OATAO - Open Archive Toulouse Archive Ouverte Open Access Week

Text mining assisted review of the literature on Li-O2 batteries

Torayev, Amangeldi and Magusin, Pieter C M M and Grey, Clare P and Merlet, Céline and Franco, Alejandro A Text mining assisted review of the literature on Li-O2 batteries. (2019) Journal of Physics Materials, 2 (4). 044004. ISSN 2515-7639

[img]
Preview
(Document in English)

PDF (Publisher's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
1MB

Official URL: https://doi.org/10.1088/2515-7639/ab3611

Abstract

The high theoretical capacity of Li-O2 batteries attracts a lot of attention and this field has expanded significantly in the last two decades. In a more general way, the large number of articles being published daily makes it difficult for researchers to keep track of the progress in science. Here we develop a text mining program in an attempt to facilitate the process of reviewing the literature published in a scientific field and apply it to Li-O2 batteries. We analyze over 1800 articles and use the text mining program to extract reported discharge capacities, for the first time, which allows us to show the clear progress made in recent years. In this paper, we focus on three main challenges of Li-O2 batteries, namely the stability-cyclability, the low practical capacity and the rate capability. Indeed, according to our text mining program, articles dealing with these issues represent 86% of the literature published in the field. For each topic, we provide a bibliometric analysis of the literature before focusing on a few key articles which allow us to get insights into the physics and chemistry of such systems. We believe that text mining can help readers find breakthrough papers in a field (e.g. by identifying papers reporting much higher performances) and follow the developments made at the state of the art (e.g. by showing trends in the numbers of papers published—a decline in a given topic probably being the sign of limitations). With the progress of text mining algorithms in the future, the process of reviewing a scientific field is likely to become more and more automated, making it easier for researchers to get the 'big picture' in an unfamiliar scientific field.

Item Type:Article
Additional Information:Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Other partners > Collège de France (FRANCE)
Other partners > Ecole Nationale Supérieure de Chimie de Paris - ENSCP (FRANCE)
Other partners > Ecole Nationale Supérieure de Chimie de Montpellier - ENSCM (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
Other partners > Institut polytechnique de Grenoble (FRANCE)
Other partners > Institut universitaire de France - IUF (FRANCE)
Other partners > Sorbonne Université (FRANCE)
Other partners > University of Cambridge (UNITED KINGDOM)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UPS (FRANCE)
Other partners > Université de Nantes (FRANCE)
Other partners > Université de Picardie Jules Verne (FRANCE)
Other partners > Université de Pau et des Pays de l'Adour - UPPA (FRANCE)
Other partners > Université de Haute Alsace - UHA (FRANCE)
Other partners > Université de Montpellier (FRANCE)
Laboratory name:
Funders:
ALISTORE European Research Institute - European Union’s Horizon 2020 - European Research Council (ERC)
Statistics:download
Deposited By: cirimat webmestre
Deposited On:08 Oct 2019 11:53

Repository Staff Only: item control page