Article
Materials Science, Multidisciplinary
Haejoo Moon, Younghun Kim
Summary: The study investigated the use of core-satellite structures in photothermal-mediated catalytic reactions, enhancing the photothermal effect by adjusting the structure and spacing of HAuNPs. The results demonstrated that localized heating effectively increased catalytic activity without the need to heat the entire solution additionally.
ACS APPLIED POLYMER MATERIALS
(2021)
Article
Chemistry, Multidisciplinary
Ibrahim Dubdub, Mohammed Al-Yaari
Summary: An artificial neural network (ANN) model was developed to predict the pyrolysis of mixed plastics efficiently, showing excellent agreement with experimental data. A sensitivity analysis revealed that temperature was the most sensitive input parameter in the model.
APPLIED SCIENCES-BASEL
(2021)
Letter
Thermodynamics
Cherifa Kara Mostefa Khelil, Badia Amrouche, Kamel Kara, Aissa Chouder
Summary: This study examines the impact of different Artificial Neural Networks on fault diagnosis in PV installations. It shows that RBF ANNs affect the algorithm's reaction rate, while BPNNs and GRNN present the best results in terms of speed, high precision, and classification efficiency. PNN also stands out for achieving 100% accuracy in key statistical concepts.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Energy & Fuels
Omer Boyukdipi, Gokhan Tuccar, Hakan Serhad Soyhan
Summary: The experimental study investigated the effects of NH3 as a fuel additive on engine vibration parameters, revealing that increasing levels of NH3 additive led to increased engine vibration and had a negative impact on engine vibration when blended with sunflower biodiesel. High accuracy rates were achieved in predicting vibration data through artificial neural networks models.
Article
Engineering, Multidisciplinary
M. A. Maia, I. B. C. M. Rocha, P. Kerfriden, F. P. van der Meer
Summary: Driven by the need for faster numerical simulations, the use of machine learning techniques is rapidly growing in computational solid mechanics, especially in concurrent multiscale finite element analysis. Surrogate models are being used to approximate microscopic behavior and accelerate simulations, but challenges related to their data-driven nature compromise their reliability. This study introduces a neural network that incorporates classical constitutive models to introduce non-linearity and address these challenges. The network demonstrates the ability to predict unloading/reloading behavior without prior training, unlike popular data-hungry models such as RNNs.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Engineering, Biomedical
Hasan Turker, Bekir Aksoy, Koray Ozsoy
Summary: This study investigated the production of dental guides using additive manufacturing stereo lithography (SLA) technology and utilized artificial intelligence to analyze the dimensional aperture values. The results showed that the SLA-produced dental guides were compatible with the mandible bone, and the most suitable guide design was determined. Through the use of artificial neural network models, an accuracy rate of 99% was achieved.
JOURNAL OF THE MECHANICAL BEHAVIOR OF BIOMEDICAL MATERIALS
(2022)
Article
Engineering, Civil
Mahesh Shelke, S. N. Londhe, P. R. Dixit, Pravin Kolhe
Summary: This study compared the performance of a conceptual semi distributed HEC-HMS model and an ANN-based model in predicting reservoir inflow in the Koyna reservoir catchment area. The results showed that the semi distributed HEC-HMS model performed slightly better than the ANN model. This research is significant for the planning of reservoir operations in the Koyna reservoir catchment area.
WATER RESOURCES MANAGEMENT
(2023)
Review
Biochemistry & Molecular Biology
Waad H. Abuwatfa, Nour AlSawaftah, Naif Darwish, William G. Pitt, Ghaleb A. Husseini
Summary: Membrane fouling is a major challenge for pressure-driven membrane processes. Various factors affect membrane fouling, and several models, including artificial neural networks (ANNs), have been developed to predict fouling. ANNs are powerful tools for nonlinear mapping and can capture complex relationships between input and output variables. This review focuses on the use of ANNs for membrane fouling prediction, providing insights into their strengths, weaknesses, potential, and areas of improvement.
