Extrusion-based 3D printing/bioprinting is a promising approach to generate patient-specific tissue engineered grafts. However, a major challenge in extrusion-based 3D printing/bioprinting is that most currently used ...
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Reinforcement Learning (RL) is an extraordinarily paradigm that aims to solve a complex problem. This technique leverages the traditional feedforward networks with temporal-difference learning to overcome supervised a...
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We highlight with first-principles molecular dynamics the persistence of intrinsic h111i Ti off-centerings for BaTiO3 in its cubic paraelectric phase. Intriguingly, these are inconsistent with the Pm¯3m space gro...
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Background To achieve efficient solar-to-chemical energy conversion in photocatalysis, it is crucial to develop visible-light-driven catalysts with excellent charge transport properties and superior activity. Methods ...
Background To achieve efficient solar-to-chemical energy conversion in photocatalysis, it is crucial to develop visible-light-driven catalysts with excellent charge transport properties and superior activity. Methods Here, p-n heterostructured Bi 2 O 3 /BiVO 4 (BO/BVO) composites were synthesized via a spray-drying method, incorporating synergistic effects of oxygen vacancies and asphaltene-derived carbon coating. Significant findings The built-in electric field at the p −type BO and n-type BVO interface, combined with the electron sink effect of the coated graphitic carbon layer, enhances charge transfer while suppressing charge recombination. As a result, the carbon coated BO/BVO (C-BO/BVO) heterojunction reveals markedly improved efficiency for photodegradation of methylene blue (MB) in comparison with pure BO and BVO. Under visible light irradiation, the C-BO/BVO composite achieves an MB decomposition efficiency of 92.1 %, which is approximately 1.78, 2.23, and 2.98 times higher than that of BO/BVO, pure BVO, and pure BO, respectively. As a result, the C-BO/BVO composites exhibit superior degradation performance for MB and tetracycline (TC), achieving high rate constants of 6.51 × 10 −2 min −1 and 7.55 × 10 −3 min −1 , respectively. The C-BO/BVO photocatalysts also exhibit exceptional antibacterial activity against Escherichia coli ( E. coli ). Additionally, their biocompatibility has been assessed using an in vivo zebrafish embryo model, highlighting their potential for future biomedical applications.
Matrix factorization (MF) technique has been widely utilized in recommendation systems due to the precise prediction of users' interests. Prior MF-based methods adapt the overall rating to make the recommendation ...
ISBN:
(数字)9781728128207
ISBN:
(纸本)9781728128214
Matrix factorization (MF) technique has been widely utilized in recommendation systems due to the precise prediction of users' interests. Prior MF-based methods adapt the overall rating to make the recommendation by extracting latent factors from users and items. However, in real applications, people's preferences usually vary with time; the traditional MF-based methods could not properly capture the change of users' interests. In this paper, by incorporating the recurrent neural network (RNN) into MF, we develop a novel recommendation system, M-RNN-F, to effectively describe the preference evolution of users over time. A learning model is proposed to capture the evolution pattern and predict the user preference in the future. The experimental results show that M-RNN-F performs better than other state-of-the-art recommendation algorithms. In addition, we conduct the experiments on real world dataset to demonstrate the practicability.
Reinforcement Learning (RL) is an extraordinarily paradigm that aims to solve a complex problem. This technique leverages the traditional feedforward networks with temporal-difference learning to overcome supervised a...
ISBN:
(数字)9781728128207
ISBN:
(纸本)9781728128214
Reinforcement Learning (RL) is an extraordinarily paradigm that aims to solve a complex problem. This technique leverages the traditional feedforward networks with temporal-difference learning to overcome supervised and unsupervised real-world problems. However, RL is one of state-of-the-art topic due to the opaque aspects in design and implementation. Also, in which situation we will get performance gain from RL is still unclear. Therefore, This study firstly examines the impact of Experience Replay in Deep Q-Learning agent with Self-Driving Car application. Secondly, The impact of Eligibility Trace in RNN A3C agents with Breakout AI game application is studied. Our results indicated that these two techniques enhance RL performance by more than 20 percent as compared with traditional RL methods.
Electron transport materials (ETM) play an important role in the improvement of efficiency and stability for inverted perovskite solar cells (PSCs). This work reports an efficient ETM, named PDI‐C 60 , by the combina...
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Electron transport materials (ETM) play an important role in the improvement of efficiency and stability for inverted perovskite solar cells (PSCs). This work reports an efficient ETM, named PDI‐C 60 , by the combination of perylene diimide (PDI) and fullerene. Compared to the traditional PCBM, this strategy endows PDI‐C 60 with slightly shallower energy level and higher electron mobility. As a result, the device based on PDI‐C 60 as electron transport layer (ETL) achieves high power conversion efficiency (PCE) of 18.6 %, which is significantly higher than those of the control devices of PCBM (16.6 %) and PDI (13.8 %). The high PCE of the PDI‐C 60 ‐based device can be attributed to the more matching energy level with the perovskite, more efficient charge extraction, transport, and reduced recombination rate. To the best of our knowledge, the PCE of 18.6 % is the highest value in the PSCs using PDI derivatives as ETLs. Moreover, the device with PDI‐C 60 as ETL exhibits better device stability due to the stronger hydrophobic properties of PDI‐C 60 . The strategy using the PDI/fullerene hybrid provides insights for future molecular design of the efficient ETM for the inverted PSCs.
Carbon nanotubes (CNT) is one of the most investigated materials before the invention of graphene. 1 Due to its unique properties, CNTs have been used in various applications include sensors, solar cells, transistors...
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Carbon nanotubes (CNT) is one of the most investigated materials before the invention of graphene. 1 Due to its unique properties, CNTs have been used in various applications include sensors, solar cells, transistors, etc. 2 However, it makes difficult to make nano-devices based on CNTs at a large scale. For this reason, formation of composite with conducting polymers is a good way for modification of CNT. 3 In this study, CNT was modificated with poly(3-hexylthiophene) by using RF rotating plasma. The obtained modified graphene particles were characterized with FTIR, SEM-EDS, XRD and TGA methods.
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