Solution Conditions Research Articles

How Salt and Temperature Drive Reentrant Condensation of Aβ40.

Within the framework of liquid-liquid phase separation (LLPS), biomolecular condensation orchestrates vital cellular processes, and its dysregulation is implicated in severe pathological conditions. Recent studies highlight the role of intrinsically disordered proteins (IDPs) in LLPS, yet the influence of microenvironmental factors has remained a puzzling factor. Here, via computational simulation of the impact of solution conditions on LLPS behavior of neurologically pathogenic IDP Aβ40, we chanced upon a salt-driven reentrant condensation phenomenon, wherein Aβ40 aggregation increases with low salt concentrations (25-50 mM), followed by a decline with further salt increments. An exploration of the thermodynamic and kinetic signatures of reentrant condensation unveils a nuanced interplay between protein electrostatics and ionic strength as potential drivers. Notably, the charged residues of the N-terminus exhibit a nonmonotonic response to salt screening, intricately linked to the recurrence of reentrant behavior in hydrophobic core-induced condensation. Intriguingly, our findings also unveil the reappearance of similar reentrant condensation phenomena under varying temperature conditions. Collectively, our study illuminates the profoundly context-dependent nature of Aβ40s liquid-liquid phase separation behavior, extending beyond its intrinsic molecular framework, where microenvironmental cues wield significant influence over its aberrant functionality.

Robust Optimality and Duality for Nonsmooth Multiobjective Programming Problems with Vanishing Constraints Under Data Uncertainty

This article investigates robust optimality conditions and duality results for a class of nonsmooth multiobjective programming problems with vanishing constraints under data uncertainty (UNMPVC). Mathematical programming problems with vanishing constraints constitute a distinctive class of constrained optimization problems because of the presence of complementarity constraints. Moreover, uncertainties are inherent in various real-life problems. The aim of this article is to identify an optimal solution to an uncertain optimization problem with vanishing constraints that remains feasible in every possible future scenario. Stationary conditions are necessary conditions for optimality in mathematical programming problems with vanishing constraints. These conditions can be derived under various constraint qualifications. Employing the properties of convexificators, we introduce generalized standard Abadie constraint qualification (GS-ACQ) for the considered problem, UNMPVC. We introduce a generalized robust version of nonsmooth stationary conditions, namely a weakly stationary point, a Mordukhovich stationary point, and a strong stationary point (RS-stationary) for UNMPVC. By employing GS-ACQ, we establish the necessary conditions for a local weak Pareto solution of UNMPVC. Moreover, under generalized convexity assumptions, we derive sufficient optimality criteria for UNMPVC. Furthermore, we formulate the Wolfe-type and Mond–Weir-type robust dual models corresponding to the primal problem, UNMPVC.

Vanadium oxide thin films growth by a chemical solution deposition method and its pH sensor toward bio-sensing devices

Abstract We report on the evaluation of the structure of VOx thin films under different fabrication conditions of the chemical solution deposition method and the pH response of an extended gate field effect transistor (EGFET)-type pH sensor. Polycrystalline V2O5 thin films of 30 and 60 nm thick and amorphous VOx thin films of about 30 nm thick were successfully prepared under different precursor solution conditions. Using polycrystalline V2O5 thin films and amorphous VOx thin films as an extended gate (EG) electrode, protypes of EGFET-type pH sensors were fabricated and investigated their pH response characteristics. The average pH responsibility of 64 mV/pH, which exceeded the theoretical Nernst limit, was observed over a wide pH range from 2.4 to 8.0 in the amorphous VOx extended gate sensor. It was also found that stable pH response measurements could be performed on amorphous VOx film samples.

On a Dini Type Blow-Up Condition for Solutions of Higher Order Nonlinear Differential Inequalities

On a Dini Type Blow-Up Condition for Solutions of Higher Order Nonlinear Differential Inequalities

On the interior regularity conditions of a suitable weak solution to 3D MHD equations

On the interior regularity conditions of a suitable weak solution to 3D MHD equations

Optical dromions for M-fractional Kuralay equation via complete discrimination system approach along with sensitivity analysis and quasi-periodic behavior

