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  • Open access
  • 68 Reads
Optical coatings: application and metrology.

The development of optical coatings experienced rapid growth in the last few decades for a wide range of applications. The strong demand is motivated by the progress of new generation sources, large-scale facilities, new lithography arrangements, innovative methods for materials science investigation, biosensors, and instruments for space and solar physics observations. The research activities carried out at the Institute for Photonics and Nanotechnologies (Padova) of the National Research Council range from the design and characterization of optical components for space activities to the development of nanostructured coatings for several tools, including biosensors and surface plasmon resonance devices. The expertise, the recent results, the ongoing activities and the future developments will be presented and reviewed in this forum.

  • Open access
  • 66 Reads

Elimination of foreign bodies from seed mixtures or their calibration for use as seed material, but also the fractional classification of granular materials requires separating surfaces with vibrating motion. The paper presents some aspects regarding the working process of a perforated sheet metal screen with external conical surface having an oscillating (alternative) circular movement on the horizontal. The results of some experimental researches on the movement of the material on the sieve for various kinematic parameters of the sieve (amplitude and frequency of oscillation) are presented. The conical sieve, suspended at the top and bottom at three points, was tested for sieving rapeseed in order to estimate the influence of the oscillation frequency on the separation process. The separation intensity curves were plotted on the sieve generator, and by regression analysis with the normal distribution law the coefficients of the equation and the correlation with the experimental data were determined. The movement of the material on the sieve and its working process, in general, was appreciated by the position of the maximum distribution curve depending on the oscillation frequency of the sieve, considering that the normal distribution law correlates very well the data obtained from experiments.

  • Open access
  • 138 Reads
Analysis of NO2 pollution over Bangladesh between the two covid-19 caused lockdown in 2020 and 2021 using Sentinel 5P products.

Due to the covid-19 pandemic, all countries around the world have imposed nationwide lockdowns to control the spreading of the virus. During the lockdown period, many countries had seen a drastic drop in air pollution. In Bangladesh, two nationwide lockdowns were imposed on 26th March- 30th May in 2020 and on 3rd April till the study period of 31 May in 2021. This study was aimed to analyze the NO2 pollution over Bangladesh during the two periods of lockdown. Tropospheric NO2 column spatial configuration was measured over Bangladesh using Sentinel 5P data. Map of monthly average concentration of Tropospheric NO2 of 2020 and 2021 over Bangladesh had been produced using HARP toolkit and python. Then the map was compared with the same period Sentinel 5P products map of 2019. It had been found that during the first lockdown in Bangladesh between 26th March- 30th May-2020, a drastic decrease of NO2 concentration had been observed in April month but increased in May. But during the second lockdown from 3rd April in 2021, the NO2 concentration was found much higher in concentration. Most of the pollution occurred in the Dhaka district. During the second lockdown, the restriction was much easier than the first one that impacted the NO2 concentration. This kind of study can be the essence for the authority to look closely at air quality and use sentinel data to improve air quality monitoring in the future.

  • Open access
  • 59 Reads
Normal range of motion of lower extremity joints in Mongolian subjects

It is important to identify the normal range of motion (ROM) of the human joints for both biomechanical and clinical applications. Many diseases and injuries can impair joint mobility, which results in a decline in ROM or changes the gait characteristics. In addition, the decline in ROM is associated with aging as well as an abnormal lifestyle. For health care providers, including physicians and therapists, the restoration of normal ROM is a difficult task. The severity of impaired joint mobility or the postoperative rehabilitation process must be evaluated in comparison with a normal reference value. However, there is no studies have reported the ROM of the Mongolian subjects. In this study, we measured the hip, knee, and ankle joint angles using a multiple wearable inertial sensors. Ten healthy young subjects participated. The sensors were placed on the chest, tummy, thigh, shank, and foot. The 3D motion data were collected during the walking with normal speed and values were analyzed with Matlab software. In our knowledge, it is first to analyze the normal ROM of the Mongolian male subjects. The wearable sensor technology can be applied to both indoor and outdoor environments without any restrictions. The collected data can be reference values for evaluating the disability of the motion and performance in rehabilitation programs.

