Optimal Results Research Articles

Preliminary investigation of the effects of high-temperature thermal activation on the activity of graphite tailings and the mechanical properties of graphite tailings mortar

The disposal of graphite tailings, a byproduct of graphite mining, poses significant recycling challenges. This study explores eco-friendly cement mortar by partially replacing sand with activated graphite tailings, focusing on waste utilization. Activating the pozzolanic properties of graphite tailings at various thermal activation temperatures converts them into high-quality binder materials. Quantitative assessments of graphite tailings' activity, conducted through ion leaching tests and raw material diffraction analysis, produce an activity index Ka, evaluating their performance under different thermal activation temperatures. Variable testing of substitution rates and activation temperatures examines changes in mechanical properties, microstructure, and pore distribution of the graphite tailings mortar. Optimum results reveal a 30 % substitution rate and a 750°C activation temperature. Graphite tailings, as pozzolanic binder materials, enhance mortar strength by improving particle size distribution, reducing "micro-area bleeding" and "boundary effects". After thermal activation, reactive minerals on the tailings' surface undergo secondary hydration reactions with cementitious products, modifying chemical structures and micro-morphology at the interface. Semi-quantitative XRD analysis and FT-IR spectroscopy characterize the ensuing chemical reactions, unveiling molecular bond and functional group changes. The superior performance of thermally activated graphite tailings primarily stems from increased active SiO2 and Al2O3 post-activation. The active SiO2 and Al2O3 reacts with cementitious products to form C-(A)-S-H gels, filling micro-pores at the tailings-mortar interface, which is the key characteristic that enables graphite tailings to serve as high-quality pozzolanic binder aggregates. A predictive model for compressive strength, utilizing reference models from solid waste mortar concrete, quantifies the positive impact of chemical activation. The theoretical results obtained from this predictive model closely align with the experimental findings.

Experimental and Statistical Assessment for Hydrogen-Powered Dual-fuel Diesel Engine Using a Novel Biodiesel Blend at Variable Injection Pressure

Hydrogen has been proven to be a potential fuel alternative in the area of field of transportation and power generation. The study aimed to improve the efficiency of a dual-fuel engine running on a blend of biodiesels and hydrogen by optimizing operating parameters. This involved varying the injection pressure of pilot fuel (220, 240, and 260 bar) and adjusting engine load (ranging from 20% to 100% in increments of 20% in five steps). The results indicate that maximum brake thermal efficiency of 28.11 % and liquid fuel substitution by 85% when the injection pressure of pilot fuel was set to 240 bar at 100 % engine load. At 100% load, setting the injection pressure of fuel to 240 bar resulted in a substantial drop in the emissions of carbon monoxide and hydrocarbons by 9.27% and 47.61%, respectively. The response surface methodology specified that the optimized value of the engine load and pilot fuel injection pressure was found to be 55.93% and 242.731 bar, respectively for achieving optimum results of response variables from the engine.

An adaptive differential evolution algorithm driven by multiple probabilistic mutation strategies for influence maximization in social networks

Influence maximization is a critical research topic of social network analysis, particularly with the increasing involvement of individuals in the global networked society. The purpose of the problem is to identify k influential nodes from the social network and activate them initially to maximize the expected number of influenced nodes at the end of the spreading process. Although some meta-heuristics based on swarm intelligence or biological evolution have been proposed to tackle this intractable problem, further investigation is required to refine the exploration and exploitation operations based on the iterative information from the evolutionary process. In this paper, an adaptive differential evolution algorithm driven by multiple probabilistic mutation strategies is proposed for the influence maximization problem. In order to enhance the evolutionary capability of the later stages of the discrete differential evolution, the mutation in the framework, consisting of three policies, namely comprehensive learning particle swarm mutation strategy, differential mutation strategy, and perturbation strategy, is implied based on different probabilistic models. An adaptive local search strategy is presented to improve the local optimum results based on a potential alternative library consisting of structural hole nodes, which guides the differential evolution to find a more optimal solution. Experimental results on six real-world social networks demonstrate the competitive performance of the proposed algorithm in terms of both efficacy and efficiency compared to state-of-the-art algorithms.

