Article
Polymer Science
Gerald Munoz, Alain Dequidt, Nicolas Martzel, Ronald Blaak, Florent Goujon, Julien Devemy, Sebastien Garruchet, Benoit Latour, Etienne Munch, Patrice Malfreyt
Summary: The Elastic Network Model (ENM) approach reintroduces spatial resolution by considering the network at the level of its topological constraints, predicting the macroscopic properties of polymer networks up to the point of failure, and highlighting the effects of topology and structure on the mechanical properties of polymer networks. Significant differences in mechanical responses arise between networks with similar topology but different spatial structure, while the dispersion of cross-link valency has a negligible impact.
Article
Polymer Science
Moises Bustamante-Torres, Victor H. Pino-Ramos, David Romero-Fierro, Sandra P. Hidalgo-Bonilla, Hector Magana, Emilio Bucio
Summary: The design of a new pH-sensitive hydrogel for localized prophylactic release of ciprofloxacin and silver nanoparticles was presented in this study. The hydrogel was copolymerized using gamma rays and characterized using various analytical techniques. The antimicrobial activity of the hydrogel against E. coli and MRSA was evaluated in vitro.
Article
Mathematics, Applied
Yuxiang Chen, Zhihao Ge
Summary: In this paper, a multiphysics finite element method is proposed for the quasi-static thermoporoelasticity model with small Peclet number. The method is proved to have existence, uniqueness, stability and optimal convergence order. Numerical examples are provided to verify the theoretical results.
JOURNAL OF SCIENTIFIC COMPUTING
(2022)
Article
Mathematics, Applied
A. Magana, R. Quintanilla
Summary: This study focuses on the behavior of solutions to porous-thermo-elastic problems over time, specifically when one variable is considered quasi-static. The analysis involves three different situations and utilizes both classical Fourier law and Green-Naghdi heat conduction models.
ZEITSCHRIFT FUR ANGEWANDTE MATHEMATIK UND PHYSIK
(2021)
Article
Mathematics, Applied
Jing Zhang, Hongxing Rui
Summary: The standard Galerkin method for fully coupled nonlinear thermo-poroelastic model problems is studied in this paper. The semi discrete and fully discrete finite element schemes are established, and the stability of this method is obtained. Error estimates for the displacement, pressure, and temperature are derived. Finally, several numerical examples are given to illustrate the accuracy of the method.
COMPUTERS & MATHEMATICS WITH APPLICATIONS
(2022)
Article
Polymer Science
Wenjie Wang, Guitian Tai, Yixuan Li, Junqi Sun
Summary: Highly elastic, healable, and durable electrolyte membranes named PU-IL-MX have been developed by complexing poly(urea-urethane), ionic liquids, and MXene nanosheets. The membranes exhibit a tensile strength of approximately 3.86 MPa, a strain at break of approximately 281.89%, and can conduct protons under anhydrous, high-temperature conditions. The PU-IL-MX membranes also demonstrate excellent IL retention properties and can retain their weight and proton conductivity even under highly humid conditions.
MACROMOLECULAR RAPID COMMUNICATIONS
(2023)
Article
Chemistry, Multidisciplinary
Mustafa S. Hamad, Catherine Boissier, Victor M. Calo, Julian D. Gale, Sten O. Nilsson Lill, Gordon M. Parkinson, Andrew L. Rohl
Summary: Two computational methods were used to study the slip deformation of simple molecular crystalline materials, revealing the critical influence of internal molecular rotations on slip barriers and deformation mechanisms. The results also provide new interpretations of slip-observed morphologies.
Article
Chemistry, Multidisciplinary
Yunfei Wang, Song Zhang, Guillaume Freychet, Zhaofan Li, Kai-Lin Chen, Chih-Ting Liu, Zhiqiang Cao, Yu-Cheng Chiu, Wenjie Xia, Xiaodan Gu
Summary: Wearable devices can benefit from the use of stretchable conjugated polymers (CPs). Traditional design assumes that low elastic modulus (E) is crucial for achieving high stretchability, but this research challenges this notion. It is discovered that the degree of entanglement, rather than softness alone, determines stretchability. Even rigid CPs can exhibit high stretchability. Two model CPs with different elastic moduli are studied to further investigate mechanical behavior and deformation mechanisms. By challenging the conventional design metric of low E for high stretchability and highlighting the importance of entanglement, this research hopes to expand the range of CPs available for use in wearable devices.
