Δ-machine learning-driven discovery of double hybrid organic-inorganic perovskites

2022 ◽  
Author(s):  
Jialu Chen ◽  
Wenjun Xu ◽  
Rui Qin Zhang
Keyword(s):  
Machine Learning ◽  
Optoelectronic Properties ◽  
Production Costs ◽  
Photovoltaic Applications

Double hybrid organic-inorganic perovskites (DHOIPs) with excellent optoelectronic properties and low production costs are promising in photovoltaic applications. However, DHOIPs still have not been investigated thoroughly, due to their structural...

Optoelectronic properties of naphthalene bis-benzimidazole based derivatives and their photovoltaic applications

2021 ◽  
pp. 113373
Author(s):  
Mahnoor Javed ◽  
Afifa Farhat ◽  
Sobia Jabeen ◽  
Rasheed Ahmad Khera ◽  
Muhammad Khalid ◽  
...  
Keyword(s):  
Optoelectronic Properties ◽  
Photovoltaic Applications

Optoelectronic Properties of (CH3NH3)3Sb2I9 Thin Films for Photovoltaic Applications

2016 ◽  
Vol 1 (1) ◽  
pp. 309-314 ◽  
Author(s):  
Jan-Christoph Hebig ◽  
Irina Kühn ◽  
Jan Flohre ◽  
Thomas Kirchartz
Keyword(s):  
Thin Films ◽  
Optoelectronic Properties ◽  
Photovoltaic Applications

Effect of solvent vapour annealing on bismuth triiodide film for photovoltaic applications and its optoelectronic properties

2020 ◽  
Vol 8 (35) ◽  
pp. 12173-12180
Author(s):  
Muthu Gomathy M. Pandian ◽  
Dhruba B. Khadka ◽  
Yasuhiro Shirai ◽  
Shodruz Umedov ◽  
Masatoshi Yanagida ◽  
...  
Keyword(s):  
Photovoltaic Device ◽  
Optoelectronic Properties ◽  
Ambient Conditions ◽  
Solvent Vapour ◽  
Effect Of Solvent ◽  
Photovoltaic Applications ◽  
Bismuth Triiodide

The annealing ambient conditions affect the morphology and optoelectronic quality of bismuth triiodide film and hence impact on the photovoltaic device parameters.

scholarly journals First-principles DFT insights into the structural, elastic, and optoelectronic properties of α and β-ZnP2: implications for photovoltaic applications

2019 ◽  
Vol 31 (26) ◽  
pp. 265501 ◽  
Author(s):  
Aleksandar Živković ◽  
Barbara Farkaš ◽  
Veikko Uahengo ◽  
Nora H de Leeuw ◽  
Nelson Y Dzade
Keyword(s):  
First Principles ◽  
Optoelectronic Properties ◽  
Photovoltaic Applications

Tuning the optoelectronic properties of benzodithiophene based donor materials and their photovoltaic applications

2022 ◽  
Vol 137 ◽  
pp. 106150
Author(s):  
Ayesha Naveed ◽  
Rasheed Ahmad Khera ◽  
Urwah Azeem ◽  
Iqra Zubair ◽  
Afifa Farhat ◽  
...  
Keyword(s):  
Optoelectronic Properties ◽  
Photovoltaic Applications

Optoelectronic properties of oligomers based on tetrazine, benzothiadiazole, benzodithiophene and thiophene moieties for photovoltaic applications: a theoretical study

RSC Advances ◽  
2015 ◽  
Vol 5 (102) ◽  
pp. 83960-83968 ◽  
Author(s):  
Massimo Ottonelli ◽  
Marina Alloisio ◽  
Ivana Moggio ◽  
M. Isabel Martinez Espinosa ◽  
Eduardo Arias
Keyword(s):  
Theoretical Study ◽  
Optoelectronic Properties ◽  
Design Approach ◽  
Organic Photovoltaic ◽  
Photovoltaic Devices ◽  
Organic Photovoltaic Devices ◽  
Theoretical Design ◽  
Photovoltaic Applications