A rapid-convergent particle swarm optimization approach for multiscale design of high-permeance seawater reverse osmosis systems

Directly solving sophisticated partial differential equation constrained optimization problems is not only extremely time-consuming, but also very hard to find unique optimal solutions. Here, we propose stable and efficient surrogate models for seawater reverse osmosis desalination processes that enable thorough quantitative description of hydrodynamics and local transport characteristics in narrow flow channels. Without iteratively solving complex multi-physics simulation problem taking several hours, the proposed multi-scale design optimization framework significantly reduces the problem complexity by computing the surrogate models in seconds. Moreover, a fast-converging active subspace particle swarm optimization framework is proposed to address the optimal design problem. Compared to the standard particle swarm optimization algorithm, the proposed method enhances the average optimum by 14% and the standard deviation of optimum results for multiple runs is reduced by no less than ten times. The optimized desalination system achieves 9% reduction on energy consumption and 30% improvement on water production efficiency.

Modeling the Impact of Wind Drag Coefficient on Wind-Driven Currents in Lake Taihu, China

The wind drag coefficient, Cd, has a great influence on the numerical results obtained from shallow lakes. To analyze the modeling impacts of Cd on wind-driven currents, a series of numerical simulations of Lake Taihu were conducted at three grid resolutions (800 m × 800 m, 400 m × 400 m, and 100 m × 100 m) using the empirical formulae of Flather (F76), Large and Pond (LP81), Large and Yeager (LY04), Andreas (A12), and Gao (G20). The G20 formula produced the optimum results of all the formulae for both the water level and velocity simulations; however, the grid resolution was found to have a significant influence on simulation in G20 cases. Thus, the G20 formula is only recommended when using a high-resolution grid to meet the accuracy requirements of analyzing wind-driven currents in the numerical modeling of Lake Taihu. A combination of the A12 formula and a coarse grid is preferred when taking computational efficiency into consideration.

Identical Parallel Machine Scheduling to Minimize Makespan Using Suggested Algorithm Method at XYZ Company

XYZ company produce the various shape of motor spare parts product. The company has threeidentical parallel spot welding machines that use a random method of production scheduling, basedon machine capacity without any sequence of jobs, and only use daily production targets given tooperators. Based on the data, the actual scheduling of the machines has a very large completion timedifference between each machine, or the machine loading is uneven. As a result, the makespanbecomes longer with a value of 440000 seconds (26 days). This research aims to minimize the existingmakespan by giving proposed scheduling, using the suggested algorithm method, which has a smallnumber of iterations and has an optimal result. The method begins with the longest processing timesequence rule which is used as the upper bound for the first iteration, then continued to calculate thelower bound and machine workload. The calculation stops at the 15th iteration because the completiontime value exceeds the lower and upper bound so that the optimal scheduling taken is scheduled inthe 14th iteration with a makespan value of 914412 seconds (16 days). The proposed scheduling canminimize the makespan from the actual schedule by 38%.

Modelling and optimizing the impact resistance of engineered cementitious composites with Multiwalled carbon nanotubes using response surface methodology

