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Reversible cross‐linking of thermoplastic materials allows thermoplastic processing before and after cross‐linking, while improving the mechanical and thermal properties after cross‐linking during the use phase. Hence, reversible cross‐linking can play an important role in establishing circularity by enabling mechanical recycling of a cross‐linked material after de‐linking. This exploratory study investigates 1‐(5‐(Aminoethyl)‐2‐nitrophenyl)ethanol as a photolabile cross‐linker (PXL) for polyamide 6 (PA6). The PXL is melt‐mixed with PA6 in two concentrations and processed into samples, which are investigated by Dynamic Mechanical Analysis (DMA) and Differential Scanning Calorimetry (DSC) before and after UV‐exposure. The addition of the PXL increases the storage modulus from 1.585 MPa for neat PA6 to 2.550 MPa for PA6 with 3 wt.% PXL and 3.470 MPa for PA6 with 6 wt.% PXL, respectively. Exposure to UV radiation decreases the storage modulus with increasing exposure time. The crystallinity decreases from 33,32% for neat PA6 to 30,71% for PA6 with 3 wt.% PXL and to 29,71% for PA6 with 6 wt.% PXL When the samples with PXL are exposed to UV‐radiation, an increase in the crystallinity is observed. The results of this exploratory study indicate that PA6 can be cross‐linked with 1‐(5‐(Aminoethyl)‐2‐nitrophenyl)ethanol and that de‐linking through UV‐exposure is possible. This exploratory study investigates the reversible cross‐linking of polyamide 6 by the photolabile cross‐linker 1‐(5‐(Aminoethyl)‐2‐nitrophenyl)ethanol (PXL). The storage modulus of the material increases with increasing PXL content and decreases when the exposure time of the samples is increased. The crystallinity of the samples decreases with increasing PXL content and increases after exposing the samples to UV radiation.

This publication introduces a novel open-access 31 P Nuclear Magnetic Resonance (NMR) shift database. With 14,250 entries encompassing 13,730 distinct molecules from 3,648 references, this database offers a comprehensive repository of organic and inorganic compounds. Emphasizing single-phosphorus atom compounds, the database facilitates data mining and machine learning endeavors, particularly in signal prediction and Computer-Assisted Structure Elucidation (CASE) systems. Additionally, the article compares different models for 31 P NMR shift prediction, showcasing the database’s potential utility. Hierarchically Ordered Spherical Environment (HOSE) code-based models and Graph Neural Networks (GNNs) perform exceptionally well with a mean squared error of 11.9 and 11.4 ppm respectively, achieving accuracy comparable to quantum chemical calculations.

Aqueous‐organic redox flow batteries (RFBs) have gained considerable interest in recent years, given their potential for an economically viable energy storage at large scale. This, however, strongly depends on both the robustness of the underlying electrolyte chemistry against molecular decomposition reactions as well as the device's operation. With regard to this, the presented study focuses on the use of in situ IR spectroscopy in combination with a multivariate curve resolution approach to gain insight into both the molecular structures of the active materials present within the electrolyte as well as crucial electrolyte state parameters, represented by the electrolyte's state of charge (SOC) and state of health (SOH). To demonstrate the general applicability of the approach, methyl viologen (MV) and bis(3‐trimethylammonium)propyl viologen (BTMAPV) are chosen, as viologens are frequently used as negolytes in aqueous‐organic RFBs. The study's findings highlight the impact of in situ spectroscopy and spectral deconvolution tools on the precision of the obtainable SOC and SOH values. Furthermore, the study indicates the occurrence of multiple viologen dimers, which possibly influence the electrolyte lifetime and charging characteristics. The accurate assessment of battery state variables in organic redox flow batteries is crucial for a long service life. Using in situ IR spectroscopy and a multivariate curve resolution‐alternating least squares (MCR‐ALS) algorithm, different viologen scaffolds are analyzed, enabling the extraction of the state of charge (SOC), state of health (SOH) parameters and the identification of possible solution structures.

Here, we present a protocol for the one-step synthesis of the title compound in quantitative yield using adapted Vilsmeier conditions. The product was characterized by 1H-,2H-,13C-NMR-, as well as IR and Raman spectroscopy. Spectral data are given in detail.

Graph Neural Networks (GNNs) are powerful tools for predicting chemical shifts in Nuclear Magnetic Resonance (NMR) spectroscopy. In this paper, we improve the state-of-the-art mean absolute error (MAE) on the Ilm-NMR-P31 dataset for the prediction of 31 P shifts from 11.4 ppm to 8.88 ppm by proposing a lightweight GNN which is based on the Metalayer-Framework. Furthermore, we analyze the performance of our model depending on the size of the training dataset and compare our model with different state-of-the-art models. Finally, we demonstrate how the GNN’s predictions can be interpreted and visualized with respect to the underlying molecular structures. Using GNNExplainer, we analyze the best- and worst-predicted molecules and perform feature ablations to assess the model’s reliance on specific input features. Furthermore, we validate the model’s physical plausibility by extracting learned substituent effects: the GNN autonomously rediscovers established empirical rules, accurately reproducing the$$\\beta$$β -deshielding and$$\\gamma$$γ -shielding increments of neutral phosphines. Finally, we show that the SDGNN outperforms standard vacuum DFT calculations and HOSE code baselines. Scientific Contribution: The proposed lightweight GNN based on the Metalayer framework improves the state-of-the-art 31 P NMR shift prediction. By systematically varying the training-set size and benchmarking against multiple state-of-the-art models, we provide a standardized performance comparison that was previously lacking for 31 P NMR shift prediction. Using GNNExplainer and targeted feature ablations, we relate the model’s predictions to specific molecular substructures and input features. By quantitatively verifying that the model learns fundamental physical trends like substituent increments and identifying specific error sources, we provide a level of chemical interpretability and validation that goes beyond prior black-box approaches.

