Clean Boundaries Research Articles

Microstructure, Thermal and Mechanical Properties of AlN-MgO Composites Prepared by Spark Plasma Sintering

AlN-MgO composites with different compositions were prepared by spark plasma sintering, and the effects of their composition on their microstructure, thermal properties, and mechanical properties were systemically investigated. MgO compositions in the AlN-MgO composites were controlled from 20 to 80 wt%. The results indicated that a phase transition did not occur during the sintering process, and different solid solutions were formed within the MgO and AlN lattices. The AlN-MgO composites exhibited finer-grain microstructures than those of the sintered pure AlN and MgO samples. Transmission electron microscopy analysis showed that both oxygen-rich, low-density grain boundaries and clean boundaries with spinel phases were present in the composites. The sintered pure AlN sample exhibited the highest thermal conductivity (53.2 W/mK) and lowest coefficient of thermal expansion (4.47 × 10<sup>-6</sup> /K) at 100 °C. And, the sintered pure MgO sample exhibited moderate thermal conductivity (39.7 W/mK) and a high coefficient of thermal expansion (13.05 × 10<sup>-6</sup> /K). With increasing MgO contents in the AlN-MgO composites, however, the thermal conductivity of the AlN-MgO composites decreased, from 33.3 to 14.9 W/mK, while their coefficient of thermal expansion generally increased, from 6.49×10<sup>-6</sup> to 10.73×10<sup>-6</sup> /K with increasing MgO content. The composite with an MgO content of 60 wt% exhibited the best mechanical properties overall. Thus, the composition and microstructure of AlN-MgO composites have a determining effect on their thermal and mechanical properties.

Natural-like generation of grain boundary models and the combined effects of microstructural elements and lithiation on the plastic behavior of TiO2: A computational study

Microstructural features of ceramics have a significant effect on mechanical properties and ion transport in different technologies ranging from optical materials to renewable energy generation and storage. Atomistic modeling is usually done on postulated grain boundaries which may not account for the distributions of different grain boundaries in real materials. We generate microstructural features of TiO2 with molecular dynamics (MD) by simulating a heat treatment; in this way, a natural-like distribution of grain sizes and boundaries is obtained. We used a model system with about 545,000 atoms and obtained structures with average grain sizes ranging about 6 to 12 nm and trending with the annealing temperature. While the model is nano- rather than micro-meter structured, it present key microstructural features. We observe ‘clean’ grain boundaries as well as boundaries with an amorphous interlayer. The clean boundaries feature low-index surfaces. When an amorphous interlayer is present, higher-index surfaces can be observed. The resulting structures show significant softening (reduction of maximum stress) vs. ideal crystalline titania, with isotropy of mechanical properties. Elastic constants, besides significant isotropy, show moderate stiffening due to the presence of grains. When introducing lithium, we observe segregation at grain boundaries, and lithiation of grainy structures leads to further softening.

Microscopy aided detection of the self-intercalation mechanism and in situ electronic properties in chromium selenide.

Two-dimensional (2D) chromium-based self-intercalated materials Cr1+nX2 (0 ≤ n ≤ 1, X = S, Se, Te) have attracted much attention because of their tunable magnetism with good environmental stability. Intriguingly, the magnetic and electrical properties of the materials can be effectively tuned by altering the coverage and spatial arrangement of the intercalated Cr (ic-Cr) within the van der Waals gap, contributing to different stoichiometries. Several different Cr1+nX2 systems have been widely investigated recently; however, those with the same stoichiometric ratio (such as Cr1.25Te2) were reported to exhibit disparate magnetic properties, which still lacks explanation. Therefore, a systematic in situ study of the mechanisms with microscopy techniques is in high demand to look into the origin of these discrepancies. Herein, 2D self-intercalated Cr1+nSe2 nanoflakes were synthesized as a platform to conduct the characterization. Combining scanning transmission electron microscopy (STEM) and scanning tunneling microscopy (STM), we studied in depth the microscopic structure and local electronic properties of the Cr1+nSe2 nanoflakes. The self-intercalation mechanism of ic-Cr and local stoichiometric-ratio variation in a Cr1+nSe2 ultrathin nanoflake is clearly detected at the nanometer scale. Scanning tunneling spectroscopy (STS) measurements indicate that Cr1.5Se2/Cr2Se2 and Cr1.25Se2 exhibit conductive and semiconductive behaviors, respectively. The STM tip manipulation method is further applied to manipulate the microstructure of Cr1+nSe2, which successfully produces clean zigzag-type boundaries. Our systematic microscopy study paves the way for the in-depth study of the magnetic mechanism of 2D self-intercalated magnets at the nano/micro scale and the development of new magnetic and spintronic devices.