Engineered Cementitious Composites (ECC) are highly regarded in construction owing to their tensile ductility and crack control capabilities, making them suitable for various structural applications. The accumulation of multi-walled carbon nanotubes (MWCNTs) further enhances their mechanical properties. However, there’s a significant knowledge gap concerning MWCNTs-ECC impact resistance. The objective of this study is to tackle the challenges associated with evaluating, optimizing, and predicting MWCNTs-ECC impact resistance to ensure its safe and widespread use in critical infrastructure by applying response surface methodology (RSM). Moreover, the 13 mixtures of ECC combined with several quantities of PVA fiber and MWCNTs as input elements were utilized to calculate the first (E1) and final (E2) impact energies. The findings demonstrated that the MWCNTs-ECC combinations’ impact resistance improved as the input ingredient concentrations increased. Besides, the optimum E1 and E2 of ECC combined with 1% of PVA fiber were noted by 1398 Joules and 12,956 Joules at 0.065% of MWCNTs on 28 days respectively. Furthermore, Response prediction models for E1 and E2 were created, and after being validated with an analysis of variance (ANOVA), it was determined that they had high R2 readings of 99.30% and 99.07%, correspondingly. The optimization process produced an ideal number of input variables for MWCNTs and PVA fiber, respectively, of 0.066% and 1%, with a desirability value of 100%. Moreover, it is recommended that the usage of 0.066% of MWCNTs in ECC combined with 1.0–1.50% PVA fiber provides optimum results for the construction industry.

Structural and Electrochemical Properties of Binary ZnO:Al Nanocomposites as Anode for Lithium-ion Batteries

In conventional lithium-ion batteries (LIBs), carbon compounds are commonly utilised as the anode owing to their great performance, low cost, and abundance. However, due to the limited storage capability of pure carbon materials that restrict further improvement of LIBs, zinc oxide (ZnO) has been one of the promising anode materials to be used as an alternative to strengthen the electrochemical performance of LIBs due to its high theoretical capacity of 987 mAh g-1. This study aims to synthesise ZnO:Al nanowires using the hot-tube thermal evaporation method. Three types of samples are made using this method by varying the concentration of 0 wt% (S1), 3wt% (S2), and 6 wt% (S3) of aluminium (Al) during the Al deposition process. The EDX findings indicated that the sample has a high proportion of zinc (Zn) and oxygen (O), with the S3 sample having the highest Al concentration after being deposited. The most substantial diffraction peak for XRD of all samples was found at (101), exhibiting a single crystalline hexagonal structure with optimum growth direction on the c-axis. For EIS analysis, the S3 sample has the lowest bulk resistance and maximum ionic conductivity. In conclusion, the ZnO sample with 3 wt% of Al as a dopant was selected as the optimum result to synthesise a homogenous surface of ZnO:Al with good crystallinity by using a hot-tube thermal evaporation process and giving the best conductivity in electrochemical performance.

Predicting Binge Eating Disorder Using Machine Learning Methods

Eating disorders are enduring conditions characterized by elevated rates of mortality and morbidity, presenting a serious threat to life. Among these disorders, binge eating disorder is the most prevalent. Therefore, it is an important health problem that often results in obesity worldwide. This study was conducted to evaluate the eating attitudes and behaviors of university students and predict binge eating disorder using machine learning methods. The study was carried out on 306 individuals (117 males, 189 females). Individuals' personal characteristics were questioned with the questionnaire form. The Bulimic Investigatory Test Edinburgh (BITE) test was used to determine whether individuals taking part in the study had binge eating disorder. In this study, in which binge eating disorder was classified, different artificial neural network models were created by changing the basic parameters, and the optimum model was assessed accordingly. Among the models created with different layers and activation functions, the optimum results were obtained using the number of fully connected layers as 2, first and second layers' sizes as 10, and ReLU, a non-linear activation function, in the Bilayered Neural Network structure. This study is the first trial in which binge eating disorder is predicted using machine learning methods, and we believe that machine learning is an important tool to help researchers and clinicians diagnose, prevent, and treat eating disorders at an early stage.

