Evidence map›Paper›PMID 35389549›Full record

ArticleChemphyschem : a European journal of chemical physics and physical chemistry2022

Introducing Alternative Algorithms for the Determination of the Distribution of Relaxation Times.

Tobias G Bergmann, Nicolas Schlüter

Abstract read
In one paragraph

Article in Chemphyschem : a European journal of chemical physics and physical chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Introducing Alternative Algorithms for the Determination of the Distribution of Relaxation Times.Chemphyschem : a European journal of chemical physics and physical chemistry · 2022
    Article
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

2 authors.

Tobias G BergmannInstitute of Environmental and Sustainable Chemistry, Technische Universität Braunschweig, Braunschweig, Germany.ORCID 0000-0001-8139-722X
Nicolas SchlüterInstitute of Energy and Process Systems Engineering, Technische Universität Braunschweig, Braunschweig, Germany.ORCID 0000-0002-6805-7906

Funding

Bundesministerium für Bildung und Forschung 03XP0355BBundesministerium für Wirtschaft und Energie 03ETE017A
6 · The paper itself

Abstract

Impedance spectroscopy is a powerful characterization method to evaluate the performance of electrochemical systems. However, overlapping signals in the resulting impedance spectra oftentimes cause misinterpretation of the data. The distribution of relaxation times (DRT) method overcomes this problem by transferring the impedance data from the frequency domain into the time domain, which yields DRT spectra with an increased resolution. Unfortunately, the determination of the DRT is an ill-posed problem, and appropriate mathematical regularizations become inevitable to find suitable solutions. The Tikhonov algorithm is a widespread method for computing DRT data, but it leads to unlikely spectra due to necessary boundaries. Therefore, we introduce the application of three alternative algorithms (Gold, Richardson Lucy, Sparse Spike) for the determination of stable DRT solutions and compare their performances. As the promising Sparse Spike deconvolution has a limited scope when using one single regularization parameter, we furthermore replaced the scalar regularization parameter with a vector. The resulting method is able to calculate well-resolved DRT spectra.

Indexed as

AlgorithmsElectric Impedancedistribution of relaxation timeselectrochemical impedance spectroscopygold deconvolutionsparse spike deconvolutionTikhonov regularization

Identifiers

PMID35389549
PMCPMC9400974

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.