Machine Learning-optimized synthesis of doped TiO2 with improved photocatalytic performance: A multi-step workflow supported by designed wet-lab experiments

2021 ◽  
pp. 160534
Author(s):  
Bowen Gao ◽  
Mingxuan Sun ◽  
Zhipeng Ding ◽  
Wenzhu Liu
Keyword(s):  
Machine Learning ◽  
Photocatalytic Performance ◽  
Doped Tio2 ◽  
Lab Experiments ◽  
Wet Lab

scholarly journals Graph contextualized attention network for predicting synthetic lethality in human cancers

2021 ◽  
Author(s):  
Yahui Long ◽  
Min Wu ◽  
Yong Liu ◽  
Jie Zheng ◽  
Chee Keong Kwoh ◽  
...  
Keyword(s):  
Synthetic Lethality ◽  
Critical Role ◽  
Design Feature ◽  
Cost Effective ◽  
Attention Network ◽  
New Genes ◽  
Lab Experiments ◽  
Proposed Model ◽  
Multiple Feature ◽  
Wet Lab

Abstract Motivation Synthetic Lethality (SL) plays an increasingly critical role in the targeted anticancer therapeutics. In addition, identifying SL interactions can create opportunities to selectively kill cancer cells without harming normal cells. Given the high cost of wet-lab experiments, in silico prediction of SL interactions as an alternative can be a rapid and cost-effective way to guide the experimental screening of candidate SL pairs. Several matrix factorization-based methods have recently been proposed for human SL prediction. However, they are limited in capturing the dependencies of neighbors. In addition, it is also highly challenging to make accurate predictions for new genes without any known SL partners. Results In this work, we propose a novel graph contextualized attention network named GCATSL to learn gene representations for SL prediction. First, we leverage different data sources to construct multiple feature graphs for genes, which serve as the feature inputs for our GCATSL method. Second, for each feature graph, we design node-level attention mechanism to effectively capture the importance of local and global neighbors and learn local and global representations for the nodes, respectively. We further exploit multi-layer perceptron (MLP) to aggregate the original features with the local and global representations and then derive the feature-specific representations. Third, to derive the final representations, we design feature-level attention to integrate feature-specific representations by taking the importance of different feature graphs into account. Extensive experimental results on three datasets under different settings demonstrated that our GCATSL model outperforms 14 state-of-the-art methods consistently. In addition, case studies further validated the effectiveness of our proposed model in identifying novel SL pairs. Availability Python codes and dataset are freely available on GitHub (https://github.com/longyahui/GCATSL) and Zenodo (https://zenodo.org/record/4522679) under the MIT license.

scholarly journals The Photocatalytic and Antibacterial Performance of Nitrogen-Doped TiO2: Surface-Structure Dependence and Silver-Deposition Effect

Nanomaterials ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 2261 ◽  
Author(s):  
Abdul Wafi ◽  
Erzsébet Szabó-Bárdos ◽  
Ottó Horváth ◽  
Mihály Pósfai ◽  
Éva Makó ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Visible Light ◽  
Surface Area ◽  
Photocatalytic Performance ◽  
Visible Region ◽  
Bet Surface Area ◽  
Doped Tio2 ◽  
X Ray ◽  
Nitrogen Doped ◽  
Visible Light Driven

Catalysts for visible-light-driven oxidative cleaning processes and antibacterial applications (also in the dark) were developed. In order to extend the photoactivity of titanium dioxide into the visible region, nitrogen-doped TiO2 catalysts with hollow and non-hollow structures were synthesized by co-precipitation (NT-A) and sol–gel (NT-U) methods, respectively. To increase their photocatalytic and antibacterial efficiencies, various amounts of silver were successfully loaded on the surfaces of these catalysts by using a facile photo-deposition technique. Their physical and chemical properties were evaluated by using scanning electron microscopy (SEM), transmission electron microscopy–energy dispersive X-ray spectroscopy (TEM–EDS), Brunauer–Emmett–Teller (BET) surface area, X-ray diffraction (XRD), and diffuse reflectance spectra (DRS). The photocatalytic performances of the synthesized catalysts were examined in coumarin and 1,4-hydroquinone solutions. The results showed that the hollow structure of NT-A played an important role in obtaining high specific surface area and appreciable photoactivity. In addition, Ag-loading on the surface of non-hollow structured NT-U could double the photocatalytic performance with an optimum Ag concentration of 10−6 mol g−1, while a slight but monotonous decrease was caused in this respect for the hollow surface of NTA upon increasing Ag concentration. Comparing the catalysts with different structures regarding the photocatalytic performance, silverized non-hollow NT-U proved competitive with the hollow NT-A catalyst without Ag-loading for efficient visible-light-driven photocatalytic oxidative degradations. The former one, due to the silver nanoparticles on the catalyst surface, displayed an appreciable antibacterial activity, which was comparable to that of a reference material practically applied for disinfection in polymer coatings.

