A series of poly-3-alkylthiophenes (P3ATs) with butyl (P3BT), hexyl (P3HT), and octyl (P3OT) sidechains and well-defined molecular weights (MWs) were synthesized using Grignard metathesis polymerization. The MWs of P3HTs and P3OTs obtained via gel permeation chromatography agreed well with the calculated MWs ranging from approximately 10 to 70 kDa. Differential scanning calorimetry results showed that the crystalline melting temperature increased with increasing MWs and decreasing alkyl side-chain length, whereas the crystallinity of the P3ATs increased with the growth of MWs. An MW-dependent red shift was observed in the UV–Vis and photoluminiscence spectra of the P3ATs in solution, which might be a strong evidence for the extended effective conjugation occurring in polymers with longer chain lengths. The photoluminescence quantum yields of pristine films in all polymers were lower than those of the diluted solutions, whereas they were higher than those of the phenyl-C61-butyric acid methyl ester-blended films. The UV–Vis spectra of the films showed fine structures with pronounced red shifts, and the interchain interaction-induced features were weakly dependent on the MW but significantly dependent on the alkyl side-chain length. The photovoltaic device performances of the P3BT and P3HT samples significantly improved upon blending with a fullerene derivative and subsequent annealing, whereas those of P3OTs mostly degraded, particularly after annealing. The optimal power conversion efficiencies of P3BT, P3HT, and P3OT were 2.4%, 3.6%, and 1.5%, respectively, after annealing with MWs of ~11, ~39, and ~38 kDa, respectively.

This paper is devoted to the development of a deep learning- (DL-) based model to detect crack fractures on concrete surfaces. The developed model for the classification of images was based on a DL Convolutional Neural Network (CNN). To train and validate the CNN model, a database containing 40,000 images of concrete surfaces (with and without cracks) was collected from the available literature. Several conditions on the concrete surfaces were taken into account such as illumination and surface finish (i.e., exposed, plastering, and paint). Various error measurement criteria such as accuracy, precision, recall, specificity, and F1-score were employed for accessing the quality of the developed model. Results showed that for the training dataset (50% of the database), the precision, recall, specificity, F1-score, and accuracy were 99.5%, 99.8%, 99.5%, 99.7%, and 99.7%, respectively. On the other hand, for the validating dataset, the precision, recall, specificity, F1-score, and accuracy are 96.5%, 98.8%, 96.6%, 97.7%, and 97.7%, respectively. Thus, the developed CNN model may be considered valid because it performs the classification of cracks well using the testing data. It is also confirmed that the developed DL-based model was robust and efficient, as it can take into account different conditions on the concrete surfaces. The CNN model developed in this study was compared with other works in the literature, showing that the CNN model could improve the accuracy of image classification, in comparison with previously published results. Finally, in further work, such model could be combined with Unmanned Aerial Vehicles (UAVs) to increase the productivity of concrete infrastructure inspection.

In this paper, the chromium, Cr (VI), ion adsorption ability of oyster shell samples collected from two sea regions in Vietnam (Phu Yen province and Quang Ninh province) was investigated and compared. The oyster shell samples were calcined at different temperatures and denatured by using ethylenediaminetetraacetic acid (EDTA). The Cr (VI) ion adsorption ability of the prismatic (PP) and nacreous (NP) shell layers of oysters was also evaluated. The characteristics of oyster shell samples before and after treatment were determined by using analysis methods including XRD, IR, BET, UV-Vis, and FESEM. The Langmuir, Freundlich, Temkin, and Dubinin–Radushkevich models and fit statistic equations were used to study the adsorption isotherms of Cr (VI) ion adsorption by oyster shells. The Cr (VI) ions adsorption kinetic has been set up using four reaction models consisting of first-order, pseudo-first-order, second-order, and pseudo-second-order reaction models. Effects of experimental factors on the Cr (VI) ion adsorption process using oyster shells were also investigated and discussed in this work.

Bicycle helmets are designed to reduce the acceleration/deceleration of the head during impacts. During an accident, the main parts of a helmet responsible for shock absorption are the energy-absorbing liner and the outer shell. In the present study, several geometrical structures of convex acrylonitrile butadiene styrene plastic shell, such as a truncated conical form and a semi-spherical form, were investigated for their energy-absorbing capacity. Moreover, several liner dimension parameters were also optimised. The energy was absorbed in these structures by a combination of folding and collapsing. The idea of a sandwich liner using the aforementioned structures was also constructed to analyse its advantages compared to their single structures. This study performs finite element analyses on helmet impact tests using LS-DYNA software based on the EN1078 standards. The purpose of the present study was to determine a structure of liner that reaches high effective protection and to design helmet models with single and dual liners. The final work in this study was performed to optimise the height cone of linear semi-sphere cones. This innovative design for a helmet liner demonstrated high energy-absorbing capabilities. Moreover, this helmet impact test model can serve as a valuable tool to assist future development of safety-helmet technologies.