We devise reduced-dimension metrics for effectively measuring the distance between two points (i.e., microstructures) in the microstructure space and quantifying the pathway associated with microstructural evolution, ...
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We devise reduced-dimension metrics for effectively measuring the distance between two points (i.e., microstructures) in the microstructure space and quantifying the pathway associated with microstructural evolution, based on a recently introduced set of hierarchical n-point polytope functions Pn. The Pn functions provide the probability of finding particular n-point configurations associated with regular n polytopes in the material system, and are a special subset of the standard n-point correlation functions Sn that effectively decompose the structural features in the system into regular polyhedral basis with different symmetries. The nth order metric Ωn is defined as the L1 norm associated with the Pn functions of two distinct microstructures. By choosing a reference initial state (i.e., a microstructure associated with t0=0), the Ωn(t) metrics quantify the evolution of distinct polyhedral symmetries and can in principle capture emerging polyhedral symmetries that are not apparent in the initial state. To demonstrate their utility, we apply the Ωn metrics to a two-dimensional binary system undergoing spinodal decomposition to extract the phase separation dynamics via the temporal scaling behavior of the corresponding Ωn(t), which reveals mechanisms governing the evolution. Moreover, we employ Ωn(t) to analyze pattern evolution during vapor deposition of phase-separating alloy films with different surface contact angles, which exhibit rich evolution dynamics including both unstable and oscillating patterns. The Ωn metrics have potential applications in establishing quantitative processing-structure-property relationships, as well as real-time processing control and optimization of complex heterogeneous material systems.
Despite the weak, van-der-Waals interlayer coupling, photoinduced charge transfer vertically across atomically thin interfaces can occur within surprisingly fast, sub-50fs timescales. Early theoretical understanding o...
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The rapid advance of Information and Communication Technology (ICT) in recent times and the current pandemic caused by COVID-19 have profoundly transformed society and the economy in most of the world. The education s...
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The rapid advance of Information and Communication Technology (ICT) in recent times and the current pandemic caused by COVID-19 have profoundly transformed society and the economy in most of the world. The education sector has benefited from this ICT-driven revolution, which has provided and expanded multiple new tools and teaching methods that did not exist just a few decades ago. In light of this technological change, virtual laboratories (VLs) based on the use of virtual reality (VR) have emerged, which are increasingly used to facilitate the teaching-learning process in a wide range of training activities, both academic and professional types. The set of advantages offered by this type of VL, the main of which are listed in this article, has made its use increasingly common as support for engineering classes at universities. This paper presents a study involving 420 engineering students from Spanish and Portuguese universities and associated analyses on the assessment of different parameters in various VLs designed by the authors. The results obtained indicate that, in general, VR-based VLs are widely accepted and demanded by students, who likewise consider real laboratories (RLs) necessary in face-to-face teaching. In the current post-COVID-19 educational scenario, VLs and RLs will coexist within the new hybrid models that combine face-to-face and online teaching and learning.
The revolution in materials in the past century was built on a knowledge of the atomic arrangements and the structure-property relationship. The sine qua non for obtaining quantitative structural information is single...
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The revolution in materials in the past century was built on a knowledge of the atomic arrangements and the structure-property relationship. The sine qua non for obtaining quantitative structural information is single crystal crystallography. However, increasingly we need to solve structures in cases where the information content in our input signal is significantly degraded, for example, due to orientational averaging of grains, finite size effects due to nanostructure, and mixed signals due to sample heterogeneity. Understanding the structure property relationships in such situations is, if anything, more important and insightful, yet we do not have robust approaches for accomplishing it. In principle, machine learning (ML) and deep learning (DL) are promising approaches since they augment information in the degraded input signal with prior knowledge learned from large databases of already known structures. Here we present a novel ML approach, a variational query-based multi-branch deep neural network that has the promise to be a robust but general tool to address this problem end-to-end. We demonstrate the approach on computed powder x-ray diffraction (PXRD), along with partial chemical composition information, as input. We choose as a structural representation a modified electron density we call the Cartesian mapped electron density (CMED), that straightforwardly allows our ML model to learn material structures across different chemistries, symmetries and crystal systems. When evaluated on theoretically simulated data for the cubic and trigonal crystal systems, the system achieves up to 93.4% average similarity with the ground truth on unseen materials, both with known and partially-known chemical composition information, showing great promise for successful structure solution even from degraded and incomplete input data. The approach doesn’t presuppose a crystalline structure and the approach are readily extended to other situations such as nanomaterials and te
Do high-entropy alloys and ceramics have their grain boundary (GB) counterparts? As the concept of high-entropy grain boundaries (HEGBs) was initially proposed in 2016, this article provides the first complete and rig...
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Device models show GeSn lasers are limited by weak electron and photon confinement. Adding carbon offers strong conduction band offsets, freeing SiGeSn layers for separate confinement heterostructures, reducing thresh...
Device models show GeSn lasers are limited by weak electron and photon confinement. Adding carbon offers strong conduction band offsets, freeing SiGeSn layers for separate confinement heterostructures, reducing thresholds. Photoluminescence from recent growths of GeC and GeSnC quantum wells will be presented.
Solid-state control of the thermal conductivity of materials is of exceptional interest for novel devices such as thermal diodes and switches. Here, we demonstrate the ability to continuously tune the thermal conducti...
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Pesticide monitoring has received considerable attention in the past couple of decades because uncontrolled use of pesticides has resulted in significant risks to the ecosystem, animal bodies, and humans. This study c...
Pesticide monitoring has received considerable attention in the past couple of decades because uncontrolled use of pesticides has resulted in significant risks to the ecosystem, animal bodies, and humans. This study confers a novel approach to detecting 2,2-dichloro vinyl dimethyl phosphate (DDVP) organophosphate pesticides using fluorescence quenching. Herein, a novel nickel–benzene-1,4 dicarboxylic acid metal–organic framework (Ni-BDC-MOF) is synthesized. The solvothermal method is used to synthesize the metal–organic framework of the nickel ion. Ni-BDC-MOF highlights good sensitivity, selectivity, and rapid luminous response to DDVP with 47.31 nM as the detection limit. Further, our suggested optical sensor was assessed for selectivity and real-world samples. This selectivity indicates the sensor’s potential for accurately assessing DDVP in the complex matrices present in wastewater, and it opens up new applications for environmental monitoring. This sensor can be highly beneficial in enhancing environmental monitoring and treatment methods, given the emergence of DDVP contamination. The advancement of selectivity, sensitivity, and applicability in complex contexts such as wastewater or environmental detection contributes to further improving DDVP detection.
During the laser powder bed fusion (LPBF) process, laser-material interaction ignites complex dynamic physical phenomena incorporating the melt pool, gas and powder, which jointly influences the process stability. Whi...
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