Article
Food Science & Technology
Iman Golpour, Ana Cristina Ferrao, Fernando Goncalves, Paula M. R. Correia, Ana M. Blanco-Marigorta, Raquel P. F. Guine
Summary: This research study focused on evaluating the total phenolic compounds and antioxidant activity of strawberries under different experimental extraction conditions using Artificial Neural Networks (ANNs). The results indicated that ANN is a potential method for estimating the targeted compounds and activity of strawberries, showcasing its effectiveness in predicting health benefits.
Article
Construction & Building Technology
S. N. Londhe, P. S. Kulkarni, P. R. Dixit, A. Silva, R. Neves, J. de Brito
Summary: Concrete carbonation is an important issue in Civil Engineering and Material Science fields. This study used statistical modeling and Soft Computing techniques like Artificial Neural Networks and Genetic Programming to predict the carbonation coefficient in concrete, showing that these models perform better than Multiple Linear Regression in handling the nonlinear influence of relative humidity on concrete carbonation.
JOURNAL OF BUILDING ENGINEERING
(2021)
Review
Engineering, Chemical
Saikat Sinha Ray, Rohit Kumar Verma, Ashutosh Singh, Mahesh Ganesapillai, Young-Nam Kwon
Summary: In recent years, deep learning and machine learning have emerged as potential technologies widely applied in the fields of science, engineering, and technology, specifically in the optimization of seawater desalination and water treatment processes. Artificial intelligence has played a key role in addressing issues such as monitoring, management, and labor costs. This article thoroughly reviews the application of AI in the water treatment and seawater desalination sectors, compares conventional modeling approaches with artificial neural network modeling, and discusses challenges, shortcomings, and future prospects. The use of AI mechanisms in data processing, optimization, modeling, prediction, and decision-making during water treatment and seawater desalination processes are emphasized, along with innovative trends in these areas.
Article
Chemistry, Analytical
C. Tsekos, S. Tandurella, W. de Jong
Summary: This study evaluates the estimation of main products from lignocellulosic biomass pyrolysis using neural networks as modeling tools. By analyzing data from 32 published studies, simplified models were proposed to predict yields. The results show that the simplified models perform best for char and gas products, while the full network is more effective for the liquid product.
JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS
(2021)
Article
Engineering, Chemical
Meerambika Behera, Nitika Tiwari, Aradhana Basu, Smruti Rekha Mishra, Shirsendu Banerjee, Sankha Chakrabortty, Suraj K. Tripathy
Summary: The Maghemite/ZnO nanocomposites showed higher catalytic activity in the reduction of p-nitrophenol compared to pure oxides. Parameter investigation and optimization revealed the maximum reduction efficiency under specific conditions. The developed material has potential commercial applications due to its stable catalytic performance over multiple cycles.
ADVANCED POWDER TECHNOLOGY
(2021)
Article
Chemistry, Applied
Patricia Horta-Fraijo, Elena Smolentseva, Andrey Simakov, Miguel Jose-Yacaman, Brenda Acosta
Summary: In this study, silver nanoparticles, cations, and clusters dispersed in A4 zeolite were successfully synthesized using microwave irradiation and ion-exchange methods. The sample prepared under microwave irradiation showed higher catalytic performance, with a TOF value 7.5 times higher than the reference sample obtained via ion-exchange method. The synthesis conditions and components used in microwave-assisted synthesis offer an ultra-fast method for the synthesis of highly active catalysts.
MICROPOROUS AND MESOPOROUS MATERIALS
(2021)
Article
Computer Science, Artificial Intelligence
Arunabha M. Roy, Rikhi Bose, Veera Sundararaghavan, Raymundo Arroyave
Summary: This paper presents an efficient and robust data-driven deep learning framework for linear continuum elasticity problems. The methodology is based on Physics Informed Neural Networks (PINNs) and uses a multi-objective loss function for accurate representation of field variables. Multiple independent artificial neural networks are trained to obtain accurate solutions, which are shown to have excellent agreement with analytical solutions in benchmark problems. The framework combines the benefits of classical methods and deep learning techniques to construct lightweight, accurate, and robust neural networks.
