Density Gradients, Cellular Structure and Thermal Conductivity of High-Density Polyethylene Foams by Different Amounts of Chemical Blowing Agent.

Polymers (Basel)

Cellular Materials Laboratory (CellMat), Condensed Matter Physics Department, Faculty of Science, Campus Miguel Delibes, University of Valladolid, Paseo de Belén 7, 47011 Valladolid, Spain.

Published: September 2022

LDPE (low-density polyethylene) foams were prepared using the improved compression moulding technique (ICM) with relative densities ranging from 0.3 to 0.7 and with different levels of chemical blowing agents (from 1% to 20%). The density gradients, cellular structure and thermal conductivity of the foams were characterized. The density and amount of CBA used were found to have a significant effect on the cellular structure both at the mesoscale (density gradients) and at the microscale (different cell sizes and cell densities). In addition, the thermal conductivity of the samples is very sensitive to the local structure where the heat flux is located. The technique used to measure this property, the Transient Plane Source method (TPS), makes it possible to detect the presence of density gradients. A simple method for determining these gradients based on thermal conductivity data was developed.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573033PMC
http://dx.doi.org/10.3390/polym14194082DOI Listing

Publication Analysis

Top Keywords

density gradients
16
thermal conductivity
16
cellular structure
12
gradients cellular
8
structure thermal
8
polyethylene foams
8
chemical blowing
8
density
5
structure
4
thermal
4

Similar Publications

Self-supervised learning (SSL) has gained attention in the medical field as a deep learning approach utilizing unlabeled data. The Jigsaw puzzle task in SSL enables models to learn both features of images and the positional relationships within images. In breast cancer diagnosis, radiologists evaluate not only lesion-specific features but also the surrounding breast structures.

View Article and Find Full Text PDF

Context: Exploration for renewable and environmentally friendly energy sources has become a major challenge to overcome the depletion of fossil fuels and their environmental hazards. Therefore, solar cell technology, as an alternative solution, has attracted the interest of many researchers. In the present work, the CsXInBr (X = Cu or Ag) compounds as lead-free halide perovskites have been studied due to their direct energy gap in the range of solar energy, thermodynamic stability, low effective mass of electrons, and high absorption coefficient.

View Article and Find Full Text PDF

Background: In population health research, rurality is often defined using broad population density measures, which fail to capture the diverse and complex characteristics of rural areas. While researchers have developed more nuanced approaches to study neighborhood and area effects on health in urban settings, similar methods are rarely applied to rural environments. To address this gap, we aimed to explore dimensions of contextual heterogeneity across rural settings in the US.

View Article and Find Full Text PDF

Prediction of Pt, Ir, Ru, and Rh complexes light absorption in the therapeutic window for phototherapy using machine learning.

J Cheminform

January 2025

PROMOCS Laboratory, Department of Chemistry and Chemical Technologies, University of Calabria, Arcavacata di Rende (CS), Italy.

Effective light-based cancer treatments, such as photodynamic therapy (PDT) and photoactivated chemotherapy (PACT), rely on compounds that are activated by light efficiently, and absorb within the therapeutic window (600-850 nm). Traditional prediction methods for these light absorption properties, including Time-Dependent Density Functional Theory (TDDFT), are often computationally intensive and time-consuming. In this study, we explore a machine learning (ML) approach to predict the light absorption in the region of the therapeutic window of platinum, iridium, ruthenium, and rhodium complexes, aiming at streamlining the screening of potential photoactivatable prodrugs.

View Article and Find Full Text PDF

China, the world's largest carbon emitter, plays a pivotal role in achieving carbon neutrality. This study systematically analyzes the impact of landscape indices on carbon emissions from rural settlements across more than 2800 counties using eight supervised machine learning models. To assess variable influences under diverse conditions, we also employed the SHapley Additive exPlanations (SHAP) and Accumulated Local Effects (ALE) methods.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!