ADVANCED FUNCTIONAL MATERIALS
(2023)
Article
Chemistry, Multidisciplinary
Zaoming Wang, Christian Villa Santos, Alexandre Legrand, Frederik Haase, Yosuke Hara, Kazuyoshi Kanamori, Takuma Aoyama, Kenji Urayama, Cara M. Doherty, Glen J. Smales, Brian R. Pauw, Yamil J. Colon, Shuhei Furukawa
Summary: The study demonstrates that aging is an effective strategy to control the hierarchical network of supramolecular gels, allowing tuning of the linked MOP gel network over multiple length scales. This provides an opportunity to engineer the structure and permanent porosity of amorphous materials for further applications.
Article
Chemistry, Applied
Anna A. Skuredina, Anastasia S. Tychinina, Irina M. Le-Deygen, Sergey A. Golyshev, Natalya G. Belogurova, Elena V. Kudryashova
Summary: The highly cross-linked sulfobutyl ether derivative of beta-cyclodextrin in combination with the antibacterial drug moxifloxacin forms polymeric nanoparticles with efficient drug encapsulation and extended antibacterial activity, showcasing potential for new pharmaceutical formulations.
REACTIVE & FUNCTIONAL POLYMERS
(2021)
Article
Materials Science, Multidisciplinary
C. R. Lear, M. R. Chancey, R. Flanagan, J. G. Gigax, M. T. Hoang, D. R. Jones, H. Kim, D. T. Martinez, B. M. Morrow, N. Mathew, Y. Wang, N. Li, J. R. Payton, M. B. Prime, S. J. Fensin
Summary: Damage and degradation caused by low-temperature irradiation presents challenges for storing radioactive materials and peripheral components in nuclear reactors. To understand the mechanical behavior of such materials, both quasi-static and dynamic strain rates need to be tested. This study overcame the limitations of neutron-irradiated and ion-irradiated materials by using surface-sensitive Richtmyer-Meshkov instability tests. The data compiled from nanopillar compression, nanoindentation, and RMI testing on helium-implanted copper showed that helium bubbles did not significantly affect the strength at high strain rates, unlike at quasi-static strain rates.
Article
Mechanics
Noelia Bazarra, Alberto Castejon, Jose R. Fernandez, Ramon Quintanilla
Summary: This study focuses on a one-dimensional thermoelastic problem with type III thermal law and quasi-static microvoids, analyzing it numerically with a coupled linear system and discrete approximations using the finite element method and backward Euler method. The research proves discrete stability, error estimates, and linear convergence, and demonstrates the accuracy of the approximation through numerical simulations.
Article
Polymer Science
Kun Woo Park, Zoran Zujovic, Erin M. Leitao
Summary: The discovery of inverse vulcanization has led to the development of stable polysulfide materials synthesized from affordable sulfur. This research explores the use of two siloxane dienes as cross-linkers and successfully synthesizes two series of cross-linked polysulfides. The properties and performance of these materials are characterized and analyzed.
Article
Engineering, Mechanical
Suchao Xie, Pengfei Chen, Ning Wang, Jin Wang, Xuanjin Du
Summary: A study was conducted on the performance of circular tubes subjected to radial extrusion under different geometrical configurations, revealing that indenters could produce axial grooves without buckling instability. Deformation and driving force-displacement responses were analyzed through numerical simulation, with effects of different geometrical configurations on crashworthiness also examined based on the validated finite element model.
INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
(2021)
Article
Physics, Fluids & Plasmas
Manoj Kumar Maurya, Celine Ruscher, Debashish Mukherji, Manjesh Kumar Singh
Summary: Indentation is a common experimental technique to study the mechanics of polymeric materials. In this work, the authors investigate the structure-property relationship in highly cross-linked polymer networks using computational indentation. The results are compared with the elastic-plastic deformation model, and it is found that highly cross-linked polymers harden upon indentation.
Article
Mechanics
Xiaoying Zhuang, Hongwei Guo, Naif Alajlan, Hehua Zhu, Timon Rabczuk
Summary: This paper introduces a Deep Autoencoder based Energy Method (DAEM) for the bending, vibration, and buckling analysis of Kirchhoff plates. The DAEM utilizes higher-order continuity, integrates a deep autoencoder and the minimum total potential principle, and serves as an unsupervised feature learning method. It efficiently identifies patterns, minimizes total potential energy, extracts fundamental frequencies and critical buckling loads, alleviates gradient problems, and improves computational efficiency and generality through transfer learning.