Graph Neural Networks (GNNs) are powerful tools for predicting chemical shifts in Nuclear Magnetic Resonance (NMR) spectroscopy. In this paper, we improve the state-of-the-art mean absolute error (MAE) on the Ilm-NMR-P31 dataset for the prediction of P shifts from 11.4 ppm to 8.88 ppm by proposing a lightweight GNN which is based on the Metalayer-Framework. Furthermore, we analyze the performance of our model depending on the size of the training dataset and compare our model with different state-of-the-art models. Finally, we demonstrate how the GNN's predictions can be interpreted and visualized with respect to the underlying molecular structures. Using GNNExplainer, we analyze the best- and worst-predicted molecules and perform feature ablations to assess the model's reliance on specific input features. Furthermore, we validate the model's physical plausibility by extracting learned substituent effects: the GNN autonomously rediscovers established empirical rules, accurately reproducing the -deshielding and -shielding increments of neutral phosphines. Finally, we show that the SDGNN outperforms standard vacuum DFT calculations and HOSE code baselines.Scientific Contribution: The proposed lightweight GNN based on the Metalayer framework improves the state-of-the-art P NMR shift prediction. By systematically varying the training-set size and benchmarking against multiple state-of-the-art models, we provide a standardized performance comparison that was previously lacking for P NMR shift prediction. Using GNNExplainer and targeted feature ablations, we relate the model's predictions to specific molecular substructures and input features. By quantitatively verifying that the model learns fundamental physical trends like substituent increments and identifying specific error sources, we provide a level of chemical interpretability and validation that goes beyond prior black-box approaches.

The straightforward synthesis of a urea polymer network is presented. Commercially available monomers are polymerized using light-induced polymerization, resulting in networks crosslinked by hindered urea molecules. These moieties are reversible and, thus, can be converted into the starting compounds (that is, isocyanate and amine) by a simple thermal treatment. This process is monitored using differential scanning calorimetry as well as Raman and infrared spectroscopy. Furthermore, the self-healing ability of these polymer networks is investigated using scratch-healing tests as well as bulk-healing investigations using tensile testing. The resultant materials have a high E -modulus, are able to heal scratches at temperatures above 70 °C multiple times and their mechanical properties can be partially regenerated. The underlying healing mechanism is based on the reversible opening of the urea bonds and exchange reactions between two functional groups, which were confirmed from a spectroscopic analysis. In summary, these new materials are a new type of intrinsically healable polymers and provide a first step toward hard and healable polymers. Self-healing polymers: Learning the hard stuff One of the hardest self-healing polymers ever reported has been prepared using the reversible bonds of sterically hindered urea groups. Polymers that can re-form internal chemical links after being scratched or cracked are usually subject to design constraints that lower their mechanical strength. To overcome these constraints, Martin D. Hager and colleagues from Friedrich Schiller University Jena, Germany, created a series of poly(methacrylate) polymers bearing reversible urea units. By simply exposing the starting reagents to brief flashes of light, they prepared a cross-linked polymer featuring the urea units on the poly(methacrylate) chains resulting in mechanically tough materials. After optimizing the cross-link density, the team deliberately scratched the polymer and then heated it to begin the self-healing process. Temperatures of about 100 degree Celsius were sufficient to open urea bonds up and initiate material repair. New intrinsic self-healing polymers with outstanding mechanical performance are presented. For this purpose, sterically hindered amines were utilized to crosslink isocyanate containing poly(methacrylates) resulting in urea crosslinked networks. The reversibility of the urea bond during thermal treatment could be utilized to induce self-healing ability and could be proven using various techniques.

Here, we present a protocol for the one-step synthesis of the title compound in quantitative yield using adapted Vilsmeier conditions. The product was characterized by [sup.1] H-,[sup.2] H-,[sup.13] C-NMR-, as well as IR and Raman spectroscopy. Spectral data are given in detail.

This publication introduces a novel open-access P Nuclear Magnetic Resonance (NMR) shift database. With 14,250 entries encompassing 13,730 distinct molecules from 3,648 references, this database offers a comprehensive repository of organic and inorganic compounds. Emphasizing single-phosphorus atom compounds, the database facilitates data mining and machine learning endeavors, particularly in signal prediction and Computer-Assisted Structure Elucidation (CASE) systems. Additionally, the article compares different models for P NMR shift prediction, showcasing the database's potential utility. Hierarchically Ordered Spherical Environment (HOSE) code-based models and Graph Neural Networks (GNNs) perform exceptionally well with a mean squared error of 11.9 and 11.4 ppm respectively, achieving accuracy comparable to quantum chemical calculations.

In the package corr2D two-dimensional correlation analysis is implemented in R. This paper describes how two-dimensional correlation analysis is done in the package and how the mathematical equations are translated into R code. The paper features a simple tutorial with executable code for beginners, insight into at the calculations done before the correlation analysis, a detailed look at the parallelization of the fast Fourier transformation based correlation analysis and a speed test of the calculation. The package corr2D offers the possibility to preprocess, correlate and postprocess spectroscopic data using exclusively the R language. Thus, corr2D is a welcome addition to the toolbox of spectroscopists and makes two-dimensional correlation analysis more accessible and transparent.