Quantitative and Non‐Quantitative Assessments of Enzymatic Electrosynthesis: A Case Study of Parameter Requirements

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Quantitative and Non-Quantitative Assessments of Enzymatic Electrosynthesis: A Case Study of Parameter Requirements

Compare, contrast, scrutinize and decide: Various process options can be used to bring enzyme processes to industrial application; these include electroenzymatic processes. To evaluate the processes quantitatively, appropriate performance indicators must be determined. In addition, there are non-quantitative variables that need to be considered. This article shows how laboratory processes can be evaluated and how options for action can be identified.


Abstract

The integration of enzymatic and electrochemical reactions offers a unique opportunity to optimize production processes. Recently, an increasing number of laboratory-scale enzymatic electrosyntheses have shown impressive performance indicators, leading to scientific interest in technical implementation. However, important process parameters are missing in most of the relevant literature. On one hand, this is due to the large variety of relevant performance indicators. On the other hand, enzyme technologists and electrochemists use different parameters to describe a process. In this article, we review the most important performance indicators in electroenzymatic processes and suggest that in order to allow quantitative comparison, these indicators should be reported in all respective publications. In addition to quantitative parameters, non-quantitative assessments often need to be included in a final evaluation. Examples of such parameters are sustainability, contribution to the UN Sustainable Development Goals or interactions with the overall process. We demonstrate the evaluation of processes using hydrogen peroxide-dependent peroxygenases. The strength of the proposed evaluation system lies in its ability to identify weaknesses in a process at an early stage of development. Finally, it can be concluded that all evaluated enzymatic electrosynthesis do not yet meet typical industrial requirements for an enzyme-based process.

Data‐Driven Analysis of Amine‐Based Sorbents for CO2 Removal from the Atmosphere

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Data-Driven Analysis of Amine-Based Sorbents for CO2 Removal from the Atmosphere

CO2 sorbents are considered key components of the direct air capture (DAC) process. Various types of amines are widely applied for CO2 capture from the atmosphere. Their activities and efficiencies depend on various factors including their support material, operating conditions, and environment. Statistical data analysis is done to provide more insights into the performance of these amines for DAC.


Abstract

Available data on amine-based sorbents used for the direct air capture (DAC) process were gathered and analyzed to identify the correlations between various aspects of these sorbents and the operating conditions they are used in. It is demonstrated that a moderately high temperature (∼ 50 °C) can help with higher CO2 capture capacity. The effect of sorbent preparation method on its activity and stability was studied. Also, the influence of amine groups and support choice on amine efficiency and CO2 capture capacity was discussed. The DAC process conditions proved to play a major role in determining the optimal sorbent. An outlook for characteristics to be sought for in future DAC sorbents for CO2 removal is proposed.

Unlocking High‐Performance Supercapacitor Behavior and Sustained Chemical Stability of 2D Metallic CrSe2 by Optimal Electrolyte Selection

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Unlocking High-Performance Supercapacitor Behavior and Sustained Chemical Stability of 2D Metallic CrSe2 by Optimal Electrolyte Selection

Supercapacitor: 2D metallic conductor CrSe2 synthesized at scale as crystalline powder remains stable in acidic conditions and outperforms high surface area carbon in supercapacitor applications


Abstract

Supercapacitors are energy storage devices with the ability to rapidly charge and discharge, making them a valuable complement to battery systems. To maximize their fast-charging capabilities, identifying materials and methods to enhance their energy density is crucial. In this work, we carried out a comprehensive study of an emerging 2D dichalcogenide, CrSe2, as a supercapacitor material. We demonstrate that CrSe2 can be obtained at ambient temperature through deintercalation of a relevant KCrSe2 precursor using a 0.5 M solution of I2 in acetonitrile. Although CrSe2 decomposed in 1 M KOH, it was found to be chemically stable in common electrolytes such as H2SO4, Li2SO4, and Na2SO4. Despite low surface area CrSe2 reached a specific capacitance of 27 F g−1 in 1 M H2SO4 and, thus consistently outperformed high surface carbon black. Computational studies suggested that the metallic conductivity of CrSe2 was likely the primary factor contributing to the superior performance of this 2D chalcogenide over high surface carbon analogues.

The increased Diels–Alder reactivity of umpolung tropone: analysis of individual atoms and bonds using QTAIM and IQA along complete IRC paths

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The increased Diels–Alder reactivity of umpolung tropone: analysis of individual atoms and bonds using QTAIM and IQA along complete IRC paths

The IQA@IRC protocol enables us to see, in a much deeper detail, how the energy of each atom or group is varying along the IRC. It acts like a magnifying glass, allowing us to see things that are unreachable from the standard IRC analysis.