Article
Polymer Science
M. Karimi, M. Habibizadeh, K. Rostamizadeh, M. Khatamian, B. Divband
Article
Chemistry, Applied
Saeed Jafarirad, Maryam Salmasi, Baharak Divband, Mohammadhassan Sarabchi
JOURNAL OF RARE EARTHS
(2019)
Article
Chemistry, Applied
Sara Fazli-Shokouhi, Farzad Nasirpouri, Maasoumeh Khatamian
JOURNAL OF COATINGS TECHNOLOGY AND RESEARCH
(2019)
Article
Materials Science, Multidisciplinary
M. Safari Gezaz, S. Mohammadi Aref, M. Khatamian
MATERIALS CHEMISTRY AND PHYSICS
(2019)
Article
Engineering, Environmental
Maasumeh Khatamian, Baharak Divband, Robab Shahi
JOURNAL OF WATER PROCESS ENGINEERING
(2019)
Article
Engineering, Environmental
Sepideh Mahjouri, Morteza Kosari-Nasab, Elham Mohajel Kazemi, Baharak Divband, Ali Movafeghi
JOURNAL OF HAZARDOUS MATERIALS
(2020)
Article
Materials Science, Multidisciplinary
M. Saket Osgouei, M. Khatamian, H. Kakili
MATERIALS CHEMISTRY AND PHYSICS
(2020)
Article
Materials Science, Multidisciplinary
Sanam Mohandesnezhad, Younes Pilehvar-Soltanahmadi, Effat Alizadeh, Arash Goodarzi, Soodabeh Davaran, Masoumeh Khatamian, Nosratollah Zarghami, Mohammad Samiei, Marzieh Aghazadeh, Abolfazl Akbarzadeh
MATERIALS CHEMISTRY AND PHYSICS
(2020)
Article
Environmental Sciences
Maryam Tahmasebpoor, Shamin Hosseini Nami, Masoumeh Khatamian, Leila Sanaei
Summary: Fe-Clin granules, obtained by modifying clinoptilolite with iron oxide, exhibit enhanced affinity towards arsenic pollutant and can be widely used within a wide range of pH. These granules can remove As(V) in a very short amount of time and have a high adsorption capacity.
ENVIRONMENTAL TECHNOLOGY
(2022)
Article
Biotechnology & Applied Microbiology
Baharak Divband, Bahareh Pouya, Mehdi Hassanpour, Mahdieh Alipour, Roya Salehi, Reza Rahbarghazi, Sahriar Shahi, Zahra Aghazadeh, Marziyeh Aghazadeh
Summary: This study developed bFGF-loaded PCL/CS scaffolds and investigated their effects on angiogenesis in hDPSCs. The results showed that the PCL/CS/bFGF group significantly increased cell adhesion and survival rate, as well as the expression levels of VEGFR-2, Tie2, and Angiopoietin-1 genes.
BIOMED RESEARCH INTERNATIONAL
(2022)
Article
Materials Science, Biomaterials
Mahdi Khalilnejad, Baharak Divband, Nahideh Gharehaghaji, Tohid Mortezazadeh
Summary: This study developed a high potential nanosystem that combines imaging contrast media and treatment strategies for dual-modal CT/MRI and pH-responsive 5FU drug delivery.
INTERNATIONAL JOURNAL OF POLYMERIC MATERIALS AND POLYMERIC BIOMATERIALS
(2023)
Article
Food Science & Technology
Maryam Azizi-Lalabadi, Mahmood Alizadeh-Sani, Baharak Divband, Ali Ehsani, David Julian McClements
FOOD RESEARCH INTERNATIONAL
(2020)
Article
Multidisciplinary Sciences
Ebrahim Sadeghi, Maryam Saket Oskoui, Masoumeh Khatamian
SN APPLIED SCIENCES
(2019)
Article
Chemistry, Physical
Baharak Divband, Azadeh Jodaie, Masumeh Khatmian
IRANIAN JOURNAL OF CATALYSIS
(2019)
Article
Materials Science, Multidisciplinary
Madeeha Riaz, Manahil Najam, Hina Imtiaz, Farooq Bashir, Tousif Hussain
Summary: This study focuses on the structural and biological analysis of Zn-Cu based biodegradable alloys for orthopedic applications. The results indicate that the alloys have good electrical conductivity and biocompatibility, with potential for promoting bone growth and healing process. Additionally, the alloys exhibit a low corrosion rate and improved corrosion resistance.