  • Open access
  • 367 Reads
Overlooked Ionic Phenomena Affecting the Electrical Conductivity of Liquid Crystals

Liquid crystal devices such as displays, various tunable optical components, and sensors are becoming increasingly ubiquitous. Basic physical properties of liquid crystal materials can be controlled by external physical fields thus making liquid crystal devices dynamically reconfigurable. The tunability of liquid crystals offers exciting opportunities for the development of new applications, including advanced electronic and photonic devices, by merging the concepts of flat optics, tunable metasurfaces, nanoplasmonics, and soft matter biophotonics. As a rule, the tunability of liquid crystals is achieved by applying an electric field. This field reorients liquid crystals and changes their physical properties. Ions, typically present in liquid crystals in minute quantities, can alter the reorientation of liquid crystals through the well-known screening effect. Because the electrical conductivity of thermotropic liquid crystals is normally caused by ions, an understanding of sources of ion generation in liquid crystals is of utmost importance to existing and emerging technologies relying on such materials. That is why measurements of electrical conductivity of liquid crystals is a standard part of their material characterization. Measuring the electrical conductivity of liquid crystals is a very delicate process. In this paper, we discuss overlooked ionic phenomena caused by interactions of ions with substrates of the liquid crystal cells. These interactions affect the measured values of the DC electrical conductivity of liquid crystals and make them dependent on the cell thickness.

  • Open access
  • 71 Reads
Idea of rapid preparation of fatty acid methyl ester using in situ derivatization from fresh horse mussel
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Published: 15 October 2021 by MDPI in 2nd International Electronic Conference on Applied Sciences session Chemistry

The analysis of the fatty acid (FA) profile requires multiple preparation steps, which are lipid extraction followed by derivatization of the FA into fatty acid methyl ester (FAME). The procedures are time-consuming, generally require large volumes of sample sizes and solvents. This report proposes a technique for the preparation of FAME from fresh horse mussels without a step of lipid extraction. A rapid in situ derivatization using N, N‐dimethylformamide dimethyl acetal (DMF-DMA) methylation followed by alkali-transesterification was examined. Direct alkali-transesterification of the fresh sample gave only 58.7% FAME with 12.4% triglyceride and 21.1% FFA. The alkali in situ method showed low conversion efficiency due to the initial sample contains high contents of moisture and FFA (75.11% of the fresh sample and 14.3% of total oil, respectively). The reaction was developed by using two steps in situ derivatization. A 50 mg sample was methylated with 1 mL of DMF-DMA (100 °C, 15 min), followed by transesterified with 10 mL of 1% (w/v) NaOH in methanol (60 °C, 3 min). The FAME yield of 79.9% with 7.8% triglyceride and 8.5% FFA was obtained. The two steps in situ derivatization gave a promising result with the higher conversion with lower FFA. However, increasing the conversion efficiency as well as the variety of samples should be further studied.

  • Open access
  • 84 Reads
Ultra-short pulse lasers – materials - applications

From 2000, the average laser power of ultra-short (sub-1 ps) pulsed lasers has increased as Power = 2N/2 with N – number of the years from beginning of the trend, which parallels Moore’s law for number of transistors in an integrated circuit. Initially based on the chirped pulse amplification (CPA), which was awarded the Nobel prize in 2018, more recent approaches exploit different cavity geometries as well as amplification via the divided pulse and coherent beam combination. These strategies further increase the extracted power from solid state and fiber laser systems and makes them more compact. Ultra-short lasers with powers in the sub-1 kW range, ~1 mJ pulse energies and at the repetition rates up to ~1 MHz have become available. New modes of laser operation brings the capability to combine ultra-short pulses into MHz-GHz bursts with a controlled number of pulses per burst. It was shown that this burst mode of operation delivers ablation rates for metal and dental tissue on the order of 3 mm3/min. This is the rate that reaches that of current Electrical Discharge Machining/Grinding (EDM/G) CNC tools. This parity between material removal rate by discharge spark and laser beam was achieved in 2016. The burst mode advantage is in the possibility to fine tune material removal to the most efficient fluence [J/cm2], which is empirically determined to be e2= 7.4 times larger than the ablation threshold for the given material. Fine tuning to the optimum ablation rate is achieved by changing pulse number per irradiation spot, using beam scanning, and control of number of pulses per burst. For comparison of different fabrication conditions, the volume [mm3] ablated per 1 W average power per time 1 min: Va ~ mm3/W/min ~ mm3/(W.s) ~ mm3/J is used. This is the ablated volume-per-energy delivered by laser for subtractive machining (3D(-) printing). Interestingly, we show here that the volumetric energy density Energy/Volume ~ J/mm3 is the right measure for the additive mode of 3D(+) printing by ultra-short laser pulses. It is not surprising that accounting for the energy deposition in the volume of light-matter interaction is the essential measure for the both additive and subtractive 3D(+) and 3D(-) modes of 3D fabrication.

