A new data-driven computational framework is developed to assist in the design and modeling of new material systems and structures. The proposed framework integrates three general steps: (1) design ...of experiments, where the input variables describing material geometry (microstructure), phase properties and external conditions are sampled; (2) efficient computational analyses of each design sample, leading to the creation of a material response database; and (3) machine learning applied to this database to obtain a new design or response model.
In addition, the authors address the longstanding challenge of developing a data-driven approach applicable to problems that involve unacceptable computational expense when solved by standard analysis methods – e.g. finite element analysis of representative volume elements involving plasticity and damage. In these cases the framework includes the recently developed “self-consistent clustering analysis” method in order to build large databases suitable for machine learning. The authors believe that this will open new avenues to finding innovative materials with new capabilities in an era of high-throughput computing (“big-data”).
•Unified data-driven framework for design and modeling of materials and structures.•Integration of design of experiments, computational analyses, and machine learning.•Avoids curse of dimensionality when using self-consistent clustering analyses method.
The discovery of efficient and accurate descriptions for the macroscopic behavior of materials with complex microstructure is an outstanding challenge in mechanics of materials. A mechanistic, ...data-driven, two-scale approach is developed for predicting the behavior of general heterogeneous materials under irreversible processes such as inelastic deformation. The proposed approach includes two major innovations: (1) the use of a data compression algorithm, k-means clustering, during the offline stage of the method to homogenize the local features of the material microstructure into a group of clusters; and (2) a new method called self-consistent clustering analysis used in the online stage that is valid for any local plasticity laws of each material phase without the need for additional calibration. A particularly important feature of the proposed approach is that the offline stage only uses the linear elastic properties of each material phase, making it efficient. This work is believed to open new avenues in parameter-free multi-scale modeling of complex materials, and perhaps in other fields that require homogenization of irreversible processes.
In this paper, we propose a new homogenization algorithm, virtual clustering analysis (VCA), as well as provide a mathematical framework for the recently proposed self-consistent clustering analysis ...(SCA) (Liu et al. in Comput Methods Appl Mech Eng 306:319–341,
2016
). In the mathematical theory, we clarify the key assumptions and ideas of VCA and SCA, and derive the continuous and discrete Lippmann–Schwinger equations. Based on a key postulation of “once response similarly, always response similarly”, clustering is performed in an offline stage by machine learning techniques (k-means and SOM), and facilitates substantial reduction of computational complexity in an online predictive stage. The clear mathematical setup allows for the first time a convergence study of clustering refinement in one space dimension. Convergence is proved rigorously, and found to be of second order from numerical investigations. Furthermore, we propose to suitably enlarge the domain in VCA, such that the boundary terms may be neglected in the Lippmann–Schwinger equation, by virtue of the Saint-Venant’s principle. In contrast, they were not obtained in the original SCA paper, and we discover these terms may well be responsible for the numerical dependency on the choice of reference material property. Since VCA enhances the accuracy by overcoming the modeling error, and reduce the numerical cost by avoiding an outer loop iteration for attaining the material property consistency in SCA, its efficiency is expected even higher than the recently proposed SCA algorithm.
As a promising powder-based additive manufacturing technology, selective laser melting (SLM) has gained great popularity in recent years. However, experimental observation of the melting and ...solidification process is very challenging. This hinders the study of the physical mechanisms behind a variety of phenomena in SLM such as splashing and balling effects, and further poses challenges to the quality control of the products. Powder-scale computational models can reproduce the multi-physics process of SLM. In this study, we couple the Finite Volume Method (FVM) and Discrete Element Method to model the deposition of powder particles, and use the FVM to model the melting process, both with ambient air. In particular, a cutting-edge sharp surface capturing technique (iso-Advector) is incorporated into the Volume of Fluid Model to reconstruct the interface between different phases during the melting process. Iso-Advector is then used to capture and reconstruct the interface between molten material and ambient air, which is further used as a solid boundary for spreading the next powder layer. As such, 3D geometrical data is exchanged between these two stages repeatedly to reproduce the powder spreading-melting process of SLM incorporating different scan paths on multiple powder layers. To demonstrate the effectiveness of the powder-scale multi-physics modeling framework, typical scenarios with different fabrication parameters (Ti–6Al–4V powder) are simulated and compared with experimental observations available in literature.
This communication reports novel luminescent rhenium(I)–polypyridine complexes appended with a perylene diimide (PDI) or benzoperylene monoimide (BPMI) moiety through a non‐conjugated linker. The ...photophysical and photochemical properties originating from the interactions of the metal polypyridine and perylene units were exploited to afford new cellular reagents with thiol‐sensing capability and excellent photocytotoxic activity.
Conjugates of phosphorescent rhenium(I)–polypyridines and fluorescent perylene derivatives were exploited to afford cellular reagents for sensing and photocytotoxic applications.
