Interaction of surface-modified silica nanoparticles with clay minerals

Abstract In this study, the adsorption of 5-nm silica nanoparticles onto montmorillonite and illite is investigated. The effect of surface functionalization was evaluated for four different surfaces: unmodified, surface-modified with anionic (sulfonate), cationic (quaternary ammonium (quat)), and nonionic (polyethylene glycol (PEG)) surfactant. We employed ultraviolet–visible spectroscopy to determine the concentration of adsorbed nanoparticles in conditions that are likely to be found in subsurface reservoir environments. PEG-coated and quat/PEG-coated silica nanoparticles were found to significantly adsorb onto the clay surfaces, and the effects of electrolyte type (NaCl, KCl) and concentration, nanoparticle concentration, pH, temperature, and clay type on PEG-coated nanoparticle adsorption were studied. The type and concentration of electrolytes were found to influence the degree of adsorption, suggesting a relationship between the interlayer spacing of the clay and the adsorption ability of the nanoparticles. Under the experimental conditions reported in this paper, the isotherms for nanoparticle adsorption onto montmorillonite at 25 °C indicate that adsorption occurs less readily as the nanoparticle concentration increases

An Investigation of Barriers to Adopt Green Innovation Among Manufacturing Organizations in Vietnam

This research aims to identify the main barriers to green innovation in Vietnam manufacturing organizations. This study began by reviewing the relevant literature and providing a solid theoretical framework to understand the determinants of green innovation for manufacturing firms in the global context. It also helps internal and external stakeholders figure out what influence and how to implement green innovation more efficiently by removing all impediments. Additionally, this article is considered a valuable and rational evidence for prioritizing and directing innovation policies in the manufacturing industry. Based on numerical data from 143 employees at middleand upper-level managers among manufacturing companies around Vietnam, the study found that deficiency of financial resources primarily significantly impacts green innovation adoption, followed by the uncertainty of market demand and lack of government support. However, with limited observations, the investigation did not observe the dynamic effect of green innovation over periods and only focused on the manufacturing sector instead of different industries for generalizing the research results. Moreover, the circumstances of green innovation would be diverse in other nations. Keywords: green innovation, manufacturing organizations, government supports, financial barriers, market barrier

Stability investigations of isotropic and anisotropic exponential inflation in the Starobinsky-Bel-Robinson gravity

In this paper, we would like to examine whether a novel Starobinsky-Bel-Robinson gravity model admits stable exponential inflationary solutions with or without spatial anisotropies. As a result, we are able to derive an exact de Sitter inflationary to this Starobinsky-Bel-Robinson model. Furthermore, we observe that an exact Bianchi type I inflationary solution does not exist in the Starobinsky-Bel-Robinson model. However, we find that a modified Starobinsky-Bel-Robinson model, in which the sign of coefficient of $R^2$ term is flipped from positive to negative, can admit the corresponding Bianchi type I inflationary solution. Unfortunately, stability analysis using the dynamical system approach indicates that both of these inflationary solutions turn out to be unstable. Interestingly, we show that a stable de Sitter inflationary solution can be obtained in the modified Starobinsky-Bel-Robinson gravity.Comment: 26 pages, 2 figures. V2 with the abstract revised to improve its clarity, some relevant references added, and some typos fixed. All main calculations and conclusions remain unchanged. Comments are welcom

Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigation

The work discusses the use of machine learning algorithms for anomaly detection in medical image analysis and how the performance of these algorithms depends on the number of annotators and the quality of labels. To address the issue of subjectivity in labeling with a single annotator, we introduce a simple and effective approach that aggregates annotations from multiple annotators with varying levels of expertise. We then aim to improve the efficiency of predictive models in abnormal detection tasks by estimating hidden labels from multiple annotations and using a re-weighted loss function to improve detection performance. Our method is evaluated on a real-world medical imaging dataset and outperforms relevant baselines that do not consider disagreements among annotators.Comment: This is a short version submitted to the Midwest Machine Learning Symposium (MMLS 2023), Chicago, IL, US