High average power ~sub-kW systems are targeting industrial applications. To handle high laser power, new beam delivery systems are developed for distribution of energy in a very well controlled and precise way over the workpiece. Photonic crystal fibers (holy-fibers), flexible delivery units, and polygon scanners with beam travel rates up to 1 km/s are readily available. These further contribute to compactness, versatility, and safety of high-power handling. This is especially important for open space and field deployable applications, e.g., surface texturing by ablation ripples for creation hydrophobic, anti-icing, and biocidal surfaces. These applications are particularly suitable for fast beam scanning techniques.

Here we overview recent development of 3D(+/-) printing from development of lasers, beam delivery tools, applications and materials. New polymerizable mixtures of colloidal particles, standard photo-polymerisable resists/resins can be tailored for required material composition. Calcination of the polymerised composites can be transferred into a glass, polycrystalline or ceramic state with feature sizes down to nanoscale.

  • Open access
  • 58 Reads
Development of a Predictive Model for Mild Cognitive Impairment in Parkinson's Disease with Normal Cognition by Combining Kernel-based Machine Learning and C5.0

It has been reported that mild cognitive impairment (MCI), known as the preclinical phase of dementia, may last up to seven years and appropriate therapeutic interventions in the MCI stage can delay the progression to dementia approximately five years. As a result, many studies have focused on detecting MCI, known as an intermediate stage between normal aging and Alzheimer's dementia, as soon as possible. As longitudinal studies on Parkinson's disease have reported that patients with Parkinson's disease frequently suffer from cognitive impairment, recent studies have paid more attention to mild cognitive impairment in Parkinson's disease (PDMCI) as well as Alzheimer’s MCI. Although PDMCI occurs frequently in patients with Parkinson's disease, the characteristics of PDMCI are known much less than those of Alzheimer's MCI and those of vascular MCI. Although a number of previous studies have reported that the most critical characteristic of PDMCI is executive function impairment due to frontal lobe dysfunction found at an early stage, it is hard to detect it only with the degree of executive function because early-stage MCI due to Alzheimer disease or vascular dementia shows executive function impairment. In particular, since Parkinson's disease progresses slowly and symptoms appear little by little, patients and caregivers can perceive the cognitive problems caused by PDMCI as the cognitive frailty in the normal aging process. Therefore, it is hard to diagnose it early. MCI is diagnosed based on an interview, evaluation of cognitive function through standardized neuropsychological tests, and brain imaging. However, brain imaging has limitations to be used for early diagnosis purposes because although it can detect the presence of cerebrovascular disease and brain atrophy, it can find them only when these symptoms are very advanced. Therefore, neuropsychological tests also evaluating cognitive function are known to be effective screening tests for detecting MCI early. On the other hand, studies in the medical field have steadily predicted the risk probability or high-risk groups of a disease using data mining in recent years. However, it is challenging to accurately predict diseases with single machine learning (learner). For example, the artificial neural network technique has a limitation of not being able to explain the derived results but it offers high prediction accuracy. On the other hand, the decision tree technique allows clinicians to easily interpret the results derived from it, but it is exposed to a higher overfitting risk than other machine learning algorithms such as SVM, the results of it can be altered by the type and order of input variables, and the accuracy of it can be lowered depending on them. To overcome these limitations, a hybrid model combining artificial neural network and decision tree model has been used recently to develop a model that has higher predictive power and explanatory power compared to single machine learning. This study developed a PDMCI predictive model considering health behaviors, environmental factors, medical history, physical function, depression, and cognitive level using a hybrid model combining C-SVM and C5.0 and provided baseline data for the prevention and early management of Parkinson's dementia. This study analyzed 185 patients with Parkinson's disease (75 Parkinson's disease patients with normal cognition, and 110 patients with PDMCI) after being approved by the Research Ethics Review Committee of the National Biobank of Korea. This study used 48 variables (diagnostic data), including motor symptoms of Parkinson's disease, non-motor symptoms of Parkinson's disease, and sleep disorders, as explanatory variables. This study chose “C5.0” implemented by Kuhn et al. (2013) for the decision tree algorithm and “kernel-based machine learning (kernlab)” implemented by Karatzoglou et al. (2016) for the SVM to develop a PDMCI predictive model. The kernlab algorithm includes a polynomial kernel function (polydot), a linear kernel function (vanilladot), and a radial basis kernel function (RBFdot) that enable nonlinear SVM analysis. This study developed seven machine learning models using blending (3 hybrid models (polydot+C5.0, vanilladot+C5.0, and RBFdot+C5.0) and four single machine learning models (polydot, vanilladot, RBFdot, and C5.0)). This study compared the predictive performance of these developed models using the 10-folds cross-validation method. The results of this study showed that the RBFdot+C5.0 was the model with the best performance to predict PDMCI in Parkinson’s disease patients with normal cognition (AUC=0.88) among the seven machine learning models. It is necessary to develop a customized screening program for detecting PDMCI in Parkinson’s disease patients with normal cognition early based on the results of this study.