This paper investigates new analytical wave solutions for the fractional Kuralay-IIB equations (FK-IIBE), including a new definition of the fractional derivative. This model works in disciplines such as ferromagnetic materials, optical fibers, wave mixing, and nonlinear optics. The integrable motion of space curves can be determined by our governing model. This research holds great importance in the area of optical fibers, exact and analytical solitons solution. The polynomial method’s complete discrimination system (CDSPM) is used to identify a variety of exact and analytical soliton solutions such as periodic, hyperbolic, rational, Jacobian elliptic (JE), and trigonometric functions. Additionally, we also convert the JE function into a solitary wave (SW) solution. To provide a comprehensive understanding of the dynamic aspects of the system, we also address bifurcation points, quasi-periodic behavior, sensitivity analysis, Poincarè maps, time series profile, and critical solution conditions. The research offers straightforward, efficient algorithms that can consistently and effectively address FK-IIBE. Machine learning technologies are also utilized to satisfy the criteria defined by dynamic analysis. Python regression learner tools are used for reverse engineering to analyze the mathematical model’s equilibrium based on parameter intervals and thresholds determined by dynamical analysis.

Investigating the effects of different chemical conditions in aqueous solutions on the adsorption of Eu(III) onto illite colloids

Investigating the effects of different chemical conditions in aqueous solutions on the adsorption of Eu(III) onto illite colloids

Precipitation Refinement via Aging Time Modification for Enhancing Wear Resistance in Ti–V–Nb Microalloyed High‐Manganese Steels

It is imperative to ensure an even distribution of refined precipitates to enhance wear resistance. Herein, different heat treatments are to control the size and distribution of precipitates. The treatments include water quenching at 1100 °C followed by aging at 400 °C for 24, 60, and 84 h, designated as AT24, AT60, and AT84, respectively. The microstructure, mechanical properties, and impact wear properties of high‐manganese steel are investigated under solution and aging conditions using scanning electron microscopy, field‐emission scanning electron microscopy, tensile testing, and impact abrasive wear testing. Notably, the absence of nanoscale precipitates largely accounts for the poor wear resistance of as‐casting steel, whereas the strengthening effect of larger micrometer‐sized precipitates is insufficient. After the solution and aging treatment, nanosized precipitates continuously form within the matrix, conducive to the formation of the deeper work‐hardening layer, thereby improving the wear resistance. The fine micrometer‐sized precipitates and evenly distributed nanoscale precipitates in AT60 actively contribute to toughness. Additionally, these precipitates interact with slip dislocations, providing stronger strengthening via the Orowan looping mechanism. The wear mechanisms of steel can be transformed from wide, deep pits to shallow grooves and microcutting by extending the aging time.

Witte’s conditions for uniqueness of solutions to a class of Fractal-Fractional ordinary differential equations

In this paper, Witte's conditions for the uniqueness solution of nonlinear differential equations with integer and non-integer order derivatives are investigated. We present a detailed analysis of the uniqueness solutions of four classes of nonlinear differential equations with nonlocal operators. These classes include classical and fractional ordinary differential equations in fractal calculus. For each case, theorems and lemmas and their proofs are presented in detail.

YOLOv8-licorice: a lightweight salt-resistance detection method for licorice based on seed germination state.

Seeds will display different germination states during the germination process, and their good or bad state directly influences the subsequent growth and yield of the crop. This study aimed to address the difficulties of obtaining the images of seed germination process in all time series and studying the dynamic evolution law of seed germination state under stress conditions. A licorice sprouting experiment was performed using a seed sprouting phenotype acquisition system to obtain images of the sprouting process of licorice in full-time sequence. A labeled dataset of licorice full-time sequence sprouting process images was constructed based on the four states of unsprouted, sprouted, cracked, and shelled in the sprouting process. An optimized model, YOLOv8-Licorice, was developed based on the YOLOv8-n model and its effectiveness was demonstrated by comparative and ablation tests. Different salt stress environments were simulated via NaCl aqueous solution concentration, and germination experiments of licorice seeds were performed under different salt stresses. The germination state of licorice under different salt stress environments was detected using the YOLOv8-Licorice detection model. Percentage curve of licorice seeds in an unsprouted state displayed a continuous decreasing trend. For the percentage curve of licorice seeds in the sprouted state, an increasing and then decreasing trend was observed under the condition of 0-200 mmol/L NaCl solution, and a continuous increasing trend was observed under the condition of 240-300 mmol/L NaCl solution. Licorice seeds in the cracked state demonstrated percentage curves with an increasing and then decreasing trend under the condition of 0-140 mmol/L NaCl solution and a continuous increasing trend under the condition of 160-300 mmol/L NaCl solution. The percentage curve of licorice seeds in shelled state displayed a continuous increasing trend in 0-200 mmol/L NaCl solution condition and remained horizontal in 220-300 mmol/L NaCl solution condition. Overall, this study provides a valuable method involving the seed sprouting phenotype acquisition system and the proposed method for detecting the germination state of licorice seeds. This method serves as a valuable reference to comprehensively understand the seed sprouting process under triggering treatment.