Application of indocyanine green in the management of oral cancer: a literature review

Abstract Indocyanine green is a cyanine dye that displays fluorescent properties in the near-infrared region. Indocyanine green has good water solubility and can bind to plasma proteins in the body. After binding, it can display green fluorescence when irradiated by near-infrared fluorescence. Owing to its good imaging ability and low side effects, indocyanine green is widely used in the diagnosis and treatment of various tumors. Indocyanine green-assisted sentinel lymph node biopsy helps determine clean tumor boundaries, helps surgeons remove primary tumors completely, assists in microvascular anastomosis in head and neck repair and reconstruction, reduces operation time, evaluates blood perfusion to monitor flap status. In addition, indocyanine green has great potential in photodynamic therapy to specifically kill tumor cells. However, despite the benefits, studies regarding the application of indocyanine green in oral cancer are limited. Therefore, we conducted a literature review to explore the application of indocyanine green in oral cancer to benefit clinicians involved in the diagnosis and treatment of this disease.

Morpheme-Based Neural Machine Translation Models for Low-Resource Fusion Languages

Neural approaches, which are currently state-of-the-art in many areas, have contributed significantly to the exciting advancements in machine translation. However, Neural Machine Translation (NMT) requires a substantial quantity and good quality parallel training data to train the best model. A large amount of training data, in turn, increases the underlying vocabulary exponentially. Therefore, several proposed methods have been devised for relatively limited vocabulary due to constraints of computing resources such as system memory. Encoding words as sequences of subword units for so-called open-vocabulary translation is an effective strategy for solving this problem. However, the conventional methods for splitting words into subwords focus on statistics-based approaches that mainly conform to agglutinative languages. In these languages, the morphemes have relatively clean boundaries. These methods still need to be thoroughly investigated for their applicability to fusion languages, which is the main focus of this article. Phonological and orthographic processes alter the borders of constituent morphemes of a word in fusion languages. Therefore, it makes it difficult to distinguish the actual morphemes that carry syntactic or semantic information from the word’s surface form, the form of the word as it appears in the text. We, thus, resorted to a word segmentation method that segments words by restoring the altered morphemes. We also compared conventional and morpheme-based NMT subword models. We could prove that morpheme-based models outperform conventional subword models on a benchmark dataset.

Complex Magnetic Anomaly Detection Using Structured Low-Rank Approximation With Total Variation Regularization

In the field of magnetic anomaly detection (MAD), the anomaly signal is easily submerged by ambient electromagnetic interference. Though the existing noise suppression methods can effectively improve the signal-to-noise ratio (SNR), there are still some intractable problems, such as signal distortion and boundary blur. To solve these problems, a novel MAD method based on structured low rank (SLR) and total variation (TV) regularization constraints is proposed in this letter. The noise suppression performance is improved by leveraging the structured low rankness of the signal. To preserve clean boundaries of the anomalies, an anisotropic TV regularization constraint is employed in the approach. Comparing the SLR-TV method with four state-of-the-art methods with extensive field tests, the results demonstrate that the proposed SLR-TV method achieves the greatest SNR improvement by about 63.24% and the best structural similarity (SSIM) improvement by about 53.02% over other methods in the range from −40 to 0 dB, showing the utility and high fidelity of the proposed framework in low SNR.