EUROPEAN JOURNAL OF MECHANICS A-SOLIDS
(2021)
Article
Engineering, Geological
Xiaoying Zhuang, Fei Zheng, Hong Zheng, Yu-Yong Jiao, Timon Rabczuk, Peter Wriggers
Summary: The study introduces a cover-based strategy for the detection and resolution of contacts between irregular convex polygons, using loops and filter criteria to establish potential contact cover lists and update contact covers iteratively. The proposed method is implemented in discontinuous deformation analysis for efficient and robust contact analysis of irregular blocks in both 2D and potentially 3D cases.
INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS
(2021)
Article
Computer Science, Interdisciplinary Applications
Aydin Shishegaran, Hesam Varaee, Timon Rabczuk, Gholamreza Shishegaran
Summary: This paper introduces a novel hybrid model to predict the compressive strength of concrete using HCVCM method, and demonstrates that the HCVCM-ANFIS model can predict the compressive strength more accurately than other models.
COMPUTERS & STRUCTURES
(2021)
Article
Materials Science, Multidisciplinary
Xinyue Wu, Yabin Jin, Abdelkrim Khelif, Xiaoying Zhuang, Timon Rabczuk, Bahram Djafari-Rouhani
Summary: The study proposes topological metamaterials in the Hertz frequency range, consisting of concrete pillars in a honeycomb lattice on the soil ground. By breaking the inversion symmetry of the unit cell, a non-trivial bandgap is formed, analogous to the quantum valley Hall effect. The robustness of the topological interface between two different crystal phases against defects and disorders is quantitatively analyzed. Additionally, a harvesting energy device is designed using the robust and compact topological edge state, showing functionality in both reducing surface vibration and energy harvesting.
MECHANICS OF ADVANCED MATERIALS AND STRUCTURES
(2022)
Article
Chemistry, Physical
Bohayra Mortazavi, Fazel Shojaei, Timon Rabczuk, Xiaoying Zhuang
Summary: Building on the experimental achievement, this study conducted simulations based on first principles to explore the properties of graphene-like BeO, MgO, and CaO monolayers, revealing their desirable thermal stability, mechanical strength, and electronic band gaps. Increasing the weight of metal atoms showed a substantial decline in the electronic band gap, mechanical strength, and thermal conductivity of the monolayers. This work highlights the potential of insulating BeO nanosheets as promising candidates for designing components with high thermal conductivities.
Article
Chemistry, Multidisciplinary
Dongil Shin, Andrea Cupertino, Matthijs H. J. de Jong, Peter G. Steeneken, Miguel A. Bessa, Richard A. Norte
Summary: The article introduces the important role of mechanical resonators in driving the next generation technologies to operate in room-temperature environments. Through bioinspired design and guidance of machine learning, a spiderweb nanomechanical resonator with novel isolated vibration modes has been developed, exhibiting quality factors above 1 billion, and it is more simple and cost-effective in manufacturing.
ADVANCED MATERIALS
(2022)
Article
Mechanics
Bokai Liu, Nam Vu-Bac, Xiaoying Zhuang, Xiaolong Fu, Timon Rabczuk
Summary: In this study, a data-driven approach based on a stochastic full-range multiscale model is proposed for predicting the thermal conductivity of CNT reinforced polymeric nano-composites (PNCs). Machine learning techniques are employed to efficiently design new PNCs based on the prediction results.
COMPOSITE STRUCTURES
(2022)
Article
Materials Science, Composites
Bokai Liu, Nam Vu-Bac, Xiaoying Zhuang, Xiaolong Fu, Timon Rabczuk
Summary: We propose a stochastic integrated machine learning approach for predicting the macroscopic thermal conductivity in carbon nanotube reinforced polymeric composites. By utilizing different machine learning models and optimizing hyperparameters with particle swarm optimization, this approach reduces computational cost and provides valuable insights in the computational design of new composites for thermal management applications.
