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Machine learning-assisted equivalent circuit identification for dielectric spectroscopy of polymers
(2024)
Polymers have become indispensable across fields of application, and understanding their structure–property relationships and dynamic behaviour is essential for performance optimization. Polymer membranes, particularly ion exchange membranes, play a crucial role in renewable energy conversion technologies, fuel cells, solar energy conversion, and energy storage. In this context, broadband dielectric spectroscopy (BDS) offers a powerful, non-destructive approach to investigate the electrical response and relaxation dynamics of polymers. These properties are investigated by parametrizing the system’s impedance response in terms of a network of circuit elements, i.e. the electrical equivalent circuit (EEC), whose impedance resembles the one of the system under investigation. However, the determination of the EEC from BDS data is challenging due to system complexity, interdependencies of circuit elements, and researcher biases. In this work, we propose a novel approach that incorporates a convolutional neural network (CNN) model to predict the EEC topology. By reducing user bias and enhancing data analysis, this approach aims to make BDS accessible to both experienced users and those with limited expertise. We show that the combination of machine learning and BDS provides valuable insights into the dynamic behaviour of polymer membranes, thus facilitating the design and characterization of tailored polymers for various applications. We also show that our model outperforms state-of-the-art machine learning methods with a top-5 accuracy of around 80% for predicting the circuit topology and a parameter fitting error as low as 0.05%.
Over the last few years, the Federal Institute for material research (BAM, Berlin) together with the Centre for the Study of Manuscript Cultures (CSMC, University of Hamburg) have initiated a systematic material investigation of black inks produced from Late Antiquity to the Middle Ages (ca. fourth century CE–fourteenth/fifteenth centuries CE), aimed primarily at extending and complementing findings from previous sporadic studies. Part of this systematic investigation has focused on Egyptian Coptic manuscripts, and the present preliminary study is one of its outputs. It centres on a corpus of 45 Coptic manuscripts—43 papyri and 2 ostraca—preserved at the Palau-Ribes and Roca-Puig collections in Barcelona. The manuscripts come from the Monastery of Apa Apollo at Bawit, one of the largest monastic settlements in Egypt between the Late Antiquity and the Early Islamic Period (sixth–eighth centuries CE). The composition of their black inks was investigated in situ using near-infrared reflectography (NIRR) and X-ray fluorescence (XRF). The analyses determined that the manuscripts were written using different types of ink: pure carbon ink; carbon ink containing iron; mixed inks containing carbon, polyphenols and metallic elements; and iron-gall ink. The variety of inks used for the documentary texts seems to reflect the articulate administrative system of the monastery of Bawit. This study reveals that, in contrast to the documents, written mostly with carbon-based inks, literary biblical texts were written with iron-gall ink. The frequent reuse of papyrus paper for certain categories of documents may suggest that carbon-based inks were used for ephemeral manuscripts, since they were easy to erase by abrasion.
Simulation-Assisted Augmentation of Missing Wedge and Region-of-Interest Computed Tomography Data
(2024)
This study reports a strategy to use sophisticated, realistic X-ray Computed Tomography (CT) simulations to reduce Missing Wedge (MW) and Region-of-Interest (RoI) artifacts in FBP (Filtered Back-Projection) reconstructions. A 3D model of the object is used to simulate the projections that include the missing information inside the MW and outside the RoI. Such information augments the experimental projections, thereby drastically improving the reconstruction results. An X-ray CT dataset of a selected object is modified to mimic various degrees of RoI and MW problems. The results are evaluated in comparison to a standard FBP reconstruction of the complete dataset. In all cases, the reconstruction quality is significantly improved. Small inclusions present in the scanned object are better localized and quantified. The proposed method has the potential to improve the results of any CT reconstruction algorithm.
The complex nature of liquid water saturation of polymer electrolyte fuel cell (PEFC) catalyst layers (CLs) greatly affects the device performance. To investigate this problem, we present a method to quantify the presence of liquid water in a PEFC CL using small-angle X-ray scattering (SAXS). This method leverages the differences in electron densities between the solid catalyst matrix and the liquid water filled pores of the CL under both dry and wet conditions. This approach is validated using ex situ wetting experiments, which aid the study of the transient saturation of a CL in a flow cell configuration in situ. The azimuthally integrated scattering data are fitted using 3D morphology models of the CL under dry conditions. Different wetting scenarios are realized in silico, and the corresponding SAXS data are numerically simulated by a direct 3D Fourier transformation. The simulated SAXS profiles of the different wetting scenarios are used to interpret the measured SAXS data which allows the derivation of the most probable wetting mechanism within a flow cell electrode.
The aim of this study is to develop nanometer-thin epoxy-based films on aluminium alloy AA2024-T3 as a model coating system for high resolution corrosion studies. Spin coating was used for the layer-by-layer (LbL) deposition of poly-(ethylenimine) (PEI) and poly([o-cresyl glycidyl ether]-co-formaldehyde) (CNER) bilayers. The film chemistry and the cross-linking process were characterized by means of Fourier-transform infrared spectroscopy (FTIR).
Ellipsometric data confirmed the linear increase of film thickness. The potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) results indicate the improvement of the film barrier properties with increasing film thickness. Mapping of the topography and the volta potential was performed by means of scanning Kelvin probe force microscopy (SKPFM).