Synergistic Hankel Structured Low-Rank Approximation With Total Variation Regularization for Complex Magnetic Anomaly Detection

In the field of magnetic anomaly detection (MAD), the anomaly signal is easy to be submerged by ambient electromagnetic interference. Though the existing noise suppression methods can effectively improve the signal-to-noise ratio (SNR), there are still some intractable problems, such as signal distortion and boundary blur. To solve these problems, a novel MAD method based on structured low-rank (SLR) and total variation (TV) regularization constraints is proposed in this paper. To be specific, a new framework SLR-TV, which mainly contains abnormal signal acquisition, objective function solution, and inverse transform operation is constructed. The noise suppression performance is improved by leveraging the structured low-rankness of the signal. To preserve clean boundaries of the anomalies, an anisotropic TV regularization constraint is employed in the approach. Comparing the SLR-TV with five state-of-the-art methods with extensive synthetic and field tests, the results demonstrate that the proposed SLR-TV method achieves the highest SNR improvement by about 15.62% and the best structural similarity (SSIM) improvement by about 62.95% over other methods in the range from -40 dB to 0 dB, showing the utility and high-fidelity of the proposed framework in low SNR.

Dynamically Induced Large‐Scale, Selective, and Vertical Structure Growth of MoS2 Nanosheets

Molybdenum disulfide (MoS2) has recently gained attention due to the exciting and various nanostructures created by different synthesis processes and their superior physical and chemical properties. Although large‐scale production with various nanostructures is achieved, only patterned film growth is demonstrated in specific locations. Traditional photolithography and etching processes are not suitable for patterning of 3D structures due to the huge height difference. The large‐scale patterned growth of MoS2 with different nanostructures is critical for sophisticated, customized electronic and optoelectronic devices. Herein, for the first time, the synthesis of large‐scale and patterned MoS2 nanosheets with vertically standing morphology (v‐MoS2 NS) using a chemical vapor deposition (CVD) method and a TiO2‐induced layer is reported. This method enables the preparation of patterned v‐MoS2 NS arrays with clean boundaries, uniform distributions, controllable locations, and various shapes, which can be used to fabricate various functional devices without etching. Next, it is demonstrated that the photodetectors can be fabricated directly using square‐patterned v‐MoS2 NS arrays. The method proposed could be extended to form other 2D materials with vertical NS structures and provides a lot of new chances to legitimately design functional devices and systems in the future.

Cascading loss and loss risk multipliers amid a changing climate in the Pacific Islands.

Human society has experienced, and will continue to experience, extensive loss and damage from worsening anthropogenic climate change. Despite our natural tendencies to categorise and organise, it can be unhelpful to delineate clean boundaries and linear understandings for complex and messy concepts such as loss and damage. Drawing on the perspectives of 42 local and regional Pacific Islander stakeholders, an underexplored resource for understanding loss and damage, we explore the complexity and interconnectedness of non-economic loss and damage (NELD). According to participants, Pacific Islander worldviews, knowledge systems and cosmologies often make it difficult to separate and evaluate NELD independently, challenging the nomenclature of NELD categories developed through international mechanisms. Instead, NELD understandings are often centred on the interdependencies between losses, including the cascading flow-on effects that can occur and the nature of some losses as risk multipliers (i.e. one loss creating the risk for further losses). Most notably, losses to biodiversity, ecosystem services and land are critically linked to, and have cascading effects on, livelihoods, knowledge, ways of life, wellbeing, and culture and heritage. We argue that loss and damage is not always absolute, and that there are NELD that are arguably reparable. Concerning, however, is that biodiversity loss, as a risk multiplier, was considered the least reparable by participants. We put forward that NELD understandings must consider interconnectivity, and that biodiversity and ecosystem conservation and restoration must be the focus for interventions to prevent irreparable and cascading losses from climate change in the Pacific Islands.

Fuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy.

Deep learning has been successfully applied to many classification problems including underwater challenges. However, a long-standing issue with deep learning is the need for large and consistently labeled datasets. Although current approaches in semi-supervised learning can decrease the required amount of annotated data by a factor of 10 or even more, this line of research still uses distinct classes. For underwater classification, and uncurated real-world datasets in general, clean class boundaries can often not be given due to a limited information content in the images and transitional stages of the depicted objects. This leads to different experts having different opinions and thus producing fuzzy labels which could also be considered ambiguous or divergent. We propose a novel framework for handling semi-supervised classifications of such fuzzy labels. It is based on the idea of overclustering to detect substructures in these fuzzy labels. We propose a novel loss to improve the overclustering capability of our framework and show the benefit of overclustering for fuzzy labels. We show that our framework is superior to previous state-of-the-art semi-supervised methods when applied to real-world plankton data with fuzzy labels. Moreover, we acquire 5 to 10% more consistent predictions of substructures.

