| 1. |
- Liang, Jie, et al.
(författare)
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A multi-dimensional quasi-zeolite with 12 x 10 x 7-ring channels demonstrates high thermal stability and good gas adsorption selectivity
- 2016
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Ingår i: Chemical Science. - : Royal Society of Chemistry. - 2041-6520 .- 2041-6539. ; 7:5, s. 3025-3030
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Tidskriftsartikel (refereegranskat)abstract
- A novel quasi-zeolite PKU-15, with a rare 3-dimensional structure containing interconnected large (12-ring), medium (10-ring) and small (7-ring) multi-pore channels, was hydrothermally synthesised and characterised. A unique tri-bridging O2- anion is found to be encapsulated in the cage-like (Ge,Si)(12)O-31 building unit and energetically stabilises the PKU-15 framework. The removal of this oxygen atom would convert PKU-15 into a hypothetical zeolite PKU-15H. Thus, PKU-15 can be considered as a unique 'quasi-zeolite', which bridges porous germanates and zeolites. Owing to the absence of terminal Ge-OH groups in its structure, PKU-15 shows a remarkably high thermal stability of up to 600 degrees C. PKU-15 is also the first microporous germanate that exhibits permanent porosity, with a BET area of 428 m(2) g(-1) and a good adsorption affinity toward CO2.
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| 2. |
- Wang, Kang, et al.
(författare)
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Luminescent metal-halide perovskites: fundamentals, synthesis, and light-emitting devices
- 2024
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Ingår i: Science in China Series B. - : SCIENCE PRESS. - 1674-7291 .- 1869-1870. ; 67:6, s. 1776-1838
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Forskningsöversikt (refereegranskat)abstract
- Metal-halide perovskites have garnered considerable research attention as highly efficient light emitters in recent years due to their outstanding optoelectronic properties with remarkable tunability and excellent solution processabilities. Substantial advancements have been achieved in the development of novel halide perovskites, and the exploitations of these materials in light-emitting devices. This review comprehensively outlines recent breakthroughs in metal-halide perovskites, encompassing the rational design of perovskite materials with tunable light emission properties, the controllable growth of single crystal for a deeper understanding of their structure-property relationships, as well as the fundamental insights into the photophysics and carrier dynamics in perovskite systems. Additionally, it provides an overview of recent applications of perovskite materials in high-performance light-emitting diodes (LEDs) and lasers.
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| 3. |
- Ablikim, M., et al.
(författare)
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Search for h(c) -> pi(+)pi(-) J/psi via psi(3686) -> pi(0)pi(+)pi(-) J/psi
- 2018
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Ingår i: Physical Review D. - : American Physical Society. - 2470-0010 .- 2470-0029. ; 97:5
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Tidskriftsartikel (refereegranskat)abstract
- Using a data sample of 448.1 x 10(6) psi(3686) events collected with the BESIII detector operating at the BEPCII, we perform search for the hadronic transition h(c)-> pi(+)pi(-) J/psi via psi (3686) -> pi(0)hc. No signals of the transition are observed, and the upper limit on the product branching fraction B(sigma(3686) -> pi(0)h(c))B(h(c) -> pi(+)pi(-) J/psi) at the 90% confidence level (C. L.) is determined to be 2.0 x 10(-6). This is the most stringent upper limit to date.
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| 4. |
- Ghayour, Laleh, et al.
(författare)
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Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms
- 2021
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Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 13:7
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Tidskriftsartikel (refereegranskat)abstract
- With the development of remote sensing algorithms and increased access to satellite data, generating up-to-date, accurate land use/land cover (LULC) maps has become increasingly feasible for evaluating and managing changes in land cover as created by changes to ecosystem and land use. The main objective of our study is to evaluate the performance of Support Vector Machine (SVM), Artificial Neural Network (ANN), Maximum Likelihood Classification (MLC), Minimum Distance (MD), and Mahalanobis (MH) algorithms and compare them in order to generate a LULC map using data from Sentinel 2 and Landsat 8 satellites. Further, we also investigate the effect of a penalty parameter on SVM results. Our study uses different kernel functions and hidden layers for SVM and ANN algorithms, respectively. We generated the training and validation datasets from Google Earth images and GPS data prior to pre-processing satellite data. In the next phase, we classified the images using training data and algorithms. Ultimately, to evaluate outcomes, we used the validation data to generate a confusion matrix of the classified images. Our results showed that with optimal tuning parameters, the SVM classifier yielded the highest overall accuracy (OA) of 94%, performing better for both satellite data compared to other methods. In addition, for our scenes, Sentinel 2 date was slightly more accurate compared to Landsat 8. The parametric algorithms MD and MLC provided the lowest accuracy of 80.85% and 74.68% for the data from Sentinel 2 and Landsat 8. In contrast, our evaluation using the SVM tuning parameters showed that the linear kernel with the penalty parameter 150 for Sentinel 2 and the penalty parameter 200 for Landsat 8 yielded the highest accuracies. Further, ANN classification showed that increasing the hidden layers drastically reduces classification accuracy for both datasets, reducing zero for three hidden layers.