MATERIALS CHEMISTRY AND PHYSICS
(2024)
Article
Materials Science, Multidisciplinary
Rijo Rajeev, Sk Safikul Islam, Anitha Varghese, Gurumurthy Hegde, Suryasarathi Bose
Summary: In this study, a facile and selective electrochemical sensor was developed for the sensing of guanosine. The sensor utilized a unique porous structure and ordered framework, enabling linear detection of guanosine concentration in the range of 0.123-720 μM under specific conditions.
MATERIALS CHEMISTRY AND PHYSICS
(2024)
Article
Materials Science, Multidisciplinary
Rafael V. M. Freire, Dominique Celeste de A. Dias, Jose Yago Rodrigues Silva, Dayane Kelly Dias do Nascimento Santos, Larissa T. Jesus, Ricardo O. Freire, Severino A. Junior
Summary: This study reports the extraction and isolation of euphol from nature, its adsorption in nanosized ZIF-8, and the efficacy of this system against cancer cells. Experimental and simulation results show that ZIF-8 can enhance the effectiveness of euphol against cancer cells and selectively target cancer cells.
MATERIALS CHEMISTRY AND PHYSICS
(2024)
Article
Materials Science, Multidisciplinary
Manal A. Awad, Awatif A. Hendi, Maha M. Almoneef, Maymunah Alwehaibi, Khalid M. Ortashi, Wadha Alenazi, Fatimah S. Alfaifi, Shareefa Alahmariye, Asma Alangery, Warda Ali Alghoubiri, Haia Aldosari
Summary: In this study, magnesium-doped zinc oxide nanoparticles were synthesized and characterized. The research findings show that magnesium doping can alter the crystal structure and optical properties of zinc oxide, while enhancing its dielectric constant.
MATERIALS CHEMISTRY AND PHYSICS
(2024)
Article
Materials Science, Multidisciplinary
F. J. Willars-Rodriguez, I. R. Chaverz-Urbiola, M. A. Hernandez-Landaverde, A. Zavala-Franco, E. A. Chavez-Urbiola, P. Vorobiev, Yu V. Vorobiev
Summary: This study focuses on manganese doped CdS thin films synthesized by chemical bath deposition. The incorporation of Mn2+ cations in CdS was found to influence the crystalline structure, morphology, and optoelectronic properties. Doped thin films exhibited a uniform hexagonal structure, changed growth orientation, and showed scale-like and needle-like morphologies. The bandgap and rectification speed of Schottky diodes were modified by introducing manganese. This study suggests the potential for affordable high-speed optoelectronic devices.
MATERIALS CHEMISTRY AND PHYSICS
(2024)
Article
Materials Science, Multidisciplinary
Mehdi Javidi, Hooman Karimi Abadeh, Fatemeh Namazi, Hamid Reza Yazdanpanah, Narjes Shirvani Shiri
Summary: This study investigated the synergistic effect of temperature, solution velocity, and sulphuric acid concentration on the corrosion behavior of carbon steel using response surface methodology. The results showed that temperature affected anodic reactions, solution velocity influenced cathodic reactions, and acid concentration altered the corrosion mechanisms by changing the properties of the surface layer.
MATERIALS CHEMISTRY AND PHYSICS
(2024)
Article
Materials Science, Multidisciplinary
R. Sakthivel, Thirumoorthy Kulandaivel, Kirankumar Venkatesan Savunthari, K. Mohanraj, Hans-Uwe Dahms, Aswin kumar Anbalagan, Manjunath Rangasamy, Kien-Voon Kong
Summary: In this study, saturated fatty acids were incorporated with silane to modify viscose fabric, resulting in superhydrophobic and superoleophilic properties. The modified fabric showed excellent separation efficiency for oil and organic solvents, with high absorption capacity. The modified fabric also exhibited durability and retained its properties in harsh conditions.
MATERIALS CHEMISTRY AND PHYSICS
(2024)
Article
Materials Science, Multidisciplinary
Wei Zhang, Hong Lei, Wenqing Liu, Zefang Zhang, Yi Chen, Xiaogang Hu, Xiangshan Ye
Summary: In this study, EDTA-grafted alumina composite abrasives were produced by a two-step process for the CMP of sapphire substrates. Experimental results showed that the modified abrasives exhibited better dispersion properties and significantly improved polishing efficiency, with higher material removal rates and lower surface roughness. The combination of chemical reaction and mechanical action enhanced the CMP performance.