A fowl politics of urban dwelling. Or, Ybor City’s republic of noise

ABSTRACT This essay traces how Ybor City’s feral chickens are rhetorically and materially situated in urban space, and how Tampa’s Ybor City emerges as a “sanctuary city” for feral creatures. More specifically, I map how chicken advocates and chickens take part in constructing what an urban space should look, smell, and sound like, and whose “voices” are included in shaping the neighborhood. Michel de Certeau helps guide me to consider how chickens and their advocates attempt to foster a feral republic of noise, a place in which chicken feet and rooster crows frustrate anthropocentric constructions of place. “Chicken rhetorics” interrupt desires to create and sustain clean boundaries between urban and rural, guest and host, chicken and human, purity and dirt, political actor and agricultural product. “Fowl” assemblages perform “pedestrian speech acts that remix what it means to dwell in a multi-species urban space.”

Regionalization study of maximum daily temperature based on grid data by an objective hybrid clustering approach

Regionalization plays an important role in climatology and hydrology research, as it can be used to understand the changing pattern of a hydroclimate variable better and explore its underlying drivers effectively by identifying homogeneous regions which are determined by topography and climatic conditions. However, as climatic drivers change over time, homogeneous regions should vary when the intra-annual patterns are of interest. In this paper we investigate the regionalization and changing patterns of maximum daily temperature in China based on the maximum air temperature (Tmax) derived from the CN05.1 daily surface air temperature gridded data from 1981 to 2010. We also propose an objective variable-based regionalization approach by combining the rotated empirical orthogonal function (REOF) method and K-means clustering analysis, to determine homogeneous regions effectively for such a large volume of daily grid data. The REOF approach is not able to identify discrete regional boundaries easily, but it is efficient at determining the number of regions and their rough spatial location. Furthermore, while clustering analysis suffers from convergence when the number of observations is large, it is able to provide clean boundaries between regions. The hybrid method tends to overcome their individual disadvantages while maintaining their individual advantages. The results show that the patterns of homogeneous regions have distinct features in both summer and winter seasons. The reasonableness of regionalization is then examined by the first principal component within regions, which shows the homogeneity within regions, and the scatterplots of Tmax time series between regions, which show the heterogeneity between regions. Furthermore, the boxplots of normalized Tmax within regions for both summer and winter seasons also clearly show the different characteristics of inter-annual variability in each region. In order to reveal detailed process of the change in the number and pattern of regions, we further conducted the regionalization for individual months and explored the potential linkages between regionalization and climatic conditions over time. This research is noteworthy in promoting research on regionalization with consideration of seasonal and intra-annual variation as well as extreme temperature events. The proposed hybrid regionalization method is broadly applicable to other regionalization studies.

Life as Adaptive Capacity: Bringing New Life to an Old Debate

Whatever we take “life” to mean, it must involve an attempt to describe the objective reality beyond scientists’ biases. Traditionally, this is thought to involve comparing our scientific categories to “natural kinds.” But this approach has been tainted with an implicit metaphysics, inherited from Aristotle, that does not fit biological reality. In particular, we must accept that biological categories will never be specifiable in terms of necessary and sufficient conditions or shared underlying physical structures that produce clean boundaries. Biology blurs all lines and failure to embrace this unique feature has blocked attempts to reach consensus on the meaning of “life.” Thus, while the three classical accounts (thermodynamic, metabolic, and evolutionary) all fall short of offering a complete definition, their advocates fail to realize that they share the same view of life’s ultimate, functional hallmark: its uniquely rich adaptive capacity. I develop an account of life as adaptive capacity that sidesteps debates about the relative importance of specific mechanisms and the precise location of boundaries to bring the three classical accounts together under a shared conceptual framework.

