scholarly journals Excitonic effects in absorption spectra of carbon dioxide reduction photocatalysts

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Tathagata Biswas ◽  
Arunima K. Singh
Keyword(s):  
State Of The Art ◽  
Computational Cost ◽  
Carbon Dioxide Reduction ◽  
Binding Energies ◽  
Data Driven ◽  
Screening Criteria ◽  
Mott Model ◽  
Solar Energy Harvesting ◽  
Photocatalytic Application ◽  
Excitonic Effects

AbstractThe formation and disassociation of excitons play a crucial role in any photovoltaic or photocatalytic application. However, excitonic effects are seldom considered in materials discovery studies due to the monumental computational cost associated with the examination of these properties. Here, we study the excitonic properties of nearly 50 photocatalysts using state-of-the-art Bethe–Salpeter formalism. These ~50 materials were recently recognized as promising photocatalysts for CO2 reduction through a data-driven screening of 68,860 materials. Here, we propose three screening criteria based on the optical properties of these materials, taking excitonic effects into account, to further down select six materials. Furthermore, we study the correlation between the exciton binding energies obtained from the Bethe–Salpeter formalism and those obtained from the computationally much less-expensive Wannier–Mott model for these chemically diverse ~50 materials. This work presents a paradigm towards the inclusion of excitonic effects in future materials discovery for solar-energy harvesting applications.

scholarly journals Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2371
Author(s):  
Matthieu Dubarry ◽  
David Beck
Keyword(s):  
Cobalt Oxide ◽  
State Of The Art ◽  
Data Driven ◽  
Nickel Aluminum ◽  
Li Ion Battery ◽  
Diagnosis And Prognosis ◽  
Detailed Statistical Analysis ◽  
Li Ion ◽  
Synthetic Datasets ◽  
Nickel Manganese

The development of data driven methods for Li-ion battery diagnosis and prognosis is a growing field of research for the battery community. A big limitation is usually the size of the training datasets which are typically not fully representative of the real usage of the cells. Synthetic datasets were proposed to circumvent this issue. This publication provides improved datasets for three major battery chemistries, LiFePO4, Nickel Aluminum Cobalt Oxide, and Nickel Manganese Cobalt Oxide 811. These datasets can be used for statistical or deep learning methods. This work also provides a detailed statistical analysis of the datasets. Accurate diagnosis as well as early prognosis comparable with state of the art, while providing physical interpretability, were demonstrated by using the combined information of three learnable parameters.

scholarly journals Excitonic effects in the optical spectra of Li2SiO3 compound

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nguyen Thi Han ◽  
Vo Khuong Dien ◽  
Ming-Fa Lin
Keyword(s):  
Optical Properties ◽  
State Of The Art ◽  
Plasmon Mode ◽  
First Principle ◽  
Response Behavior ◽  
Electronic And Optical Properties ◽  
Physical Chemical ◽  
Excitonic Effects ◽  
Energy Loss Functions ◽  
Excitonic Effect

AbstractLi2SiO3 compound exhibits unique electronic and optical properties. The state-of-the-art analyses, which based on first-principle calculations, have successfully confirmed the concise physical/chemical picture and the orbital bonding in Li–O and Si–O bonds. Especially, the unusual optical response behavior includes a large red shift of the onset frequency due to the extremely strong excitonic effect, the polarization of optical properties along three-directions, various optical excitations structures and the most prominent plasmon mode in terms of the dielectric functions, energy loss functions, absorption coefficients and reflectance spectra. The close connections of electronic and optical properties can identify a specific orbital hybridization for each distinct excitation channel. The presented theoretical framework will be fully comprehending the diverse phenomena and widen the potential application of other emerging materials.

scholarly journals A Cofacial Metal-Organic Framework Based Photocathode for Carbon Dioxide Reduction

2021 ◽  
Author(s):  
Bowen Ding ◽  
Bun Chan ◽  
Nicholas Proschogo ◽  
Marcello Solomon ◽  
Cameron Kepert ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Energy Harvesting ◽  
Solar Energy ◽  
Metal Organic Framework ◽  
Carbon Dioxide Reduction ◽  
Solar Energy Harvesting ◽  
Metal Organic ◽  
Organic Framework