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| 5. |
- Hu, Ji-Chong, et al.
(författare)
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Oxidation behavior of Ni-based superalloy GH4738 under tensile stress
- 2024
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Ingår i: Rare Metals. - : NONFERROUS METALS SOC CHINA. - 1001-0521 .- 1867-7185.
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Tidskriftsartikel (refereegranskat)abstract
- Revealing the oxidation behavior of superalloys is crucial for optimizing material properties and extending service life. This study investigated the oxidation behavior of superalloy GH4738 under stress states at 850 degrees C. High-throughput specimens were fabricated to withstand different stresses at the same time. Isothermal oxidation samples were analyzed using the mass gain method to obtain oxidation kinetic curves. The results show that the external stress below 200 MPa could improve the oxidation resistance of the GH4738. With tensile stress increasing, the oxide layer becomes thinner, denser and more complete, while internal oxidation decreases. The tensile stress alters the structure of the external oxide layer from a two-layer to a three-layer configuration. The Cr2O3 oxide layer inhibits the outward diffusion of Ti, leading to Ti enrichment at the oxide-matrix interface and altering the oxidation mechanism of GH4738.
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| 6. |
- Nhu, Viet-Ha, et al.
(författare)
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Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran
- 2020
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Ingår i: Applied Sciences. - Switzerland : MDPI. - 2076-3417. ; 10:15
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Tidskriftsartikel (refereegranskat)abstract
- This paper aims to apply and compare the performance of the three machine learningalgorithms–support vector machine (SVM), bayesian logistic regression (BLR), and alternatingdecision tree (ADTree)–to map landslide susceptibility along the mountainous road of the SalavatAbad saddle, Kurdistan province, Iran. We identified 66 shallow landslide locations, based on fieldsurveys, by recording the locations of the landslides by a global position System (GPS), Google Earthimagery and black-and-white aerial photographs (scale 1: 20,000) and 19 landslide conditioningfactors, then tested these factors using the information gain ratio (IGR) technique. We checked thevalidity of the models using statistical metrics, including sensitivity, specificity, accuracy, kappa,root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC).We found that, although all three machine learning algorithms yielded excellent performance, theSVM algorithm (AUC=0.984) slightly outperformed the BLR (AUC=0.980), and ADTree (AUC=0.977) algorithms. We observed that not only all three algorithms are useful and effective tools foridentifying shallow landslide-prone areas but also the BLR algorithm can be used such as the SVMalgorithm as a soft computing benchmark algorithm to check the performance of the models in future.
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| 7. |
- Nhu, Viet-Ha, et al.
(författare)
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Shallow Landslide Susceptibility Mapping : A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms
- 2020
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Ingår i: International Journal of Environmental Research and Public Health. - Switzerland : MDPI. - 1661-7827 .- 1660-4601. ; 17:8, s. 1-30
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Tidskriftsartikel (refereegranskat)abstract
- Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.
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| 8. |
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| 9. |
- Yang, Xun, et al.
(författare)
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Diamond Based Optical Metasurfaces for Broadband Wavefront Shaping in Harsh Environment
- 2024
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Ingår i: Laser and Photonics Reviews. - 1863-8899 .- 1863-8880. ; In Press
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Tidskriftsartikel (refereegranskat)abstract
- Metasurface enables a new class of “meta-optics” that can manipulate light at subwavelength scale. Despite that versatile metasurfaces have been demonstrated based on a wide range of materials, the vulnerability of conventional materials to harsh environments, i.e., low resistance to corrosion, low transparency at short wavelength, and lack of thermal/mechanical stability, greatly limit their applications in extreme conditions. Diamond is well-known for exceptional properties, including the highest thermal conductivity, high damage resistance, extraordinary hardness, and chemical inertness. Therefore, diamond based metasurface is generally expected to benefit from its material merits for extreme use. However, the performance of diamond metasurface in harsh environments remains unexplored up to date. To address this question, this work is designed to study the suitability of single-crystal diamond based metasurface for broadband applications under harsh environments. As an example, diamond metasurfaces with representative functionalities, including holographic wavefront-shaping, DUV-focusing, are investigated under high-temperature, acid/alkali, and abrasive conditions, respectively. The findings prove the capability of diamond metasurfaces for applications in broadband and harsh conditions, which not only provides a practical and scalable scheme to encode on-demand functionalities into diamond, but also unlocks a capable candidate to develop robust, large bandwidth, and durable meta-optics for advanced wavefront shaping under extreme conditions.
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