Recycling spent battery components into highly efficient hydrogen and oxygen evolution electro-nano-catalysts

Inspired by the 'waste-to-resource' strategic theme for environmental recovery, here we present the recycling of spent dry cells and spent Lithium-ion batteries aiming to achieve the preparation of cheap and efficient electrocatalysts for Proton Exchange membrane electrolyzers instead of the precious-based electrocatalysts. Graphite doped with manganese (IV) oxide nanoparticles, graphite doped with manganese (IV) oxide and aluminum oxide nanoparticles, lithium cobalt oxide doped with copper oxide nanoparticles, and lithium cobalt oxide doped with copper oxide and zinc oxide nanoparticles synthesis procedures are done via hydrothermal method at a pressure of 4.5 Mpa at 180 ○C. Electrochemical characterization is done to determine overpotential, tafel slope, and electrocatalytic stability via a three-electrode system. The optimum results are observed by the graphite doped with manganese (IV) oxide nanoparticles, which show -55.06 mV and -41.33 mV/dec as overpotential and tafel slope at -10 mA/cm2 for hydrogen evolution reaction with surface area of 40.87 m2/g and 21.05 nm as crystal size and Graphite doped with manganese oxide and aluminum oxide nanoparticles, which show 321.58 mV and 174.14 mV/dec as overpotential and Tafel slope for oxygen evolution reaction at 10 mA/cm2 with surface area of 7.64 m2/g and 44.43 nm as crystal size.

All-inside Technique versus Conventional Full-tunnel Reconstruction for Anterior Cruciate Ligament Tears of the Knee: A Systematic Review and Meta-analysis

Introduction: There is a paucity of evidence in literature regarding the efficacy of all-inside anterior cruciate ligament reconstruction (AIACLR) vis-à-vis the traditionally used full-tunnel (FT) technique. The primary aim of this review, thus, was to shed light on this discrepancy and determine which of the two methods had more optimum results. Methods: PubMed and Cochrane Library databases were systematically searched by two independent reviewers. Selection criteria were laid down as per the patient, intervention, control, and outcome (PICO) format. All included studies were checked for quality and methodological strength using well-defined risk-of-bias assessment tools. Techniques were compared with respect to their graft and tunnel dimensions, functional outcome scores, and complication rates. Data analysis was carried out using the RevMan 5.3® software. Results: A total of 12 articles (six randomized control trials or randomized clinical trials and six prospective/retrospective cohort studies) with 880 knees were included in this study. In most instances of AIACLR, a short and thick quadrupled semitendinosus graft was used for reconstruction. Suspensory fixation devices alone or in combination with interference screws were used for graft fixation. The two techniques were comparable in terms of their functional outcomes, with a slightly lower graft failure rate with AIACLR. Conclusion: The AI technique provides a safe and reliable alternative to conventional ACLR in terms of comparable functional results, reduced postoperative pain, and lower graft failure rates. However, more comparative trials with long-term follow-ups are needed before a definitive statement can be put forward.

Scleral buckling for retinal reattachment while retaining long-standing glass intraocular foreign bodies and review of literature

Intraocular foreign bodies (IOFBs) can cause significant visual morbidity. Management of rhegmatogenous retinal detachment (RD) with an IOFB can be challenging. We report a case of a retained glass IOFB for 27 years, with RD secondary to atrophic holes in a lattice. We opted for a conservative approach with scleral buckling for the RD, while leaving the IOFB undisturbed. As the foreign body was remote in location with respect to the RD and removal was likely to be traumatic, the IOFB was left in situ. A tailor-made approach for each case, with regular follow-up examinations, can yield optimum results.

Speed and accuracy investigations of neural network algorithms for ionospheric modelling at an equatorial region

