The Effect of Salinized Nano ZrO2 Particles on the Microstructure, Hardness, and Wear Behavior of Acrylic Denture Tooth Nanocomposite

The wear of acrylic denture teeth is a serious problem that can change the vertical dimensions of dentures. This study evaluates the effect of adding salinized nano ZrO2 particles on the microstructure, hardness, and wear resistance of acrylic denture teeth. Heat polymerizing polymethyl methacrylate resin was mixed with salinized ZrO2 at concentrations of 5 wt% and 10 wt%. Acrylic resin specimens without filler addition were used as a control group. SEM/EDS analyses were performed and the Vickers’ hardness was evaluated. Two-body wear testing was performed using a chewing simulator with a human enamel antagonist. After subjecting the samples to 37,500 cycles, both height loss and weight loss were used to evaluate the wear behavior. The microstructural investigation of the reinforced-denture teeth indicates sound nanocomposite preparation using the applied regime without porosity or macro defects. The addition of zirconium oxide nanofillers to PMMA at both 5% and 10% increased the microhardness, with values of up to 49.7 HV. The wear mechanism in the acrylic base material without nanoparticle addition was found to be fatigue wear; a high density of microcracks were found. The addition of 5 wt% ZrO2 improved the wear resistance. Increasing the nanoparticles to 10 wt% ZrO2 further improved the wear resistance, with no microcracks found.

Modified Activated Carbon for Copper Ion Removal from Aqueous Solution

Because of increasing environmental awareness, it is becoming more important to remove harmful elements from water solutions. This study used activated carbon (AC) derived from waste wood-based panels as the base material, oxidized with nitric acid (OAC), and grafted with iminodiacetic acid (IDA-OAC) to improve the adsorption capacity and affinity for metals. The characterization of AC, OAC, and IDA-OAC was conducted via FTIR, SEM, N2 adsorption and desorption analysis, elemental analysis, Boehm titration, and point of zero charge (PZC). The instrument studies proved the modified increasing of the functional groups of the adsorbents. Moreover, batch and column experiments were conducted to evaluate the ability of the three adsorbents to remove copper ions from aqueous solution. In batch sorption, IDA-OAC had the highest adsorption capacity (84.51 mg/g) compared to OAC (54.74 mg/g) and AC (24.86 mg/g) at pH 5. The breakthrough point (Ct/Ci = 0.05) of copper ions for IDA-OAC occurred much later than AC in the column experiment (AC = 19 BV, IDA-OAC = 52 BV). The Langmuir isotherm and pseudo-second-model kinetics modeling could better fit with the data obtained from the batch sorption of AC, OAC, and IDA-OAC. The significant capacity and reusability of IDA-OAC displayed high applicability for water treatment.

The Use of Drone Photo Material to Classify the Purity of Photovoltaic Panels Based on Statistical Classifiers

The subject of this work is the analysis of methods of detecting soiling of photovoltaic panels. Environmental and weather conditions affect the efficiency of renewable energy sources. Accumulation of soil, dust, and dirt on the surface of the solar panels reduces the power generated by the panels. This paper presents several variants of the algorithm that uses various statistical classifiers to classify photovoltaic panels in terms of soiling. The base material was high-resolution photos and videos of solar panels and sets dedicated to solar farms. The classifiers were tested and analyzed in their effectiveness in detecting soiling. Based on the study results, a group of optimal classifiers was defined, and the classifier selected that gives the best results for a given problem. The results obtained in this study proved experimentally that the proposed solution provides a high rate of correct detections. The proposed innovative method is cheap and straightforward to implement, and allows use in most photovoltaic installations.

Denture Acrylic Resin Material with Antibacterial and Protein-Repelling Properties for the Prevention of Denture Stomatitis

Denture stomatitis is a multifactorial pathological condition of the oral mucosa that affects up to 72% of denture wearers. It is commonly seen on the palatal mucosa and characterized by erythema on the oral mucosa that are in contact with the denture surface. The aim of this study was to incorporate 2-methacryloyloxyethyl phosphorylcholine (MPC) and dimethylaminohexadecyl methacrylate (DMAHDM) into a high impact polymethylmethacrylate heat-cured denture base acrylic resin as a potential treatment for denture stomatitis. We used a comparative study design to examine the effect of incorporating MPC as a protein repellent agent and DMAHDM as an antifungal agent to prevent the adherence of Candida albicans to the denture base material. The dual incorporation of MPC and DMAHDM reduced C. albicans biofilm colony-forming unit by two orders of magnitude when compared to the control group devoid of the bioactive agents. Although the addition of MPC and DMAHDM alone or in combination significantly reduced the flexural strength of the material, they showed reduced roughness values when compared to control groups. This new denture acrylic resin provides the benefit of enhancing C. albicans biofilm elimination through dual mechanisms of action, which could potentially reduce the prevalence of denture stomatitis.

