| 1. |
- Li, Zhiyang, et al.
(författare)
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Magnetic carbon nanotube modified S-scheme TiO2-x/g-C3N4/CNFe heterojunction coupled with peroxymonosulfate for effective visible-light-driven photodegradation via enhanced interfacial charge separation
- 2023
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Ingår i: Separation and Purification Technology. - : Elsevier. - 1383-5866 .- 1873-3794. ; 308
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Tidskriftsartikel (refereegranskat)abstract
- To remediate water bodies contaminated with organic micropollutants, recyclable and visible-light-driven coupled photocatalysis-peroxymonosulfate (PMS) activation systems were established by synthesizing magnetic-carbon-nanotubes (CNFe) modified TiO2-x/g-C3N4/CNFe (TCNCNFe) S-scheme heterojunction with oxygen vacancies (O-v) by a simple hydrothermal-calcination approach. The introduction of O-v and CNFe enhances the visible-light-harvesting efficiency and the internal electric field across the heterojunction accompanying favorable energy band bending could effectively migrate the photoexcited electrons along the S-scheme mechanism, thus highly suppressing in situ recombination and improving charge separation. Therefore the TCNCNFe-(30-500)/PMS/Vis system achieved 95.4% removal efficiency of atrazine after 30 min irradiation, meanwhile exhibited excellent recyclability without metal ion leaching due to the unique pod-like nanostructure of CNFe. Moreover, the impacts of certain various reaction variables on pollutant removal were explored to evaluate the practical application potential. Interestingly, the biotoxicity of the treated reaction filtrate was significantly alleviated compared to that of ATZ solution. Furthermore, the exploration of photocatalytic reaction mechanism revealed that the dominant reactive oxidizing species contributed in the following order: h(+) > (OH)-O-center dot > O-center dot(2)- > (SO4-)-S-center dot, and the feasible photodegradation pathway of atrazine was presented based on the determined in-termediates. Hence, this research work holds great promise in ecological environment protection using sustainable solar energy.
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| 2. |
- Nešić, Nebojša, et al.
(författare)
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Automated segmentation of cell organelles in volume electron microscopy using deep learning
- 2024
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Ingår i: Microscopy Research and Technique. - 1059-910X .- 1097-0029.
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Tidskriftsartikel (refereegranskat)abstract
- Recent advances in computing power triggered the use of artificial intelligence in image analysis in life sciences. To train these algorithms, a large enough set of certified labeled data is required. The trained neural network is then capable of producing accurate instance segmentation results that will then need to be re-assembled into the original dataset: the entire process requires substantial expertise and time to achieve quantifiable results. To speed-up the process, from cell organelle detection to quantification across electron microscopy modalities, we propose a deep-learning based approach for fast automatic outline segmentation (FAMOUS), that involves organelle detection combined with image morphology, and 3D meshing to automatically segment, visualize and quantify cell organelles within volume electron microscopy datasets. From start to finish, FAMOUS provides full segmentation results within a week on previously unseen datasets. FAMOUS was showcased on a HeLa cell dataset acquired using a focused ion beam scanning electron microscope, and on yeast cells acquired by transmission electron tomography. Research Highlights: Introducing a rapid, multimodal machine-learning workflow for the automatic segmentation of 3D cell organelles. Successfully applied to a variety of volume electron microscopy datasets and cell lines. Outperforming manual segmentation methods in time and accuracy. Enabling high-throughput quantitative cell biology.
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