MATERIALS CHEMISTRY AND PHYSICS
(2024)
Article
Materials Science, Multidisciplinary
Shumaila Rafaqat, Bushra Perveen, Warda Raqba, Warda Imran, Arshad Hussain, Naeem Ali
Summary: This study developed a MnP-based biosensor for quantitative measurement of dye concentrations using electrochemical signals. The effects of two different dyes on MnP activity were investigated, with one dye showing inhibitory effects and the other dye having no effect. The study demonstrates the potential application of enzyme-based biosensors in dye detection and toxicological monitoring.
MATERIALS CHEMISTRY AND PHYSICS
(2024)
Article
Materials Science, Multidisciplinary
Jinyan Shi, Oguzhan Yavuz Bayraktar, Baris Bayrak, Burak Bodur, Ali Oz, Gokhan Kaplan, Abdulkadir Cuneyt Aydin
Summary: The elemental composition of precursors is crucial for the performance development of geopolymers. The use of lime instead of metakaolin increases the fluidity and mechanical properties of geopolymers, while the addition of gypsum decreases them. Furthermore, higher lime content exacerbates the negative effect of gypsum.
MATERIALS CHEMISTRY AND PHYSICS
(2024)
Article
Materials Science, Multidisciplinary
Aayush Gupta, Kaveri Ajravat, Loveleen K. Brar, O. P. Pandey, Pandey Rajagopalan
Summary: This study focuses on the performance of Mn3O4-ZnO composite material in wastewater treatment and energy storage applications, and presents a detailed comparative analysis. Results show that the composite material with equal concentrations of Mn3O4 and ZnO exhibits excellent photocatalytic activity and high capacitance.
MATERIALS CHEMISTRY AND PHYSICS
(2024)
Article
Materials Science, Multidisciplinary
V. Murugabalaji, Matruprasad Rout, Harsh Soni, Biranchi Narayan Sahoo
Summary: This study focuses on the corrosion characteristics of AA 7075 and AA 7075 based hybrid composite fabricated using stir casting and hot rolling techniques. The results show that the hybrid composite produced by hot cross rolling exhibits better corrosion resistance compared to the base metal. The addition of a small amount of graphite improves the bonding between the matrix and reinforcements, and the hot cross rolling enhances this bonding, leading to the formation of a strong passivation oxide layer and increased charge transfer resistance, thereby improving corrosion resistance.
MATERIALS CHEMISTRY AND PHYSICS
(2024)
Article
Materials Science, Multidisciplinary
Fangkun Ning, Qinghao Shi, Shuping Kong, Weitao Jia, Lifeng Ma
Summary: The paper investigates a new method of rolling sheets with variable chamfering amounts in both the transversal and normal directions. The feasibility of the technological process was tested through simulation and compared with experimental results. Three important process parameters, temperature, stress, and flow velocity, were used to evaluate the effects on chamfering amount before determining the optimal angle. The spread formula for evaluating the shape quality of the plate after ECR was obtained through testing and theory.
MATERIALS CHEMISTRY AND PHYSICS
(2024)
Article
Materials Science, Multidisciplinary
Aqeel Abbas, M. A. Hussein, Mohamed Javid
Summary: In this study, the AM60 magnesium alloy was processed using high-energy ball milling, and the results showed that different reinforcement agents had certain effects on particle size, crystallite size, lattice strain, and dislocation density.
MATERIALS CHEMISTRY AND PHYSICS
(2024)
Article
Materials Science, Multidisciplinary
D. S. Mahmoud, E. M. Eldesouki, W. M. Abd El-Gawad
Summary: The development of flexible and lightweight microwave-absorbing materials has become a trendy topic. This study focuses on enhancing the microwave-absorbing performance of butadiene-acrylonitrile rubber (NBR) by incorporating novel reinforcing nanofillers. The results show that the NBR nanocomposite with a loading of 16 parts per hundred rubber (phr) of LiFe 20%/Si has the best microwave-absorbing performance.
MATERIALS CHEMISTRY AND PHYSICS
(2024)