Variation of phenotypic and physiological traits of Robinia pseudoacacia L. from 20 provenances

To select elite Robinia pseudoacacia L. germplasm resources for production, 13 phenotypes and three physiological indicators of 214 seedlings from 20 provenances were systematically evaluated and analyzed. The leaf phenotypic and physiological coefficients of variation among the genotypes ranged from 3.741% to 19.599% and from 8.260% to 42.363%, respectively. The Kentucky provenance had the largest coefficient of variation (18.541%). The average differentiation coefficients between and within provenances were 34.161% and 38.756%, respectively. These close percentages showed that R. pseudoacacia presented high genetic variation among and within provenances, which can be useful for assisted migration and breeding programs. Furthermore, based on the results of correlations, principal component analysis and cluster analysis, breeding improvements targeting R. pseudoacacia’s ornamental value, food value, and stress resistance of were performed. Forty and 30 excellent individuals, accounting for 18.692% and 14.019%, respectively, of the total resources. They were ultimately screened, after comprehensively taking into considering leaf phenotypic traits including compound leaf length, leaflet number and leaflet area and physiological characteristics including proline and soluble protein contents. These selected individuals could provide a base material for improved variety conservation and selection.

EFFECT OF WELDING PROCESS PARAMETERS ON TENSILE OF LOW CARBON STEEL 283 G.C

In this study, three welding methods are used. The purpose to investigation the effects of SMAW, SAW, and gas tungsten arc welding (GTAW) on the tensile stress of low carbon steel conforming to ASTM 283 c. 8mm thick plates are used as base material for butt welded joints. The tensile properties of the welded joints were evaluated and the results were compared by experts using the Taguchi method to design three levels of each parameter (current, voltage and displacement speed). From this research, it is found that compared to metal shielded arc welding and submerged arc welding, the pulling effect of the gas shielded welding joint of the tungsten electrode is the best. This is mainly due to the presence of The results of using analysis of variance (ANOVA) to estimate important parameters show that welding current and speed of the weld have a significant effect on tensile stress .the experimental results are in agreement with predicted results, and the maximum error is 3%..

Interface Reactions Responsible for Run-Out in Active Brazing: Part 4

This study examined the interface reaction between Ag-xAl filler metals having x = 0.2, 0.5, or 1.0 wt-% and Kovar™ base materials. The present investigation used the braze joint test sample configuration. The brazing conditions were 965°C (1769°F), 5 min; 995°C (1823°F), 20 min, and a vacuum of 10–7 Torr. Run-out was absent from all test samples. Combining these results with those of the Part 2 study that used high-Al, Ag-xAl filler metals (x = 2.0, 5.0, and 10 wt-%) established these conditions for run-out: Ag-xAl filler metals having x ≥ 2.0 wt-% Al, which result in reaction layer compositions, and (Fe, Ni, Co)y Alz , having z ≥ 26 at.-% Al. The limited occurrences of run-out lobes resulted from the surface tension effect that quickly reduced the driving force for additional run-out events. The interface reactions were controlled by a driving force that was an expressed function of filler metal composition (Ag-xAl) and brazing temperature, as opposed to simply thermally activated rate kinetics. The differences of reaction layer composition and thickness confirmed that the interface reactions differed between the braze joint and sessile drop configurations. Collectively, the findings from the Parts 1–4 investigations concluded that the most-effective means to mitigate run-out is to place a barrier coating on the Kovar base material that will prevent formation of the (Fe, Ni, Co)y Alz reaction layer.

Hyperspectral Remote Sensing of TiO2 Concentration in Cementitious Material Based on Machine Learning Approaches

Titanium dioxide (TiO2) is a photocatalyst that can be used to remove nitrogen oxide (NOx). When applied to cementitious materials, it reacts with photons in sunlight or artificially generated light to reduce the concentration of particulate matter in the atmosphere. The concentration of TiO2 applied to the cementitious surface is difficult to quantify in a non-destructive manner after its application; however, knowledge of this residual amount is important for inspection and the evaluation of life expectancy. This study proposes a remote sensing technique that can estimate the concentration of TiO2 in the cementitious surface using a hyperspectral sensor. In the experiment, cement cores of varying TiO2 concentration and carbon contents were prepared and the surfaces were observed by TriOS RAMSES, a directional hyperspectral sensor. Machine-learning-based algorithms were then trained to estimate the TiO2 concentration under varying base material conditions. The results revealed that the best-performing algorithms produced TiO2 concentration estimates with a ~6% RMSE and a correlation close to 0.8. This study presents a robust machine learning model to estimate TiO2 and activated carbon concentration with high accuracy, which can be applied to abrasion monitoring of TiO2 and activated carbon in concrete structures.

Sensitivity Analysis of FAARFIELD Rigid Airport Pavement Thickness Determination

FAARFIELD is a common mechanistic-empirical software that uses a combination of layered elastic and finite element methods for the determination of rigid aircraft pavement thickness. The primary input parameters are the aircraft type, mass and departures, concrete flexural strength, sub-base material and thickness, as well as subgrade support characteristic. A parametric sensitivity analysis, including three common commercial aircraft and four subgrade conditions, determined that concrete thickness was most sensitive to concrete strength and aircraft mass. The concrete thickness was least sensitive to the sub-base material and thickness and was moderately sensitive to the subgrade condition and aircraft departures. These relative sensitivities were consistent when the results were analysed based on average percentage change in concrete thickness, the average slope of lines of best fit for normalised parameter values and the coefficients of a numeric linear regression for concrete thickness. It is recommended that designers focus their attention on accurately estimating realistic concrete strength and aircraft mass values, as these parameters had the greatest influence on concrete thickness.