Abstract

A fruitful debate took place recently in literature, discussing the enhanced Diels–Alder reactivity of tropone derivatives for which the carbonyl polarity was reversed by means of umpolung. Karas et al. sustained that the umpolung increases the antiaromatic character of the ring, affecting the highest occupied molecular orbital (HOMO)/least unoccupied molecular orbital (LUMO) energies, speeding up the reaction. Tiekink et al. challenged this interpretation by sustaining that the asynchronicity of the reaction mechanisms, rather than orbital energy perturbation, was the main responsible for the smaller reaction barriers. We shed light on this dispute by computing full interaction quantum atom (IQA) and quantum theory of atoms in molecules (QTAIM) analyses over complete intrinsic reaction coordinate (IRC) paths for the Diels–Alder reaction of tropone and its umpolung derivatives, using the same systems studied by Karas et al. and Tiekink et al. Our results confirm that the asynchronicity is indeed very high for those reactions with smaller reaction barriers and offer an atom-by-atom and bond-by-bond analysis of the entire IRC pathways. Even though asynchronicity and lower reactions barriers seem to be related, antiaromaticity and lower barriers are related as well, but discussing both these interpretations does not necessarily require arguments on HOMO/LUMO energies to be invoked.

Oxidative Depolymerization of Polyphenylene Oxide into Benzoquinone

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Poly(2,6-dimethyl-1,4-phenylene oxide) (PPO) is one of the most important engineering plastics commonly utilized in various fields. Herein, chemical recycling of PPO was performed via oxidative depolymerization to form 2,6-diemthyl-p-benzoquionone (26DMBQ) as a sole aromatic product in 66% yield using nitronium ions (NO2+) as a mild oxidant. Mechanistic studies revealed that PPO is oxidized by NO2+ generated from the combination of a silicotungstic acid and nitrate salts, and then subsequently attacked by H2O to achieve C–O bond cleavage, resulting in the formation of 26DMBQ, which was sublimed at the headspace of the reaction vessel in pure form. 26DMBQ was applied to polymerization with dianilines to form polyimides. Thus, an upgrade recycling process of PPO was demonstrated.

Detection of Per‐ and Polyfluoroalkyl Substances in High‐Protein Food Products

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Abstract

Per- and polyfluoroalkyl substances (PFAS) belong to the emerging class of persistent organohalogenated contaminants in the environment. We determined the levels of 10 PFAS in selected samples representing different food types, with a special focus on those rich in protein such as fish, meat and meat preparations, liver, eggs, and leguminous vegetables. Such determinations were based on the Quick Easy Cheap Effective Rugged Safe extraction procedure followed by micro-high-performance liquid chromatography–tandem mass spectrometry. The most frequently found was perfluorooctanoic acid, in 84% of the food samples. However, its maximum measured concentration was 0.50 ng g–1, in a herring sample. The highest concentrations were for perfluorobutanoic acid (35 ng g–1 measured in a pork liver sample) and perfluorooctane sulfonate (12 ng g–1 measured in a herring sample). Because these compounds may bioaccumulate in human tissues by dietary intake, further research into their impact on human health is called for. Environ Toxicol Chem 2023;00:1–10. © 2023 SETAC

Implementation of machine learning protocols to predict the hydrolysis reaction properties of organophosphorus substrates using descriptors of electron density topology

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Abstract

Prediction of catalytic reaction efficiency is one of the most intriguing and challenging applications of machine learning (ML) algorithms in chemistry. In this study, we demonstrated a strategy for utilizing ML protocols applied to Quantum Theory of Atoms In Molecules (QTAIM) parameters to predict the ability of the A17 L47K catalytic antibody to covalently capture organophosphate pesticides. We found that the novel “composite” DFT functional B97-3c could be effectively employed for fast and accurate initial geometry optimization, aligning well with the input dataset creation. QTAIM descriptors proved to be well-established in describing the examined dataset using density-based and hierarchical clustering algorithms. The obtained clusters exhibited correlations with the chemical classes of the input compounds. The precise physical interpretation of the QTAIM properties simplifies the explanation of feature impact for both supervised and unsupervised ML protocols. It also enables acceleration in the search for entries with desired properties within large databases. Furthermore, our findings indicated that Ridge Regression with Laplacian kernel and CatBoost Regressor algorithms demonstrated suitable performance in handling small datasets with non-trivial dependencies. They were able to predict the actual reaction barrier values with a high level of accuracy. Additionally, the CatBoost Classifier proved reliable in discriminating between “active” and “inactive” compounds.