Innovative and robust photosensitisation materials play a cardinal role in advancing the combined effort towards efficient solar energy harvesting. Here, we demonstrate the photocathode functionality of a Metal-Organic Framework (MOF)...

scholarly journals Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2778 ◽  
Author(s):  
Mohsen Azimi ◽  
Armin Eslamlou ◽  
Gokhan Pekcan
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
High Speed ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Data Driven ◽  
Structural Health ◽  
Promising Tool ◽  
Significant Attention

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

First principles study of the structural, electronic and optical properties of crystalline o-phenanthroline

2016 ◽  
Vol 30 (14) ◽  
pp. 1650077 ◽  
Author(s):  
Hajar Nejatipour ◽  
Mehrdad Dadsetani
Keyword(s):  
Optical Properties ◽  
Electronic Structure ◽  
First Principles ◽  
Binding Energies ◽  
Many Body ◽  
Generalized Gradient ◽  
Generalized Gradient Approximation ◽  
Independent Particle ◽  
Excitonic Effects ◽  
Comprehensive Study

In a comprehensive study, structural properties, electronic structure and optical response of crystalline o-phenanthroline were investigated. Our results show that in generalized gradient approximation (GGA) approximation, o-phenanthroline is a direct bandgap semiconductor of 2.60 eV. In the framework of many-body approach, by solving the Bethe–Salpeter equation (BSE), dielectric properties of crystalline o-phenanthroline were studied and compared with phenanthrene. Highly anisotropic components of the imaginary part of the macroscopic dielectric function in o-phenanthroline show four main excitonic features in the bandgap region. In comparison to phenanthrene, these excitons occur at lower energies. Due to smaller bond lengths originated from the polarity nature of bonds in presence of nitrogen atoms, denser packing, and therefore, a weaker screening effect, exciton binding energies in o-phenanthroline were found to be larger than those in phenanthrene. Our results showed that in comparison to the independent-particle picture, excitonic effects highly redistribute the oscillator strength.

scholarly journals Plant Leaf Disease Recognition Using Depth-Wise Separable Convolution-Based Models

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 511
Author(s):  
Syed Mohammad Minhaz Hossain ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
Takeshi Koshiba
Keyword(s):  
State Of The Art ◽  
Computational Cost ◽  
Region Of Interest ◽  
Number Of Clusters ◽  
Plant Leaf ◽  
Leaf Disease ◽  
Automatic Initialization ◽  
Adequate Accuracy ◽  
Model Size ◽  
High Computational Cost

Proper plant leaf disease (PLD) detection is challenging in complex backgrounds and under different capture conditions. For this reason, initially, modified adaptive centroid-based segmentation (ACS) is used to trace the proper region of interest (ROI). Automatic initialization of the number of clusters (K) using modified ACS before recognition increases tracing ROI’s scalability even for symmetrical features in various plants. Besides, convolutional neural network (CNN)-based PLD recognition models achieve adequate accuracy to some extent. However, memory requirements (large-scaled parameters) and the high computational cost of CNN-based PLD models are burning issues for the memory restricted mobile and IoT-based devices. Therefore, after tracing ROIs, three proposed depth-wise separable convolutional PLD (DSCPLD) models, such as segmented modified DSCPLD (S-modified MobileNet), segmented reduced DSCPLD (S-reduced MobileNet), and segmented extended DSCPLD (S-extended MobileNet), are utilized to represent the constructive trade-off among accuracy, model size, and computational latency. Moreover, we have compared our proposed DSCPLD recognition models with state-of-the-art models, such as MobileNet, VGG16, VGG19, and AlexNet. Among segmented-based DSCPLD models, S-modified MobileNet achieves the best accuracy of 99.55% and F1-sore of 97.07%. Besides, we have simulated our DSCPLD models using both full plant leaf images and segmented plant leaf images and conclude that, after using modified ACS, all models increase their accuracy and F1-score. Furthermore, a new plant leaf dataset containing 6580 images of eight plants was used to experiment with several depth-wise separable convolution models.

scholarly journals High-Fidelity Simulated Players for Interactive Narrative Planning