Rapid screening of prednisolone acetate adulterants in health foods using colloidal gold immunochromatographic assay

In this study, a rapid detection method utilizing colloidal gold immunochromatography (CG-ICA) was developed for the detection of illegally added prednisone acetate in health foods. Initially, the preparation conditions of colloidal gold solution were optimized. The optimal potassium carbonate dosage, antibody diluent type, antibody dosage, probe labeling time, blocking time and BSA dosage were determined. Technical analysis was performed to ensure that the established CG-ICA exhibited satisfactory color development and inhibition rates. Under optimized conditions, the cut-off value of CG-ICA was 250 μg/kg. The assay demonstrated a sensitivity of 100 %, a false positive rate of 8 %, and a false negative rate of 0, indicating high specificity for prednisone acetate. The results obtained from testing actual samples were consistent with those obtained using LC-MS/MS, thereby verifying the reliability of the developed method. This method offers robust support for the rapid detection of illegally added prednisone acetate in health foods.

A Strategy for Quantifying Microplastic Particles in Membrane Filtration Processes using Flow Cytometry

Microplastic (MP) pollution is ubiquitous in the aquatic environment, with significant quantities of MPs originating from municipal wastewater treatment plants. Efforts to evaluate and implement MP removal processes are underway, with membrane technologies often recommended as an "ideal" solution. A key challenge in evaluating these technologies involves efficiently quantifying MP concentrations in samples. Here, flow cytometry (FC) is demonstrated as an effective technique to obtain concentration measurements of plastic microbeads (MBs; 1-5 μm) suspended in water with/without added humic acid. Regardless of solution conditions, MB concentrations were easily quantified via FC. Subsequently, two microfiltration membranes were challenged to these suspensions. As measured via FC, the 0.45 μm membrane demonstrated effective MB rejection (>99%) whereas the 5 μm membrane exhibited a broad range of rejections (40% to >95%) depending on solution conditions and filtration time. Finally, a model was formulated utilizing FC forward light scattering intensity measurements to estimate MB sizes in samples. Using the model, a 33% reduction in median MB size, on average, was noted across the 5 μm membrane when filtering MBs suspended in humic acid solution, affirming a preferential permeation of smaller particles. Overall, this study advances MP quantification techniques towards validating removal processes.

Predictive Model Building for Aggregation Kinetics Based on Molecular Dynamics Simulations of an Antibody Fragment.

Computational methods including machine learning and molecular dynamics simulations have strong potential to characterize, understand, and ultimately predict the properties of proteins relevant to their stability and function as therapeutics. Such methods would streamline the development pathway by minimizing the current experimental testing required for many protein variants and formulations. The molecular understanding of thermostability and aggregation propensity has advanced significantly along with predictive algorithms based on the sequence-level or structural-level information on a protein. However, these approaches focus largely on a comparison of protein sequence variations to correlate the properties of proteins to their stability, solubility, and aggregation propensity. For therapeutic protein development, it is of equal importance to take into account the impact of the formulation conditions to elucidate and predict the stability of the antibody drugs. At the macroscopic level, changing temperature, pH, ionic strength, and the addition of excipients can significantly alter the kinetics of protein aggregation. The mechanisms controlling aggregation kinetics have been traced back to a combination of molecular features, including conformational stability, partial unfolding to aggregation-prone states, and the colloidal stability governed by surface charges and hydrophobicity. However, very little has been done to evaluate these features in the context of protein dynamics in different formulations. In this work, we have combined a range of molecular features calculated from the Fab A33 protein sequence and molecular dynamics simulations. Using the power of advanced, yet interpretable, statistical tools, it has been possible to uncover greater insights into the mechanisms behind protein stability, validating previous findings, and also develop models that can predict the aggregation kinetics within a range of 49 different solution conditions.

