| 1. |
- Fan, Qunping, 1989, et al.
(författare)
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High-performance all-polymer solar cells enabled by a novel low bandgap non-fully conjugated polymer acceptor
- 2021
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Ingår i: Science in China Series B. - : Springer Nature. - 1674-7291 .- 1869-1870. ; 64, s. 1380-1388
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Tidskriftsartikel (refereegranskat)abstract
- Anon-fully conjugated polymer as a new class of acceptor materials has shown some advantages over its small molecular counterpart when used in photoactive layers for all-polymer solar cells (all-PSCs), despite a low power conversion efficiency (PCE) caused by its narrow absorption spectra. Herein, a novel non-fully conjugated polymer acceptor PFY-2TS with a low bandgap of similar to 1.40 eV was developed, via polymerizing a large pi-fused small molecule acceptor (SMA) building block (namely YBO) with a non-conjugated thioalkyl linkage. Compared with its precursor YBO, PFY-2TS retains a similar low bandgap but a higher LUMO level. Moreover, compared with the structural analog of YBO-based fully conjugated polymer acceptor PFY-DTC, PFY-2TS shows similar absorption spectrum and electron mobility, but significantly different molecular crystallinity and aggregation properties, which results in optimal blend morphology with a polymer donor PBDB-T and better device physical processes in all-PSCs. As a result, PFY-2TS-based all-PSCs achieved a PCE of 12.31% with a small energy loss of 0.56 eV enabled by the reduced non-radiative energy loss (0.24 eV), which is better than that of 11.08% for the PFY-DTC-based ones. Our work clearly demonstrated that non-fully conjugated polymers as a new class of acceptor materials are very promising for the development of high-performance all-PSCs.
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| 2. |
- Jin, Yi, et al.
(författare)
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Self-aware distributed deep learning framework for heterogeneous IoT edge devices
- 2021
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Ingår i: Future generations computer systems. - : Elsevier BV. - 0167-739X .- 1872-7115. ; 125, s. 908-920
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Tidskriftsartikel (refereegranskat)abstract
- Implementing artificial intelligence (AI) in the Internet of Things (IoT) involves a move from the cloud to the heterogeneous and low-power edge, following an urgent demand for deploying complex training tasks in a distributed and reliable manner. This work proposes a self-aware distributed deep learning (DDL) framework for IoT applications, which is applicable to heterogeneous edge devices aiming to improve adaptivity and amortize the training cost. The self-aware design including the dynamic self-organizing approach and the self-healing method enhances the system reliability and resilience. Three typical edge devices are adopted with cross-platform Docker deployment: Personal Computers (PC) for general computing devices, Raspberry Pi 4Bs (Rpi) for resource-constrained edge devices, and Jetson Nanos (Jts) for AI-enabled edge devices. Benchmarked with ResNet-32 on CIFAR-10, the training efficiency of tested distributed clusters is increased by 8.44x compared to the standalone Rpi. The cluster with 11 heterogeneous edge devices achieves a training efficiency of 200.4 images/s and an accuracy of 92.45%. Results prove that the self-organizing approach functions well with dynamic changes like devices being removed or added. The self-healing method is evaluated with various stabilities, cluster scales, and breakdown cases, testifying that the reliability can be largely enhanced for extensively distributed deployments. The proposed DDL framework shows excellent performance for training implementation with heterogeneous edge devices in IoT applications with high-degree scalability and reliability.
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| 3. |
- Li, Sirui, et al.
(författare)
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Glioma grading, molecular feature classification, and microstructural characterization using MR diffusional variance decomposition (DIVIDE) imaging
- 2021
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Ingår i: European Radiology. - : Springer Science and Business Media LLC. - 0938-7994 .- 1432-1084. ; 31:11, s. 8197-8207
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Tidskriftsartikel (refereegranskat)abstract
- Objective: To evaluate the potential of diffusional variance decomposition (DIVIDE) for grading, molecular feature classification, and microstructural characterization of gliomas. Materials and methods: Participants with suspected gliomas underwent DIVIDE imaging, yielding parameter maps of fractional anisotropy (FA), mean diffusivity (MD), anisotropic mean kurtosis (MKA), isotropic mean kurtosis (MKI), total mean kurtosis (MKT), MKA/MKT, and microscopic fractional anisotropy (μFA). Tumor type and grade, isocitrate dehydrogenase (IDH) 1/2 mutant status, and the Ki-67 labeling index (Ki-67 LI) were determined after surgery. Statistical analysis included 33 high-grade gliomas (HGG) and 17 low-grade gliomas (LGG). Tumor diffusion metrics were compared between HGG and LGG, among grades, and between wild and mutated IDH types using appropriate tests according to normality assessment results. Receiver operating characteristic and Spearman correlation analysis were also used for statistical evaluations. Results: FA, MD, MKA, MKI, MKT, μFA, and MKA/MKT differed between HGG and LGG (FA: p = 0.047; MD: p = 0.037, others p < 0.001), and among glioma grade II, III, and IV (FA: p = 0.048; MD: p = 0.038, others p < 0.001). All diffusion metrics differed between wild-type and mutated IDH tumors (MKI: p = 0.003; others: p < 0.001). The metrics that best discriminated between HGG and LGGs and between wild-type and mutated IDH tumors were MKT and FA respectively (area under the curve 0.866 and 0.881). All diffusion metrics except FA showed significant correlation with Ki-67 LI, and MKI had the highest correlation coefficient (rs = 0.618). Conclusion: DIVIDE is a promising technique for glioma characterization and diagnosis. Key Points: • DIVIDE metrics MKIis related to cell density heterogeneity while MKAand μFA are related to cell eccentricity. • DIVIDE metrics can effectively differentiate LGG from HGG and IDH mutation from wild-type tumor, and showed significant correlation with the Ki-67 labeling index. • MKIwas larger than MKAwhich indicates predominant cell density heterogeneity in gliomas. • MKAand MKIincreased with grade or degree of malignancy, however with a relatively larger increase in the cell eccentricity metric MKAin relation to the cell density heterogeneity metric MKI.
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| 4. |
- Yin, Yongguang, et al.
(författare)
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Possible alkylation of inorganic Hg(II) by photochemical processes in the environment
- 2012
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Ingår i: Chemosphere. - : Elsevier. - 0045-6535 .- 1879-1298. ; 88:1, s. 8-16
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Tidskriftsartikel (refereegranskat)abstract
- The methylation of inorganic Hg by anaerobic bacteria in aquatic environments is considered to be the major pathway for methylmercury (MeHg) production. However, recent research has suggested that abiotic or chemical methylation by humic substances and other low-molecular-weight organic compounds in natural environments is also possible. Here, the aqueous photo-transformation of Hg(2+) to organomercurials was investigated in the presence of ketones, aldehydes and low molecular weight organic acids under UV irradiation. MeHg and/or ethylmercury (EtHg) were identified as the main organomercurial products by multiple analytical techniques, including chromatography-atomic spectrometry and molecular mass spectrometry and further confirmed by stable isotope tracer experiments. The yield of organomercurials was markedly influenced by pH, NaCl concentration, alkylation donor concentration and the presence of chelating ligands in the aqueous solution. Electron paramagnetic resonance spectrometry demonstrated that the radical reaction was not the predominating alkylation pathway, although methyl radicals were detected in the photo-alkylation procedure. A mechanism based on intra-molecular alkyl transfer in the Hg(2+)-low-molecular-weight organic compound complex is proposed. The present work helps us better understand of MeHg and EtHg photo-generation in natural environments.
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