| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Gilshtein, Evgeniia
Technical University of Denmark
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (16/16 displayed)
- 2024Electrochemical activation of Fe-LiF conversion cathodes in thin-film solid-state batteriescitations
- 2024Polypy: a framework to interpret polymer properties from mass spectroscopy datacitations
- 2024Polypy:A Framework to Interpret Polymer Properties from Mass Spectroscopy Datacitations
- 2024Polypy: A Framework to Interpret Polymer Properties from Mass Spectrometry Datacitations
- 2023Colloidal ternary telluride quantum dots for tunable phase change optics in the visible and near-infraredcitations
- 2023Controlled li alloying by postsynthesis electrochemical treatment of Cu 2 ZnSn(S, Se) 4 absorbers for solar cellscitations
- 2022Photonic sintering of oxide ceramic films: effect of colored Fe x O y nanoparticle pigmentscitations
- 2022High-quality graphene using boudouard reactioncitations
- 2022High-Quality Graphene Using Boudouard Reaction
- 2022Photonic Sintering of Oxide Ceramic Films: Effect of Colored FexOy Nanoparticle Pigmentscitations
- 2021Millisecond photonic sintering of iron oxide doped alumina ceramic coatingscitations
- 2021In situ lithiated ALD niobium oxide for improved long term cycling of layered oxide cathodes: a thin-film model studycitations
- 2021Influence of the rear interface on composition and photoluminescence yield of CZTSSe absorbers: a case for an Al 2 O 3 intermediate layercitations
- 2020Revealing the perovskite formation kinetics during chemical vapour depositioncitations
- 2020ALD-ZnMgO and absorber surface modifications to substitute CdS buffer layers in co-evaporated CIGSe solar cellscitations
- 2019Inkjet-printed and deep-UV-annealed YAlO x dielectrics for high-performance IGZO thin-film transistors on flexible substratescitations
Places of action
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article
Polypy: a framework to interpret polymer properties from mass spectroscopy data
Abstract
Mass spectroscopy (MS) is a robust technique for polymer characterization, and it can provide the chemical fingerprint of a complete sample regarding polymer distribution chains. Nevertheless, polymer chemical properties such as polydispersity (Pd), average molecular mass (MᵅB), weight average molecular mass (Mᵆ4) and others are not determined by MS, as they are commonly characterized by gel permeation chromatography (GPC). In order to calculate polymer properties from MS, a Python script was developed to interpret polymer properties from spectroscopic raw data. Polypy script can be considered a peak detection and area distribution method, and represents the result of combining the MS raw data filtered using Root Mean Square (RMS) calculation with molecular classification based on theoretical molar masses. Polypy filters out areas corresponding to repetitive units. This approach facilitates the identification of the polymer chains and calculates their properties. The script also integrates visualization graphic tools for data analysis. In this work, aryl resin (poly(2,2-bis(4-oxy-(2-(methyloxirane)phenyl)propan) was the study case polymer molecule, and is composed of oligomer chains distributed mainly in the range of dimers to tetramers, in some cases presenting traces of pentamers and hexamers in the distribution profile of the oligomeric chains. Epoxy resin has MᵅB = 607 Da, Mᵆ4 = 631 Da, and polydispersity (Pd) of 1.015 (data given by GPC). With Polypy script, calculations resulted in MᵅB = 584.42 Da, Mᵆ4 = 649.29 Da, and Pd = 1.11, which are consistent results if compared with GPC characterization. Additional information, such as the percentage of oligomer distribution, was also calculated and for this polymer matrix it was not possible to retrieve it from the GPC method. Polypy is an approach to characterizing major polymer chemical properties using only MS raw spectra, and it can be utilized with any MS raw data for any polymer matrix.