The size, shape, surface property and material composition of polymer-coated nanoparticles (NPs) are four important parameters in designing efficient NP-based carriers for targeted drug delivery. ...However, due to the complex interplay between size, shape and surface property, most studies lead to ambiguous descriptions of the relevance of shape. To clarify its influence on the cellular uptake of PEGylated NPs, large scale molecular simulations have been performed to study differently shaped convex NPs, such as sphere, rod, cube and disk. Comparing systems with identical NP surface area, ligand-receptor interaction strength, and grafting density of the polyethylene glycol, we find that the spherical NPs exhibit the fastest internalization rate, followed by the cubic NPs, then rod- and disk-like NPs. The spherical NPs thus demonstrate the highest uptake among these differently shaped NPs. Based on a detailed free energy analysis, the NP shape effect is found to be mainly induced by the different membrane bending energies during endocytosis. The spherical NPs need to overcome a minimal membrane bending energy barrier, compared with the non-spherical counterparts, while the internalization of disk-like NPs involves a strong membrane deformation, responsible for a large free energy barrier. Besides, the free energy change per tethered chain is about a single kBT regardless of NP shape, as revealed by our self-consistent field theory calculations, where kB and T denote Boltzmann constant and temperature, respectively. Thus, the NP shape only plays the secondary role in the free energy change of grafted PEG polymers during internalization. We also find that star-shaped NPs can be quickly wrapped by the cell membrane, similar to their spherical counterparts, indicating star-shaped NPs can be used for drug delivery with high efficacy. Our findings seem to provide useful guidance in the molecular design of PEGylated NPs for controllable cellular uptake and help establish quantitatively rules in designing NP-based vectors for targeted drug delivery.
Additive manufacturing (AM) processes produce unique microstructures compared with other manufacturing processes because of the large thermal gradient, high solidification rate and other local ...temperature variations caused by the repeated heating and melting. However, the effect of these thermal profiles on the microstructure is not thoroughly understood. In this work, a 3D cellular automaton method is coupled to a finite volume method to predict the grain structure of an alloy, e.g. Inconel 718, fabricated by AM. The heat convection due to thermocapillary flow inside the melt pool is resolved by the finite volume method for a real and accurate temperature field, while an enriched grain nucleation scheme is implemented to capture epitaxial grain growth following the mechanism identified from experiments. Simulated microstructure results are shown to be in qualitative agreement with experimental result and the effects of the process parameters on both thermal characteristics and the grain structure are identified. The 3D cellular automaton finite volume method results establish our approach as a powerful technique to model grain evolution for AM and to address the process-structure-property relationship.
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•A 3D cellular automata finite volume method for additive manufacturing is presented, including an enriched nucleation model•The grain structure in the Directed Energy Deposition of IN718 alloy is simulated by the proposed method•The effects of the processing parameters on the resulting grain structure of IN718 alloy are elaborated via the simulation•Sandwich- and zig-zag patterns of grain structure observed in experiments are captured and explained by the proposed model
Metallic powder bed-based additive manufacturing technologies have many promising attributes. The single track acts as one fundamental building unit, which largely influences the final product ...quality such as the surface roughness and dimensional accuracy. A high-fidelity powder-scale model is developed to predict the detailed formation processes of single/multiple-track defects, including the balling effect, single track nonuniformity and inter-track voids. These processes are difficult to observe in experiments; previous studies have proposed different or even conflicting explanations. Our study clarifies the underlying formation mechanisms, reveals the influence of key factors, and guides the improvement of fabrication quality of single tracks. Additionally, the manufacturing processes of multiple tracks along S/Z-shaped scan paths with various hatching distance are simulated to further understand the defects in complex structures. The simulations demonstrate that the hatching distance should be no larger than the width of the remelted region within the substrate rather than the width of the melted region within the powder layer. Thus, single track simulations can provide valuable insight for complex structures.
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Phosphorescent probes often show sensitive response toward analytes at a specific wavelength. However, oxygen quenching usually occurs at the same wavelength and thus hinders the accurate detection ...of analytes. In this study, we have developed dual‐emissive iridium(III) complexes that exhibit phosphorescence responses to copper(II) ions at a wavelength distinct from that where oxygen quenching occurs. The complexes displayed colorimetric phosphorescence response in aqueous solutions under different copper(II) and oxygen conditions. In cellular imaging, variation in oxygen concentration over a large range from 5 % to 80 % can modulate the intensity and lifetime of green phosphorescence without affecting the response of red phosphorescence toward intracellular copper(II) ions.
We reported dual‐emissive iridium(III) complexes bearing a di(2‐picolyl)‐amine (DPA) unit, which exhibited green and red dual phosphorescence in aqueous solutions. The complexes exhibited static and dynamic phosphorescence quenching in response to copper(II) ions and oxygen, respectively, at different wavelengths, achieving colorimetric emission response in solutions and multichannel response in live cell imaging.
Although the strain‐promoted sydnone–alkyne cycloaddition reaction has been utilized for bioconjugation, its potential applications in bioorthogonal labeling and imaging in live cells have not been ...explored. This communication reports novel bioorthogonal imaging reagents with environment‐sensitive emission properties through the modification of sydnone with cyclometalated iridium(III) polypyridine complexes. These complexes displayed significant emission enhancement and lifetime elongation upon reaction with strained alkyne derivatives, and were utilized to label cyclooctyne‐modified proteins and ceramide molecules in live cells. Additionally, the manipulation of the photocytotoxicity of the complexes through the use of a bioorthogonal reagent was demonstrated.
Novel bioorthogonal imaging reagents with environment‐sensitive emission properties were developed through the modification of sydnone with cyclometalated iridium(III) polypyridine complexes.
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