Neural networks are very efficient tools for modeling, including ionospheric modeling. The training algorithm is important for achieving the optimum performance of the trained network. This research is therefore meant to evaluate and compare the performances of ten neural network training algorithms based on their prediction accuracies, and the duration/times taken by each of the algorithms to establish the optimum result. The neural networks were trained using electron density measurements by Radio Occultation (RO) technique from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) satellites. Data for the period 2006 through 2021 was used. The networks were trained using about 2.9 million data points collected from the Kano region, Nigeria (5-degree rectangular region around geographic: 12.00° N, 8.59° E) after performing data quality control. The training algorithms considered in the work include: Bayesian Regularization (BR); Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFG); Conjugate Gradient with Powell/Beale (CGB); Fletcher-Reeves Conjugate Gradient (CGF); Gradient descent with momentum and adaptive learning rate (GDX); Levenberg Marquardt (LM); One Step Secant (OSS); Polak-Ribiére Conjugate Gradient (CGP); Resilient Backpropagation (RP); and Scaled Conjugate Gradient (SCG). The results showed that the BR and the LM algorithms gave the best performances in minimizing the errors of prediction (the mean RMSEs are respectively 112 and 114 ×103 electrons/cm3), but the RP algorithm, which came third in terms of accuracy, was significantly faster than both the LM and BR algorithms. The worst-performing algorithm in terms of accuracy was the GDX algorithm, although it was the fastest algorithm. The BFG algorithm was the worst-performing algorithm in terms of a combination of speed and accuracy. The developed neural network model was validated using ionosonde electron density measurements obtained from Ilorin, Nigeria (geographic: 8.5° N, 4.5° E; geomagnetic: 1.8° S). A comparison of the neural network, the NeQuick, and the IRI model predictions relative to the ionosonde measurements indicate that the neural network model was the best-performing model; the NN model predictions minimized the mean absolute errors (MAEs) in ∼44% of 399 ionosonde profiles investigated, the IRI model did so in ∼32%, and the NeQuick did so in ∼24%. The MAEs of the NeQuick however exhibited the best (least) variance. In overall, the NN model gave the least (best) mean of the MAEs (∼73 × 103 cm−3), compared to ∼82 × 103 cm−3 given by both the NeQuick and the IRI models, further supporting the idea that neural networks are excellent for present-day ionospheric modeling.

Increasing Physical Soil Quality by Using Rice Straw Biomass

Paddy straw as an agricultural waste can be returned to the soil to increase the organic matter content. This study aims to determine the optimum inoculant treatment of rice paddy straw on soil quality. The straw handling was carried out by spreading the straw on paddy fields and sprayed with four inoculant treatments (0, 3, 6, 9, and 12 L/ha). The incubation process was carried out for 21 days and incorporated in the soil through plowing. Each treatment was repeated three times so that the total treatment was 15 units. The variables observed in each treatment were soil quality. The soil quality parameters observed were particle density, bulk density, porosity, field capacity and pH. ANOVA statistical analysis which was continued by the Tukey HSD Post Hoc Test to determine the effect of treatment on soil quality. The results showed that the inoculant treatments showed a significant difference (p-value=0.05) in soil volume, density, and porosity. P4 gave the optimum results which are statistically not significantly different from P3. P4 shows the physical properties of the soil which consist of the soil bulk density of 0.61 g/cm3; particle density 1.74 g/cm3; porosity was 64.91%, and pH 6.68. The utilization of straw biomass as source of organic material contributes to zero waste rice cultivation. Keywords: Effective microorganisms, Rice straw, Soil density, Soil pH, Soil porosity.

Particle Shape-Based Evaluation of the Leaching of Sphalerite Ore in Dilute Acid Solutions

In this study, the effects of changes in particle shapes on dissolution efficiencies in zinc (Zn) recovery from a lead-zinc (Pb-Zn) ore by acid leaching method were investigated. In the experiments with nitric acid (HNO3), sulfuric acid (H2SO4), and hydrochloric acid (HCl), particle size (75-106-150 µm), solids ratio (5-10-15-20-25%), leaching time (30-60-120-180-240 min), acid dosage (0.25-0.5-1-2-5 M) and pulp temperature (30-40-50-60-70 oC) parameters were analyzed. Optimum results were obtained under the conditions of 75 µm particle size, 15% solids ratio, 120 min leaching time, 0.5 M acid dosage, and 50°C pulp temperature for H2SO4; 106 µm particle size, 25% solids ratio, 60 min leaching time, 0.5 M acid dosage, and 70°C pulp temperature for HCl; 75 µm particle size, 20% solids ratio, 60 min leaching time, 1 M acid dosage, and 50°C pulp temperature for HNO3. As a consequence of the tests performed under these optimized conditions, 97.32%, 96.38% and 96.06% Zn dissolution efficiencies were obtained. Within the context of particle shape factor research, microscope images of the leaching residues were obtained from the experiments in which the pulp temperature, acid dosage, and leaching time parameters were examined. The samples obtained from the experiments with all three acids were compared with the ore samples, and the impacts of changes in circularity, roundness, and solidity values on dissolution efficiencies were interpreted.