On stability of nonlinear difference equations and some of their applications

The issues of stability in solving nonlinear difference equations were considered. Based on a generalized difference analog of the well-known Bihari lemma, stability conditions for a trivial solution based on initial data were obtained, and an a priori estimate of stability under permanent disturbances was determined. The results were used to study the stability of solving explicit and implicit difference schemes approximating nonlinear parabolic equations.

A Combined Physical-Mechanical and Hydrometallurgical Approach for Recovering Valuable Metals from Spent Lithium-ion Batteries

In this work, the chemical composition of electrode materials from two samples of lithium-ion batteries (LiB) is comprehensively investigated. The material balance of the physical and mechanical processing of the LiBs mixture is determined. The developed dry process scheme made it possible to extract the following components (wt.%): 15.6 plastic (ABS), 1.89 electronic materials (PP), 59.1 black mass (three types), 6.43 plastic (PVC), 2.97 Al, 6.31 Cu and 7.1 magnetic fraction (Fe). The thermodynamics of reductive leaching of LiCoO<sub>2</sub> in the H<sub>2</sub>SO<sub>4</sub>-HCOOH system was studied. It was calculated that the Gibbs energy of the leaching reaction at 363 K is -327.4 kJ/mol, the equilibrium constant is 2.02×10<sup>44</sup>. All these factors showed the potential of using formic acid as a reducing agent instead of the commonly used hydrogen peroxide solution. It is known to be unstable, since when the solution is heated from 20 to 50<sup>0</sup> C, the decomposition of H<sub>2</sub>O<sub>2</sub> increases 20 times, and the presence of copper sulfate, which usually accompanies the decomposition of the black mass in a sulfuric acid medium, leads to the destruction of 76% of hydrogen peroxide. Therefore, from a practical point of view, it was of interest to study the kinetics of cobalt dissolution from the cathode material in the H<sub>2</sub>SO<sub>4</sub>-HCOOH system. It was shown that the Crank-Ginstling-Braunstein equation agrees satisfactorily with the experimental data, which indicates intra-diffusion limitation of dissolution. The activation energy was determined. The optimal composition and conditions of the leaching solution for dissolving cobalt, lithium and associated transition metals from the black mass were determined. The thermodynamics and kinetics of dissolution of Ag, Au and Pd from electrode materials in a bromide-bromine solution were also studied.

Multi-armed Bandit Experimental Design: Online Decision-Making and Adaptive Inference

Multi-armed bandit has been well known for its efficiency in online decision-making in terms of minimizing the loss of the participants’ welfare during experiments (i.e., the regret). In clinical trials and many other scenarios, the statistical power of inferring the treatment effects (i.e., the gaps between the mean outcomes of different arms) is also crucial. Nevertheless, minimizing the regret entails harming the statistical power of estimating the treatment effect because the observations from some arms can be limited. In this paper, we investigate the trade-off between efficiency and statistical power by casting the multi-armed bandit experimental design into a minimax multi-objective optimization problem. We introduce the concept of Pareto optimality to mathematically characterize the situation in which neither the statistical power nor the efficiency can be improved without degrading the other. We derive a useful sufficient and necessary condition for the Pareto optimal solutions to the minimax multi-objective optimization problem. Additionally, we design an effective Pareto optimal multi-armed bandit experiment that can be tailored to different levels of the trade-off between the two objectives. Moreover, we extend the design and analysis to the setting where the outcome of each arm consists of an adversarial baseline reward and a stochastic treatment effect, demonstrating the robustness of our design. Finally, motivated by clinical trials, we examine the setting where the employed experiment must split the experimental units into a small number of batches, and we propose a flexible Pareto optimal design. This paper was accepted by George Shanthikumar, data science. Funding: The authors thank the Massachusetts Institute of Technology (MIT)-IBM partnership in Artificial Intelligence and the MIT Data Science Laboratory for support. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00492 .

