Search

Shock-induced flow separation is important in hypersonic intake unstart and for unsteady aero-thermal loads. This work presents a computational study of shock-boundary layer interaction (SBLI) at hypersonic Mach numbers. The objective is to accurately predict the separation bubble size for oblique shock impingement on a turbulent boundary layer and for two-dimensional axisymmetric cone-flare SBLI cases. We use the Reynolds-averaged Navier Stokes method with an advanced shock-strength-dependent turbulence model. A recently developed transport equation-based shock sensor is employed to identify the location and strength of shock waves. The computational results are found to match experimental measurements of surface pressure and skin-friction coefficient for a series of test cases, highlighting the effects of Mach number, shock generator angle, and wall cooling on the size of the separation bubble. Results are also found to conform to the scaling of SBLI separation size found in the literature. This is one of the few such corroborations for hypersonic axisymmetric SBLI. [ABSTRACT FROM AUTHOR]

An analytical framework is presented here for estimating the limiting area contraction for a family of curved compression ramp (CCR) supersonic air intakes with self-starting capability via overboard spillage. Flow compression in CCR intakes occurs through a combination of a curved ramp shock wave and a series of compression waves. In general, such a combination offers the best features of the canonical air intake families. That is, pressure recovery in CCR intakes is better than simple ramp (planar ramp) air intakes, and pressure ratios in CCR intakes are better than Prandtl-Mayer air intakes. A modified Kantrowitz criterion for startability is proposed to accommodate the nonuniform Mach number distribution in the cross-stream direction at the intake throat that is inevitably realized in CCR air intakes. The starting characteristics of different design configurations that fall within the family of CCR air intakes are analyzed, and a comparison is made with planar ramp and Prandtl-Meyer intakes. Inviscid flow numerical experiments of intake starting were carried out to successfully verify the starting characteristics predicted by the analytical model. The analytical framework presented here can be employed as a general tool for optimizing air intake design based on startability, efficiency, and other performance considerations. [ABSTRACT FROM AUTHOR]

Since the proposition of the Hofmeister series, guanidinium (Gdm) salts hold a special mention in protein science owing to their contrasting effect on protein(s) depending on the counteranion(s). For example, while GdmCl is known to act as a potential protein denaturant, Gdm 2 SO 4 offers minimal effect on protein structure. Despite the fact that theoretical studies reckon the formation of ion-pairing to be responsible for such behavior, experimental validation of this hypothesis is still in sparse. In this study, we combine electrochemical impedance spectroscopy (EIS) and THz spectroscopy to underline the effect of GdmCl and Gdm 2 SO 4 on a model amide molecule N -methylacetamide (NMA). Molecular dynamics (MD) simulation studies predict that Gdm 2 SO 4 forms heteroion pairing in water, which inhibits Gdm + ions to approach NMA molecules, while in case of GdmCl, Gdm + ions directly interact with NMA. The experimental findings on ion hydration, specifically the detailed analysis of the ion-water rattling mode, which appears in the THz frequency domain, unambiguously endorse this hypothesis. Our study establishes the fact that the propensity of ion-pairing in Gdm salts dictates their (de)stabilization effect on proteins.

The unique physicochemical properties inherent to nanoscale materials have unveiled numerous potential applications, spanning beyond the pharmaceutical and medical sectors into various consumer industries like food and cosmetics. Consequently, humans encounter nanomaterials through diverse exposure routes, giving rise to potential health considerations. Noteworthy among these materials are silica and specific metallic nanoparticles, extensively utilized in consumer products, which have garnered substantial attention due to their propensity to accumulate and induce adverse effects in the liver. This review paper aims to provide an exhaustive examination of the molecular mechanisms underpinning nanomaterial-induced hepatotoxicity, drawing insights from both in vitro and in vivo studies. Primarily, the most frequently observed manifestations of toxicity following the exposure of cells or animal models to various nanomaterials involve the initiation of oxidative stress and inflammation. Additionally, we delve into the existing in vitro models employed for evaluating the hepatotoxic effects of nanomaterials, emphasizing the persistent endeavors to advance and bolster the reliability of these models for nanotoxicology research., Competing Interests: Conflict-of-interest statement: All authors have no conflicts of interest to disclose., (©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.)

Metabolic reactions in the body transform the administered drug into metabolites. These metabolites exhibit diverse biological activities. Drug metabolism is the major underlying cause of drug overdose-related toxicity, adversative drug effects and the drug's reduced efficacy. Though metabolic reactions deactivate a drug, drug metabolites are often considered pivotal agents for off-target effects or toxicity. On the other side, in combination drug therapy, one drug may influence another drug's metabolism and clearance and is thus considered one of the primary causes of drug-drug interactions. Today with the advancement of machine learning, the metabolic fate of a drug candidate can be comprehensively studied throughout the drug development procedure. Naïve Bayes, Logistic Regression, k-Nearest Neighbours, Decision Trees, different Boosting and Ensemble methods, Support Vector Machines and Artificial Neural Network boosted Deep Learning are some machine learning algorithms which are being extensively used in such studies. Such tools are covering several attributes of drug metabolism, with an emphasis on the prediction of drug-drug interactions, drug-target-interactions, clinical drug responses, metabolite predictions, sites of metabolism, etc. These reports are crucial for evaluating metabolic stability and predicting prospective drug-drug interactions, and can help pharmaceutical companies accelerate the drug development process in a less resourcedemanding manner than what in vitro studies offer. It could also help medical practitioners to use combinatorial drug therapy in a more resourceful manner. Also, with the help of the enormous growth of deep learning, traditional fields of computational drug development like molecular interaction fields, molecular docking, quantitative structure-toactivity relationship (QSAR) studies and quantum mechanical simulations are producing results which were unimaginable couple of years back. This review provides a glimpse of a few contextually relevant machine learning algorithms and then focuses on their outcomes in different studies., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)