The effect of hydrothermal conditions on surface properties of synthesized nano SBA-15 using sodium silicate

A successful synthesis of mesoporous Santa Barbara amorphous (SBA-15) with nanoparticle size was attempted using prepared sodium silicate from Iraqi high silica sand. EO20PO70EO20 copolymer was used as a template at highly acidic conditions (pH < 2). The effect of crystallization temperature of (100, 110, 120, and 130 0C), and crystallization time of (24, 48, 72, and 120 h) on surface properties was studied. The experiments were characterized using XRD, FTIR, AFM, BET, and FESEM. The XRD and FTIR tests represent amorphous SBA-15 without any impurities. Decreasing average particle size distribution increased the surface area, and decreased the porosity (pore volume, and pore size) in all experiments. The texture properties were surface area of 210-756 cm2/g, volume pore of 0.28-0.7 cm3/g, and pore size of 1.94-13.45 nm. The optimum result of surface area was achieved at hydrothermal conditions of 110 0C, and 24 h, while optimum results of pore volume, and pore size were achieved at hydrothermal conditions of 100 0C, and 120 h. Semi-spherical shape of particles appeared at hydrothermal conditions of 120 0C and 24 h. This morphology was transferred to small rods at 130 0C, and to semi-platelets at 120 h.

Wee1 inhibitor optimization through deep-learning-driven decision making

Deep learning has gained increasing attention in recent years, yielding promising results in hit screening and molecular optimization. Herein, we employed an efficient strategy based on multiple deep learning techniques to optimize Wee1 inhibitors, which involves activity interpretation, scaffold-based molecular generation, and activity prediction. Starting from our in-house Wee1 inhibitor GLX0198 (IC50 = 157.9 nM), we obtained three optimized compounds (IC50 = 13.5 nM, 33.7 nM, and 47.1 nM) out of five picked molecules. Further minor modifications on these compounds led to the identification of potent Wee1 inhibitors with desirable inhibitory effects on multiple cancer cell lines. Notably, the best compound 13 exhibited superior cancer cell inhibition, with IC50 values below 100 nM in all tested cancer cells. These results suggest that deep learning can greatly facilitate decision-making at the stage of molecular optimization.

Optimization of Image Quality in Pelvis Lymphoscintigraphy SPECT/CT Using Discovery NM/CT 670

