We report a method for effectively and homogeneously incorporating carbon nanotubes (CNTs) in the form of double-wall (DWCNTs) and multi-wall (MWCNTs) structures into commercial paints without the use of additives, surfactants, or chemical processes. The process involves the physical mixing of the nanotubes and polymers using the cavitation energy of an ultrasonic bath. It is a simple, fast method that allows for uniform distribution of carbon nanotube bundles within the polymer for direct application. Due to the hydrophobic properties of the carbon nanotubes as grown, we used paint samples containing 0.3% by mass of both types of CNTs and observed an improvement in waterproofing through wettability and water absorption through immersion tests on the samples. Different solvents such as water, formaldehyde, and glycerin were used, and the results showed an increase in paint impermeability of 30% and 25% with the introduction of DWCNTs and MWCNTs, respectively. This indicates a promising, economically viable, and revolutionary method for applying nanotechnology in the polymer industry.

The food industry progressively requires innovative and environmentally safe packaging materials with increased physical, mechanical, and barrier properties. Due to its unique properties, cellulose has several potential applications in the food industry as a packaging material, stabilizing agent, and functional food ingredient. A coffee pod is a filter of cellulosic, non-rigid, ready-made material containing ground portions and pressed coffee prepared in dedicated machines. In our study, we obtained, with homogenization and sonication, cellulose micro/nanoparticles from three different coffee pods. It is known that nanoparticulate systems can enter live cells and, if ingested, could exert alterations in gastrointestinal tract cells. Our work aims to investigate the response of HT-29 cells to cellulose nanoparticles from coffee pods. In particular, the subcellular effects between coffee-embedded nanocellulose (CENC) and cellulose nanoparticles (NC) were compared. Finally, we analysed the pathologic condition (Cytolethal Distending Toxin (CDT) from Campylobacter jejuni) on the same cells conditioned by NC and CENC. We evidenced that, for the cellular functional features analysed, NC and CENC pre-treatments do not worsen cell response to the C. jejuni CDT, also pointing out an improvement of the autophagic flux, particularly for CENC preconditioning.

Alginate-silver nanocomposites in the form of spherical beads and films were prepared using a green approach by using the aqueous extract of Ajwa date seeds. The nanocomposites were fabricated by in situ reduction and gelation by ionotropic crosslinking using calcium ions in solution. The rich phytochemicals of the date seed extract played a dual role as a reducing and stabilizing agent in the synthesis of silver nanoparticles. The formation of silver nanoparticles was studied using UV-Vis absorption spectroscopy, and a distinct surface plasmon resonance peak at 421 nm characteristic of silver nanoparticles confirmed the green synthesis of silver nanoparticles. The morphology of the nanocomposite beads and film was compact, with an even distribution of silver nanoclusters. The catalytic property of the nanocomposite beads was evaluated for the degradation of 2-nitrophenol in the presence of sodium borohydride. The degradation followed pseudo-first-order kinetics with a rate constant of 1.40 × 10⁻³ s⁻¹ at 23 ℃ and an activation energy of 18.45 kJ mol⁻¹. The thermodynamic parameters, such as changes in enthalpy and entropy, were evaluated to be 15.22 kJ mol⁻¹ and −197.50 J mol⁻¹ K⁻¹, respectively. The nanocomposite exhibited properties against three clinically important pathogens (gram-positive and gram-negative bacteria).

The initiation of tapering, sparked by heightened inflation in the United States, reverberates across global markets, with notable implications for Indonesia. This study delved into the nuanced impact of tapering on Sharia-compliant stocks in both Indonesia and Malaysia. The rationale behind selecting Sharia stocks for analysis lies in their composition, featuring companies boasting low debt-to-asset and equity ratios, thereby positing robust resilience in the face of the Federal Reserve’s implementation of tapering. Employing a time series dataset with a weekly sampling period spanning from January to September 2022, the analysis adopted the Error Correction Model (ECM) within a multiple regression framework to circumvent potential spurious regression pitfalls. The results of this study indicate that the impact of tapering off policy in Indonesia has a positive impact in the short term and long term, while in Malaysia it tends to be insignificant in the short term and has a positive impact from the US 10-year bond yield variable and a negative impact from US 1-Year Treasury Bills. This result is interesting because it differs from the general theory. The causal factors include the agility of the Indonesian central bank in maintaining the benchmark interest rate spread with the Fed, the economic stability of both countries, and the increasing trend of coal, with Indonesia being one of the largest producers of the commodity. Investors, in navigating these intricate dynamics, may find strategic insights derived from this research invaluable for shaping their investment decisions. while government policymakers may use them as a reference for shaping policies related to Sharia stock investments, including the incorporation of artificial intelligence.

This research aims to analyze the relationship between financial literacy variables and financial inclusion, the relationship between financial literacy variables and financial technology, and the relationship between financial technology variables and financial inclusion. The analysis of this research is to learn more about how financial literacy and the use of financial technology influence financial inclusion. This type of research is associative quantitative. Next, the relationship between these variables is explained using statistical formulas. Consequently, the term for this research is “quantitative research”. The study population is the number of people who use financial services. For this sampling, the purposive random sampling method was used. The following criteria are determined in sampling: 1) Minimum age 17 years, this is intended to take the minimum age standard in sampling and is considered capable of understanding the contents of the questionnaire statements. 2) Have ever used financial services. In this study, 11 question items were used to measure 3 variables, so this study used the largest range, namely 231 respondents. The intervention variable will be used as a reference for the Partial Least Square (PLS) method to analyze this research data. This study uses a causal model (causal modelling, relationships, and influence) or path analysis. The hypothesis that will be discussed in this research is tested using the Structural Equation Model (SEM), which is operated with Smart PLS. The results of this research show that financial literacy has a positive and significant impact on financial inclusion in society. Financial literacy has a positive and significant impact on financial technology. financial technology has a positive and significant impact on financial inclusion, financial technology can offset the impact of financial literacy on financial inclusion. The results of this research are used as input for the community so that they pay more attention to their internal human resources related to financial products that can be used for investment. With knowledge of the right financial products, it is hoped that they can create good financial behaviour so that an awareness of the importance of carrying out good financial planning. For financial institutions, it is hoped that this can increase easy access to financial products and services, in particular credit for businesses as additional capital for the community.

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.