 × 106) (group A) or with killed T. annulata vaccine (2 × 109 schizonts) with Freund's adjuvant (group B), along with an infected group (group C) and a healthy control group (group D). The protection of vaccinated calves was estimated with challenge infection. Our results showed that with a single shot of live-attenuated and killed vaccine with a booster dose elicited cell-mediated immune responses in immunized calves. We observed a significant elevation in CD4 + and CD8 + T cells in immunized calves. A significant difference in the CD8 + T cell response between the post-challenge stage of killed and live vaccine (p < 0.0001) was observed, whereas no other difference was found at both pre- and post-immunization stages. A similar finding was recorded for the CD4 + T cells at a post-challenge stage, where a significant difference was seen between killed and live vaccine (p < 0.0001). Another significant difference was observed between the CD8 + T cells and CD4 + T cells at the post-challenge stage in the live vaccine group, where there was a significantly higher induction of CD4 + T cell response (p < 0.0001).

Graph data science and machine learning for the detection of COVID-19 infection from symptoms.

Background: COVID-19 is an infectious disease caused by SARS-CoV-2. The symptoms of COVID-19 vary from mild-to-moderate respiratory illnesses, and it sometimes requires urgent medication. Therefore, it is crucial to detect COVID-19 at an early stage through specific clinical tests, testing kits, and medical devices. However, these tests are not always available during the time of the pandemic. Therefore, this study developed an automatic, intelligent, rapid, and real-time diagnostic model for the early detection of COVID-19 based on its symptoms. Methods: The COVID-19 knowledge graph (KG) constructed based on literature from heterogeneous data is imported to understand the COVID-19 different relations. We added human disease ontology to the COVID-19 KG and applied a node-embedding graph algorithm called fast random projection to extract an extra feature from the COVID-19 dataset. Subsequently, experiments were conducted using two machine learning (ML) pipelines to predict COVID-19 infection from its symptoms. Additionally, automatic tuning of the model hyperparameters was adopted. Results: We compared two graph-based ML models, logistic regression (LR) and random forest (RF) models. The proposed graph-based RF model achieved a small error rate = 0.0064 and the best scores on all performance metrics, including specificity = 98.71%, accuracy = 99.36%, precision = 99.65%, recall = 99.53%, and F1-score = 99.59%. Furthermore, the Matthews correlation coefficient achieved by the RF model was higher than that of the LR model. Comparative analysis with other ML algorithms and with studies from the literature showed that the proposed RF model exhibited the best detection accuracy. Conclusion: The graph-based RF model registered high performance in classifying the symptoms of COVID-19 infection, thereby indicating that the graph data science, in conjunction with ML techniques, helps improve performance and accelerate innovations.

Unsteady ternary hybrid-nanofluid flow over an expanding/shrinking cylinder with multiple slips: a Yamada-Ota model implementation.

The primary objective of this investigation is to examine the thermal state of an unsteady ternary hybrid-nanofluid flow over an expanding/shrinking cylinder. The influence of radiation along with a non-uniform thermal source/sink is taken into account to expedite heat distribution. Multiple slips are considered at the cylinder interface. The mathematical model is simplified by incorporating appropriate transformations. A numerical solution is obtained using the bvp4c algorithm. The flow characteristics and behavior of the trihybrid nanoliquid exhibit significant changes when the cylinder expands or contracts. The effects of various emerging parameters are analyzed using graphical representations. The velocity field shows an opposite trend when the unsteadiness and mass transfer parameters are increased. The thermal field improves with higher values of the non-uniform source/sink parameter but deteriorates with an increase in the thermal slip parameter. The drag force increases with higher values of the unsteadiness parameter, while it decreases with amplified values of the mass suction and velocity slip parameters. A strong correlation is observed with previous studies which validates and strengthens the credibility of the present analysis.

Toward a Realistic Surface State of Ru in Aqueous and Gaseous Environments.

Identifying the surface species is critical in developing a realistic understanding of supported metal catalysts working in water. To this end, we have characterized the surface species present at a Ru/water interface by employing a hybrid computational approach involving an explicit description of the liquid water and a possible pressure of H2. On the close-packed, most stable Ru(0001) facet, the solvation tends to favor the full dissociation of water into atomic O and H in contrast with the partially dissociated water layer reported for ultra-high-vacuum conditions. The solvation stabilization was found to reach -0.279 J m2, which results in stable O and H species on Ru(0001) in the presence of liquid water even at room temperature. Conversely, introducing even a small H2 pressure (10-2 bar) results in a monolayer of chemisorbed H at the interface, a general trend found on the three most exposed facets of Ru nanoparticles. While hydroxyls were often hypothesized as possible surface species at the Ru/water interface, this computational study clearly demonstrates that they are not stabilized by liquid water and are not found under realistic reductive catalytic conditions.