Evidence map›Paper›PMID 39053903›Full record

ReviewACS nano2024

Modeling of Nanomaterials for Supercapacitors: Beyond Carbon Electrodes.

Sheng Bi, Lisanne Knijff, Xiliang Lian, Alicia van Hees, Chao Zhang, Mathieu Salanne

Abstract readReview
In one paragraph

Review in ACS nano, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

6 authors.

Sheng BiPhysicochimie des Électrolytes et Nanosystèmes Interfaciaux, Sorbonne Université, CNRS, F-75005 Paris, France.ORCID 0000-0001-8804-7353
Lisanne KnijffDepartment of Chemistry - Ångström Laboratory, Uppsala University, Lägerhyddsvägen 1, BOX 538, Uppsala 75121, Sweden.
Xiliang LianPhysicochimie des Électrolytes et Nanosystèmes Interfaciaux, Sorbonne Université, CNRS, F-75005 Paris, France.ORCID 0009-0008-2110-0111
Alicia van HeesDepartment of Chemistry - Ångström Laboratory, Uppsala University, Lägerhyddsvägen 1, BOX 538, Uppsala 75121, Sweden.
Chao ZhangDepartment of Chemistry - Ångström Laboratory, Uppsala University, Lägerhyddsvägen 1, BOX 538, Uppsala 75121, Sweden.ORCID 0000-0002-7167-0840
Mathieu SalanneRéseau sur le Stockage Electrochimique de l'Energie (RS2E), FR CNRS 3459, 80039 Amiens Cedex, France.ORCID 0000-0002-1753-491X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Capacitive storage devices allow for fast charge and discharge cycles, making them the perfect complements to batteries for high power applications. Many materials display interesting capacitive properties when they are put in contact with ionic solutions despite their very different structures and (surface) reactivity. Among them, nanocarbons are the most important for practical applications, but many nanomaterials have recently emerged, such as conductive metal-organic frameworks, 2D materials, and a wide variety of metal oxides. These heterogeneous and complex electrode materials are difficult to model with conventional approaches. However, the development of computational methods, the incorporation of machine learning techniques, and the increasing power in high performance computing now allow us to tackle these types of systems. In this Review, we summarize the current efforts in this direction. We show that depending on the nature of the materials and of the charging mechanisms, different methods, or combinations of them, can provide desirable atomic-scale insight on the interactions at play. We mainly focus on two important aspects: (i) the study of ion adsorption in complex nanoporous materials, which require the extension of constant potential molecular dynamics to multicomponent systems, and (ii) the characterization of Faradaic processes in pseudocapacitors, that involves the use of electronic structure-based methods. We also discuss how recently developed simulation methods will allow bridges to be made between double-layer capacitors and pseudocapacitors for future high power electricity storage devices.

Indexed as

2D materialsDouble layerMachine-learningMetal−organic frameworkMetal oxidesMolecular dynamicsMXenePseudocapacitors

Identifiers

PMID39053903
PMCPMC11308780

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.