2018 ◽  
Author(s):  
Pengcheng Wang ◽  
Jonathan Rowe ◽  
Wookhee Min ◽  
Bradford Mott ◽  
James Lester
Keyword(s):  
State Of The Art ◽  
Data Driven ◽  
High Fidelity ◽  
Interactive Narrative ◽  
Interaction Data ◽  
Convolutional Networks ◽  
Novel Approach ◽  
Adaptation Policies ◽  
Narrative Planning ◽  
Prior State

Interactive narrative planning offers significant potential for creating adaptive gameplay experiences. While data-driven techniques have been devised that utilize player interaction data to induce policies for interactive narrative planners, they require enormously large gameplay datasets. A promising approach to addressing this challenge is creating simulated players whose behaviors closely approximate those of human players. In this paper, we propose a novel approach to generating high-fidelity simulated players based on deep recurrent highway networks and deep convolutional networks. Empirical results demonstrate that the proposed models significantly outperform the prior state-of-the-art in generating high-fidelity simulated player models that accurately imitate human players’ narrative interactions. Using the high-fidelity simulated player models, we show the advantage of more exploratory reinforcement learning methods for deriving generalizable narrative adaptation policies.

scholarly journals Very early identification of a bimodal frictional behavior during the post-seismic phase of the 2015 M<sub><i>w</i></sub>8.3 Illapel, Chile, earthquake

2021 ◽  
Author(s):  
Cedric Twardzik ◽  
Mathilde Vergnolle ◽  
Anthony Sladen ◽  
Louisa L. H. Tsang
Keyword(s):  
Template Matching ◽  
State Of The Art ◽  
Data Driven ◽  
Spatiotemporal Evolution ◽  
Seismic Slip ◽  
The North ◽  
Chile Earthquake ◽  
Gnss Data ◽  
Matching Techniques

Abstract. It is well-established that the post-seismic slip results from the combined contribution of seismic slip and aseismic slip. However, the partitioning between these two modes of slip remains unclear due to the difficulty to infer detailed and robust descriptions of how both evolve in space and time. This is particularly true just after a mainshock when both processes are expected to be the strongest. Using state-of-the-art sub-daily processing of GNSS data, along with dense catalogs of aftershocks obtained from template-matching techniques, we unravel the spatiotemporal evolution of post-seismic slip and aftershocks over the first 12 hours following the 2015 Mw8.3 Illapel, Chile, earthquake. We show that the very early post-seismic activity occurs over two regions with distinct behaviors. To the north, post-seismic slip appears to be purely aseismic and precedes the occurrence of late aftershocks. To the south, aftershocks are the primary cause of the post-seismic slip. We suggest that this difference in behavior could be inferred only few hours after the mainshock, and thus could contribute to a more data-driven forecasts of long-term aftershocks.

scholarly journals Machine Learning for the Development of Data Driven Turbulence Closures in Coolant Systems

2020 ◽  
Author(s):  
James Hammond ◽  
Francesco Montomoli ◽  
Marco Pietropaoli ◽  
Richard D. Sandberg ◽  
Vittorio Michelassi
Keyword(s):  
Heat Transfer ◽  
Complex Geometry ◽  
Gene Expression Programming ◽  
Turbine Blades ◽  
Computational Cost ◽  
Data Driven ◽  
Internal Cooling ◽  
Cooling Channels ◽  
Turbulence Closures ◽  
Improved Model

Abstract This work shows the application of Gene Expression Programming to augment RANS turbulence closure modelling for flows through complex geometry, designed for additive manufacturing. Specifically, for the design of optimised internal cooling channels in turbine blades. One of the challenges in internal cooling design is the heat transfer accuracy of the RANS formulation in comparison to higher fidelity methods, which are still not used in design on account of their computational cost. However, high fidelity data can be extremely valuable for improving current lower fidelity models and this work shows the application of data driven approaches to develop turbulence closures for an internally ribbed duct. Different approaches are compared and the results of the improved model are illustrated; first on the same geometry, and then for an unseen predictive case. The work shows the potential of using data driven models for accurate heat transfer predictions even in non-conventional configurations.

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