Preparation of K-doped TiO2 nanostructures by wet corrosion and their sunlight-driven photocatalytic performance

2016 ◽  
Vol 379 ◽  
pp. 33-38 ◽  
Author(s):  
Eunhye Shin ◽  
Saera Jin ◽  
Jiyoon Kim ◽  
Sung-Jin Chang ◽  
Byung-Hyuk Jun ◽  
...  
Keyword(s):  
Photocatalytic Performance ◽  
Doped Tio2 ◽  
Tio2 Nanostructures

Fabrication of nitrogen-doped TiO2 monolith with well-defined macroporous and bicrystalline framework and its photocatalytic performance under visible light

2014 ◽  
Vol 34 (3) ◽  
pp. 809-816 ◽  
Author(s):  
Olim Ruzimuradov ◽  
Suvankul Nurmanov ◽  
Mirabbos Hojamberdiev ◽  
Ravi Mohan Prasad ◽  
Aleksander Gurlo ◽  
...  
Keyword(s):  
Visible Light ◽  
Photocatalytic Performance ◽  
Doped Tio2 ◽  
Nitrogen Doped

Photocatalytic Performance of Anion Doped TiO2 on the Degradation of Complex Organic Matrix

2016 ◽  
Vol 19 (2) ◽  
Author(s):  
Nazmiye Cemre Birben ◽  
Ceyda Senem Uyguner-Demirel ◽  
Sibel Sen-Kavurmaci ◽  
Yelda Yalçın Gürkan ◽  
Nazlı Türkten ◽  
...  
Keyword(s):  
Photocatalytic Performance ◽  
Organic Matrix ◽  
Doped Tio2 ◽  
Complex Organic

AbstractTiO

scholarly journals DeepCLIP: predicting the effect of mutations on protein–RNA binding with deep learning

2020 ◽  
Author(s):  
Alexander Gulliver Bjørnholt Grønning ◽  
Thomas Koed Doktor ◽  
Simon Jonas Larsen ◽  
Ulrika Simone Spangsberg Petersen ◽  
Lise Lolle Holm ◽  
...  
Keyword(s):  
Protein Binding ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Sequence Data ◽  
Genomic Context ◽  
Functional Changes ◽  
Lab Experiments ◽  
Sequence Variations ◽  
Wet Lab ◽  
Nuclear Shuttling

Abstract Nucleotide variants can cause functional changes by altering protein–RNA binding in various ways that are not easy to predict. This can affect processes such as splicing, nuclear shuttling, and stability of the transcript. Therefore, correct modeling of protein–RNA binding is critical when predicting the effects of sequence variations. Many RNA-binding proteins recognize a diverse set of motifs and binding is typically also dependent on the genomic context, making this task particularly challenging. Here, we present DeepCLIP, the first method for context-aware modeling and predicting protein binding to RNA nucleic acids using exclusively sequence data as input. We show that DeepCLIP outperforms existing methods for modeling RNA-protein binding. Importantly, we demonstrate that DeepCLIP predictions correlate with the functional outcomes of nucleotide variants in independent wet lab experiments. Furthermore, we show how DeepCLIP binding profiles can be used in the design of therapeutically relevant antisense oligonucleotides, and to uncover possible position-dependent regulation in a tissue-specific manner. DeepCLIP is freely available as a stand-alone application and as a webtool at http://deepclip.compbio.sdu.dk.

In situ synthesis of carbon doped TiO2 nanotubes with an enhanced photocatalytic performance under UV and visible light

Carbon ◽  
2017 ◽  
Vol 125 ◽  
pp. 544-550 ◽  
Author(s):  
Lijun Ji ◽  
Yuheng Zhang ◽  
Shiyong Miao ◽  
Mindong Gong ◽  
Xi Liu
Keyword(s):  
Visible Light ◽  
Tio2 Nanotubes ◽  
Photocatalytic Performance ◽  
In Situ Synthesis ◽  
Doped Tio2 ◽  
Carbon Doped
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