  • Open access
  • 69 Reads
Sorting of nickelocene-filled single-walled carbon nanotubes by density gradient centrifugation by conductivity type

Applications of single-walled carbon nanotubes (SWCNTs) require the nanotube samples with uniform properties. The filling of SWCNTs is a promising method of tailoring their properties. Other way to obtain the samples with homogeneous properties is to perform the separation of filled nanotubes by conductivity type. In this work, we performed the sorting of nickelocene-filled SWCNTs by density gradient centrifugation to metallic and semiconducting fractions. The obtained samples were characterized by Raman spectroscopy and X-ray photoelectron spectroscopy. The investigation showed that the samples have homogenous properties, high quality and high purity. The encapsulated nickelocene has n-doping effect on metallic and semiconducting SWCNTs. The samples were annealed in vacuum at 360-1200°C to grow inner tubes inside SWCNTs, and the electronic properties of these samples were investigated. The annealing of nickelocene-filled SWCNTs leads to decomposition of molecules with the formation of nickel carbides and pure nickel inside double-walled carbon nanotubes (DWCNTs). It was shown that annealing of nickelocene-filled SWCNTs at 360-600°C leads to n-doping of SWCNTs, whereas annealing at 800-1200°C results in p-doping of SWCNTs.

  • Open access
  • 62 Reads
Interaction between Wnt/β-catenin pathway, dental materials and dentine formation.

The Wnt/β-catenin pathway participates in various physiological processes. The bind of Wnt ligand to Frizzled and LRP5/6 receptors promotes the signal transduction, ensuring an elevate concentration of β-catenin in the cytosol that migrates to the nucleus, interacting with transcription factors. Its abnormal regulation leads to early dysfunctional events. The degradation of β-catenin is regulated by GSK3β and CK1α interactions, and several proteins modulate Wnt/β-catenin pathway such as lithium, Dkk1 and indirubin isomers. The aim of this study is to review the scientific knowledge in order to highlight the role of Wnt/β-catenin pathway in response to dental biomaterials for reparative dentine formation following tooth damage, by triggering the natural process of dentinogenesis. Wnt activation via GSK-3 inhibitor drugs increases dentine secretion, although Wnt inhibition does not impair dentine secretion. Moreover, the interaction of the Wnt/β-catenin with dental materials and the effect on pulp was detected. Recently, odontogenic/osteogenic gene expression in human dental pulp stem cells (hDPSCs) cultured with various concentrations of Mineral Trioxide Aggregate (MTA) was evaluated and differentiation of hDPSCs induced by MTA extract and mediated by pathway was observed. Furthermore, pulp injury caused by resinous monomers with or without the presence of GSK-3 inhibitor Lithium was assessed, indicating the activation of Wnt signaling after exposure to TEGDMA and a cumulative effect with the co-treatment with Lithium. In conclusion, the development of a concept of biological repair based on the role of the Wnt/β-catenin pathway in dentin formation seems to offer a new translational approach into development of future treatments.