COMPOSITES SCIENCE AND TECHNOLOGY
(2022)
Article
Engineering, Multidisciplinary
Bernardo P. Ferreira, F. M. Andrade Pires, M. A. Bessa
Summary: This article introduces adaptivity in Clustering-based Reduced Order Models (ACROMs) and applies it to Self-Consistent Clustering Analysis (SCA). The Adaptive Self-Consistent Clustering Analysis (ASCA) method improves predictions for materials with history-dependent localization phenomena. The method consists of three main building blocks and proposes solutions to further enhance the adaptive process.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Engineering, Multidisciplinary
N. Vu-Bac, T. Rabczuk, H. S. Park, X. Fu, X. Zhuang
Summary: In this study, a novel formulation using nonlinear kinematics and material models is proposed to couple elasticity and solvent transport in stimuli-responsive gels. The identification of external stimuli to generate specific target shapes is achieved through an inverse methodology that chemomechanically couples large deformation and mass transport. Numerical examples demonstrate the capability of identifying the required external stimuli and accurately reconstructing target shapes, including those involving elastic instabilities or softening.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Bokai Liu, Nam Vu-Bac, Xiaoying Zhuang, Weizhuo Lu, Xiaolong Fu, Timon Rabczuk
Summary: This article introduces a web-based framework based on the R shiny package with functional back-end server in machine learning methods. A 4-tiers architecture is programmed to achieve users' interactive design and visualization via a web browser. Many data-driven methods such as Random Forest, Gradient Boosting Machine, Artificial and Deep neural networks are integrated into this framework. Moreover, a robust gradient-free optimization technique, the Particle Swarm Optimization, is used for hyper-parameters tuning. K-fold Cross Validation is applied to avoid over-fitting. R2 and RMSE are used to evaluate the trained models. The contributions include a systematic framework for materials prediction with machine learning approaches, a user-friendly web-based platform, and integrated optimization and visualization into the framework with pre-set algorithms. This computational framework is designed for researchers and materials engineers to do preliminary designs before experimental studies. The performance of the web-based framework is demonstrated through 2 case studies.
ADVANCES IN ENGINEERING SOFTWARE
(2023)
Article
Computer Science, Artificial Intelligence
Aleksandr Dekhovich, David M. J. Tax, Marcel H. F. Sluiter, Miguel A. Bessa
Summary: The human brain can learn tasks without forgetting, but deep neural networks suffer from catastrophic forgetting. CP & S addresses this by finding a subnetwork responsible for each task during training and selecting the correct subnetwork for inference, eliminating forgetting and enabling knowledge transfer across subnetworks.
APPLIED INTELLIGENCE
(2023)
Review
Chemistry, Multidisciplinary
Bohayra Mortazavi, Xiaoying Zhuang, Timon Rabczuk, Alexander V. Shapeev
Summary: Since their introduction in 2007, machine learning interatomic potentials (MLIPs) have gained increasing interest as a more accurate and reliable alternative to empirical interatomic potentials (EIPs) in molecular dynamics calculations. Recently, MLIPs have been successfully applied in analyzing mechanical properties and failure responses, surpassing both EIPs and density functional theory (DFT) calculations. In this mini-review, we discuss the basic principles and development strategies of MLIPs, highlight their robustness in mechanical property analysis through examples, and emphasize their advantages over EIPs and DFT methods. MLIPs also offer the unique ability to combine the robustness of DFT with continuum mechanics for first-principles multiscale modeling of mechanical properties in nanostructures. Challenges and future directions for MLIP-based molecular dynamics simulations are also outlined.
MATERIALS HORIZONS
(2023)
Article
Engineering, Multidisciplinary
Aleksandr Dekhovich, O. Taylan Turan, Jiaxiang Yi, Miguel A. Bessa
Summary: Data-driven modeling in mechanics is advancing rapidly, but cooperation is hindered by the forgetting problem of artificial neural networks. The authors developed a continual learning method applied to solid mechanics for predicting history-dependent plasticity behavior. This work aims to foster cooperative strategies in the mechanics community.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Abhilash Chandrashekar, Pierpaolo Belardinelli, Miguel A. Bessa, Urs Staufer, Farbod Alijani
Summary: Dynamic atomic force microscopy (AFM) is a key platform for characterizing the topological and nanomechanical properties of novel materials. In this study, machine learning and data science are used to characterize tip-sample forces purely from experimental data with high resolution. The results show the potential of combining machine learning with experimental methodologies in dynamic AFM characterization to study transient processes and complex chemical and biological phenomena in real-time.
NANOSCALE ADVANCES
(2022)