High thermal conductivity of spark plasma sintered silicon carbide ceramics with yttria and scandia

AbstractA fully dense SiC ceramic with a room‐temperature thermal conductivity of 262 W·(m·K)−1 was obtained via spark plasma sintering β‐SiC powder containing 0.79 vol% Y2O3‐Sc2O3. High‐resolution transmission electron microscopy revealed two different SiC‐SiC boundaries, that is, amorphous and clean boundaries, in addition to a fully crystallized junction phase. A high thermal conductivity was attributed to a low lattice oxygen content and the presence of clean SiC‐SiC boundaries.

Preparation and properties of sintering additive-free AlN–BN composite ceramics by hot-pressing sintering

With high activity h-BN powders as raw materials and without the using of any sintering additives, AlN–BN composite ceramics with 0–30 vol% were prepared by hot-pressing sintering in N2 at 1850 °C. Effect of h-BN content on the microstructure and properties of the composite ceramics was investigated. The results show that fully dense composite ceramics with h-BN content up to 20 vol% can be obtained and all composites show a fine-grain-microstructure with clean and clear boundaries. No “card room structure” of h-BN was formed in the samples. With the increasing h-BN content, the composites show a decreased bending strength, fracture toughness, Vickers hardness, thermal conductivity and dielectric constant. The application of high activity raw materials produces a significant grain-refinement strengthening effect and makes the composites exhibit a much higher strength and toughness compared with the results reported by other researchers. Due to the absence of sintering additive, the composite ceramics also show a quite low dielectric loss at both low and high frequency. An optimal comprehensive properties are obtained for the AlN-20 vol% BN composite: bending strength of 410 ± 21 MPa, fracture toughness of 4.21 ± 0.19 MPa·m1/2, Vickers hardness of 7.7 ± 0.2 GPa, thermal conductivity of 46.6 W·m−1·K−1, dielectric constant of 7.4 (1 MHz) and 7.48 (18 GHz), dielectric loss of 6.7 × 10−4 (1 MHz) and 6.18 × 10−3 (18 GHz).

Investigations on the Optimal Support Vector Machine Classifiers for Predicting Design Feasibility in Analog Circuit Optimization

In simulation-based circuit optimization, many simulation runs may be wasted while evaluating infeasible designs, i.e. the designs that do not meet the constraints. To avoid such a waste, this paper investigates the use of support vector machine (SVM) classifiers in predicting the design's feasibility prior to simulation and the optimal selection of the SVM parameters, namely, the Gaussian kernel shape parameter γ and the misclassification penalty parameter C. These parameters affect the complexity as well as the accuracy of the model that SVM represents. For instance, the higher γ is good for detailed modeling and the higher C is good for rejecting noise in the training set. However, our empirical study shows that a low γ value is preferable due to the high spatial correlation among the circuit design candidates while C has negligible impacts due to the smooth and clean constraint boundaries of most circuit designs. The experimental results with an LC-tank oscillator example show that an optimal selection of these parameters can improve the prediction accuracy from 80 to 98% and model complexity by 10×.

Grain-growth effect on dielectric nonlinearity of BaTiO3-based multi-layer ceramic capacitors

A significant difference in dielectric nonlinearity was contrasted between fine- and coarse-grained BaTiO3-based multilayer ceramic capacitors. Grain growth resulted in a decrease in dielectric constant in low field but a steep increase with increase in alternating current field, which can be associated with a decrease in reversible and a significant increase in irreversible domain wall contribution from Preisach analysis. Fine-grained specimens showed almost cubic structure despite ferroelectric domain contrasts, which is anticipated to significantly reduce strain incompatibility during domain wall motion, and clean domain boundaries with no lattice defects. However, coarse-grained specimens with high aspect ratio of the tetragonal lattice should accompany lattice distortion with increased intergranular constraints during domain wall motion, and many lattice defects were observed near domain boundaries. These results demonstrate experimentally the presence of weak pinning centers in coarse-grained specimens, which inhibit domain wall motion in low alternating current fields. Long-range motion occurs beyond the threshold field and results in an abrupt increase in dielectric constant.

