Effect of Temperature Changes on the Vibration Transmissibility of XPE and PE Packaging Cushioning Material

Polyethylene (PE) and its variations are among the most traditional materials used for cushioning in packaging systems. The role of these materials is to prevent damages during handling and distribution processes from physical events such as vibration stress. This study presents new results on the characterization of properties of PE and XPE (cross-linked polyethylene) packaging materials, which have significant relevance as a protective mechanism due to their vibration transmissibility and frequency curve properties. The main goal of this study is the evaluation of vibration transmissibility of PE and XPE cushion material at varied real temperature and static load conditions through a series of experiments using a vibration tester and climate chamber to determine the peak frequencies, vibration transmissibility, and damping ratios. The results can be used by engineers in the package-design process, and can be useful in different distribution conditions. Three different kinds of static loads and a 0.5 oct/min sine sweep of vibration test were used to find the peak frequencies and vibration transmissibility at −20 °C, 0 °C, 20 °C and 40 °C to estimate the damping ratios. The results provided a better understanding of the materials and can assist in the design of suitable protective packaging systems.

Compensation of Measurement Uncertainty in a Remote Fetal Monitor

The perinatal mortality rate is very high throughout the world. A fetal monitor may be used remotely, and this would tackle the problem of continuous monitoring of high-risk pregnancies. There is evidence that current technology is of low reliability, and, therefore, of low precision to identify fetal health. In medical technological implementation, a safe, efficient, and reliable operation must be guaranteed, and the main problem is that remote fetal monitor gathers just a few samples, so the hypothesis of classical theory is not met. We are proposing an approach that improves the data’s lack of reliability that accompanies the use of a remote fetal monitor. The method refers to how, by using the existing technologies and the initial experimental data, it is possible to apply probabilistic models that are truly representative of each application. This leads to the characterization of properties of the statistics used to generate a representative probabilistic model without the need to consider the hard suppositions. Results show that, for every case study, it was possible to improve estimations of measurement uncertainty. The proposed method is a useful tool to increase the reliability of medical technology, especially for pieces of equipment where a health care professional is not available.

Multivariate Model Based on UV-Vis Spectroscopy and Regression in Partial Least Squares for Determination of Diameter and Polydispersity of Silver Nanoparticles in Colloidal Suspensions

In the universe of nanomaterials, silver nanoparticles (AgNPs) have attracted the attention of researchers because of their optical, catalytic, antimicrobial, fungicidal, and bactericidal properties. Recently, studies have correlated the toxicity and efficacy of antimicrobial activity with surface-volume ratio, morphology, polydispersity, ligand types, particle size, and stability of AgNPs. Soon, the need for characterization of properties such as diameter and polydispersity is clear. The methodologies conventionally used for characterization of AgNPs, although accurate, are generally expensive and laborious and can degrade the sample. Thus, the development of methodologies based on UV-Vis spectroscopy and chemometric techniques appears as an alternative for the characterization of diameter and polydispersity of the nanoparticles. For the development of the methodology in question, 50 samples were synthesized, varying the type, volume, and concentration of the reagents in order to increase the diameter and polydispersity values. All samples were analyzed by DLS and UV-Vis spectroscopy. For the construction of multivariate calibration models, the calibration and validation sets were selected using the SPXY algorithm, and their predictive capacity was evaluated based on the method figures. The model that presented the best predictive capacity was the one built with the pretreated spectra with the 1st derivative with a 15-point window and 2nd-order polynomial, providing prediction errors of 5.31% and 4.43% for diameter and polydispersity, respectively.