Localization factor: A new parameter for the quantitative characterization of surface structure with atomic force microscopy (AFM)

Nanoarchitectonics for Ultrathin Gold Films Deposited on Collagen Fabric by High-Power Impulse Magnetron Sputtering

Gold nanoparticles conjugated with collagen molecules and fibers have been proven to improve structure strength, water and enzyme degradation resistance, cell attachment, cell proliferation, and skin wound healing. In this study, high-power impulse magnetron sputtering (HiPIMS) was used to deposit ultrathin gold films (UTGF) and discontinuous island structures on type I collagen substrates. A long turn-off time of duty cycle and low chamber temperature of HiPIMS maintained substrate morphology. Increasing the deposition time from 6 s to 30 s elevated the substrate surface coverage by UTGF up to 91.79%, as observed by a field emission scanning electron microscope. X-ray diffractometry analysis revealed signature low and wide peaks for Au (111). The important surface functional groups and signature peaks of collagen substrate remained unchanged according to Fourier transform infrared spectroscopy results. Multi-peak curve fitting of the Amide I spectrum revealed the non-changed protein secondary structure of type I collagen, which mainly consists of α-helix. Atomic force microscopy observation showed that the roughness average value shifted from 1.74 to 4.17 nm by increasing the deposition time from 13 s to 77 s. The uneven surface of collagen substrate made quantification of thin film thickness by AFM difficult. Instead, UTGF thickness was measured using simultaneously deposited glass specimens placed in an HiPIMS chamber with collagen substrates. Film thickness was 3.99 and 10.37 nm at deposition times of 13 and 77 s, respectively. X-ray photoelectron spectroscopy showed preserved substrate elements on the surface. Surface water contact angle measurement revealed the same temporary hydrophobic behavior before water absorption via exposed collagen substrates, regardless of deposition time. In conclusion, HiPIMS is an effective method to deposit UTGF on biomedical materials such as collagen without damaging valuable substrates. The composition of two materials could be further used for biomedical purposes with preserved functions of UTGF and collagen.

Two-step heat fusion kinetics and mechanical performance of thermoplastic interfaces

Thermoplastic polymers and composites are ubiquitous in the industry for their reshaping and fusing capabilities at elevated temperatures. The quality of heat-fused thermoplastic interfaces is of great concern for adhesion, coating, and welding applications, especially those between dissimilar materials. Kinetic evolution of the microstructures defines the mechanical performance of heat-fusion thermoplastic interfaces, which is studied here using polyethylene and polypropylene as an example. Key factors such as the viscosity and compatibility of polymers and the time and temperature of fusion are discussed by combining molecular-level simulations and structural-level hot-compression experiments. Inter-diffusion and entanglement of polymer chains are identified as the two elementary kinetic steps of the fusion, which dominate the control on the stiffness and strength of the interfaces, respectively. Experimental data shows that the quality of fused interfaces can be improved by reducing the viscosity and the interaction parameter. Following the same set of time-scaling relations as identified in the simulations, the two-step characteristics and their effects on the stiffness and strength are experimentally validated. Both simulation and the experiment results show that Young’s modulus of fused interfaces recovers faster than the strength that is controlled by polymer entanglement to a large extent, rather than diffusion. These findings add insights into the design of fusion processes, laying the ground for the applications of thermoplastic polymers and composites.

Application of Structural Entropy and Spatial Filling Factor in Colonoscopy Image Classification

For finding colorectal polyps the standard method relies on the techniques and devices of colonoscopy and the medical expertise of the gastroenterologist. In case of images acquired through colonoscopes the automatic segmentation of the polyps from their environment (i.e., from the bowel wall) is an essential task within computer aided diagnosis system development. As the number of the publicly available polyp images in various databases is still rather limited, it is important to develop metaheuristic methods, such as fuzzy inference methods, along with the deep learning algorithms to improve and validate detection and classification techniques. In the present manuscript firstly a fuzzy rule set is generated and validated. The former process is based on a statistical approach and makes use of histograms of the antecedents. Secondly, a method for selecting relevant antecedent variables is presented. The selection is based on the comparision of the histograms computed from the measured values for the training set. Then the inclusion of the Rényi-entropy-based structural entropy and the spatial filling factor into the set of input variables is proposed and assessed. The beneficial effect of including the mentioned structural entropy of the entropies from the hue and saturation (H and S) colour channels resulted in 65% true positive and 60% true negative rate of the classification for an advantageously selected set of antecedents when working with HSV images.

On Structural Entropy and Spatial Filling Factor Analysis of Colonoscopy Pictures

Colonoscopy is the standard device for diagnosing colorectal cancer, which develops from little lesions on the bowel wall called polyps. The Rényi entropies-based structural entropy and spatial filling factor are two scale- and resolution-independent quantities that characterize the shape of a probability distribution with the help of characteristic curves of the structural entropy–spatial filling factor map. This alternative definition of structural entropy is easy to calculate, independent of the image resolution, and does not require the calculation of neighbor statistics, unlike the other graph-based structural entropies.The distant goal of this study was to help computer aided diagnosis in finding colorectal polyps by making the Rényi entropy based structural entropy more understood. The direct goal was to determine characteristic curves that can differentiate between polyps and other structure on the picture. After analyzing the distribution of colonoscopy picture color channels, the typical structures were modeled with simple geometrical functions and the structural entropy–spatial filling factor characteristic curves were determined for these model structures for various parameter sets. A colonoscopy image analying method, i.e., the line- or column-wise scanning of the picture, was also tested, with satisfactory matching of the characteristic curve and the image.