Physically consistent immersed boundary method: A framework for predicting hydrodynamic forces on particles with coarse meshes

In the present paper, a fluid-particle coupling method is directly derived from the Navier-Stokes equations (NSE) by applying the concept of volume-filtering, yielding a physically consistent methodology to incorporate solid wall boundary conditions in the volume-filtered flow solution, thereby allowing to solve the governing flow equations on non-body conforming meshes. The resulting methodology possesses similarities with a continuous forcing immersed boundary method (IBM) and is, therefore, termed physically consistent IBM (PC-IBM). Based on the recent findings of Hausmann et al. [1], the closures arising in the volume-filtered NSE are closed by suitable models or even expressed analytically. The PC-IBM is fully compatible with the large eddy simulation framework, as the volume-filtered NSE converge to the filtered NSE away from solid boundaries. The potential of the PC-IBM is demonstrated by means of different particle-laden flow applications, including a dense packing of fixed particles and periodic settling of 500 particles. The new methodology turns out to be capable of accurately predicting the fluid forces on particles with relatively coarse mesh resolutions. For the flow around isolated spheres, a spatial resolution of six fluid mesh cells per diameter is sufficient to predict the drag force with less than 10% deviation from highly resolved simulation for the investigated particle Reynolds numbers ranging from 10 to 250.

Novel polyamide nanofiltration membrane PEI-(β-CD@g-C3N5)/TMC based on microfiltration substrate for efficient separation of lithium and magnesium

With the development of industry, the scarcity of lithium resources is becoming increasingly prominent. Therefore, the development of high-performance nanofiltration (NF) membranes for lithium extraction from salt lakes has become crucial. Herein, β-Cyclodextrin (β-CD) and amino-rich graphitic carbon nitride (g-C3N5) sol were uniformly mixed to form β-CD@g-C3N5, which was then mixed with polyetherimide (PEI) monomer and subjected to interfacial polymerization (IP) reaction with trimesoyl chloride (TMC) on a 0.22 μm microfiltration (MF) membrane to prepare the PEI-(β-CD@g-C3N5)/TMC composite NF membrane. Through thin plate theory and gradient operation pressure filtration tests, it was found that the composite NF membrane prepared on the MF membrane substrate was not only defect-free but also exhibited superior compression resistance. In Li+/Mg2+ filtration tests, the PEI-(β-CD@g-C3N5)/TMC membrane showed superior NF performance, with a Li+/Mg2+ selectivity coefficient of 38.85 and a permeability of 8.91 L·m−2·h−1·bar−1. This was attributed to the effect of β-CD@g-C3N5, which could thin the separation layer, slightly enlarge and more uniformly distribute the pores, increase hydrophilicity, and enhance positive charge; meanwhile, the cavity structure provided by β-CD helped to effectively separate Li+/Mg2+. Furthermore, under conditions of mixed salt solutions with different Mg2+/Li+ mass ratios and 80 h of continuous filtration, the membrane exhibited excellent stability. This work introduces innovative membrane materials and design concepts to the field of lithium extraction via NF, potentially paving the way for the industrial application of NF technology.

Thermal conductivity in solid solutions of lithium niobate tantalate single crystals from 300 K up to 1300 K

Lithium niobate tantalate (LiNb1−xTaxO3, LNT) solid solutions offer exciting new possibilities for applications ranging from optics, piezotronics, and electronics beyond the capabilities of the widely used singular compounds of lithium niobate (LiNbO3, LN) or lithium tantalate (LiTaO3, LT). Crystal growth of homogeneous LNT single crystals by the Czochralski method is still challenging. One key aspect of homogeneous growth is the accurate knowledge of thermal conductivity through the crystal boule during the growth, which is central to control the crystal growth. Therefore, the temperature dependent thermal conductivity of pure LN, LT, and LNT solid solutions, as well as of selected doped LN and LT crystals (Mg, Zn) was investigated across the temperature range from 300 to 1300 K. The results that span across the whole composition range can directly be applied for optimizing growth conditions of both LNT solid solutions as well as doped and undoped LN and LT crystals.

Existence and Approximate Controllability of Random Impulsive Neutral Functional Differential Equation with Finite Delay

This study investigates second-order neutral functional differential equations with delays, prevalent in various scientific and engineering fields. These equations, characterized by their neutral nature and delays, present unique challenges within Banach spaces. The research focuses on the existence and approximate controllability of solutions, using advanced mathematical tools like cosine family theory and the Leray-Schauder theorem to establish rigorous solution conditions. These theoretical results are empirically validated through practical examples, enhancing understanding of real-life behavior and bridging theory with practice. The study’s findings advance the understanding of delayed feedback systems, facilitating effective control strategies and practical engineering solutions, thereby contributing significantly to dynamical systems and control theory.