The results indicate the presence of a homogeneous film structure, while the intermetallic phases can still be identified below the coating. The SKPFM Analysis confirmed that the model films are suitable for investigation of corrosion processes at the coating/metal interface.
The application of characterization methods with high spatial resolution to the analysis of buried coating/metal interfaces requires the design and use of model systems. Herein, an epoxy-like thin film is used as a model coating resembling the epoxy-based coatings and adhesives widely used in technical applications. Spin coating is used for the deposition of a 30 nm-thin bilayer (BL) composed of poly-(ethylenimine) (PEI) and poly[(o-cresyl glycidyl ether)-co-formaldehyde] (CNER). Fourier-transform infrared spectroscopy (FTIR) results confirm that the exposure of coated AA2024-T3 (AA) samples to the corrosive electrolyte solution does not cause the degradation of the polymer layer. In situ atomic force microscopy (AFM) studies are performed to monitor local corrosion processes at the buried interface of the epoxy-like film and the AA2024-T3 aluminum alloy surface in an aqueous electrolyte solution. Hydrogen evolution due to the reduction of water as the cathodic corrosion reaction leads to local blister formation. Based on the results of the complementary energy-dispersive X-ray spectroscopy (EDX) analysis performed at the same region of interest, most of the hydrogen evolved originates at the vicinity of Mg-containing intermetallic particles.
We introduce herein boron-dipyrromethene (BODIPY) dyes as a new class of fluorophores for the design of reporter dyes for supramolecular host–guest complex formation with cucurbit[7]uril (CB7). The BODIPYs contain a protonatable aniline nitrogen in the meso-position of the BODIPY chromophore, which was functionalized with known binding motifs for CB7. The unprotonated dyes show low fluorescence due to photoinduced electron transfer (PET), whereas the protonated dyes are highly fluorescent. Encapsulation of the binding motif inside CB7 positions the aniline nitrogen at the carbonyl rim of CB7, which affects the pKa value, and leads to a host-induced protonation and thus to a fluorescence increase. The possibility to tune binding affinities and pKa values
is demonstrated and it is shown that, in combination with the beneficial photophysical properties of BODIPYs, several new applications of host–dye reporter pairs can be implemented. This includes indicator displacement assays with favourable absorption and
emission wavelengths in the visible spectral region, fluorescence correlation spectroscopy, and noncovalent surface functionalization
with fluorophores.
Additive manufacturing offers a range of novel applications. However, the manufacturing process is complex and the production of almost defect-free parts with high reliability and durability is still a challenge. Thermography is a valuable tool for process surveillance, especially in metal additive manufacturing processes. The high process temperatures allow one to use cameras usually operating in the visible spectral range. Here, we compare the results of measurements during the manufacturing process of a commercial laser metal deposition setup using a mid wavelength infrared camera with those from a short wavelength infrared camera and those from a visual spectrum high-speed camera with band pass filter in the near infrared range.
Mass spectrometry-based proteomics provides a holistic snapshot of the entire protein set of living cells on a molecular level. Currently, only a few deep learning approaches exist that involve peptide fragmentation spectra, which represent partial sequence information of proteins. Commonly, these approaches lack the ability to characterize less studied or even unknown patterns in spectra because of their use of explicit domain knowledge. Here, to elevate unrestricted learning from spectra, we introduce ‘ad hoc learning of fragmentation’ (AHLF), a deep learning model that is end-to-end trained on 19.2 million spectra from several phosphoproteomic datasets. AHLF is interpretable, and we show that peak-level feature importance values and pairwise interactions between peaks are in line with corresponding peptide fragments. We demonstrate our approach by detecting post-translational modifications, specifically protein phosphorylation based on only the fragmentation spectrum without a database search. AHLF increases the area under the receiver operating characteristic curve (AUC) by an average of 9.4% on recent phosphoproteomic data compared with the current state of the art on this task. Furthermore, use of AHLF in rescoring search results increases the number of phosphopeptide identifications by a margin of up to 15.1% at a constant false discovery rate. To show the broad applicability of AHLF, we use transfer learning to also detect cross-linked peptides, as used in protein structure analysis, with an AUC of up to 94%.
In recent years, thermoextraction/desorption-gas chromatography/mass spectrometry (TED-GC/MS) has been developed as a rapid detection method for the determination of microplastics (MP) mass contents in numerous environmentally relevant matrices and, in particular, for the measurement of polymers in water samples without time-consuming sample preparation. The TED-GC/MS method was applied to investigate a typical European municipal wastewater system for possible MP masses. Such investigations are important in view of the recent revision of the Urban Wastewater Treatment Directive. Four different representative sampling sites were selected: greywater (domestic wastewater without toilet), combined sewer, and influent and effluent of a wastewater treatment plant (WWTP). All samples were collected by fractional filtration. Filtration was carried out over mesh sizes of 500, 100, 50, and in some cases, 5 µm. Polyethylene (PE), polypropylene (PP), and polystyrene (PS) were detected in all samples, with the PE fraction dominating in all cases. Styrene-butadiene rubber which serves as an indication of tire abrasion, was only found in the influent of the WWTP. The highest MP mass contents were found in the combined sewer, so MP can become a source of pollution during heavy rain events when the capacity limits of the effluent are reached, and the polluted effluent is released uncontrolled into the environment. Based on the studies, MP retention from the WWTP could be estimated to be approximately 96%. Few trends in polymer type or mass contents were detected within the different fractions of the samples or when comparing samples to each other.