Effects of Al2O3 on (1−x)[SrO–SiO2–B2O3]–xAl2O3 glass sealant for intermediate temperature solid oxide fuel cell

The effects of Al2O3 on (1−x)[SrO–SiO2–B2O3]–xAl2O3 (SASB, x=0, 0.02, 0.04, 0.06, 0.08, and 0.1) sealing glasses for intermediate temperature solid oxide fuel cell (SOFC) have been investigated to determine the proper amount of Al2O3 for the application of the SASB glasses to a SOFC sealant. The structural, thermal, mechanical and adhesion properties were determined by using DTA, TMA, XRD, SEM, and Raman spectroscopy. It is found that the thermal expansion coefficients of the glasses and crystallized samples are 8.0–11.0×10−6°C−1, and ~10.0×10−6°C−1, respectively, which are within the required values for the SOFC sealant. The change of mode population in Raman scattering analysis shows that Al2O3 acts as a network modifier. When annealed at 800°C, the glasses with x=0 and 0.1 start to crystallize in 5h, but the sample with x=0.6 mostly remains as a glass phase even after 50h, and this crystallization behavior has a close relationship with adhesion properties. Comparing the cross-sectional views of SEM images between x=0 and x=0.06, the sample with x=0.06 shows homogeneous morphologies with good adhesion and clean surface boundaries between the sample and substrate. But for the x=0 case, the boundaries are not clearly defined and devitrifications with heterogeneous morphologies exist.

Characterization of Hot Deformation Behavior of a Novel Al–Cu–Li Alloy Using Processing Maps

The isothermally compression deformation behavior of an elevated Cu/Li weight ratio Al–Cu–Li alloy was investigated under various deformation conditions. The isothermal compression tests were carried out in a temperature range from 300 to 500 °C and at a strain rate range from 0.001 to 10 s−1. The results show that the peak stress level decreases with temperature increasing and strain rate decreasing, which is represented by the Zener–Hollomon parameter Z in the hyperbolic sine equation with the hot deformation activation energy of 218.5 kJ/mol. At low Z value, the dynamic recrystallized grain is well formed with clean high-angle boundaries. At high Z value, a high dislocation density with poorly developed cellularity and considerable fine dynamic precipitates are observed. Based on the experimental data and dynamic material model, the processing maps at strain of 0.3, 0.5 and 0.7 were developed to demonstrate the hot workability of the alloy. The results show that the main softening mechanism at high Z value is precipitate coarsening and dynamic recovery; the dynamic recrystallization of the alloy can be easily observed as ln Z ≤ 29.44, with peak efficiency of power dissipation of around 70%. At strains of 0.3, 0.5 and 0.7, the flow instability domains are found at higher strain rates, which mainly locate at the upper part of processing maps. In addition, when the strain rate is 0.001 or 0.02 s−1, there is a particular instability domain at 300–350 °C.

Fabrication of Al<sub>2</sub>O<sub>3</sub>/YAG eutectic ceramics utilizing a fast combustion reaction heating method

Al2O3/Y3Al5O12(YAG) eutectic ceramics are considered to be candidate materials for use in high temperature and oxidizing environments owing to their remarkable thermodynamic stabilities. Many techniques have been used to prepare eutectic ceramics, including the Bridgeman, edge-defined film-fed growth, micro-pulling-down, floating zone, and laser zone re-melting methods. However, these methods use complex high temperature equipment. In this work, simple equipment was used to achieve combustion reaction heating with the advantages of a high heating rate (2000℃/min), high temperature, and high cooling rate. Using this set-up we could melt and fabricate eutectic ceramics. The microstructure and physical properties of the obtained Al2O3/YAG eutectic ceramics were studied. The eutectic ceramics consisted of Al2O3 and YAG phases. The two phases exhibited coupled growth with well-matched and clean phase boundaries. Several special morphologies were obtained besides the typical Chinese script microstructure, and their formation mechanisms are discussed. The Vickers hardness of the eutectic ceramics was 20.52 GPa, which is higher than that previously reported for any binary eutectic ceramic system. The fracture toughness was 2.64 MPa m1/2, which is slightly higher than about 2 MPa m1/2 previously reported. The crack propagated in a straight line from the indentation corner and did not deflect at the interface between the Al2O3 and YAG domains, compared with the intergranular fracture in the hot-pressed sample. This weak interaction of the crack path with the microstructure was induced by the absence of residual stress and the excellent bonding between the eutectic phases. Samples treated at 1500℃ for 20 h exhibited no weight loss or obvious change in microstructure, indicating the high temperature stability of these eutectic ceramics.