Evidence map›Paper›PMID 39472726›Full record

ArticleScientific reports2024

Prediction of power conversion efficiency parameter of inverted organic solar cells using artificial intelligence techniques.

Mustapha Marzouglal, Abdelkerim Souahlia, Lakhdar Bessissa, Djillali Mahi, Abdelaziz Rabehi, Yahya Z Alharthi, Amanuel Kumsa Bojer, Aymen Flah, Mosleh M Alharthi, Sherif S M Ghoneim

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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Mustapha MarzouglalLaboratory of Studies and Development of Semiconductor and Dielectric Materials, LeDMaScD, University Amar Telidji of Laghouat, BP 37G Route of Ghardaïa, Laghouat, 03000, Algeria.
Abdelkerim SouahliaTelecommunication and Smart Systems Laboratory, Faculty of Sciences and Technology, Ziane Achour University, Djelfa, Algeria.
Lakhdar BessissaMaterials Science and Informatics Laboratory, MSIL, Ziane Achour University of Djelfa, Road Moudjbara, PO Box 3117, Djelfa, 17000, Algeria.
Djillali MahiLaboratory of Studies and Development of Semiconductor and Dielectric Materials, LeDMaScD, University Amar Telidji of Laghouat, BP 37G Route of Ghardaïa, Laghouat, 03000, Algeria.
Abdelaziz RabehiTelecommunication and Smart Systems Laboratory, Faculty of Sciences and Technology, Ziane Achour University, Djelfa, Algeria. abdelaziz.rabehi@univ-djelfa.dz.
Yahya Z AlharthiDepartment of Electrical Engineering, College of Engineering, University of Hafr Albatin, Hafr Al Batin, 39524, Saudi Arabia.
Amanuel Kumsa BojerEthiopian Artificial Intelligence Institute, PO Box 40782, Addis Ababa, Ethiopia. amanuel.kumsa@aau.edu.et.
Aymen FlahProcesses, Energy, Environment, and Electrical Systems, National Engineering School of Gabès, University of Gabès, Gabes, Tunisia.
Mosleh M AlharthiDepartment of Electrical Engineering, College of Engineering, Taif University, Taif, 21944, Saudi Arabia.
Sherif S M GhoneimDepartment of Electrical Engineering, College of Engineering, Taif University, Taif, 21944, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Organic photovoltaic (OPV) cells are at the forefront of sustainable energy generation due to their lightness, flexibility, and low production costs. These characteristics make OPVs a promising solution for achieving sustainable development goals. However, predicting their lifetime remains challenging task due to complex interactions between internal factors such as material degradation, interface stability, and morphological changes, and external factors like environmental conditions, mechanical stress, and encapsulation quality. In this study, we propose a machine learning-based technique to predict the degradation over time of OPVs. Specifically, we employ multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks to predict the power conversion efficiency (PCE) of inverted organic solar cells (iOSCs) made from the blend PTB7-Th:PC

Indexed as

DegradationInverted organic solar cellsLong short-term memoryMachine learningMulti-layer perceptronPower conversion efficiencyPrediction

Identifiers

PMID39472726
PMCPMC11522405

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.