In this article, a fast theoretical design approach for addressing the synthesis of new conjugated co-polymers for applications in organic photovoltaic devices is discussed.

scholarly journals Cost-effectiveness of Microsoft Academic Graph with machine learning for automated study identification in a living map of coronavirus disease 2019 (COVID-19) research

2021 ◽  
Vol 6 ◽  
pp. 210
Author(s):  
Ian Shemilt ◽  
Anneliese Arno ◽  
James Thomas ◽  
Theo Lorenc ◽  
Claire Khouja ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cost Effectiveness ◽  
Active Learning ◽  
Systematic Reviews ◽  
Production Costs ◽  
Sensitivity Analyses ◽  
Relative Advantage ◽  
Base Case ◽  
Network Graph ◽  
Microsoft Academic

Background: Conventionally, searching for eligible articles to include in systematic reviews and maps of research has relied primarily on information specialists conducting Boolean searches of multiple databases and manually processing the results, including deduplication between these multiple sources. Searching one, comprehensive source, rather than multiple databases, could save time and resources. Microsoft Academic Graph (MAG) is potentially such a source, containing a network graph structure which provides metadata that can be exploited in machine learning processes. Research is needed to establish the relative advantage of using MAG as a single source, compared with conventional searches of multiple databases. This study sought to establish whether: (a) MAG is sufficiently comprehensive to maintain our living map of coronavirus disease 2019 (COVID-19) research; and (b) eligible records can be identified with an acceptably high level of specificity. Methods: We conducted a pragmatic, eight-arm cost-effectiveness analysis (simulation study) to assess the costs, recall and precision of our semi-automated MAG-enabled workflow versus conventional searches of MEDLINE and Embase (with and without machine learning classifiers, active learning and/or fixed screening targets) for maintaining a living map of COVID-19 research. Resource use data (time use) were collected from information specialists and other researchers involved in map production. Results: MAG-enabled workflows dominated MEDLINE-Embase workflows in both the base case and sensitivity analyses. At one month (base case analysis) our MAG-enabled workflow with machine learning, active learning and fixed screening targets identified n=469 more new, eligible articles for inclusion in our living map – and cost £3,179 GBP ($5,691 AUD) less – than conventional MEDLINE-Embase searches without any automation or fixed screening targets. Conclusions: MAG-enabled continuous surveillance workflows have potential to revolutionise study identification methods for living maps, specialised registers, databases of research studies and/or collections of systematic reviews, by increasing their recall and coverage, whilst reducing production costs.

scholarly journals A Real-Time Physical Progress Measurement Method for Schedule Performance Control Using Vision, an AR Marker and Machine Learning in a Ship Block Assembly Process

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5386
Author(s):  
Taihun Choi ◽  
Yoonho Seo
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Measurement Method ◽  
Work Performance ◽  
Time Series Data ◽  
Production Costs ◽  
Series Data ◽  
Image Analysis System ◽  
Progress Management ◽  
Progress Measurement

Progress control is a key technology for successfully carrying out a project by predicting possible problems, particularly production delays, and establishing measures to avoid them (decision-making). However, shipyard progress management is still dependent on the empirical judgment of the manager, and this has led to delays in delivery, which raises ship production costs. Therefore, this paper proposes a methodology for shipyard ship block assembly plants that enables objective process progress measurement based on real-time work performance data, rather than the empirical judgment of a site manager. In particular, an IoT-based physical progress measurement method that can automatically measure work performance without human intervention is presented for the mounting and welding activities of ship block assembly work. Both an augmented reality (AR) marker-based image analysis system and a welding machine time-series data-based machine learning model are presented for measuring the performances of the mounting and welding activities. In addition, the physical progress measurement method proposed in this study was applied to the ship block assembly plant of shipyard H to verify its validity.

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