Abstract Aim A lymphoscintigraphy is a crucial diagnostic tool for visualizing lymph nodes. This scan plays a significant role in determining the treatment and recovery plan for the patients. Due to the small lymph node size, obtaining high-quality images is important to prevent inaccurate results. We aimed to identify the most effective method for enhancing image quality through postprocessing techniques and altering the image reconstruction process. Methods Two data sets were utilized in this study. First, National Electrical Manufacturers Association body phantom was filled with [99mTc]Tc-pertechnetate and prepared with and without any activity in the background of the body. Second, the images of 50 patients who underwent single-photon emission computed tomography/computed tomography imaging received [99mTc]Tc-phytate were collected. Discovery 670 GE gamma camera was used for imaging. Preprocessing of all images was performed by Xeleris and 3DSlicer 5.2.2 software was used for quantification. The effect of image reconstruction parameters such as resolution recovery (RR) algorithm, iteration, subsets, cutoff, and power in Butterworth filter, and full width at half maximum (FWHM) of Gaussian filter was assessed. The image quality index was determined based on contrast-to-noise ratio (CNR), contrast, and coefficient of variation. Results The utilization of the RR algorithm showed notable improvements equal to 74, 35, and 38% of CNR, contrast, and noise reduction, respectively. Significant differences were observed in subiteration of 40 to 112 (p-value < 0.05). The alteration of effective parameters in both smoothing filters yielded statistically significant results, leading to enhanced detectability, reduced noise, and improved contrast simultaneously. Optimum results in terms of noise reduction and CNR were achieved with subiteration (i × s) 4 × 12 using a Gaussian filter with FWHM of 4 or Butterworth filter with power of 10 and cutoff of 1. The highest contrast was observed at subiteration 40 using the Butterworth filter with cutoff of 0.5 and power of 5 or Gaussian filter with 2 mm FWHM. Qualitative analysis by two nuclear medicine specialists validated the quantified image quality. Conclusion The reconstruction setting involving subiteration 48 with the Butterworth filter using cutoff of 1 and power of 10 or 4 mm FWHM of Gaussian filter produced the highest quality images.

Digital Application of BIM Technology in the Architectural Design Stage

This paper discusses the digital application of building information model (BIM) technology in the architectural design stage. Taking the large-scale comprehensive development project of Guangxi headquarters base as an example, this paper analyzes in detail how BIM technology promotes the intelligence and refinement of the design process. Through the three-dimensional modeling and simulation analysis of BIM technology, the project design has realized the accurate transformation from concept to operation, which not only improves the design efficiency, but also ensures the construction quality and economic benefits. This paper focuses on the application of BIM in the digital design of building structure, the deepening design of steel nodes, as well as the remarkable results in the comprehensive layout optimization of mechanical and electrical pipelines. Through the collision detection and optimization design of the BIM model, the potential design conflicts and construction problems were found and solved at the initial stage of the project, ensuring the efficient promotion and smooth implementation of the project. The research results show that BIM technology, as the core digital tool in the architectural design stage, is of great significance for improving the overall design level of the construction industry and realizing intelligent construction.

Performance Properties and Finite Element Modelling of Forest-Based Bionanomaterials/Activated Carbon Composite Film for Sustainable Future

Thanks to its highly crystalline structure and excellent thermal, optical, electrical and mechanical properties, carbon and its derivatives are considered the preferred reinforcement material in composites used in many industrial applications, especially in the forest and forest products sector, including oil, gas and aviation. Since hydroxyethyl cellulose (HEC) is a biopolymer, it has poor mechanical and thermal properties. These properties need to be strengthened with various additives. This study aims to improve the thermal and mechanical properties of hydroxyethyl cellulose by preparing hydroxyethyl cellulose/activated carbon (HEC/AC) composite materials. With this study, composites were obtained for the first time and their mechanical properties were examined using a 3D numerical modeling technique. The thermal stability of the prepared composite materials was investigated via thermal gravimetric analysis (TGA). The samples were heated from 30 °C to 750 °C with a heating rate of 10 °C/min under a nitrogen atmosphere and their masses were measured subsequently. The mechanical properties of the composites were investigated via the tensile test. The viscoelastic properties of the composite films were determined with dynamic mechanical thermal analyses (DMTA) and their morphologies were examined with scanning electron microscopy (SEM) images. According to the results, the best F3 sample (films containing 3 wt.% activated carbon) had an elastic modulus of 168.3 MPa, a thermal conductivity value of 0.068 W/mK, the maximum mass loss was at 328.20 °C and the initial storage modulus at 30 °C was 206.13 MPa. It was determined that the hydroxyethyl cellulose composite films containing 3 wt.% activated carbon revealed the optimum results in terms of both thermal conductivity and viscoelastic response and showed that the obtained composite films could be used in industrial applications where thermal conductivity was required.